Coordinated optimization system for container yard loading machine scheduling and truck release

Abstract

The application discloses a kind of set up and release of collaborative optimization system of port loader scheduling, it is related to port scheduling technical field, including the following steps: step 1, data acquisition: data set is obtained by MySQL database;Step 2, data processing;Step 3, establish set up and release of collaborative optimization model of port loader scheduling, divided into two parts of constraint condition and the construction of objective function;In the present application, intelligent scheduling, efficient collaboration: according to the complex field conditions of gas transport set up and release, establish set up and release of collaborative optimization mathematical model of port loader scheduling under matching scene, the optimal loader configuration and controllable release vehicle can be accurately calculated, to ensure the efficient use of resources, through the system not only can significantly improve the efficiency of field operation, but also can promote the collaboration and smoothness of overall operation process.

Description

Technical Field

[0001] This invention relates to the field of port scheduling technology, and in particular to a collaborative optimization system for scheduling port loading and unloading loader and releasing trucks. Background Technology

[0002] With the continuous innovation and rapid development of information technology, the future development of ports is moving towards smart ports. By using big data technology, artificial intelligence, and operations optimization to restructure and rebuild the internal operations of ports, port operation efficiency can be greatly improved, and ports can be promoted towards intelligence and smartness.

[0003] The port's distribution system, serving as a vital link between the port and its economic hinterland, is a crucial foundation for the port's survival and sustainable development. Its efficiency also influences other port operations, such as stacking and transshipment. While ports primarily handle dry bulk cargo such as ore, coal, and petroleum coke, and existing ship and machinery dispatching systems are largely digitized, the road transport system, a critical component, still relies on manual scheduling of loaders and vehicle release based on on-site conditions. This results in low operational efficiency, high workload for dispatchers, and excessively long waiting times for drivers of transport vehicles.

[0004] In view of the above problems, this paper designs a collaborative optimization system for port loading and unloading and loader scheduling in the context of bulk dry cargo ports. This system can automatically issue instructions and execute plans without manual intervention, fully leveraging the operating efficiency of loaders to achieve cost reduction and efficiency improvement. From an operations research perspective, a mixed integer programming model that conforms to the port site scenario is constructed. Based on real-time field data, a reasonable and feasible loader scheduling scheme and loading results are given to ensure on-site operational efficiency and reduce waiting time for drivers inside and outside the port.

[0005] Patent "2023105174865 Port Collection and Distribution Vehicle Scheduling and Matching Method and System" discloses a port collection and distribution vehicle scheduling and matching method. This method acquires task and transportation capacity resource information through an intelligent platform, utilizes a logistics scheduling model combined with a genetic algorithm, and continuously optimizes and improves existing operational processes and transportation routes. It intelligently calculates and reconstructs the logistics operation process, solving the problem that existing port collection and distribution operations cannot automatically allocate and adjust task resources, requiring manual operation. This invention synchronizes the information flow and physical logistics of each link in port collection and distribution, effectively improving the efficiency of the port industry chain.

[0006] Patent No. 2023111566572, "A Dynamic Intelligent Scheduling Method for Port Collection and Distribution Vehicles," discloses a dynamic intelligent scheduling method for port collection and distribution vehicles. It constructs a vehicle task selection decision unit model composed of a vehicle assignment model and a dynamic task allocation model. The vehicle assignment model selects vehicles to perform collection and distribution tasks at the start of the task, while the dynamic task allocation model dynamically allocates tasks to the selected vehicles. The task selection strategy of the model is obtained by solving the Res-D3QN algorithm. The solution process is divided into two stages: the learning stage uses multi-round incremental learning to train and update the Q-network parameters; the application stage first simulates the assignment of optimal vehicles, and then performs dynamic task allocation. This invention can dynamically formulate adaptive intelligent vehicle scheduling and assignment schemes according to changes in tasks and operating environments.

[0007] Disadvantages of existing technology: (1) Manually assigning loaders is inefficient and mainly involves communication with loader drivers and port vehicle drivers by phone. The work is busy and easily disturbed by phone calls from truck drivers. It is possible to make unreasonable decisions based solely on subjective consciousness.

[0008] (2) During the vehicle release process, we will receive many calls from drivers. Drivers outside the port will ask when they can enter the port, and drivers inside the port will ask when the relevant stacking positions can be allocated loaders.

[0009] (3) There is a lack of consideration for the mutual constraints between loader scheduling and vehicle release. Loader scheduling should be affected by factors such as vehicle waiting time outside the port, waiting time inside the port, and the total number of vehicles. Whether a vehicle can be released into the port also needs to be considered comprehensively based on factors such as the location of the loader and the waiting time. At present, all these factors are limited by manual decision-making and lack rationality and scientificity.

[0010] Regarding issues (1) and (2), some drivers may have the idea that they will be assigned a loader after being put into the port and waiting. This system combines loader scheduling and automatic vehicle release intelligence, and optimizes the two parts in a coordinated manner. This can effectively prevent vehicles that cannot be assigned a loader from entering the port and waiting. After the system decides the instructions for vehicle release and loader scheduling, it can greatly reduce remote telephone communication and reduce the burden on staff. At the same time, it can push the waiting time to the driver, so that the driver is aware of the situation and solves the unknown waiting psychological aspect of the driver.

[0011] To address problem (3), a comprehensive consideration of both can be achieved by coordinating the loader and the vehicle. Summary of the Invention

[0012] The purpose of this invention is to provide a collaborative optimization system for scheduling port loaders and releasing trucks, in order to solve the problems mentioned in the background art.

[0013] The core technology of this patent lies in 1. The establishment of a collaborative optimization system for port vehicle release and loader scheduling effectively avoids manual intervention by combining the two key processes, making the loader scheduling results more reasonable, improving the efficiency of on-site port operations, reducing the workload of relevant personnel, and providing drivers with push notifications of their waiting time for entering the port, so that drivers are aware of the situation and avoid excessive and unnecessary urging and telephone interactions.

[0014] 2. Construction of a mathematical model for the coordinated optimization of port vehicle loading and unloading and loader scheduling: A mixed-integer programming model suitable for the port operation environment is built using mathematical programming methods, effectively solving the port loading and unloading operation problems of dry bulk cargo ports. The model is also flexible, adapting to different dry bulk cargo ports by dynamically adjusting the objective function weights, making it widely applicable. Furthermore, the model solves very quickly; from data processing to the solver's computation of the model, the entire process can be completed within 1 second.

[0015] To achieve the above objectives, the present invention provides the following technical solution: a collaborative optimization system for scheduling port loading machines and releasing trucks, comprising the following steps: Step 1, Data Acquisition: Obtain the data dataset through a MySQL database; Step 2, Data Processing; Step 3: Establish a collaborative optimization model for port vehicle release and loader scheduling, which consists of two parts: the construction of constraints and the objective function. Step 4: Model Solving: Import the processed data into the port collection and distribution vehicle release and loader scheduling collaborative optimization model, and use the Scip optimization solver to calculate the allocation relationship between loaders and stacking positions; Step 5, Result Processing: After obtaining the allocation relationship between loaders and stacking positions, select vehicles to release according to the pre-arranged stacking positions; Step 6: Instruction push; Step 7: Complete the operation: After the collection and distribution vehicles enter the port, they arrive at the stacking position and work together with the loader. After completing the operation, they are reloaded and leave the port.

[0016] Compared with the prior art, the beneficial effects of the present invention are: 1. In this invention, intelligent scheduling and efficient collaboration are achieved: Based on the complex on-site conditions of port transportation, a mathematical model for collaborative optimization of port vehicle release and loader scheduling under matching scenarios is established. This model can accurately calculate the optimal loader configuration and controllable release of vehicles, thereby ensuring the efficient use of resources. This system can not only significantly improve on-site operation efficiency, but also promote the collaboration and smoothness of the overall operation process.

[0017] 2. In this invention, precise matching and cost optimization are achieved: By applying a collaborative optimization model for port vehicle release and loader scheduling, loaders and stacking positions can be precisely matched based on real-time data, significantly reducing on-site labor costs. This also impacts subsequent stacking and transshipment operations at dry bulk cargo ports. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the system framework of the present invention; Figure 2 This is a schematic diagram outlining the process of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Please see Figure 1-2 This invention provides a technical solution for a collaborative optimization system that integrates port loader scheduling and truck release: Includes the following steps: Step 1, Data Acquisition: Obtain the data dataset through a MySQL database; 1.1 Obtain information on vehicles with port access plans at the current time, including cargo type, planned stacking location, stop time outside the port and stop time inside the port, workload, driver, telephone number, etc. 1.2 Obtain information on the loaders used for port collection and distribution operations at the current time, including the loader name, type of cargo being handled, location of the stack, area of ​​the stack, latitude and longitude, etc.; 1.3 Obtain cargo type information to distinguish between red and black goods. According to cargo type, "coal", "coke", "petroleum coke" and "raw coal" are listed as "black goods", which are further subdivided into specific categories; cargo types that do not belong to black goods are uniformly identified as "red goods". 1.4 Obtain cargo type information to distinguish the time it takes for a loader to load a truck. It can be mainly divided into "ore", "powder", "petroleum and natural gas and products" and "coal", with times of 4, 8, 13 and 10 minutes respectively. 1.5 Obtain stack location information and its associated area information. The stack location information is mainly latitude and longitude. Divide the entire storage yard into areas, and each stack location belongs to an associated area. 1.6 Obtain port collection and distribution monitoring information to check the plan number, ship name, number of vehicles, etc. under each plan. The status of trucks is further subdivided into entering the site, entering the port, primary load, loading and unloading, and secondary load, corresponding to different numbers.

[0021] Step 2, Data Processing; 2.1 Remove redundant data from 2.1, and handle missing values, outliers, and unnecessary duplicate values; 2.2 Data transformation and integration: organizing the key data needed for model solving.

[0022] Step 3: Establish a collaborative optimization model for port vehicle release and loader scheduling, which consists of two parts: the construction of constraints and the objective function. 3.1 Constructing Constraints: in This refers to loaders and sets of loaders. Represents stack positions and stack position sets. Loader is the primary decision variable. Assigned to stack position The value is 1 if it is true, and 0 otherwise. 3.1.1 Each loader is assigned only one stacking position:

[0023] This constraint ensures that each loader is assigned one and only one stacking position; 3.1.2 Each stack location may be assigned a maximum of one loader:

[0024] This constraint ensures that no multiple loaders are assigned to the same stack location; 3.1.3 Stacking Location Allocation Constraints:

[0025] in A value of 1 indicates that 0 loaders are assigned to a stack position; otherwise, a value of 0 indicates that the stack position is allocated to a loader. The value 1 indicates that a stack position is assigned one loader, and 0 otherwise; this constraint ensures that a stack position is either assigned one loader or zero loaders. 3.1.4 Loader allocation constraints:

[0026]

[0027] in This indicates that the constants satisfy the relationship that their sum equals 0; and Representing sets respectively The size; this constraint ensures that the model can be calculated normally even when the number of loaders is less than the number of stack positions or the number of stack positions is less than the number of loaders; 3.1.5 Flexible Constraints on Vehicle Waiting Time

[0028]

[0029]

[0030] in In section 1.4, the stack position For the corresponding type of cargo, the time it takes for one loader to complete the transport truck operation; For stack position The corresponding number of vehicles; It is a sufficiently large number; As a time-flexible constraint, the above formula integrates the number of vehicles corresponding to the stack position with the time into a flexible constraint condition; 3.2 Construction of the objective function: 3.2.1 Time for the loader to reach the stacking position:

[0031] in The time matrix consists of three parts. The first part is the travel time from the loader's location to the stacking area. The second part is a matrix showing the correspondence between the type of cargo the loader is currently handling and the type of cargo at the stacking location. If the loader is handling red cargo, it can continue handling red cargo but cannot directly handle black cargo and needs to change shovels. If the loader is handling black cargo, it can directly handle black cargo or red cargo, but black cargo is more valuable and should be avoided as much as possible. The third part is for some loaders that, due to their structural limitations, can only handle red cargo or only black cargo. The second and third parts are difficult to characterize, so they are converted into time. For cases where the conditions are not met, the time values ​​are set higher but not completely restricted to ensure the reasonableness of the results. 3.2.2 Time-flexible constraints:

[0032] This formula corresponds to constraint 3.1.5; 3.2.3 Priority Factors:

[0033] in For stack position The priority of the stack positions ensures that higher priority stack positions are allocated first. 3.2.4 Objective Function:

[0034] in The weights are adjustable to flexibly address different problem requirements.

[0035] Step 4: Model Solving: Import the processed data into the port collection and distribution vehicle release and loader scheduling collaborative optimization model, and use the Scip optimization solver to calculate the allocation relationship between loaders and stacking positions; Step 5, Result Processing: After obtaining the allocation relationship between loaders and stacking positions, select vehicles to release according to the pre-arranged stacking positions; Simultaneously, the following conditions must be met for the vehicle release rules: (1) The maximum number of vehicles allowed in the port can be dynamically adjusted according to the situation on site. (2) Under a single stacking position, ensure that the number of vehicles in the port for red cargo stacking position is less than or equal to a vehicles and the number of vehicles in the port for black cargo stacking position is less than or equal to b vehicles to avoid too many vehicles waiting. (3) According to the rules, it can be divided into stacking positions in operation and pre-arranged stacking positions. A stacking position in operation means that there is a loader working on it, and a pre-arranged stacking position means that the loader will work on the next stacking position. For a stacking position in operation, if the number of heavy vehicles in the port is less than or equal to 3, it will no longer participate in automatic release but will push the pre-arranged stacking position to the loader. The pre-arranged stacking position will put the vehicle in advance, and then the loader will go to the pre-arranged stacking position to work, so as to avoid the stacking position from being continuously filled with vehicles and continuously working. (4) Add the operation of "fixing" the loader, that is, fixing the loader in this stack, so that trucks can be put into the stack continuously, but the maximum number of vehicles in the port and the number of vehicles of the type of cargo in the stack must be met.

[0036] Step 6: Instruction push; 6.1 Loader command push: The loader scheduling system determines whether the loader's push conditions are met, that is, whether there are any unfinished vehicles at the current working position of the loader. If all vehicles at the working position have completed their work, the loader will be pushed; otherwise, it will not be pushed. 6.2 Delivery of vehicle instructions: The vehicle release system determines whether a vehicle meets the conditions for entering the port. The restrictions include the maximum number of vehicles in the port and the maximum number of vehicles per stack according to the type of cargo, whether it is red cargo or black cargo. If the conditions are met, an instruction is sent to release the vehicle into the port; if the conditions are not met, no instruction is sent. 6.3 Estimated arrival time notification: Based on the current operational status and regional workload, the approximate arrival time is pushed to the drivers corresponding to each stack location, so that the drivers are aware of the situation and reduce the frequency of calling the dispatcher.

[0037] Step 7: Complete the operation: After the collection and distribution vehicles enter the port, they arrive at the stacking position and work together with the loader. After completing the operation, they are reloaded and leave the port.

[0038] The specific implementation process of this invention is summarized as follows: 1. After accepting an order, the vehicle enters the parking lot to wait; 2. Data reading and model calculation; 3. Intelligent scheduling of loaders; 4. Automatic release of vehicles entering and leaving the port; 5. Loaders and trucks leave after loading and unloading operations at the stacking points are completed.

[0039] Terminology Explanation: (1) Vehicle release for port collection and distribution: The main operation of releasing vehicles is for vehicles outside the port that have a collection and distribution plan. If they are not released, they need to wait in the parking lot outside the port.

[0040] (2) Stop time outside the port and stop time inside the port: For road transport vehicles, the time from order acceptance to being placed inside the port is called stop time outside the port; the time from entering the port to going to the relevant stacking position to start loading and unloading is called stop time inside the port. These two times are the key indicators for the entire road transport collection and distribution of vehicles.

[0041] (3) Mixed integer programming model: a mathematical programming model in which the decision variables are integers, often used to solve problems such as task timing and equipment allocation.

[0042] (4) Mathematical optimization solver: a type of professional tool used to solve mathematical programming or simulation optimization problems. It obtains the optimal solution by solving the model to solve related problems.

[0043] (5) Red Goods and Black Goods: Goods are mainly divided into two categories based on their type: red goods and black goods. Black goods mainly include coal, petroleum coke, etc., and are of high quality. Red goods mainly include minerals, such as iron ore powder and PB powder, and are of low quality. The two types of goods must not be mixed. When the loader is processing red goods, the next shipment cannot be petroleum coke, which is a type of black goods. (6) Entry into the parking lot, entry into the port, first weighing, loading and unloading, second weighing: Entry into the parking lot means entering the port; entry into the port means being released to enter the port; first weighing means the truck is weighed for the first time; loading and unloading means the loader loads and unloads the truck; second weighing means the truck is weighed for the second time, and the weight of the goods is the second weighing minus the first weighing. Only "entry into the parking lot" means the truck is outside the port, and the others mean it is inside the port.

[0044] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A collaborative optimization system integrating port loader scheduling and truck release, characterized in that: Includes the following steps: Step 1, Data Acquisition: Obtain the data dataset through a MySQL database; Step 2, Data Processing; Step 3: Establish a collaborative optimization model for port vehicle release and loader scheduling, which consists of two parts: the construction of constraints and the objective function. Step 4: Model Solving: Import the processed data into the port collection and distribution vehicle release and loader scheduling collaborative optimization model, and use the Scip optimization solver to calculate the allocation relationship between loaders and stacking positions; Step 5, Result Processing: After obtaining the allocation relationship between loaders and stacking positions, select vehicles to release according to the pre-arranged stacking positions; Step 6: Instruction push; Step 7: Complete the operation: After the collection and distribution vehicles enter the port, they arrive at the stacking position and work together with the loader. After completing the operation, they are reloaded and leave the port.

2. The collaborative optimization system for port loading and unloading loader scheduling and truck release according to claim 1, characterized in that: Step 1 specifically involves: 1.1 Obtain information on vehicles with port access plans at the current time, including cargo type, planned stacking location, stop time outside the port and stop time inside the port, workload, driver, and telephone number; 1. Obtain the loader information used for port collection and distribution operations at the current time, including loader name, type of goods currently being handled, location of the stack, area of ​​the stack, and latitude and longitude; 1.3 Obtaining cargo type information is used to distinguish between red and black goods. According to cargo type, "coal", "coke", "petroleum coke" and "raw coal" are listed as "black goods", which are further subdivided into specific categories; cargo types that do not belong to black goods are uniformly identified as "red goods". 1.4 Obtain cargo type information to distinguish the time it takes for a loader to load a truck. It can be mainly divided into "ore", "powder", "petroleum and natural gas and products" and "coal", with times of 4, 8, 13 and 10 minutes respectively. 1.5 Obtain stack location information and its associated area information. The stack location information is mainly latitude and longitude. Divide the entire storage yard into areas, and each stack location belongs to an associated area. 1.6 Obtain port collection and distribution monitoring information to check the plan number, ship name, and number of vehicles under each plan. The status of trucks is further subdivided into entry, port entry, primary load, loading and unloading, and secondary load, corresponding to different numbers.

3. The collaborative optimization system for port loading and unloading loader scheduling and truck release according to claim 2, characterized in that: Step 2 specifically involves: 2.1 Remove redundant data from 2.1, and handle missing values, outliers, and unnecessary duplicate values; 2.2 Data transformation and integration: Organize the key data needed for model solving.

4. The collaborative optimization system for port loading and unloading loader scheduling and truck release according to claim 1, characterized in that: Step 3 specifically involves: 3.1 Construction of Constraints: in This refers to loaders and sets of loaders. Represents stack positions and stack position sets. Loader is the primary decision variable. Assigned to stack position The value is 1 if it is true, and 0 otherwise. 3.1.1 Each loader is assigned only one stacking position: ； This constraint ensures that each loader is assigned one and only one stacking position; 3.1.2 Each stack position can be assigned a maximum of one loader: ； This constraint ensures that no multiple loaders are assigned to the same stack location; 3.1.3 Stacking position allocation constraints: ； in A value of 1 indicates that 0 loaders are assigned to a stack position; otherwise, a value of 0 indicates that the stack position is allocated to a loader. The value 1 indicates that a stack position is assigned one loader, and 0 otherwise; this constraint ensures that a stack position is either assigned one loader or zero loaders. 3.1.4 Loader allocation constraints: ； ； in This indicates that the constants satisfy the relationship that their sum equals 0; and Representing sets respectively The size; this constraint ensures that the model can be calculated normally even when the number of loaders is less than the number of stack positions or the number of stack positions is less than the number of loaders; 3.1.5 Flexible constraints on vehicle waiting time; ； ； ； in In section 1.4, the stack position For the corresponding type of cargo, the time it takes for one loader to complete the transport truck operation; For stack position The corresponding number of vehicles; It is a sufficiently large number; As a time-flexible constraint, the above formula integrates the number of vehicles corresponding to the stack position with the time into a flexible constraint condition; 3.2 Construction of the objective function: 3.2.1 Time for the loader to reach the stacking position: ； in The time matrix consists of three parts. The first part is the travel time from the loader's location to the stacking area. The second part is a matrix showing the correspondence between the type of cargo the loader is currently handling and the type of cargo at the stacking location. If the loader is handling red cargo, it can continue handling red cargo but cannot directly handle black cargo and needs to change shovels. If the loader is handling black cargo, it can directly handle black cargo or red cargo, but black cargo is more valuable and should be avoided as much as possible. The third part is for some loaders that, due to their structural limitations, can only handle red cargo or only black cargo. The second and third parts are difficult to characterize, so they are converted into time. For cases where the conditions are not met, the time values ​​are set higher but not completely restricted to ensure the reasonableness of the results. 3.2.2 Time-flexible constraints: ； This formula corresponds to constraints 3, 1, and 5; Priority factors 3.2.3: ； in For stack position The priority of the stack positions ensures that higher priority stack positions are allocated first. 3.2.4 Objective Function: ； in The weights are adjustable to flexibly address different problem requirements.

5. The collaborative optimization system for port loading and unloading loader scheduling and truck release according to claim 1, characterized in that: Step 5 specifically involves: Simultaneously, the following conditions must be met for the vehicle release rules: (1) The maximum number of vehicles allowed in the port can be dynamically adjusted according to the situation on site. (2) Under a single stacking position, ensure that the number of vehicles in the port for red cargo stacking position is less than or equal to a vehicles and the number of vehicles in the port for black cargo stacking position is less than or equal to b vehicles to avoid too many vehicles waiting. (3) According to the rules, it can be divided into stacking positions in operation and pre-arranged stacking positions. A stacking position in operation means that there is a loader working on it, and a pre-arranged stacking position means that the loader will work on the next stacking position. For a stacking position in operation, if the number of heavy vehicles in the port is less than or equal to 3, it will no longer participate in automatic release but will push the pre-arranged stacking position to the loader. The pre-arranged stacking position will put the vehicle in advance, and then the loader will go to the pre-arranged stacking position to work, so as to avoid the stacking position from being continuously filled with vehicles and continuously working. (4) Add the operation of "fixing" the loader, that is, fixing the loader in this stack, so that trucks can be put into the stack continuously, but the maximum number of vehicles in the port and the number of vehicles of the type of cargo in the stack must be met.

6. The collaborative optimization system for port loading and unloading loader scheduling and truck release according to claim 1, characterized in that: Step 6 specifically involves: 6.1 Loader command push: The loader scheduling system determines whether the loader's push conditions are met, that is, whether there are any unfinished vehicles at the current working position of the loader. If all vehicles at the working position have completed their work, the loader will be pushed; otherwise, it will not be pushed. 6.2 Delivery vehicle instruction push: The vehicle release system determines whether a vehicle meets the conditions for entering the port. The restrictions include the maximum number of vehicles in the port and the maximum number of vehicles per stack according to the type of cargo, whether it is red cargo or black cargo. If the conditions are met, an instruction is sent to release the vehicle into the port; if the conditions are not met, no instruction is sent. 6.3 Estimated arrival time notification: Based on the current operational status and regional workload, the approximate arrival time is pushed to the drivers corresponding to each stack location, so that the drivers are aware of the situation and reduce the frequency of calling the dispatcher.

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