A method and system for path optimization of a cold chain vehicle
Patent Information
- Application Number
- CN202611090269.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-07-22
- Publication Date
- 2026-09-29
Smart Images
Figure CN122840841A_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of computer technology, specifically relating to a route optimization method and system for cold chain vehicles. Background Technology
[0002] With the explosive growth of fresh food e-commerce, instant retail, and cross-regional pharmaceutical distribution, cross-regional cold chain logistics has become a core infrastructure for ensuring stable consumer spending, food safety, and pharmaceutical supply. However, the industry faces severe challenges due to the dual uncertainties of strong spatial and temporal fluctuations in demand, time-varying congestion on cross-regional road networks, and complex coupling of multiple constraints. On the one hand, cross-regional fresh food orders exhibit characteristics of small batches, high frequency, and strong fluctuations in spatial and temporal distribution, with significant differences in demand between regions and time periods. Holidays and promotional activities can easily trigger pulse-like increases in orders. On the other hand, cross-regional urban roads are affected by multiple factors such as morning and evening commuting peak hours, long-distance trunk line congestion, severe weather, and emergencies, exhibiting significant dynamic congestion characteristics. The average vehicle speed during morning and evening peak hours can differ from that during off-peak hours by 3-5 times, and the transmission lag of road congestion status leads to a significant increase in the uncertainty of travel time. Traditional cold chain delivery models based on static road networks and fixed demand assumptions are trapped in a dilemma of low delivery efficiency, high cold chain losses, increased carbon emissions, and high transportation costs, which not only increases the operating costs of enterprises but also restricts the green and sustainable development of the industry.
[0003] The unique characteristics of cross-regional cold chain logistics dictate that its route optimization must simultaneously satisfy multiple constraints, such as dual-time windows, exponential decay of cargo damage, segmented refrigeration energy consumption, vehicle load and volume, and first-in-first-out cross-regional road network. It is a complex vehicle route optimization problem with multiple constraints coupled together. Existing methods for optimizing cold chain vehicle routes have significant limitations: First, traditional vehicle route optimization models are mostly based on the assumptions of fixed demand and static road networks, using average speed in a single time dimension to characterize travel time. This fails to reflect the dynamic relationship between departure time and travel time under time-varying road networks across regions, resulting in a severe disconnect between the models and actual cross-regional operational scenarios. Second, traffic flow prediction struggles to balance periodic capture and response to sudden peaks. Ordinary long short-term memory network models have a high prediction error rate for long-distance cross-regional trunk lines and urban peaks, and the generated travel time functions are prone to violating first-in-first-out constraints, leading to practically infeasible route solutions. Third, cross-regional demand forecasting often uses single-layer time-series models, ignoring the multi-granularity differences in demand characteristics at the macro-level total volume, meso-level regional level, and micro-level product category level, failing to provide accurate basis for cross-regional node-level delivery. Fourth, traditional heuristic algorithms are prone to getting trapped in local optima under multi-objective and dynamic constraints, making it difficult to balance the efficiency and solution quality of cross-regional transportation.
[0004] In existing technologies, some methods only consider time-varying road networks or demand forecasting alone, failing to achieve deep coupling between these and the multiple constraints of cross-regional cold chain logistics. Some methods do not optimize for the long distances, multiple nodes, and complex road networks inherent in cross-regional transportation, neglecting unique scenarios such as vehicle return to the nearest depot, multi-distribution-center collaboration, and segmented refrigeration energy consumption in cross-regional delivery. Other methods fail to model cargo damage attenuation, dual-layer time windows, temperature control energy consumption, and route optimization in a unified manner, resulting in poor total cost control, high fresh produce spoilage rates, and low hard time window satisfaction rates. Specifically, this manifests as: high vehicle empty-running rates and redundant mileage in independent delivery modes, leading to insufficient cross-regional collaborative scheduling capabilities; route optimization failing to incorporate dynamic changes in cross-regional traffic, easily resulting in delivery delays and late payment penalties; cargo damage and refrigeration energy consumption not accurately quantified with cross-regional transportation duration, leading to distorted cost accounting; and low algorithm solution efficiency, making it difficult to adapt to the real-time scheduling needs of large-scale cross-regional nodes.
[0005] In summary, existing cold chain logistics vehicle route optimization methods cannot meet the integrated optimization requirements of time-varying road networks, dynamic demand, and multi-constraint coupling in cross-regional transportation scenarios. There is an urgent need to construct an intelligent optimization framework that integrates cross-regional time-varying road network prediction, multi-granularity spatiotemporal demand prediction, and multi-constraint coupling modeling to achieve three-dimensional collaborative optimization of "vehicle-route-time" in cross-regional cold chain distribution, so as to support the efficient, low-carbon, and low-cost operation of cold chain logistics in complex and dynamic cross-regional environments.
[0006] Application content
[0007] The purpose of this application is to provide a route optimization method and system for cold chain vehicles to address the shortcomings of existing technologies that cannot meet the integrated optimization needs in cross-regional transportation scenarios.
[0008] To solve the above-mentioned technical problems, this application is implemented as follows:
[0009] Firstly, a route optimization method for cold chain vehicles is provided, including the following steps:
[0010] Construct a cross-regional cold chain transportation network and collect basic datasets of the transportation network. The cross-regional cold chain transportation network includes distribution center nodes, customer nodes, and energy supply nodes. The basic datasets of the transportation network include customer demand data, road network data, vehicle operation data, temperature control data, and energy supply data.
[0011] A cross-regional cold chain transportation parameter model is established based on the basic dataset of the transportation network. Specifically, customer service parameters are established based on the customer demand data, inter-node transportation parameters and cross-regional road segment parameters are established based on the road network data, vehicle operation parameters are established based on the vehicle operation data, temperature evolution parameters are established based on the temperature control data, and energy supply parameters are established based on the energy supply data.
[0012] Based on the aforementioned cold chain transportation parameter model, with the goal of minimizing overall transportation costs, the route optimization objective function for cold chain logistics vehicles is established by coupling transportation costs, refrigeration energy consumption costs, cold loss costs from opening doors, temperature degradation costs, energy replenishment costs, and cross-regional travel costs.
[0013] Customer service constraints, vehicle route continuity constraints, vehicle load constraints, time window constraints, time recursion constraints, temperature dynamic evolution constraints, door opening temperature rise constraints, energy balance constraints, temperature safety constraints, and cross-regional segmented speed constraints are coupled into the path optimization objective function to form a multi-constraint coupled path optimization model.
[0014] A mixed-integer programming solver is used to optimize the multi-constraint coupled path optimization model to obtain the optimal delivery path that satisfies temperature constraints, energy constraints, and time window constraints. The solution outputs vehicle driving route, customer access order, node arrival time, compartment temperature change information, and energy replenishment decision information.
[0015] Secondly, a route optimization system for cold chain vehicles is provided, including:
[0016] The construction module is used to build a cross-regional cold chain transportation network and collect basic datasets of the transportation network. The cross-regional cold chain transportation network includes distribution center nodes, customer nodes, and energy supply nodes. The basic datasets of the transportation network include customer demand data, road network data, vehicle operation data, temperature control data, and energy supply data.
[0017] A module is established to build a cross-regional cold chain transportation parameter model based on the transportation network basic dataset. The model includes establishing customer service parameters based on customer demand data, establishing inter-node transportation parameters and cross-regional road segment parameters based on road network data, establishing vehicle operation parameters based on vehicle operation data, establishing temperature evolution parameters based on temperature control data, and establishing energy supply parameters based on energy supply data.
[0018] The first coupling module is used to couple transportation cost, refrigeration energy consumption cost, door opening cold loss cost, temperature deterioration cost, energy replenishment cost and cross-regional travel cost based on the cold chain transportation parameter model, with the goal of minimizing the overall transportation cost, and to establish a route optimization objective function for cold chain logistics vehicles.
[0019] The second coupling module is used to couple customer service constraints, vehicle route continuity constraints, vehicle load constraints, time window constraints, time recursion constraints, temperature dynamic evolution constraints, door opening temperature rise constraints, energy balance constraints, temperature safety constraints, and cross-regional segmented speed constraints to the path optimization objective function, forming a multi-constraint coupled path optimization model.
[0020] The calculation module is used to perform optimization calculations on the multi-constraint coupled path optimization model using a mixed integer programming solver, to obtain the optimal delivery path that satisfies temperature constraints, energy constraints, and time window constraints, and outputs vehicle driving route, customer access order, node arrival time, compartment temperature change information, and energy replenishment decision information.
[0021] This application embodiment constructs a multi-constraint coupled optimization model that integrates dynamic temperature evolution, cold loss upon door opening, cross-regional road network differences, and energy replenishment decisions. This model achieves coordinated optimization of cold chain vehicle routes and resource allocation, effectively reducing overall transportation costs and improving the reliability, economy, and engineering applicability of cold chain transportation while ensuring temperature safety and delivery timeliness. Attached Figure Description
[0022] Figure 1 This is a flowchart of a route optimization method for cold chain vehicles provided in an embodiment of this application;
[0023] Figure 2 This is a structural diagram of the multi-constraint coupled optimization model provided in the embodiments of this application;
[0024] Figure 3 This is a schematic diagram of the time window and temperature energy constraint provided in the embodiments of this application;
[0025] Figure 4 This is a schematic diagram of the cross-regional cold chain logistics distribution network provided in the embodiments of this application;
[0026] Figure 5 This is a schematic diagram of the optimal delivery route for cold chain vehicles provided in the embodiments of this application;
[0027] Figure 6 This is a time-series diagram of vehicle driving and temperature changes provided in an embodiment of this application;
[0028] Figure 7 This is a schematic diagram of the structure of a route optimization system for cold chain vehicles provided in an embodiment of this application. Detailed Implementation
[0029] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0030] The embodiments of this application mainly address several technical defects of existing cold chain vehicle route optimization methods: (1) They do not model the continuous changes in the temperature of the vehicle compartment during transportation, making it difficult to truly reflect the decline in the quality of goods as temperature accumulates; (2) The instantaneous loss of cold energy caused by the opening of the vehicle door for loading and unloading at the customer's location is not fully considered, resulting in a low cost calculation result; (3) The speed limit requirements and toll differences of different road sections in cross-regional transportation are not included in the scope of route optimization considerations; (4) They lack relevant constraints on vehicle energy consumption and replenishment behavior, making it difficult for the model to be directly applied in actual engineering scenarios.
[0031] To address the aforementioned issues, this application proposes a multi-constraint coupled optimization method for cold chain logistics vehicle routes in cross-regional transportation. By constructing a mixed integer programming model that takes into account dynamic temperature changes, cold loss due to door opening, and energy replenishment constraints, the method achieves coordinated optimization of delivery route planning and cold chain safety management.
[0032] Specifically, this application focuses on the cold chain logistics scheduling and route optimization problem, particularly for cross-regional cold chain transportation scenarios, and proposes a vehicle route optimization method. Under the premise of meeting delivery time requirements and vehicle loading capacity, this method comprehensively considers factors such as real-time temperature changes in the vehicle compartment, cold energy loss due to door opening during loading and unloading, and energy replenishment. It uses a mixed-integer programming model to optimize the overall transportation task arrangement and vehicle routes. This method is mainly applicable to the transportation of goods with high temperature control requirements, such as fresh produce and pharmaceuticals, and can be implemented in the scheduling and decision-making management of regional distribution centers, urban cold chain distribution systems, and cross-regional cold chain logistics networks. Based on the Vehicle Routing Problem (VRP), cold chain logistics temperature control theory, and mixed-integer linear programming (MILP), this method falls within the research scope of extended vehicle routing problems with time windows, energy consumption constraints, and temperature constraints.
[0033] The following description, in conjunction with the accompanying drawings, details a route optimization method for cold chain vehicles provided in this application through specific embodiments and application scenarios.
[0034] like Figure 1 The diagram shown is a flowchart of a route optimization method for cold chain vehicles provided in an embodiment of this application. The method includes the following steps:
[0035] Step 101: Construct a cross-regional cold chain transportation network and collect basic datasets for the transportation network.
[0036] The cross-regional cold chain transportation network includes distribution center nodes, customer nodes, and energy supply nodes. The basic dataset of the transportation network includes customer demand data, road network data, vehicle operation data, temperature control data, and energy supply data.
[0037] Step 102: Establish a cross-regional cold chain transportation parameter model based on the basic dataset of the transportation network.
[0038] Specifically, customer service parameters are established based on the customer demand data, inter-node transportation parameters and cross-regional road segment parameters are established based on the road network data, vehicle operation parameters are established based on the vehicle operation data, temperature evolution parameters are established based on the temperature control data, and energy supply parameters are established based on the energy supply data.
[0039] Specifically, a dynamic evolution model of the compartment temperature can be constructed based on the current compartment temperature, inter-node transportation time, compartment natural temperature rise coefficient, and refrigeration unit efficiency coefficient to characterize the continuous temperature change process of cold chain vehicles during transportation. The temperature evolution parameters corresponding to the continuous temperature change process are used as components of the cold chain transportation parameter model.
[0040] Step 103: Based on the cold chain transportation parameter model, with the goal of minimizing the overall transportation cost, the transportation cost, refrigeration energy consumption cost, door opening cold loss cost, temperature deterioration cost, energy replenishment cost, and cross-regional travel cost are coupled to establish a route optimization objective function for cold chain logistics vehicles.
[0041] Specifically, the cooling loss value at the customer node can be calculated based on the service duration and temperature rise during door opening. The cooling loss cost during door opening can be determined based on the cooling loss value, and the cooling loss cost during door opening can be used as a component of the path optimization objective function.
[0042] Step 104: Couple customer service constraints, vehicle route continuity constraints, vehicle load constraints, time window constraints, time recursion constraints, temperature dynamic evolution constraints, door opening temperature rise constraints, energy balance constraints, temperature safety constraints, and cross-regional segmented speed constraints into the path optimization objective function to form a multi-constraint coupled path optimization model.
[0043] In this embodiment, the road segment type corresponding to the transportation path between nodes can also be identified based on road network data; corresponding segment driving speed and toll fee parameters can be configured according to the road segment type, and the road segment type includes at least highway segments, urban segments and speed-limited segments; the transportation time between nodes can be calculated based on the transportation distance between nodes and the corresponding segment driving speed, and the cross-regional segment speed constraint can be constructed based on the transportation time between nodes.
[0044] Step 105: Use a mixed integer programming solver to optimize the multi-constraint coupled path optimization model to obtain the optimal delivery path that satisfies temperature constraints, energy constraints, and time window constraints, and output vehicle driving route, customer access order, node arrival time, compartment temperature change information, and energy replenishment decision information.
[0045] In this embodiment, before using a mixed integer programming solver to optimize the multi-constraint coupled path optimization model, the energy replenishment requirement can be determined based on the remaining energy at the vehicle's current location and the predicted energy consumption for subsequent delivery routes. Candidate energy replenishment nodes that meet the energy replenishment requirement are selected from the energy replenishment nodes. The candidate energy replenishment nodes are added to the multi-constraint coupled path optimization model as optional stopping nodes to obtain the optimal delivery route that includes energy replenishment decisions.
[0046] This application embodiment constructs a multi-constraint coupled optimization model that integrates dynamic temperature evolution, cold loss upon door opening, cross-regional road network differences, and energy replenishment decisions. This model achieves coordinated optimization of cold chain vehicle routes and resource allocation, effectively reducing overall transportation costs and improving the reliability, economy, and engineering applicability of cold chain transportation while ensuring temperature safety and delivery timeliness.
[0047] This application provides a multi-constraint coupled optimization method for cold chain logistics vehicle routes, adapting to various conditions across regions, to meet the actual needs of cross-regional transportation. This method falls under the field of cold chain logistics scheduling and route optimization. Current route planning methods fail to fully reflect the continuous changes in vehicle temperature during transportation, accurately calculate the rapid loss of cold energy caused by opening doors for loading and unloading, and adequately consider the differences in speed limits and toll rates across different road sections. Furthermore, they do not integrate energy replenishment needs with route selection. To address these practical problems, this application uses mixed integer programming as its core tool, integrating a dynamic temperature change model, a door-opening temperature rise mutation model, and a dual energy consumption model for driving and refrigeration. Different driving and tolling rules are set for highways, urban roads, and speed-limited sections to construct a multi-constraint collaborative route optimization model. This model uses distribution centers, customer locations, and energy replenishment stations as basic network nodes, incorporating transportation costs, refrigeration energy consumption, door-opening cold loss, overheating cargo loss, replenishment costs, and cross-regional travel costs into the total cost, achieving a more realistic modeling and scheduling arrangement for the entire cross-regional cold chain transportation process. Compared with existing methods, the embodiments of this application can significantly reduce the overall distribution cost, improve the temperature control stability of goods, and the output driving route is more in line with the actual transportation conditions, and can be directly used for on-site scheduling and execution.
[0048] To achieve the above objectives, the embodiments of this application adopt the following technical solution: a multi-constraint coupling optimization method for cold chain logistics vehicle routes oriented towards cross-regional transportation, comprising the following steps:
[0049] Step 1: Establish a cross-regional cold chain transportation network and complete data modeling;
[0050] Construct a cold chain transportation network consisting of distribution centers, customer nodes, and energy replenishment stations, and provide parameterized descriptions of the key elements within the system.
[0051] Specifically, a cross-regional cold chain transportation network is constructed and data collection and preprocessing are carried out. A transportation network consisting of distribution center nodes, customer nodes, and energy supply nodes is established, and a node set N is defined, which includes distribution center O, customer node set C, and energy supply node set S.
[0052] In this embodiment, the relevant set is defined as follows:
[0053] Define the node set as:
[0054] In this context, distribution centers are collectively denoted as node O; customer points are denoted as set C; and nodes that can provide energy replenishment for refrigerated vehicles are denoted as set S.
[0055] It should be noted that energy supply nodes generally include charging stations and gas stations. When conducting route planning, these nodes are considered as selectable stopping points and incorporated into the overall scheduling decision-making process. The comprehensive cost function itself is a multi-objective coupled function. In the specific solution process, it is transformed into a single-objective optimization problem through linear weighting. The embodiments of this application are mainly applicable to fresh food, pharmaceutical products, and other goods that are sensitive to temperature changes, for use in cross-regional cold chain transportation scheduling operations.
[0056] Step 2: Collect and model basic data for the transportation system;
[0057] Specifically, the following data will be collected: customer demand information, including at least demand volume, service time, and time window; road network information, including at least mileage between nodes, speed limits and corresponding tolls for different road sections, as well as mileage, speed limits, and tolls between each energy supply node and other nodes; vehicle information, including at least loading capacity, initial energy reserves, and refrigeration performance parameters; and cold chain parameters, including at least initial temperature, ideal insulation temperature, temperature safety threshold, and parameters related to temperature changes.
[0058] In this embodiment, the following categories are specifically included: (1) Customer data: the demand for goods, service time and time window range of each customer; (2) Road network data: the driving distance between nodes, transportation time, speed limit requirements of each section and road toll; (3) Vehicle data: parameters such as the maximum load of the vehicle, initial energy storage, and power of the refrigeration unit; (4) Temperature control parameters: the initial temperature of the carriage, the ambient temperature, and the temperature safety limit; (5) Energy parameters: the energy consumption of the road section, the amount of energy that can be replenished and the minimum safe energy threshold.
[0059] Step 3: Construct a mixed-integer programming model;
[0060] Based on the data collected in step two, the optimization objective is to minimize the overall delivery cost, and a mixed integer programming model is built.
[0061] Specifically, a multi-constraint coupled optimization model for the cold chain is constructed, such as... Figure 2 As shown, based on the conventional vehicle routing optimization model, cold chain transportation characteristics and cross-regional constraints are incorporated to establish a mixed-integer programming model with the objective of minimizing comprehensive costs. These comprehensive costs include: transportation costs, refrigeration energy consumption costs, cold air loss costs from door opening, temperature quality degradation costs, energy replenishment costs, and cross-regional road tolls. A multi-dimensional constraint system is constructed, including at least the following constraints: single customer service constraint, vehicle route continuity constraint, load capacity constraint, time window constraint, time recursion constraint, temperature dynamic evolution constraint, door opening temperature rise constraint, energy consumption and replenishment balance constraint, and temperature safety red line constraint. Differential modeling is performed for cross-regional routes. Based on the speed limits and toll regulations of different regional road sections, corresponding segmented speeds are configured for each road section according to its type. The road section types at least cover highway sections, urban sections, and speed-limited sections. The transportation time between nodes is calculated by dividing the road section mileage by the corresponding segmented speed, thus reflecting the actual differences in cross-regional road networks in route optimization.
[0062] The sets involved in the model are defined as follows:
[0063] 3.1 Set Description:
[0064] O: Distribution center assembly, serving as the starting and final return point for all cold chain vehicle delivery tasks;
[0065] C: Customer node set, representing all locations that require delivery services;
[0066] S: represents the set of optional intermediate nodes in the transportation network that can serve as energy replenishment points, such as charging and refueling, supporting vehicles in making energy replenishment decisions during long-distance transportation.
[0067] The entire node set consists of distribution centers, customer nodes, and energy supply nodes, forming a complete transportation network space.
[0068] K: Available cold chain vehicle set, representing the set of homogeneous or heterogeneous cold chain transport vehicles under the jurisdiction of the distribution center.
[0069] 3.2 Parameters:
[0070] (1) Basic parameters
[0071] : Represents the actual road transport distance between node i and node j
[0072] The transportation time required for cold chain vehicles from node i to node j
[0073] Customer i's cold chain cargo delivery demand, expressed in tons (t), reflects the size of its orders and the extent to which transportation resources are used.
[0074] : The service time window allowed by customer i, used to constrain delivery timeliness.
[0075] : The total service time of a cold chain vehicle performing a delivery task at customer node i, including the time for unloading, handover and other operations, in minutes (min).
[0076] (2) Cost parameters
[0077] The unit mileage transportation cost of the vehicle on the road segment from point i to point j, expressed in yuan / km, includes driving-related expenses such as fuel and vehicle wear and tear.
[0078] Energy consumption cost of the refrigeration system per unit time.
[0079] Cost per unit of cooling loss caused by temperature rise due to opening the door for loading and unloading.
[0080] The unit cost of deterioration due to quality degradation caused by excessive temperature.
[0081] The unit cost of refueling a vehicle at a refueling station.
[0082] The toll fees payable by vehicles traveling through section i to j.
[0083] (3) Temperature-related parameters
[0084] : The real-time temperature inside the carriage when vehicle k arrives at node i.
[0085] The highest permissible temperature for cold chain cargo transportation, i.e., the upper limit of temperature control safety.
[0086] The efficiency coefficient of a refrigeration unit reflects the refrigeration system's ability to regulate the temperature of the passenger compartment.
[0087] : Temperature rise coefficient of the carriage under natural conditions.
[0088] The instantaneous increase in temperature inside the vehicle compartment when the vehicle opens its doors to unload goods at the customer's location.
[0089] (4) Energy parameters
[0090] : The total amount of available energy remaining when vehicle k arrives at node i.
[0091] The minimum energy safety limit to ensure the normal operation of a vehicle.
[0092] The total energy consumed by the vehicle during its journey from point i to point j, including driving and cooling.
[0093] The amount of energy a vehicle can replenish at supply point i.
[0094] (5) Cross-regional parameters
[0095] The average speed of the vehicle on the road segment from i to j is determined by taking values for three categories: highway, urban road, and speed-limited road.
[0096] The model parameters mentioned above cover five major categories: demand, road network, vehicles, temperature control, and cost, and can fully reflect the core elements of the entire cold chain scheduling process.
[0097] 3.3 Decision variables: including path 0-1 variables, time variables, temperature variables, energy variables and supply variables, forming a multi-dimensional coupled decision space.
[0098] : Indicates whether cold chain vehicle k performs a transportation task from node i to node j.
[0099] The time when vehicle k arrives at node i and begins service.
[0100] The interior temperature of vehicle k after completing its service at node i or upon arrival.
[0101] The remaining available energy of vehicle k at node i
[0102] Does the refrigerated transport vehicle perform an energy replenishment operation at node i?
[0103] 3.4 Objective Function
[0104] The overall objective of this application is to minimize the comprehensive cost of the entire cross-regional transportation process of cold chain logistics. This comprehensive cost includes transportation costs, refrigeration energy consumption costs, cold loss costs from opening doors, temperature degradation costs, energy replenishment costs, and cross-regional travel costs.
[0105] The refrigeration energy consumption cost is calculated based on vehicle transportation time. The longer the transportation time, the higher the refrigeration energy consumption cost. The refrigeration energy consumption cost is positively correlated with the transportation time. The specific calculation formula is as follows:
[0106]
[0107] in, The cost of refrigeration during cold chain transportation; This is the energy consumption cost coefficient per unit time for cooling; Let be the transport time from node i to j.
[0108] The cost of cold air loss when the door is opened is calculated as a function of the instantaneous temperature rise during customer node service and the service time. The degree of cold air loss when the door is opened increases with the increase of temperature rise and service time.
[0109] The specific calculation formula is as follows:
[0110]
[0111] in, Costs related to cold chain transportation losses caused by opening doors during the process; The cost coefficient for cold air loss when the door is open. Temperature rise at node opening For service hours.
[0112] The temperature degradation cost is used to characterize the loss of goods quality caused by temperature deviation from the safe range, and the temperature degradation cost satisfies the following relationship:
[0113]
[0114] in, Costs incurred due to temperature degradation during cold chain transportation; The cost coefficient for unit temperature degradation. This refers to the actual temperature inside the carriage. This is the upper limit of the safe temperature for cargo.
[0115] In this embodiment, the specific formula is expressed as follows:
[0116]
[0117] The definitions of each cost item are as follows:
[0118] Transportation costs during cold chain transportation:
[0119] Refrigeration costs during cold chain transportation:
[0120] Costs of cold chain damage caused by opening doors during cold chain transportation:
[0121] Costs resulting from temperature deterioration during cold chain transportation:
[0122] Energy replenishment costs during cold chain transportation:
[0123] Cross-regional transportation costs during cold chain logistics:
[0124] 3.5 Multi-dimensional Constraints
[0125] To ensure that the model's solution can be directly used in actual transportation and truly conforms to real physical operating rules, this method constructs multiple constraints from aspects such as single customer delivery, continuous route passage, vehicle load limit, time logic, temperature variation in the vehicle compartment, energy reserve management, and cross-regional road network characteristics. The specific contents are as follows.
[0126] 3.5.1 Customer-only service constraint
[0127] To ensure that each customer order is executed only once and to avoid duplicate deliveries or service omissions, it is stipulated that each customer node can only be accessed by one vehicle:
[0128]
[0129] 3.5.2 Vehicle route continuity: For any customer node, when a vehicle enters the node to perform a delivery task, it must leave the node and continue to perform subsequent transportation tasks, thereby ensuring that the inflow and outflow of the route at the node remain balanced.
[0130]
[0131] Vehicle origin constraint: Each cold chain vehicle must originate from the distribution center node to enter the distribution network and perform its transportation task. Vehicle destination constraint: After completing all its delivery tasks, each cold chain vehicle must return to the distribution center node to form a closed transportation path.
[0132]
[0133] 3.5.3 Vehicle capacity constraints: The total customer demand carried by each refrigerated truck during the entire delivery process shall not exceed its maximum load capacity in order to ensure that the transportation task can be completed within the physical capacity constraints.
[0134]
[0135] 3.5.4 Time Window Feasibility Constraint: The time it takes for the vehicle to arrive at the customer's location and begin service must be controlled within the customer's allowed service timeframe to meet the order's timeliness requirements. Figure 3 As shown.
[0136]
[0137] 3.5.5 Time-transfer logic constraint: When a vehicle travels from node i to node j, the time it starts serving at node j cannot be earlier than the time it left node i plus the transportation time between the two nodes.
[0138] For driving routes not selected by the model, we can set a sufficiently large constant M to automatically invalidate the corresponding time constraints without imposing additional restrictions on the values of the relevant variables.
[0139]
[0140] 3.5.6 Temperature Dynamic Evolution Constraints To clearly reflect the continuous changes in the temperature of the carriage during transportation, dynamic equations that can reflect the heat exchange process and the cooling compensation effect were added to the model.
[0141]
[0142] in, The temperature of the refrigerated truck k after it reaches its destination j. Let i be the temperature of the refrigerated truck k at its starting point i. For transportation time, The temperature rise coefficient of the carriage. This is the efficiency coefficient of the refrigeration unit.
[0143] 3.5.7 Door Opening Temperature Rise Constraint Considering that opening doors for loading and unloading will cause a sudden change in the temperature inside the vehicle, the constraint clearly states that after the customer service is completed, the temperature inside the vehicle will rise by a fixed amount based on the temperature before the service.
[0144]
[0145] 3.5.8 Cross-regional segmented speed constraints: To reflect the nonlinear variation in travel time caused by cross-regional road networks, the transport time of each segment is determined by the actual length of the segment and the average travel speed under the corresponding road type. The cross-regional path adopts a segmented speed limit model, and the transport time between nodes satisfies:
[0146]
[0147]
[0148] The speed limits change dynamically based on the type of road segment, including speed limits for expressways, urban areas, and speed-limited sections.
[0149] 3.5.9 Energy Constraints To ensure that vehicle energy use conforms to actual operating conditions, constraints require that energy fluctuations between nodes must maintain a balance between consumption and replenishment, while setting a minimum safe energy limit. The energy constraints satisfy:
[0150] and satisfy
[0151] in, Let be the remaining energy of vehicle k at node j. Let k be the remaining energy of vehicle k at node i. Let i be the energy consumption for road segment i→j. The energy supply for node i. A 0-1 variable indicating whether or not to replenish. The minimum safe energy threshold required for vehicle operation.
[0152] 3.5.10 Temperature Safety Constraints To ensure that the quality of cold chain goods is not affected, the constraints require that the temperature inside the vehicle compartment must not exceed the pre-set safety limit when the vehicle arrives at any point.
[0153]
[0154] 3.5.11 Variable type constraints:
[0155]
[0156] Step 4: Model Solving and Scheduling Scheme Generation;
[0157] A mixed-integer programming approach is used to solve the model, searching for the optimal vehicle routing scheme with the best overall cost while simultaneously satisfying time windows, temperature safety limits, energy thresholds, and capacity constraints. The final output includes: detailed vehicle routes, the access order and arrival time of each customer, the temperature change curve of the passenger compartment along the route, and the decision record of where to refuel.
[0158] Specifically, the model is solved to generate a scheduling scheme. Using a mixed integer programming solver, the optimal vehicle route scheme that meets multiple constraints such as temperature, energy and time window is obtained, and the vehicle driving route, customer access order, arrival time of each node, temperature change trend along the way and energy replenishment decision details are output.
[0159] Compared to traditional cold chain vehicle routing problem models, the embodiments in this application achieve significant improvements in modeling granularity, constraint completeness, solution stability, and engineering applicability. Specific advantages are as follows:
[0160] ① High model coupling and accurate characterization: The model incorporates dynamic temperature evolution, cold loss from door opening, cross-regional road network differences, and energy replenishment decisions into a unified optimization framework, which significantly improves the realism of cold chain transportation modeling.
[0161] ② Significantly enhanced temperature control safety: Through the synergistic effect of hard constraints on temperature safety red lines and soft constraints on temperature quality degradation, it can not only prevent quality failure events, but also allow for a moderate deviation from the ideal temperature within a controllable cost range in exchange for optimization in other aspects, thus greatly improving the temperature control compliance rate.
[0162] ③ Effective reduction in overall cost: The objective function not only includes driving costs, but also quantitatively accounts for implicit costs such as cooling energy consumption, cooling loss and quality degradation, making the optimization results closer to the true cost optimum.
[0163] ④ Strong cross-regional adaptability: The detailed modeling of segmented speed limits and differentiated tolls enables the scheduling scheme to effectively adapt to different regional road conditions and avoid distortion caused by globally uniform parameters.
[0164] ⑤ Good engineering feasibility: The built-in energy replenishment decision-making function ensures the energy endurance feasibility of long-distance cross-regional missions, and the plan can directly guide actual scheduling.
[0165] To verify the performance of this method in actual cross-regional cold chain distribution, a standard example is used below, employing the commonly used academic verification method of Gurobi exact solution, to examine it from two aspects: constraint satisfaction and cost optimization. Then, a real-world cross-regional cold chain distribution example is used to fully demonstrate the mixed-integer programming scheduling optimization method with time windows for vehicle routing provided in this application, so as to more intuitively present the operation process, key technical details, and actual optimization effects of this method.
[0166] I. Case Background and Parameter Settings
[0167] (1) Background setting of the delivery system
[0168] Suppose a large cold chain logistics company organizes cold chain transportation tasks centrally at a regional integrated distribution center (denoted as node O). Within the same delivery cycle, it needs to provide fresh and refrigerated goods delivery services to six customer nodes located in different urban areas. This delivery task has the following typical characteristics: ① The spatial distribution of customer nodes exhibits a cross-regional discrete distribution characteristic; ② Delivery orders originate from different types of customers, such as supermarkets, catering outlets, and community stores, each with differentiated time window requirements for delivery; ③ The cold chain transportation process requires strict temperature control to be completed within a limited time to ensure the quality of the goods; ④ Cold chain transportation vehicles must meet load capacity constraints, energy range constraints, and temperature safety constraints.
[0169] In this scenario, companies need to uniformly schedule a limited number of cold chain vehicles to achieve the scheduling goals of optimal routes, lowest costs, and safest temperature control.
[0170] (2) Vehicle resources and operational assumptions
[0171] The distribution center has a total of 3 identical cold chain transport vehicles, which are collectively referred to as: Each vehicle has the following uniform attributes: the maximum load capacity of the cold chain delivery vehicles is 5 tons; the vehicle's initial battery capacity is 80 kWh, and the battery capacity must be maintained at 10 kWh or more during operation; the initial temperature of the vehicle compartment is uniformly adjusted to 4℃. Transportation costs are calculated based on actual mileage, with a charge of 2 yuan per kilometer. Vehicles depart from the distribution center, complete deliveries to designated customers, and return to their origin. Each vehicle uses only one complete delivery route per trip. Toll rates vary for different road sections: 0.5 yuan per kilometer for highways, 0.2 yuan per kilometer for urban roads, and 0.3 yuan per kilometer for speed-limited sections.
[0172] (3) Customer nodes and order characteristics
[0173] Construct a cross-regional cold chain distribution network comprising one distribution center O and six customer nodes 1–6, with all nodes forming a fully connected graph, as shown below. Figure 4As shown in the diagram, the road network connects the distribution center to nodes 1, 4, and 6 via expressways, while the rest are urban or speed-limited sections, with specific speed limits of 80 km / h, 50 km / h, and 30 km / h respectively. Additionally, an energy replenishment station S is located between nodes 3 and 4, capable of replenishing 60 kWh of energy at a cost of 30 yuan per replenishment.
[0174] Let the set of client nodes be:
[0175] Each customer node corresponds to an independent order demand, whose attributes include: equivalent delivery demand, allowed service time window, and comprehensive service time window. Furthermore, customer orders can be further understood as follows: different customer orders may belong to different product types, such as fresh produce or refrigerated goods, but in the model, they are uniformly converted into equivalent transportation demand; the time window comprehensively reflects the customer's business hours, receiving capacity, and delivery time requirements; the service time is the total time of multiple operations, not just a single unloading time.
[0176] (4) Customer demand and time window data
[0177] The specific requirements parameters for customer nodes are shown in Table 1.
[0178] Table 1 Customer Node Requirements and Time Window Information
[0179]
[0180] (5) Temperature and energy parameters
[0181] Ambient temperature:
[0182] Upper temperature limit:
[0183] Energy floor:
[0184] Parameter settings:
[0185] Refrigeration unit efficiency coefficient:
[0186] Natural temperature rise coefficient of the carriage:
[0187] Instantaneous temperature rise during door opening operation:
[0188] Cost coefficient for cold air loss when the door is open:
[0189] Unit temperature degradation cost coefficient:
[0190] Unit cooling energy consumption cost coefficient:
[0191] Unit energy replenishment cost:
[0192] Energy consumption of road segment i→j:
[0193] Energy replenishment node single replenishment amount:
[0194] (6) Inter-node transport distance matrix
[0195] The transportation distances (unit: km) between nodes in the distribution network are shown in Table 2.
[0196] Table 2 Distance Matrix Between Nodes
[0197] O 0 80 120 150 60 100 140 1 80 0 65 110 40 70 95 2 120 65 0 75 85 50 60 3 150 110 75 0 100 80 55 4 60 40 85 100 0 55 105 5 100 70 50 80 55 0 70 6 140 95 60 55 105 70 0
[0198] II. Data Acquisition and Preprocessing
[0199] Before modeling for this case study, it's necessary to organize various basic data. Customer location information, which could originally be presented using GPS coordinates, is replaced by node numbers to simplify calculations. The distance matrix between nodes can be generated using GIS tools or historical driving data; however, actual road mileage is used as the basis for this calculation. During the task allocation phase, the maximum vehicle load capacity and total customer demand will be considered to ensure that the cargo load per vehicle does not exceed 5 tons. Time window parameters are derived from customer orders, and these parameter settings must meet actual delivery requirements. Once all data is organized, it is directly used as input for the mixed-integer programming model.
[0200] III. Constructing a Mixed Integer Programming Model
[0201] Based on the method described in the embodiments of this application, a multi-constraint coupled optimization model is constructed with the goal of minimizing the overall cost. The overall cost includes: transportation cost, refrigeration energy consumption cost, cold loss cost due to door opening, temperature degradation cost, energy replenishment cost, and cross-regional toll fees.
[0202] The model includes 3 vehicles, 6 customers, 3×7×7=147 binary variables and 3×6=18 continuous variables, and approximately 300 constraints.
[0203] IV. Model Solving
[0204] The model was solved using the Gurobi 9.5 solver, and the global optimal solution was obtained within 2.3 seconds. The optimal scheduling scheme and the details of each component cost are shown in Table 3.
[0205] V. Analysis of Solution Results
[0206] The optimal scheduling scheme is shown in Table 3 and Figure 5 As shown.
[0207] Table 3 Optimal Vehicle Route Scheme
[0208] 1 O → 4 → 1 → 5 → O 270 540 81 350 63 30 1064 2 O → 2 → 3 → O 345 690 103.5 240 70 0 1103.5 3 O → 6 → O 280 560 84 105 27 0 776
[0209] VI. Constraint Satisfaction Verification
[0210] (1) Time window constraint verification
[0211] The arrival times of each customer node are summarized in Table 4.
[0212] Table 4 Customer Node Arrival Time
[0213] 1 482.5 420-540 satisfy 2 480 480-600 satisfy 3 535 450-630 satisfy 4 420 420-510 satisfy 5 560 480-660 satisfy 6 510 510-720 satisfy
[0214] This demonstrates that all vehicles complete their delivery tasks within the customer-allowed time window, thus the proposed vehicle routing optimization model exhibits good feasibility under the time window constraint.
[0215] (2) Vehicle capacity constraint verification
[0216] The total amount of cargo carried by each vehicle shall not exceed the upper limit of 5 tons, thus meeting the load constraints.
[0217] (3) Temperature and energy constraint verification
[0218] like Figure 6 As shown, taking vehicle 1 as an example, when customer 4 finished serving, opening the door caused the temperature to jump from 4℃ to 9℃, but did not exceed the red line of 10℃. The refrigeration unit then operated, and the temperature dropped back to approximately 5℃ before reaching customer 1. After customer 1 served again, the temperature rose to approximately 10℃, and the transportation section continued to cool down. Throughout the entire process, the temperature never exceeded the absolute safety red line of 10℃. However, because the temperature exceeded the ideal temperature by 4℃ at certain times, a temperature quality degradation cost item was incurred. After vehicle 1 replenished its energy at the energy supply station, its range was guaranteed, avoiding mission interruption due to insufficient energy. The operating trajectories of the other vehicles also met the temperature and energy constraints.
[0219] VII. Optimization Result Analysis
[0220] A cost comparison was made between the proposed solution and the routine manual dispatching method: While manual dispatching also incorporates factors like cold-weather damage from door openings and road tolls, route planning largely relies on on-site experience. Under a unified cost accounting standard, the total cost of manual dispatching is approximately 3250 yuan, while this solution costs only 2943.5 yuan, representing a cost reduction of nearly 9.4%. Simultaneously, the total vehicle mileage decreased from 1050 kilometers to 895 kilometers, and the average vehicle loading rate increased by approximately 17%. In terms of temperature control, the temperature exceedance rate with manual dispatching was approximately 45%, while this solution did not experience any temperature exceedance issues throughout the entire process, demonstrating a significantly higher level of cargo temperature control safety.
[0221] Therefore, the method provided in this application embodiment can effectively reduce the overall cold chain distribution cost and improve scheduling efficiency and temperature protection capability under multiple strict constraints such as time window, load, temperature safety red line and energy threshold. It is applicable to cross-regional cold chain transportation scheduling of temperature-sensitive goods such as fresh food and medicine.
[0222] The multi-constraint coupling optimization method for cold chain logistics vehicle routes proposed in this application can effectively reduce overall distribution costs, improve distribution efficiency, and ensure temperature control safety under multiple constraints such as time window, vehicle capacity, temperature safety, and energy threshold. It is applicable to the scheduling of cross-regional cold chain transportation of fresh food, pharmaceutical products, and other temperature-sensitive goods.
[0223] like Figure 7 The diagram shown is a structural schematic of a route optimization system for cold chain vehicles provided in an embodiment of this application, comprising:
[0224] The construction module 710 is used to construct a cross-regional cold chain transportation network and collect basic datasets of the transportation network. The cross-regional cold chain transportation network includes distribution center nodes, customer nodes, and energy supply nodes. The basic datasets of the transportation network include customer demand data, road network data, vehicle operation data, temperature control data, and energy supply data.
[0225] Module 720 is used to establish a cross-regional cold chain transportation parameter model based on the transportation network basic dataset. Specifically, customer service parameters are established based on the customer demand data, inter-node transportation parameters and cross-regional road segment parameters are established based on the road network data, vehicle operation parameters are established based on the vehicle operation data, temperature evolution parameters are established based on the temperature control data, and energy supply parameters are established based on the energy supply data.
[0226] Specifically, module 720 is established to construct a dynamic evolution model of the compartment temperature based on the current compartment temperature, inter-node transportation time, compartment natural temperature rise coefficient, and refrigeration unit efficiency coefficient, so as to characterize the continuous temperature change process of cold chain vehicles during transportation, and to use the temperature evolution parameters corresponding to the continuous temperature change process as a component of the cold chain transportation parameter model.
[0227] The first coupling module 730 is used to establish a path optimization objective function for cold chain logistics vehicles based on the cold chain transportation parameter model, with the goal of minimizing the overall transportation cost. This function couples transportation cost, refrigeration energy consumption cost, door opening cold loss cost, temperature deterioration cost, energy replenishment cost, and cross-regional travel cost.
[0228] Specifically, the first coupling module 730 is used to calculate the cooling loss value at the customer node based on the service duration and door opening operation temperature rise corresponding to the customer node; determine the door opening cooling loss cost based on the cooling loss value; and use the door opening cooling loss cost as a component of the path optimization objective function.
[0229] The second coupling module 740 is used to couple customer service constraints, vehicle route continuity constraints, vehicle load constraints, time window constraints, time recursion constraints, temperature dynamic evolution constraints, door opening temperature rise constraints, energy balance constraints, temperature safety constraints, and cross-regional segmented speed constraints to the path optimization objective function, forming a multi-constraint coupled path optimization model.
[0230] The calculation module 750 is used to perform optimization calculations on the multi-constraint coupled path optimization model using a mixed integer programming solver, to obtain the optimal delivery path that satisfies temperature constraints, energy constraints, and time window constraints, and to output vehicle driving route, customer access order, node arrival time, compartment temperature change information, and energy replenishment decision information.
[0231] In this embodiment, the system further includes:
[0232] The identification module is used to identify the road segment type corresponding to the transportation path between nodes based on road network data; configure corresponding segment driving speed and toll fee parameters according to the road segment type, wherein the road segment type includes at least highway segments, urban segments and speed-limited segments; calculate the transportation time between nodes based on the transportation distance between nodes and the corresponding segment driving speed, and construct the cross-regional segment speed constraint based on the transportation time between nodes.
[0233] The determination module is used to determine the energy replenishment demand based on the remaining energy at the current location of the vehicle and the predicted energy consumption corresponding to the subsequent delivery route; to select candidate energy replenishment nodes that meet the energy replenishment demand from the energy replenishment nodes; and to add the candidate energy replenishment nodes as optional stopping nodes to the multi-constraint coupled path optimization model to obtain the optimal delivery route including energy replenishment decision.
[0234] This application embodiment constructs a multi-constraint coupled optimization model that integrates dynamic temperature evolution, cold loss upon door opening, cross-regional road network differences, and energy replenishment decisions. This model achieves coordinated optimization of cold chain vehicle routes and resource allocation, effectively reducing overall transportation costs and improving the reliability, economy, and engineering applicability of cold chain transportation while ensuring temperature safety and delivery timeliness.
[0235] This application also provides a computer-readable storage medium storing a computer program. When executed by a processor, this computer program implements the various processes of the above-described cold chain vehicle route optimization method embodiment and achieves the same technical effect. To avoid repetition, it will not be described again here. The computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.
[0236] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.
[0237] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.
[0238] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.
Claims
1. A route optimization method for cold chain vehicles, characterized in that, Includes the following steps: Construct a cross-regional cold chain transportation network and collect basic datasets of the transportation network. The cross-regional cold chain transportation network includes distribution center nodes, customer nodes, and energy supply nodes. The basic datasets of the transportation network include customer demand data, road network data, vehicle operation data, temperature control data, and energy supply data. A cross-regional cold chain transportation parameter model is established based on the basic dataset of the transportation network. Specifically, customer service parameters are established based on the customer demand data, inter-node transportation parameters and cross-regional road segment parameters are established based on the road network data, vehicle operation parameters are established based on the vehicle operation data, temperature evolution parameters are established based on the temperature control data, and energy supply parameters are established based on the energy supply data. Based on the aforementioned cold chain transportation parameter model, with the goal of minimizing overall transportation costs, the route optimization objective function for cold chain logistics vehicles is established by coupling transportation costs, refrigeration energy consumption costs, cold loss costs from opening doors, temperature degradation costs, energy replenishment costs, and cross-regional travel costs. Customer service constraints, vehicle route continuity constraints, vehicle load constraints, time window constraints, time recursion constraints, temperature dynamic evolution constraints, door opening temperature rise constraints, energy balance constraints, temperature safety constraints, and cross-regional segmented speed constraints are coupled into the path optimization objective function to form a multi-constraint coupled path optimization model. A mixed-integer programming solver is used to optimize the multi-constraint coupled path optimization model to obtain the optimal delivery path that satisfies temperature constraints, energy constraints, and time window constraints. The solution outputs vehicle driving route, customer access order, node arrival time, compartment temperature change information, and energy replenishment decision information.
2. The method according to claim 1, characterized in that, The establishment of a cross-regional cold chain transportation parameter model based on the aforementioned transportation network dataset specifically includes: Based on the current temperature of the vehicle compartment, the transportation time between nodes, the natural temperature rise coefficient of the compartment, and the efficiency coefficient of the refrigeration unit, a dynamic evolution model of the compartment temperature is constructed to characterize the continuous temperature change process of cold chain vehicles during transportation. The temperature evolution parameters corresponding to the continuous temperature change process are used as components of the cold chain transportation parameter model.
3. The method according to claim 1, characterized in that, The method couples transportation costs, refrigeration energy consumption costs, cold loss costs from door opening, temperature degradation costs, energy replenishment costs, and cross-regional travel costs to establish a route optimization objective function for cold chain logistics vehicles, specifically including: Calculate the cooling loss value at the customer node based on the service duration and temperature rise during door opening operations corresponding to the customer node. The cold loss cost of opening the door is determined based on the cold loss value, and the cold loss cost of opening the door is used as a component of the objective function of the path optimization.
4. The method according to claim 1, characterized in that, Also includes: Identify the road segment type corresponding to the transportation path between nodes based on road network data; Configure corresponding segmented driving speed and toll parameters according to the road segment type, wherein the road segment type includes at least highway segments, urban segments, and speed-limited segments; The inter-node transportation time is calculated based on the inter-node transportation distance and the corresponding segmented driving speed, and the cross-regional segmented speed constraint is constructed based on the inter-node transportation time.
5. The method according to claim 1, characterized in that, Before using a mixed-integer programming solver to perform optimization calculations on the multi-constraint coupled path optimization model, the following steps are also included: The energy replenishment requirement is determined based on the remaining energy at the vehicle's current location and the predicted energy consumption for subsequent delivery routes. Candidate energy supply nodes that meet the energy supply requirements are selected from the energy supply nodes; The candidate energy supply nodes are added as optional stopping nodes to the multi-constraint coupled path optimization model to obtain the optimal delivery path that includes energy supply decisions.
6. A route optimization system for cold chain vehicles, characterized in that, include: The construction module is used to build a cross-regional cold chain transportation network and collect basic datasets of the transportation network. The cross-regional cold chain transportation network includes distribution center nodes, customer nodes, and energy supply nodes. The basic datasets of the transportation network include customer demand data, road network data, vehicle operation data, temperature control data, and energy supply data. A module is established to build a cross-regional cold chain transportation parameter model based on the transportation network basic dataset. The model includes establishing customer service parameters based on customer demand data, establishing inter-node transportation parameters and cross-regional road segment parameters based on road network data, establishing vehicle operation parameters based on vehicle operation data, establishing temperature evolution parameters based on temperature control data, and establishing energy supply parameters based on energy supply data. The first coupling module is used to couple transportation cost, refrigeration energy consumption cost, door opening cold loss cost, temperature deterioration cost, energy replenishment cost and cross-regional travel cost based on the cold chain transportation parameter model, with the goal of minimizing the overall transportation cost, and to establish a route optimization objective function for cold chain logistics vehicles. The second coupling module is used to couple customer service constraints, vehicle route continuity constraints, vehicle load constraints, time window constraints, time recursion constraints, temperature dynamic evolution constraints, door opening temperature rise constraints, energy balance constraints, temperature safety constraints, and cross-regional segmented speed constraints to the path optimization objective function, forming a multi-constraint coupled path optimization model. The calculation module is used to perform optimization calculations on the multi-constraint coupled path optimization model using a mixed integer programming solver, to obtain the optimal delivery path that satisfies temperature constraints, energy constraints, and time window constraints, and outputs vehicle driving route, customer access order, node arrival time, compartment temperature change information, and energy replenishment decision information.
7. The system according to claim 6, characterized in that, The establishment module is specifically used to construct a dynamic evolution model of the compartment temperature based on the current compartment temperature, inter-node transportation time, compartment natural temperature rise coefficient, and refrigeration unit efficiency coefficient, so as to characterize the continuous temperature change process of cold chain vehicles during transportation, and to use the temperature evolution parameters corresponding to the continuous temperature change process as a component of the cold chain transportation parameter model.
8. The system according to claim 6, characterized in that, The first coupling module is specifically used to calculate the cooling loss value at the customer node based on the service duration and door opening operation temperature rise corresponding to the customer node; determine the door opening cooling loss cost based on the cooling loss value, and use the door opening cooling loss cost as a component of the path optimization objective function.
9. The system according to claim 6, characterized in that, Also includes: The identification module is used to identify the road segment type corresponding to the transportation path between nodes based on road network data; Configure corresponding segmented driving speed and toll parameters according to the road segment type. The road segment type includes at least highway segments, urban segments, and speed-limited segments. Calculate the inter-node transportation time based on the inter-node transportation distance and the corresponding segmented driving speed, and construct the cross-regional segmented speed constraint based on the inter-node transportation time.
10. The system according to claim 6, characterized in that, Also includes: The determination module is used to determine the energy replenishment needs based on the remaining energy at the vehicle's current location and the predicted energy consumption for subsequent delivery routes. Candidate energy supply nodes that meet the energy supply requirements are selected from the energy supply nodes; the candidate energy supply nodes are added as optional docking nodes to the multi-constraint coupled path optimization model to obtain the optimal delivery path that includes energy supply decisions.