A coal transportation planning method and system for large power generation groups

Abstract

The application discloses a coal transportation planning method and system for large power generation groups, and the method comprises the following steps: constructing a procurement-transportation coordination optimization model in the previous month of coal procurement based on historical procurement-transportation data, establishing a large-scale ship unified scheduling model based on the relationship between the number of ship uses and the ship load utilization rate, and constructing a procurement-transportation-inventory management two-layer optimization model based on the upper procurement-transportation coordination optimization model and the lower large-scale ship unified scheduling model; a linear programming algorithm is used to solve the upper procurement-transportation coordination optimization model, and the total procurement quantity plan and the total transportation quantity plan in the dispatching period are output; the current power plant wharf system state quantity is acquired, and based on the total procurement quantity plan, the total transportation quantity plan and the power plant wharf system state quantity, a greedy algorithm is used to solve the lower large-scale ship unified scheduling model, and the ship optimized scheduling plan is output. The application can guarantee safety production and reduce the unloading port frequency.

Description

Technical Field

[0001] This invention relates to the field of coal transportation planning technology, and specifically to a coal transportation planning method and system for large power generation groups. Background Technology

[0002] Cost control and efficient coal distribution are crucial for the development of large power generation groups. Taking one such group as an example, in 2012, coal procurement costs exceeded 30 billion yuan; transportation costs were around 2 billion yuan; and demurrage costs due to uncontrollable factors and unreasonable transportation arrangements amounted to approximately 70 million yuan. Therefore, how large power generation enterprises can balance ensuring safe production at power plants with the interests of shipping companies, avoiding unreasonable coal procurement and transportation plans, and ultimately saving on total coal costs, is a problem worthy of study.

[0003] The aforementioned practical problems mainly involve three aspects of coordination and optimization: procurement optimization, transportation optimization, and inventory management. Existing methods that comprehensively consider procurement, transportation, and inventory management have established relatively good overall optimization model frameworks, solving problems such as coal blending and maximum berthing tonnage limits at wharves. However, existing models still have certain gaps with reality, mainly in the following aspects: 1) They typically assume a limited number of transport vehicle types and a sufficiently large number available, an assumption that differs significantly from the reality of diverse ship types in a fleet; 2) They typically ignore sailing time, an assumption that contradicts the long transit times of maritime transport; 3) They do not consider the number of vessels that can berth at power plant wharves, an assumption that does not reflect the reality of a limited number of berths at power plant wharves. Without considering this constraint, concentrated vessel dispatch may cause congestion at power plant wharves, leading to unnecessary demurrage costs due to increased waiting times. Furthermore, from a methodological perspective, existing research mainly adopts an overall modeling approach, using existing mixed-integer programming algorithms or MDP (Markov Decision Process) dynamic programming methods to solve the problem. Mixed-integer programming models are often large in scale, leading to complex solutions and making it difficult to obtain a feasible solution within a reasonable timeframe. Summary of the Invention

[0004] In order to overcome the defects and shortcomings of the existing technology, the purpose of this invention is to provide a coal transportation planning method and system for large power generation groups. The shipping plan obtained by this method can better guide ship scheduling, reduce the number of unloading times, and avoid problems such as congestion at unloading terminals while ensuring safe production.

[0005] To achieve the above objectives, the present invention employs the following technical means:

[0006] The first aspect of this invention is to provide a coal transportation planning method for large power generation groups, comprising:

[0007] A coal procurement-transportation coordination optimization model is constructed based on historical procurement-transportation data. A large-scale unified ship scheduling model is established based on the relationship between the number of ships used and the utilization rate of ship deadweight. Based on the upper-level procurement-transportation coordination optimization model and the lower-level large-scale unified ship scheduling model, a two-layer optimization model for procurement-transportation-inventory management is constructed.

[0008] The upper-level procurement-transportation coordination optimization model is solved using a linear programming algorithm, and the total procurement volume plan and total transportation volume plan within the scheduling cycle are output.

[0009] Obtain the current status variables of the power plant terminal system, which include: remaining coal transportation task information, ship status information table, power plant terminal inventory information table, inventory warning time information table, and available berth number information table; based on the total procurement plan, total transportation plan, and the power plant terminal system status variables, use a greedy algorithm to solve the lower-level large-scale unified ship scheduling model and output the optimized ship scheduling plan.

[0010] As a further improvement to the present invention, the upper-level procurement-transportation coordination optimization model includes:

[0011] A. Construct the objective function by minimizing the sum of coal procurement costs and transportation costs.

[0012]

[0013] B. Purchase Quantity Balance Constraint

[0014]

[0015] C. Demand balance constraint

[0016]

[0017] D. Supplier-related constraints

[0018] 1) Maximum purchase quantity constraint

[0019]

[0020] 2) Purchase ratio constraint

[0021]

[0022] E. Decision-making constraints

[0023]

[0024]

[0025] In the formula, the subscript m represents the loading port number 1…M, the subscript n represents the unloading terminal number 1…N, the subscript s represents the supplier number 1…S, and the subscript l represents the coal type 1…L; constants This represents the unit price of coal purchases; it is a constant. This represents the unit price for ocean freight, a constant. This represents the demand for coal, a constant. Indicates the upper limit of the purchase quantity, constant α s Indicates the upper limit of the purchase ratio; variable This represents the amount of coal purchased during the scheduling period, a variable. This indicates the amount of coal transported within the scheduling cycle.

[0026] As a further improvement to the present invention, the lower-level large-scale ship unified scheduling model includes:

[0027] A. Objective function

[0028]

[0029]

[0030] B. Traffic volume planning constraints

[0031]

[0032] C. Transfer of Terminal Inventory Status

[0033]

[0034]

[0035] D. Terminal safety stock constraints

[0036]

[0037] E. Dock physical constraints

[0038] 1) Constraints on the number of berths at the wharf

[0039]

[0040] 2) Maximum berthing tonnage constraints for vessels

[0041]

[0042] F. Maximum deadweight constraints of ships

[0043]

[0044] G. Ship scheduling plan variable constraints

[0045] x t,m,n,v=f(x) 1,m',n',v ,x 2,m',n',v ...,x t-1,m',n',v (17)

[0046] H. Decision-making constraints

[0047] x t,m,n,v =0 or 1 (18)

[0048]

[0049] Where the subscript t represents the discrete time period 1…T, and the subscript v represents the ship number 1…V; constants Indicates the initial inventory level, a constant. Indicates the upper / lower limits of inventory levels, a constant d. t,n,l Represents coal consumption, a constant. Indicates the unloading time of ships at the port of discharge, a constant. Indicates the number of berths at the unloading port, a constant. Indicates the maximum deadweight of a ship, a constant. Indicates the maximum berthing tonnage at the unloading port terminal; variable The variable x represents the coal inventory at the end of time period t. t,m,n,v Let x represent the ship scheduling plan variable. t,m,n,v =1 indicates that at the end of time period t, ship v is arranged to go to route (mn), x t,m,n,v =0, indicating that at the end of time period t, no vessel v was scheduled to travel to route (mn). The variable... This represents the amount of coal loaded by ship v at the end of time period t, transporting type l coal on route (mn); the function f(x) 1,m',n',v ,x 2,m',n',v ...,x t-1,m',n',v This indicates that the current ship scheduling plan variable is a function of all historical ship scheduling plan variables.

[0050] As a further improvement to this invention, the step of using a greedy algorithm to solve the lower-level large-scale unified ship scheduling model and outputting an optimized ship scheduling plan specifically includes:

[0051] S3.1 Based on the aforementioned total procurement volume plan and total transportation volume plan: Total Transportation Plan Initial inventory at the power plant dock and the number of power plant dock berths Initialize the following information tables: Remaining Coal Transportation Task Information Table, Ship Status Information Table, Power Plant Terminal Inventory Information Table, Inventory Warning Time Information Table, and Available Berth Number Information Table, as detailed below:

[0052] Information table of remaining coal transportation tasks

[0053] Ship Status Information Form S v,t =1(21)

[0054] Power Plant Wharf Inventory Information Table

[0055] Inventory alert time information table

[0056] Available berths information table

[0057] Among them, variables This represents the number of available berths (n) at the power plant terminal at the end of time period t, used to prevent congestion at the power plant terminal. m,n Indicates the travel time for the route (mn);

[0058] S3.2 Read the available vessel information table, remaining coal transportation task information table, power plant terminal inventory information table, inventory warning time information table, and available berth number information table at time t; determine whether there are available vessels; if there are available vessels, calculate the set of routes H that can be arranged for transportation tasks. t Otherwise, let t = t + 1 and repeat the step; where set H t Let H represent the set of routes for which transportation tasks can be scheduled at the end of time period t, where any element (m, n) represents a route for which a task can be scheduled at the end of time period t. t Any element (m,n) satisfies equation (25);

[0059]

[0060] S3.3 If H t If not empty, then the set H of routes where transportation tasks can be arranged... t Select the transportation task with the shortest inventory warning time; otherwise, let t = t + 1 and return to S3.2.

[0061] S3.4 Based on the principle of ship suitability, a small-scale knapsack model is established to optimize the solution of the single coal transportation volume and transportation ships for the transportation task during this period.

[0062] S3.5 Update each information table based on the single coal transport volume and transport vessel for the transport task, and repeat the above steps until all transport tasks are arranged.

[0063] As a further improvement of the present invention, the inventory warning time parameter The specific calculation formula is as follows:

[0064]

[0065] in, T indicates the duration during which safe production can be maintained. m,n Indicates the travel time for the route (mn);

[0066] This indicates that arranging the transport of type l coal on route (mn) at the end of time period t cannot meet the minimum safety stock limit of terminal n. Require;

[0067] This indicates that the l-th type of coal transportation is scheduled on route (mn) at the end of time period t; and for different transportation tasks (m,n,l), The smaller the value, the greater the demand for coal transportation and the more urgent the transportation task.

[0068] As a further improvement to the present invention, the step of establishing a smaller-scale knapsack model based on the principle of ship suitability for loading to optimize the solution of the single coal transportation volume and transportation vessel for the transportation task during this period includes:

[0069] Define the system state variable as the remaining coal transportation task quantity information. Ship Status Information Sheet Power Plant Wharf Inventory Information Table The knapsack model is specifically as follows:

[0070] A. Objective function

[0071]

[0072]

[0073]

[0074] B. Ship set constraints can be used.

[0075] Let the available ship set at time t0 be denoted as but Any element v in the equation satisfies equation (30).

[0076]

[0077] C. Maximum berthing tonnage limits at the wharf

[0078]

[0079] D. Safety Stock Upper Limit Constraint

[0080]

[0081]

[0082] E. Principles of Ship Suitability

[0083]

[0084]

[0085] Among them, set V t This represents the set of available ships at the end of time period t, where any element represents a ship v available at the end of time period t. Represents the set of available ships at time t0; variable S v,t S represents the ship's state variable, specifically whether ship v is available at time t. v,t =1 indicates that ship v is available at time t, S v,t =0 indicates the ship is unavailable; variable The variable r represents the remaining coal transportation task (mnl). t,m,n,l This represents the portion of coal transported by ships at the end of time period t (mnl), where the transport volume is r.

[0086] Let the optimal solution of equations (27)-(35) be: That is, to arrange ship v at time t0 * The optimal single-trip transport capacity for coal is:

[0087] As a further improvement to the present invention, the updated information tables for single coal transport volume and transport vessels based on transport tasks include:

[0088] 1) Update on remaining coal transportation tasks

[0089] If a portion of the transportation volume is allocated for a transportation task (m0-n0-l0) The remaining coal transportation workload will decrease as follows:

[0090]

[0091] 2) Ship status information update

[0092] Assuming the sailing time and return time are the same, if ship v is scheduled at time t0... * When transporting coal, the time periods occupied by ships include three periods: sailing, unloading, and returning. The time period cannot be used repeatedly, as follows:

[0093]

[0094] 3) Update of power plant dock inventory information

[0095] After the coal arrives at the terminal, update the terminal inventory information as follows:

[0096]

[0097] 4) Inventory alert time update

[0098] variable This indicates the inventory warning time for a transportation task (mnl), used to measure the urgency of the task; it is used to begin transporting coal. Upon arrival at power plant dock n0, the inventory warning time is updated as follows:

[0099]

[0100] 5) The berth number information table can be updated.

[0101] If a ship is scheduled to transport coal to the power plant dock n0 at time t, then dock n0 will be at... If an occupied berth is added during a given time period, the number of available berths decreases by 1. Specifically:

[0102]

[0103] A second aspect of the present invention is to provide a coal transportation planning system for large power generation groups, comprising:

[0104] The model building module is used to build a coal procurement-transportation coordination optimization model based on historical procurement-transportation data, establish a large-scale unified ship scheduling model based on the relationship between the number of ships used and the ship deadweight utilization rate, and build a two-layer optimization model for procurement-transportation-inventory management based on the upper-level procurement-transportation coordination optimization model and the lower-level large-scale unified ship scheduling model.

[0105] The upper-level solution module is used to solve the upper-level procurement-transportation coordination optimization model using linear programming algorithms, and outputs the total procurement volume plan and total transportation volume plan within the scheduling cycle;

[0106] The lower-level solution module is used to obtain the current status variables of the power plant terminal system. These status variables include: remaining coal transportation task information, ship status information table, power plant terminal inventory information table, inventory warning time information table, and available berth number information table. Based on the total procurement plan, total transportation plan, and the power plant terminal system status variables, a greedy algorithm is used to solve the lower-level large-scale unified ship scheduling model and output the optimized ship scheduling plan.

[0107] A third aspect of the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the coal transportation planning method for large power generation groups.

[0108] A fourth aspect of the present invention is to provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the coal transportation planning method for large power generation groups.

[0109] Compared with the prior art, the present invention has the following beneficial effects:

[0110] This invention's coal transportation planning method comprehensively considers the coal procurement and transportation costs of large power generation groups, the "suitable loading principle" of shipping companies' vessels, and the safety production interests of subordinate power plants. It establishes a more realistic optimization model and, based on the analysis of the problem's characteristics, decouples the complex coal procurement-transportation-inventory management problem by establishing a two-layer optimization model to solve the coal procurement problem and the vessel scheduling problem separately. This reduces the model solution complexity caused by the unified modeling approach in traditional research and designs a greedy algorithm to solve the large-scale unified vessel scheduling problem. The optimized procurement plan can effectively reduce enterprise costs, and the optimized shipping plan can effectively guide vessel scheduling, ensuring safe production while reducing the number of unloading ports and avoiding port congestion. Attached Figure Description

[0111] Figure 1 This is a flowchart of a coal transportation planning method for large power generation groups according to the present invention.

[0112] Figure 2 This is a diagram illustrating the method steps of a specific embodiment of the present invention;

[0113] Figure 3 A graph showing the variable relationships in the procurement-transfer coordination optimization model;

[0114] Figure 4 The flowchart shows the algorithm for solving the large-scale ship scheduling problem using a greedy algorithm.

[0115] Figure 5 Timeline for task N1 at the power plant dock;

[0116] Figure 6 Chart showing inventory changes at the power plant terminal N1;

[0117] Figure 7 A specific flowchart of a coal transportation planning method for large power generation groups is provided for embodiments of the present invention;

[0118] Figure 8 This invention provides a coal transportation planning system for large power generation groups; Detailed Implementation

[0119] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. 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 should fall within the scope of protection of the present invention.

[0120] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0121] like Figure 1 As shown, the first objective of this invention is to provide a coal transportation planning method for large power generation groups, comprising the following steps:

[0122] The first aspect of this invention is to provide a coal transportation planning method for large power generation groups, comprising:

[0123] S1. A coal procurement-transportation coordination optimization model is constructed based on historical procurement-transportation data. A large-scale unified ship scheduling model is established based on the relationship between the number of ships used and the utilization rate of ship deadweight. Based on the upper-level procurement-transportation coordination optimization model and the lower-level large-scale unified ship scheduling model, a two-layer optimization model for procurement-transportation-inventory management is constructed.

[0124] S2 uses a linear programming algorithm to solve the upper-level procurement-transportation coordination optimization model and outputs the total procurement volume plan and total transportation volume plan within the scheduling cycle.

[0125] S3, obtain the current status variables of the power plant terminal system, which include: remaining coal transportation task information, ship status information table, power plant terminal inventory information table, inventory warning time information table, and available berth number information table; based on the total procurement plan, total transportation plan, and the power plant terminal system status variables, use a greedy algorithm to solve the lower-level large-scale unified ship scheduling model and output the optimized ship scheduling plan.

[0126] This invention provides a unified approach to coal procurement, transportation, and inventory management, constructing a large-scale maritime transport optimization and scheduling method with low model complexity and reasonable solution time. By unifying the procurement, transportation, and inventory management problem and establishing a two-layer optimization model, this invention reduces the model solution complexity caused by the unified modeling approach in traditional research. A greedy algorithm is designed to reduce the solution time for large-scale maritime transport inventory path optimization problems. The resulting procurement plan can effectively reduce enterprise costs, and the resulting shipping plan can effectively guide ship scheduling, ensuring safe production while reducing the number of unloading ports and avoiding port congestion.

[0127] Among them, a greedy algorithm was designed to solve the lower-level optimization model, which transformed the large-scale ship scheduling problem into a series of small-scale knapsack problems to be solved independently, thus reducing the solution time of the large-scale ship scheduling problem.

[0128] The present invention will be described in detail below with reference to specific embodiments:

[0129] This invention discloses a coal transportation planning method for large power generation groups. The main focus is on the following aspects:

[0130] First, we will consider the procurement, transportation and inventory management issues in a unified manner and establish a two-layer optimization model: the upper layer is to establish a pre-month coal procurement and transportation coordination optimization model with the minimum total cost, and the lower layer is to establish a unified ship scheduling model with the minimum number of ships used and the maximum utilization rate of ship deadweight.

[0131] Then, the linear programming algorithm is used to solve the upper-level procurement-transportation coordination optimization model, and the total procurement volume plan and total transportation volume plan within the scheduling cycle are output.

[0132] Finally, based on the total transport volume plan within the scheduling period obtained from the upper-level model, a greedy algorithm is designed to solve the large-scale unified ship scheduling problem of the lower-level model.

[0133] like Figure 2 As shown in the figure, an embodiment of the present invention discloses a coal transportation planning method for large power generation groups, including the following steps:

[0134] Step 1: Consider the procurement, transportation, and inventory management issues holistically, and establish a two-tiered optimization model. The upper tier is a pre-monthly coal procurement and transportation coordination optimization model aimed at minimizing total costs. The lower tier is a large-scale unified ship scheduling model aimed at minimizing the number of ships used and maximizing ship deadweight utilization. Details are as follows:

[0135] (1) The optimization model for upper-level procurement-transportation coordination is as follows:

[0136] A. Objective function

[0137]

[0138] Equation (1) represents the minimum sum of coal procurement costs and transportation costs.

[0139] B. Purchase Quantity Balance Constraint

[0140]

[0141] Equation (2) indicates that during the scheduling period, the total purchase volume of a certain type of coal from all suppliers at a certain port is equal to the total transportation volume of that type of coal from that port to all wharves.

[0142] C. Demand balance constraint

[0143]

[0144] Equation (3) indicates that within the scheduling cycle, the amount of coal transported from all ports to a certain terminal meets the coal consumption of that terminal.

[0145] D. Supplier-related constraints

[0146] 1) Maximum purchase quantity constraint

[0147]

[0148] Equation (4) restricts the purchase of each type of coal from each port and each supplier to not exceed its maximum supply.

[0149] 2) Purchase ratio constraint

[0150]

[0151] To prevent a supplier from monopolizing the coal market, equation (5) considers the upper limit of the purchase ratio of each supplier.

[0152] E. Decision-making constraints

[0153]

[0154]

[0155] The variable relationships in the upper-level procurement-transfer coordination optimization model are as follows: Figure 3 As shown.

[0156] In the formula, the subscript m represents the loading port number 1…M, the subscript n represents the unloading terminal number 1…N, the subscript s represents the supplier number 1…S, and the subscript l represents the coal type 1…L; constants This represents the unit price of coal purchases; it is a constant. This represents the unit price for ocean freight, a constant. This represents the demand for coal, a constant. Indicates the upper limit of the purchase quantity, constant α s Indicates the upper limit of the purchase ratio; variable This represents the amount of coal purchased during the scheduling period, a variable. This indicates the amount of coal transported within the scheduling cycle.

[0157] (2) Lower-level large-scale ship unified scheduling model

[0158] A. Objective function

[0159]

[0160]

[0161] Equation (8) represents minimizing the number of ships in use; Equation (9) represents maximizing the utilization rate of ship carrying capacity.

[0162] B. Traffic volume planning constraints

[0163]

[0164] Equation (10) indicates that the total transport volume of a certain type of coal in all time periods is equal to the planned total transport volume of that type of coal within the scheduling cycle.

[0165] C. Transfer of Terminal Inventory Status

[0166]

[0167]

[0168] Equations (11) and (12) represent the inventory status transition and initial inventory constraints of terminal n. Wherein, in tT m,n The coal transport mission scheduled at time t arrives at the port at time t. Therefore, the increase in inventory at time t is tT. m,n Coal transport volume at any given time.

[0169] D. Terminal safety stock constraints

[0170]

[0171] Equation (13) indicates that at the end of time period t, the inventory of terminal n should meet the safety stock requirements.

[0172] E. Dock physical constraints

[0173] 1) Constraints on the number of berths at the wharf

[0174]

[0175] Equation (14) indicates that at the end of time period t, the number of ships berthed at terminal n is no greater than the number of its berths. In Equation (14), the left side represents the number of ships berthed during time period t and the number of ships that have not completed unloading during time period t.

[0176] 2) Maximum berthing tonnage constraints for vessels

[0177]

[0178] Equation (15) indicates that within the scheduling cycle, the tonnage of the ships arriving at berth n is not greater than the maximum berthing tonnage of that berth.

[0179] F. Maximum deadweight constraints of ships

[0180]

[0181] Equation (16) indicates that the carrying capacity of ship v is not greater than its maximum deadweight.

[0182] G. Ship scheduling plan variable constraints

[0183] x t,m,n,v =f(x) 1,m',n',v ,x 2,m',n',v ...,x t-1,m',n',v (17)

[0184] Equation (17) indicates that ship resources must be exclusively occupied during a certain period of time. Therefore, the ship scheduling plan variable for time period t is related to the historical ship scheduling plans for all routes from time 1 to t-1.

[0185] H. Decision-making constraints

[0186] x t,m,n,v =0 or 1 (18)

[0187]

[0188] Where the subscript t represents the discrete time period 1…T, and the subscript v represents the ship number 1…V; constants Indicates the initial inventory level, a constant. Indicates the upper / lower limits of inventory levels, a constant d. t,n,l Represents coal consumption, a constant. Indicates the unloading time of ships at the port of discharge, a constant. Indicates the number of berths at the unloading port, a constant. Indicates the maximum deadweight of a ship, a constant. Indicates the maximum berthing tonnage at the unloading port terminal; variable The variable x represents the coal inventory at the end of time period t. t,m,n,v Let x represent the ship scheduling plan variable. t,m,n,v=1 indicates that at the end of time period t, ship v is arranged to go to route (mn), x t,m,n,v =0, indicating that at the end of time period t, no vessel v was scheduled to travel to route (mn). The variable... This represents the amount of coal loaded by ship v at the end of time period t, transporting type l coal on route (mn); the function f(x) 1,m',n',v ,x 2,m',n',v ...,x t-1,m',n',v This indicates that the current ship scheduling plan variable is a function of all historical ship scheduling plan variables.

[0189] Step 2: Use linear programming algorithm to solve the upper-level procurement-transportation coordination optimization model, and output the total procurement volume plan and total transportation volume plan within the scheduling cycle;

[0190] Step 3: Based on the total transport volume plan within the scheduling period obtained from the upper-level model, design a greedy algorithm to solve the large-scale unified ship scheduling problem in the lower-level model, and output the optimized ship scheduling plan. The specific algorithm flowchart is as follows: Figure 4 As shown, the specific solution method is as follows:

[0191] Step 3.1 Calculate the total transportation plan within the scheduling period based on the upper-level procurement-dispatch coordination optimization model. Initial inventory at the power plant dock and the number of power plant dock berths Initialize the various information tables as follows:

[0192] Information table of remaining coal transportation tasks

[0193] Ship Status Information Form S v,t =1(21)

[0194] Power Plant Wharf Inventory Information Table

[0195] Inventory alert time information table

[0196] Available berths information table

[0197] Step 3.2 Read the available vessel information table, remaining coal transportation task information table, power plant terminal inventory information table, inventory warning time information table, and available berth number information table at time t. If available vessels exist, calculate the set of routes H that can be arranged for transportation tasks. t Otherwise, if t = t + 1, repeat the step. Where, set H... t Let H represent the set of routes for which transportation tasks can be scheduled at the end of time period t, where any element (m, n) represents a route for which a task can be scheduled at the end of time period t. tAny element (m, n) should satisfy equation (25).

[0198]

[0199] Step 3.3 If H t If not empty, then the set H of routes where transportation tasks can be arranged... t Select the transportation task with the shortest inventory warning time (m0-n0-l0); otherwise, t=t+1, and return to step 3.2.

[0200] The inventory warning time parameter The specific calculation formula is as follows:

[0201]

[0202] in, T indicates the duration during which safe production can be maintained. m,n This indicates the travel time for the route (mn).

[0203] This indicates that arranging the transport of type l coal on route (mn) at the end of time period t cannot meet the minimum safety stock limit of terminal n. Require;

[0204] This indicates that the l-th type of coal transportation on route (mn) can be arranged at the end of time period t; and for different transportation tasks (m,n,l), The smaller the value, the greater the demand for coal transportation and the more urgent the transportation task.

[0205] Step 3.4 Based on the principle of ship suitability, establish a small-scale knapsack model to optimize the solution of the single coal transportation volume and transportation ships for the transportation task during this period.

[0206] Define the system state variable as the remaining coal transportation task quantity information. Ship Status Information Sheet Power Plant Wharf Inventory Information Table The knapsack model is specifically as follows:

[0207] A. Objective function

[0208]

[0209]

[0210]

[0211] B. Ship set constraints can be used.

[0212] Let the available ship set at time t0 be denoted as but Any element v in the equation satisfies equation (30).

[0213]

[0214] C. Maximum berthing tonnage limits at the wharf

[0215]

[0216] D. Safety Stock Upper Limit Constraint

[0217]

[0218]

[0219] E. Principles of Ship Suitability

[0220]

[0221]

[0222] Among them, set V t This represents the set of available ships at the end of time period t, where any element represents a ship v available at the end of time period t. Represents the set of available ships at time t0; variable S v,t S represents the ship's state variable, specifically whether ship v is available at time t. v,t =1 indicates that ship v is available at time t, S v,t =0 indicates the ship is unavailable; variable The variable r represents the remaining coal transportation task (mnl). t,m,n,l This represents the portion of coal transported by ships at the end of time period t (mnl), where the transport volume is r.

[0223] Let the optimal solution of equations (27)-(35) be: That is, to arrange ship v at time t0 * The optimal single-trip transport capacity for coal is:

[0224] Step 3.5 Update each information table, repeating the above steps until all transportation tasks are arranged. The update time for each information table is shown in Table 1.

[0225] Table 1 Characteristics of each information table

[0226]

[0227] The specific updates to each information table are as follows:

[0228] (1) Update on remaining coal transportation tasks

[0229] If a portion of the transportation volume is allocated for a transportation task (m0-n0-l0) The remaining coal transportation workload will decrease, as shown in equation (36).

[0230]

[0231] (2) Ship status information update

[0232] Assuming the sailing time and return time are the same, if ship v is scheduled at time t0... * When transporting coal, the time periods occupied by ships include three periods: sailing, unloading, and returning. It cannot be reused within a time period, satisfying the following constraints:

[0233]

[0234] (3) Update of power plant terminal inventory information

[0235] After the coal arrives at the terminal, update the terminal inventory information as shown in equation (38).

[0236]

[0237] (4) Inventory alert time update

[0238] variable This indicates the inventory warning time for a transportation task (mnl), used to measure the urgency of the task. Coal transportation begins. When the power plant dock n0 is reached, the inventory warning time is updated to formula (39).

[0239]

[0240] (5) The berth number information table can be updated.

[0241] If a ship is scheduled to transport coal to the power plant dock n0 at time t, then dock n0 will be at... If an occupied berth is added during a time period, the number of available berths decreases by 1, as shown in equation (40).

[0242]

[0243] The method of the present invention will be further described below with reference to specific application embodiments.

[0244] Example:

[0245] Take three power plants under a certain group as an example. The scale of the example is shown in Table 2. Equations (1)-(7) are solved using Lingo 11.0 software. Equations (8)-(19) are solved using a greedy algorithm designed in VC++.

[0246] Table 2 Scale of the Case

[0247]

[0248] The procurement and transportation volume plan is shown in Table 3. The procurement plan aims to reduce procurement and transportation costs.

[0249] Taking the actual procurement and transportation information of 13 power plants under a certain group as an example, the cost savings are calculated. After optimization, the approximate savings in procurement and transportation costs are shown in Table 4.

[0250] Table 3 Procurement and Transportation Plan

[0251]

[0252] Table 4 Cost Comparison

[0253]

[0254] The shipping plan can be obtained by the greedy algorithm, as shown in Table 5. It can be seen that: (1) the dispatch information of each route is specified to the ship name; (2) only the last transport of each route is not fully loaded, and the utilization rate of the ship's carrying capacity is relatively high; (3) the ship's occupation time includes the sailing time of each route, the unloading time of the power plant terminal and the return time of the ship. The scheduling result can be directly applied to the actual situation.

[0255] Table 5 Ship Scheduling Schedule

[0256]

[0257] A task timeline can be drawn based on the sailing times, unloading times, and return times of each route at the power plant terminal N1, such as... Figure 5 The horizontal axis represents the scheduling period; the vertical axis represents each task; the gray area represents the navigation tasks from each port to power plant terminal N1 and the return tasks; the black area represents the unloading tasks at power plant terminal N1. Observe the changes in inventory at each power plant as follows: Figure 6 .

[0258] It can be seen that: (1) the coal inventory at terminal N1 at each time meets the requirements for safe production; (2) the maximum number of berths at power plant terminal N1 is 1, therefore, the unloading time of the second and third transportation tasks is staggered. Figure 5 (The black parts of the second and third lines do not overlap), which ensures that ships arriving at the power plant dock for the third time can be safely unloaded without waiting.

[0259] To test the algorithm's performance, computational examples of different sizes were constructed, as shown in Table 6. The solution time and various performance metrics were recorded, as shown in Table 7. To compare with the standard mixed-integer programming (MIP) branch and bound algorithm, assuming a short transportation time, the lower-level problem can be transformed into a standard mixed-integer linear programming (MILP) problem.

[0260] Table 6 Different Case Sizes

[0261]

[0262] Table 7 Algorithm Performance Comparison

[0263]

[0264]

[0265] Ship deadweight utilization rate refers to the ratio of actual carrying capacity to maximum deadweight capacity. The ratio of a terminal exceeding its safety stock is defined as the percentage of terminals that cannot meet safety production requirements out of the total number of terminals, reflecting whether the scheduling plan can guarantee the safe operation of the power plant.

[0266] It can be seen that: (1) MILP can only solve small and medium-sized problems, while the greedy algorithm can solve large-scale ship scheduling problems within 10 seconds; (2) the performance indicators of the results obtained by the greedy algorithm are excellent; (3) the scheduling strategy obtained by the greedy algorithm can ensure the safe production requirements of each power plant.

[0267] like Figure 7 As shown, a second aspect of the present invention is to provide a coal transportation planning system for large power generation groups, comprising:

[0268] The model building module is used to build a coal procurement-transportation coordination optimization model based on historical procurement-transportation data, establish a large-scale unified ship scheduling model based on the relationship between the number of ships used and the ship deadweight utilization rate, and build a two-layer optimization model for procurement-transportation-inventory management based on the upper-level procurement-transportation coordination optimization model and the lower-level large-scale unified ship scheduling model.

[0269] The upper-level solution module is used to solve the upper-level procurement-transportation coordination optimization model using linear programming algorithms, and outputs the total procurement volume plan and total transportation volume plan within the scheduling cycle;

[0270] The lower-level solution module is used to obtain the current status variables of the power plant terminal system. These status variables include: remaining coal transportation task information, ship status information table, power plant terminal inventory information table, inventory warning time information table, and available berth number information table. Based on the total procurement plan, total transportation plan, and the power plant terminal system status variables, a greedy algorithm is used to solve the lower-level large-scale unified ship scheduling model and output the optimized ship scheduling plan.

[0271] like Figure 8 As shown, a third-party aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the coal transportation planning method for large power generation groups.

[0272] The coal transportation planning method for large power generation groups includes the following steps:

[0273] S1. A coal procurement-transportation coordination optimization model is constructed based on historical procurement-transportation data. A large-scale unified ship scheduling model is established based on the relationship between the number of ships used and the utilization rate of ship deadweight. Based on the upper-level procurement-transportation coordination optimization model and the lower-level large-scale unified ship scheduling model, a two-layer optimization model for procurement-transportation-inventory management is constructed.

[0274] S2 uses a linear programming algorithm to solve the upper-level procurement-transportation coordination optimization model and outputs the total procurement volume plan and total transportation volume plan within the scheduling cycle.

[0275] S3, obtain the current status variables of the power plant terminal system, which include: remaining coal transportation task information, ship status information table, power plant terminal inventory information table, inventory warning time information table, and available berth number information table; based on the total procurement plan, total transportation plan, and the power plant terminal system status variables, use a greedy algorithm to solve the lower-level large-scale unified ship scheduling model and output the optimized ship scheduling plan.

[0276] A fourth aspect of the present invention is to provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the coal transportation planning method for large power generation groups.

[0277] The coal transportation planning method for large power generation groups includes the following steps:

[0278] S1. A coal procurement-transportation coordination optimization model is constructed based on historical procurement-transportation data. A large-scale unified ship scheduling model is established based on the relationship between the number of ships used and the utilization rate of ship deadweight. Based on the upper-level procurement-transportation coordination optimization model and the lower-level large-scale unified ship scheduling model, a two-layer optimization model for procurement-transportation-inventory management is constructed.

[0279] S2 uses a linear programming algorithm to solve the upper-level procurement-transportation coordination optimization model and outputs the total procurement volume plan and total transportation volume plan within the scheduling cycle.

[0280] S3, obtain the current status variables of the power plant terminal system, which include: remaining coal transportation task information, ship status information table, power plant terminal inventory information table, inventory warning time information table, and available berth number information table; based on the total procurement plan, total transportation plan, and the power plant terminal system status variables, use a greedy algorithm to solve the lower-level large-scale unified ship scheduling model and output the optimized ship scheduling plan.

[0281] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0282] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0283] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1The function specified in one or more boxes.

[0284] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0285] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A coal transportation planning method for large power generation groups, characterized in that, include: A coal procurement-transportation coordination optimization model is constructed based on historical procurement-transportation data. A large-scale unified ship scheduling model is established based on the relationship between the number of ships used and the utilization rate of ship deadweight. Based on the upper-level procurement-transportation coordination optimization model and the lower-level large-scale unified ship scheduling model, a two-layer optimization model for procurement-transportation-inventory management is constructed. The upper-level procurement-transportation coordination optimization model is solved using a linear programming algorithm, and the total procurement volume plan and total transportation volume plan within the scheduling cycle are output. Obtain the current status variables of the power plant terminal system, which include: remaining coal transportation task information, ship status information table, power plant terminal inventory information table, inventory warning time information table, and available berth number information table; based on the total procurement plan, total transportation plan, and the power plant terminal system status variables, use a greedy algorithm to solve the lower-level large-scale unified ship scheduling model and output the optimized ship scheduling plan; The lower-level large-scale unified ship scheduling model includes: A. Objective function (8) (9) B. Traffic volume planning constraints (10) C. Transfer of Terminal Inventory Status (11) (12) D. Terminal safety stock constraints (13) E. Dock physical constraints 1) Constraints on the number of berths at the wharf (14) 2) Maximum berthing tonnage constraints for vessels (15) F. Maximum deadweight constraints of ships (16) G. Ship scheduling plan variable constraints (17) H. Decision-making constraints (18) (19) Among them, subscript t Representing discrete time period 1 …T， Subscript v Indicates ship number 1 …V ;constant Indicates the initial inventory level, a constant. / Indicates the upper / lower limit of inventory level, a constant. Represents coal consumption, a constant. Indicates the unloading time of ships at the port of discharge, a constant. Indicates the number of berths at the unloading port, a constant. Indicates the maximum deadweight of a ship, a constant. Indicates the maximum berthing tonnage at the unloading port terminal; variable Represents the coal inventory at the end of time period t, variable Indicates the variable of ship scheduling plan, At time t, it indicates that at the end of time period t, ship v is arranged to arrive at the route (mn). , indicating that at the end of time period t, no vessel v was scheduled to travel to route (mn). The variable is... This represents the amount of coal loaded by ship v at the end of time period t, transporting type l coal along route (mn); the function... This indicates that the current ship scheduling plan variable is a function of all historical ship scheduling plan variables; Subscript m Indicates the port number of the loading port. 1…M subscript n Indicates the port number of the unloading terminal. 1…N subscript l Indicates coal type 1… L ;variable This indicates the amount of coal transported within the scheduling period; The method employs a greedy algorithm to solve the lower-level large-scale unified ship scheduling model and outputs an optimized ship scheduling plan, specifically including: S3.1 Based on the aforementioned total procurement volume plan and total transportation volume plan: Total Transportation Plan Initial inventory at the power plant dock and the number of power plant dock berths Initialize the following information tables: Remaining Coal Transportation Task Information Table, Ship Status Information Table, Power Plant Terminal Inventory Information Table, Inventory Warning Time Information Table, and Available Berth Number Information Table, as detailed below: Information table of remaining coal transportation tasks (20) Ship Status Information Sheet (twenty one) Power Plant Wharf Inventory Information Table (twenty two) Inventory alert time information table (twenty three) Available berths information table (twenty four) Among them, variables This represents the number of available berths at the power plant terminal (n) at the end of time period t, used to prevent congestion at the power plant terminal. Indicates the travel time for the route (mn); S3.2 Read t The system includes tables of available vessel information, remaining coal transport capacity information, power plant terminal inventory information, inventory warning time information, and available berth information; it determines whether available vessels exist, and if so, calculates the set of routes that can be used for transport tasks. Otherwise t = t +1, re-execute this step; where, the set express t The set of routes for which transportation tasks can be scheduled at the end of the time period, where any element represents... t Flight routes where missions can be scheduled at the end of the time period (m,n), The set of routes that can be scheduled for transportation at the end of time period t Any element (m,n) satisfies equation (25); (25) in, Indicates flight route (mn) The sailing time; S3.3 If If not empty, then gather on routes where transportation tasks can be arranged. Select the transportation task with the shortest inventory warning time. ， Otherwise t = t +1, return to S3.2; S3.4 Based on the principle of ship suitability, a small-scale knapsack model is established to optimize the solution of the single coal transportation volume and transportation ships for the transportation task during this period. S3.5 Update each information table based on the single coal transport volume and transport vessel based on the transport task, and repeat the above steps until all transport tasks are arranged. The method, based on the principle of ship suitability for loading, establishes a small-scale knapsack model to optimize the solution of the single coal transportation volume and transportation vessel for the transportation task during this period, including: Define the system state variable as the remaining coal transportation task quantity information. Ship Status Information Sheet Power Plant Wharf Inventory Information Table The knapsack model is specifically as follows: A. Objective function (27) (28) (29) B. Ship set constraints can be used. remember t Available ship assemblies at time 0 are ,but any element v Satisfying equation (30) (30) C. Maximum berthing tonnage limits at the wharf (31) D. Safety Stock Upper Limit Constraint (32) (33) E. Principles of Ship Suitability (34) And it is an integer (35) Among them, set This represents the set of available ships at the end of time period t, where any element represents a ship v available at the end of time period t. express t Available ship set at time 0; variables This represents the ship's state variable, specifically whether ship v is available at time t. This indicates that ship v is available at time t. Indicates that the ship is unavailable; variable The remaining coal transportation task quantity represents the transportation task (mnl); variable This represents the portion of coal transported by ships at the end of time period t (mnl), where the transport volume is r. Let the optimal solution of equations (27)-(35) be: That is, in t Arrange ships at 0 o'clock v * The optimal single-trip transport capacity for coal is: .

2. The coal transportation planning method for large power generation groups according to claim 1, characterized in that, The upper-level procurement-transportation coordination optimization model includes: A. Construct the objective function by minimizing the sum of coal procurement costs and transportation costs. B. Purchase Quantity Balance Constraint C. Demand balance constraint D. Supplier-related constraints 1) Maximum purchase quantity constraint 2) Purchase ratio constraint E. Decision-making constraints In the formula, the subscript s Indicates supplier number 1…S ,constant This represents the unit price of coal purchases; it is a constant. This represents the unit price for ocean freight, a constant. This represents the demand for coal, a constant. Indicates the upper limit of the purchase quantity, a constant. Indicates the upper limit of the purchase ratio; variable This indicates the amount of coal procured during the scheduling cycle.

3. The coal transportation planning method for large power generation groups according to claim 1, characterized in that, The inventory warning time parameter The specific calculation formula is as follows: (26) in, This indicates the duration during which safe production can be maintained; , indicating in t Flight routes are scheduled at the end of the time period. (mn) The first l Coal transportation cannot meet the needs of the dock n Minimum safety stock level Require; ,express t Flight routes are scheduled at the end of the time period. (mn) The first l Coal transportation; and for different transportation tasks (m, n,l) , The smaller the value, the greater the demand for coal transportation and the more urgent the transportation task.

4. The coal transportation planning method for large power generation groups according to claim 1, characterized in that, The updated information tables for single coal transport volume and transport vessels based on transport tasks include: 1) Update on remaining coal transportation tasks If a transportation task is arranged (m 0 -n 0 -l 0 ) Partial volume of transport The remaining coal transportation workload will decrease as follows: (36) 2) Ship status information update Assuming the flight time and return time are the same, if t Arrange ships at 0 o'clock v * When transporting coal, the time periods occupied by ships include three periods: sailing, unloading, and returning. The time period cannot be used repeatedly, as follows: (37) 3) Update of power plant and dock inventory information After the coal arrives at the terminal, update the terminal inventory information as follows: (38) 4) Inventory alert time update variable This indicates the inventory warning time for a transportation task (mnl), used to measure the urgency of the task; it is used to begin transporting coal. To the power plant dock n At 0:00, the inventory alert time is updated to: （39） 5) The berth number information table can be updated. like t Arrange for ships to transport coal to the power plant dock at all times. n 0, then the dock n 0 in If an occupied berth is added during a given time period, the number of available berths decreases by 1. Specifically: (40)。 5. A coal transportation planning system for large power generation groups, implementing the coal transportation planning method for large power generation groups as described in any one of claims 1 to 4, characterized in that, include: The model building module is used to build a coal procurement-transportation coordination optimization model based on historical procurement-transportation data, establish a large-scale unified ship scheduling model based on the relationship between the number of ships used and the ship deadweight utilization rate, and build a two-layer optimization model for procurement-transportation-inventory management based on the upper-level procurement-transportation coordination optimization model and the lower-level large-scale unified ship scheduling model. The upper-level solution module is used to solve the upper-level procurement-transportation coordination optimization model using linear programming algorithms, and outputs the total procurement volume plan and total transportation volume plan within the scheduling cycle; The lower-level solution module is used to obtain the current status variables of the power plant terminal system. The power plant terminal system status variables include: remaining coal transportation task information, ship status information table, power plant terminal inventory information table, inventory warning time information table, and available berth number information table. Based on the total procurement volume plan, the total transportation volume plan, and the state variables of the power plant terminal system, a greedy algorithm is used to solve the lower-level large-scale unified ship scheduling model and output the optimized ship scheduling plan.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the coal transportation planning method for large power generation groups as described in any one of claims 1-4.

7. A computer-readable storage medium storing a computer program that, when executed by a processor, implements the coal transportation planning method for large power generation groups as described in any one of claims 1-4.

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