River-sea combined transportation berth quay crane cooperative scheduling optimization method in ship-ship direct rotation mode

Abstract

The invention relates to a river-sea combined transport berth quay crane cooperative scheduling optimization method in a ship-ship direct rotation mode. The method comprises the following steps: S1, obtaining information of a ship to be berthed and berth information; s2, generating initial solutions, wherein any solution is a ship sequence; s3, assigning the current solution and the optimal solution as initial solutions, and calculating target function values of the initial solutions; s4, deleting a plurality of elements from the current solution by adopting a destruction operator to obtain an incomplete ship sequence, and re-matching ships corresponding to the deleted elements to berths by adopting a repair operator; s5, calculating an objective function value of a new solution, judging whether the objective function value of the new solution is superior to the objective function value of the optimal solution and the objective function value of the current solution, and selecting whether to update; and S6, repeatedly executing the step S4 and the step S5 until a termination condition is met, allocating berths for the ships according to the optimal solution, and generating a quay crane plan. Compared with the prior art, the method can obtain an optimal berth quay crane cooperation scheme in the ship-ship direct rotation mode, the optimal berth quay crane cooperation scheme comprises a detailed berth quay crane distribution plan and a berth quay crane cooperation scheduling scheme, the staying time of the ship at the port is effectively controlled, berth scheduling and horizontal transportation time are optimized, and the optimal berth quay crane cooperation scheme is obtained on the premise that the berth time constraint is met. And efficient transportation of river-sea combined transportation containers is achieved.

Description

Technical Field

[0001] The present invention relates to the field of transportation, and particularly to an optimization method for coordinated scheduling of quay cranes in the river-sea combined transport berths under the ship-to-ship direct transfer mode. Background Art

[0002] The river-sea combined transport operation in an automated container terminal is different from the traditional container port operation and also different from the general water-to-water transshipment operation. It has the organizational characteristics of both operations and also derives new operation modes. It links the sea transportation organization system, the port operation organization system, the river transportation organization system, and the information interaction system of river-sea combined transport, and develops to form the basic connotation of the river-sea combined transport system.

[0003] As Figure 1 shown, it is the "ship-ship" operation mode, also known as the ship-to-ship direct transfer mode, which means that within the same port, a seagoing ship and a river ship are docked at different terminal berths simultaneously, and the containers on the seagoing ship (river ship) are transferred to the river ship (seagoing ship) by internal port vehicles for subsequent transportation. Under this mode, the seagoing ship and the river ship usually dock at adjacent or relatively close berths, and the container transfer process can be completed efficiently, and the entire operation process is efficient and compact.

[0004] Compared with the traditional "ship-yard-ship" mode, the ship-to-ship direct transfer mode reduces the on-site time and management cost of containers by omitting the yard link. At the same time, the automated transfer equipment inside the port can greatly improve the operation speed, ensure that the containers can be quickly transferred from the seagoing ship (or river ship) to the river ship (or seagoing ship), and avoid delays caused by ship waiting for loading and unloading. However, although the ship-to-ship direct transfer mode simplifies the transfer link, it has higher requirements for the coordination of operations within the port, and the arrival time, operation scheduling, and loading and unloading sequence of the seagoing ship and the river ship also need to be highly coordinated. In addition, due to limited port berths, the berth scheduling also needs to be reasonably arranged according to the arrival time and availability of the ships. Currently, there is no feasible solution for this highly demanding coordinated scheduling.

[0005] The above technical problems need to be solved urgently. Summary of the Invention

[0006] The purpose of the present invention is to provide an optimization method for coordinated scheduling of quay cranes in the river-sea combined transport berths under the ship-to-ship direct transfer mode to solve the problems existing in the above-mentioned prior art.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] An optimization method for coordinated scheduling of quay cranes in the river-sea combined transport berths under the ship-to-ship direct transfer mode includes:

[0009] Step S1: Obtain the information of the ships to be berthed and the berth information;

[0010] Step S2: Generate an initial solution. Any solution is a ship sequence. The elements in the ship sequence are tuples consisting of ship number and ship berth number. The ship numbers of all elements in the ship sequence cover all ships to be berthed, and the ship numbers of any two elements are different.

[0011] Step S3: Assign the current solution and the optimal solution the initial solution, and calculate the objective function value of the initial solution;

[0012] Step S4: Use the destruction operator to delete multiple elements from the current solution to obtain a fragmented ship sequence. Use the repair operator to rematch the berths of the ships corresponding to the deleted elements. Insert the generated tuple consisting of the ship number and the ship berth number into the fragmented ship sequence to obtain a new solution.

[0013] Step S5: Calculate the objective function value of the new solution.

[0014] Determine whether the objective function value of the new solution is better than that of the optimal solution. If so, assign the new solution to the optimal solution.

[0015] Determine whether the objective function value of the new solution is better than that of the current solution. If it is, then assign the current solution as the new solution. Otherwise, determine whether the new solution is accepted with the first probability. If the new solution is accepted, then assign the current solution as the new solution.

[0016] Step S6: Repeat steps S4 and S5 until the termination condition is met, allocate berths to each vessel according to the optimal solution, and generate a quay crane plan.

[0017] The process of calculating the objective function value of the initial solution in step S3 includes:

[0018] Step S3-1: Based on the initial solution and under the constraints, allocate berths to each vessel and generate a quay crane plan;

[0019] Step S3-2: Calculate the objective function value based on the generated berth allocation results and the quay crane calculation.

[0020] The objective function value is optimized by decreasing it, and its mathematical expression is:

[0021] f = βf1 + (1-β)f2

[0022] f1=λΣ i∈V1 (e i -a i )+Σ j∈V2 (e j -a j )

[0023]

[0024] Where: f is the objective function, β is the parent weight coefficient, f1 is the first sub-objective, representing minimizing the total port time of river-sea intermodal vessels, f2 is the second sub-objective, representing minimizing the total horizontal transport time of river-sea intermodal containers, λ is the weight coefficient for seagoing vessel port time, and e i Let a be the loading / unloading completion time of vessel i in the seagoing vessel pool. i Let V1 be the estimated arrival time of vessel i in the seagoing vessel group, V2 be the river vessel group, and e be the estimated arrival time of vessel i in the river vessel group. j Let a be the loading and unloading completion time of vessel j in the riverboat assembly. j B1 represents the estimated arrival time of vessel j in the riverboat assembly, B2 represents the assembly of seagoing vessels at berths, and C represents the assembly of riverboat vessels at berths. ij The volume of intermodal containers transferred directly from vessel i in the ocean-going vessel pool to vessel j in the river vessel pool via horizontal transport. The average time for a single container to be transported / round-trip from berth b1 in the ocean-going vessel berth cluster to berth b2 in the river vessel berth cluster using an intelligent guided transport vehicle. This is a binary variable; it is 1 when ship i first berths at berth b1 at time t, and 0 otherwise. T is the time interval. It is a binary variable, which is 1 when ship i first berths at berth b2 at time t, and 0 otherwise.

[0025] The destruction operators include random destruction operators and greedy destruction operators. Greedy destruction operators are used in the early stages of iteration, and random destruction operators are used when local destruction is achieved.

[0026] The random destruction operator removes multiple elements from the current solution to obtain a fragmented ship sequence.

[0027] The greedy destruction operator sequentially deletes multiple elements with the largest saving value to obtain a fragmented ship sequence, wherein the saving value is the difference between the objective function value of retaining the element and the objective function value of removing the element.

[0028] The savings value is the improved savings value, which is the ratio of the savings value to the intermodal container volume of the corresponding vessel.

[0029] The repair operators include random repair operators, greedy repair operators, and regret repair operators;

[0030] The random repair operator, greedy repair operator, and regret repair operator are each configured with operator scores. operator weight ω i At the beginning of each iteration segment, both the operator weights and operator scores are initialized to 1.

[0031] In each iteration, the update mechanism for operator weights and operator scores is as follows:

[0032]

[0033] Among them, r1, r2, and r3 are adjustment parameters.

[0034] The first probability is:

[0035] e -(new_obj-obj) / TK

[0036] Where: obj is the objective function value of the current neighborhood solution, new_obj is the objective function value of the test solution, TK is the temperature, initially set to TK0, and during the iteration process, the temperature decreases proportionally to c. TK ·TK, 0 < c TK <1.

[0037] The constraints include:

[0038] Time constraints include berthing time constraints and arrival time constraints for ships.

[0039] Spatial constraints;

[0040] Equipment constraints include the number of quay cranes allocated to each vessel being constrained by the total number of existing quay cranes at the terminal, quay crane safety distance constraints, and loading and unloading efficiency constraints.

[0041] The constraints of river-sea intermodal transport include the order of berthing times for river-sea intermodal vessels and the interrelationships or exclusions between berthing locations.

[0042] A collaborative scheduling optimization device for river-sea intermodal berths and quay cranes under ship-to-ship direct turn mode includes a memory, a processor, and a program stored in the memory. When the processor executes the program, it implements the method described above.

[0043] A storage medium having a program stored thereon, which, when executed, implements the method described above.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] 1. By setting constraints to minimize the total port dwell time of all river-sea intermodal vessels and the total horizontal transport time of river-sea intermodal containers, the ALNS algorithm is used to solve the model. This method yields the optimal coordination scheme for berth-to-quay cranes under the ship-to-ship direct transfer mode, including a detailed plan for berth-to-quay crane allocation and a coordinated scheduling scheme for berth-to-quay cranes. This method effectively controls vessel dwell time in port, optimizes berth scheduling and horizontal transport time, improves port throughput capacity, and achieves efficient "ship-to-ship, berthing-to-operation" transshipment of river-sea intermodal containers while meeting berthing time constraints.

[0046] 2. An improved savings value was designed, which improved the greedy destruction operator. The intermodal container volume of the ship itself was taken into account. This changed the priority of deleting the ship with the largest total savings value to deleting the ship with the largest savings value per unit container volume. For ships with small intermodal container volume but high original savings value, deletion can significantly reduce the objective function and improve the cost-effectiveness. This allows the optimal state to be reached more quickly and reduces the total time spent in the optimization process. Attached Figure Description

[0047] Figure 1 This is a schematic diagram illustrating the operating principle of the ship-to-ship straight-rotation mode of the present invention;

[0048] Figure 2 This is a schematic diagram of the excavated wharf of the present invention;

[0049] Figure 3 This is a schematic diagram of the ship sequence damage and repair process of the present invention;

[0050] Figure 4 This is a schematic diagram of the "sea-ship-river-ship" layout of a certain dredged port terminal operation area in an embodiment of the present invention;

[0051] Figure 5 This is a schematic diagram of the main steps of the method of the present invention;

[0052] Figure 6 This is a schematic diagram of the ALNS algorithm iteration process in an embodiment of the present invention;

[0053] Figure 7 This is a two-dimensional coordinate diagram of the location and time of the joint allocation plan for river-sea intermodal berths and quay cranes in an embodiment of the present invention. Detailed Implementation

[0054] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. These embodiments are based on the technical solution of the present invention and provide detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.

[0055] The following are explanations of some technical terms used in this invention:

[0056] (1) Excavated wharf: such as Figure 2 As shown, this type of wharf, constructed by excavating waterways and using rigid structures to support the wharf platform, is suitable for deep-water areas and provides high wave resistance. The inner shoreline of the excavated wharf is called the "riverside shoreline".

[0057] (2) Quay crane: The main equipment responsible for loading and unloading ships in river-sea intermodal transport operations. Its goal is to improve the utilization rate of quay cranes and reduce operational conflicts.

[0058] (3) Yard: A transshipment location used for temporary storage of containers.

[0059] This application applies primarily to excavated wharves.

[0060] This application firstly presents an existing container river-sea intermodal transport scheduling information platform. On this platform, registered shipping companies can transmit information on freight and various types of vessels (seagoing vessels and river vessels) to the information platform. The content includes the origin and destination ports of the goods, vessel type, vessel parameters, departure time range, container carrying capacity, and transshipment information of river-sea intermodal containers.

[0061] Before applying this application, it is first necessary to determine whether direct transport or yard transshipment should be used based on the port conditions. Specifically, this application uses the sorting-entropy weight method to determine this, including:

[0062] Step B1: Data standardization processing, specifically including:

[0063] Step B1-1: Construct the original decision matrix;

[0064] There are m modes (A1, A2) to be evaluated, each containing n indicators. A1 and A2 represent direct transport and transshipment via a yard, respectively. The original decision matrix X is:

[0065]

[0066] Where: x ij Let be the value of the i-th mode on the j-th indicator;

[0067] Step B1-2: Range standardization;

[0068] Different standardization methods are used depending on the type of indicator.

[0069] Efficiency indicators include ship synchronization and berth utilization rate, calculated using the following formulas:

[0070]

[0071] Cost-related indicators include vessel time in port and yard costs, calculated using the following formula:

[0072]

[0073] The standardized matrix is ​​as follows:

[0074]

[0075] Step B2: Entropy weight method for weight assignment, specifically including:

[0076] Step B2-1: Calculate the information entropy of the indicator;

[0077] Calculate the weight p of the j-th indicator. ij :

[0078]

[0079] Calculate information entropy:

[0080]

[0081] In the formula, when p ij When = 0, p is defined as follows: ij lnp ij =0;

[0082] Step B2-2: Calculate the indicator weights;

[0083] Calculate the difference coefficient g j :

[0084] g j =1-e j

[0085] Normalized weight w j :

[0086]

[0087] The weight vector is W = [w1, w2, ..., w n ],satisfy:

[0088]

[0089] Step B3: Improve TOPSIS calculation by introducing Mahalanobis distance and dynamic ideal solution, specifically including:

[0090] Step B3-1: Combine the normalization matrix Z with the weight vector W to obtain the weighted matrix:

[0091]

[0092] Where: z mn w represents the standardized value of the m-th pattern on the n-th indicator; mn This represents the weight of the m-th pattern on the n-th indicator;

[0093] Step B3-2: Determine the dynamic positive and negative ideal solutions:

[0094]

[0095] Where: a in This indicates that among all alternative options, the maximum or minimum value of the nth indicator is selected;

[0096] Step B3-3: Calculate the Mahalanobis distance;

[0097] Calculate the covariance matrix S:

[0098]

[0099] In the formula: m represents the number of modes to be evaluated, and A represents the weighted decision matrix. This is a vector of the mean values ​​of each indicator;

[0100] Calculation Mode A i Mahalanobis distance to the positive and negative ideal solutions:

[0101]

[0102] Step B4: Calculate the proximity;

[0103] Proximity G i Reflects the degree of closeness between the reflection model and the ideal solution:

[0104]

[0105] G i The larger the value, the more it indicates pattern A. i The better the overall performance.

[0106] In this embodiment, it has been confirmed that the direct transport mode is better for the selected port. Based on this, an optimization method for the coordinated scheduling of quay cranes at river-sea intermodal transport berths under the ship-to-ship direct transfer mode is applied, such as... Figure 4 As shown, it includes:

[0107] Step S1: Obtain information on ships to be moored and berth information, and construct a scheduling model;

[0108] The relevant parameters are defined and set as follows:

[0109] (1) Set

[0110] B1: Assembly at seagoing vessel berth. in This represents the total number of seagoing vessel berths in the river-sea intermodal transport area.

[0111] B2: Meet at the riverboat berth. in This represents the total number of riverboat berths in the river-sea intermodal transport operation area.

[0112] B: Set of berths, b∈B=B1∪B2;

[0113] Q1: Meet at the seaside pier. in This represents the total number of quay bridges on the sea side of the river-sea intermodal transport operation area.

[0114] Q2: Meet at the bridge on the riverbank. in This represents the total number of riverside bridges in the river-sea intermodal transport operation area.

[0115] Q: Set of shore bridges, q∈Q=Q1∪Q2;

[0116] (2) Model input parameters

[0117] a i The estimated arrival time of vessel i, i∈V;

[0118] c i : The number of containers that need to be unloaded from ship i, i∈V1, in TEU;

[0119] u ij : Binary variable, if container river-sea intermodal transport is carried out between riverboat j and seaboat i, i∈V1,j∈V2;

[0120] c ij : The volume of intermodal containers directly transferred from sea vessel i to river vessel j via horizontal transport, i∈V1,j∈V2;

[0121] The minimum number of quay cranes allocated to each seagoing vessel;

[0122] The minimum number of quay bridges allocated to each riverboat;

[0123] The maximum number of quay cranes allocated to each seagoing vessel;

[0124] The maximum number of quay bridges allocated to each riverboat;

[0125] η1: Loading and unloading efficiency of the seaside quay crane, unit: TEU / h;

[0126] η2: Loading and unloading efficiency of the riverside bridge, unit: TEU / h;

[0127] (3) Model auxiliary variables

[0128] s i : The start time of loading and unloading for vessel i, i∈V;

[0129] e i : The loading / unloading completion time of vessel i, i∈V;

[0130] (4) Decision variables of the scheduling model

[0131] x itb : A binary variable, which is 1 when ship i first berths at berth b at time t, and 0 otherwise, i∈V, t∈T, b∈B;

[0132] y ijb: Binary variable, which is 1 if the berthing time of ship j at berth b is later than the end time of the loading and unloading task of ship i at berth b, and 0 otherwise, i,j∈V,b∈B;

[0133] z itbq : A binary variable, which is 1 when ship i is berthed at berth b and served by quay crane q at time t, otherwise equal to 0, i∈V,t∈T,b∈B,q∈Q.

[0134] Step S2: Generate an initial solution. Any solution is a ship sequence. The elements in the ship sequence are tuples consisting of ship number and ship berth number. The ship numbers of all elements in the ship sequence cover all ships to be berthed, and the ship numbers of any two elements are different.

[0135] Step S3: Assign the current solution and the optimal solution as the initial solution, and calculate the objective function value of the initial solution. The process of calculating the objective function value of the initial solution includes:

[0136] Step S3-1: Based on the initial solution and under the constraints, allocate berths to each vessel and generate a quay crane plan;

[0137] Step S3-2: Calculate the objective function value based on the generated berth allocation results and the quay crane calculation.

[0138] In actual container river-sea intermodal transport operations, after arriving at the terminal, ships aim to berth as quickly as possible to reduce port waiting time, and simultaneously secure the optimal berth space to minimize horizontal transport time. When the distance between the sea-going and river-going vessel berths is significant, horizontal transport time increases, and transport efficiency decreases. Furthermore, when the terminal's operational systems are busy, actual operations may deviate from the original scheduling plan, leading to a decline in overall river-sea intermodal transport efficiency. Therefore, the optimization objective of the coordinated scheduling of quay cranes for container river-sea intermodal transport must consider not only the port time under different quay crane scheduling schemes for sea-going and river-going vessels, but also the impact of the relative berth positions on horizontal transport time. Thus, the specific objective function of this application is as follows:

[0139] The objective function value is optimized by decreasing it, and its mathematical expression is:

[0140] f = βf1 + (1-β)f2

[0141] f1=λΣ i∈V1 (e i -a i )+Σ j∈V2 (e j -a j )

[0142]

[0143] Where: f is the objective function, β is the parent weight coefficient, f1 is the first sub-objective, representing minimizing the total port time of river-sea intermodal vessels, f2 is the second sub-objective, representing minimizing the total horizontal transport time of river-sea intermodal containers, λ is the weight coefficient for seagoing vessel port time, and e i Let a be the loading / unloading completion time of vessel i in the seagoing vessel pool. i Let V1 be the estimated arrival time of vessel i in the seagoing vessel group, V2 be the river vessel group, and e be the estimated arrival time of vessel i in the river vessel group. j Let a be the loading and unloading completion time of vessel j in the riverboat assembly. j B1 represents the estimated arrival time of vessel j in the riverboat assembly, B2 represents the assembly of seagoing vessels at berths, and C represents the assembly of riverboat vessels at berths. ij The volume of intermodal containers transferred directly from vessel i in the ocean-going vessel pool to vessel j in the river vessel pool via horizontal transport. The average time for a single container to be transported / round-trip from berth b1 in the ocean-going vessel berth cluster to berth b2 in the river vessel berth cluster using an intelligent guided transport vehicle. This is a binary variable; it is 1 when ship i first berths at berth b1 at time t, and 0 otherwise. T is the time interval. It is a binary variable, which is 1 when ship i first berths at berth b2 at time t, and 0 otherwise.

[0144] The constraints include:

[0145] Time constraints include berthing time constraints and arrival time constraints for ships.

[0146] Spatial constraints;

[0147] Equipment constraints include the number of quay cranes allocated to each vessel being constrained by the total number of existing quay cranes at the terminal, quay crane safety distance constraints, and loading and unloading efficiency constraints.

[0148] The constraints of river-sea intermodal transport include the order of berthing times for river-sea intermodal vessels and the interrelationships or exclusions between berthing locations.

[0149] Step S4: Use the destruction operator to delete multiple elements from the current solution to obtain a fragmented ship sequence. Use the repair operator to rematch the berths of the ships corresponding to the deleted elements. Insert the generated tuple consisting of the ship number and the ship berth number into the fragmented ship sequence to obtain a new solution.

[0150] The destruction operators include random destruction operators and greedy destruction operators. Greedy destruction operators are used in the early stages of iteration, and random destruction operators are used when local destruction is achieved.

[0151] The random destruction operator removes multiple elements from the current solution to obtain a fragmented ship sequence.

[0152] The greedy destruction operator sequentially deletes multiple elements with the largest saving value to obtain a fragmented ship sequence, where the saving value is the difference between the objective function value of retaining the element and the objective function value of removing the element.

[0153] In particular, in this embodiment, the saving value is the improved saving value, which is the ratio of the saving value to the intermodal container volume of the corresponding ship. Of course, in other comparative examples, the traditional saving value can still be used.

[0154] Improving the value of savings to "the ratio of the value of savings to the corresponding intermodal container volume of the vessel" will bring the following specific changes to the node selection logic of the greedy destruction operator, the direction of solution space exploration, and the final optimization objective:

[0155] 1) The node selection criteria of the greedy destruction operator are more in line with the core needs of intermodal transport.

[0156] In the comparative example, the savings value only measures the extent to which deleting a node optimizes the objective function (total time in port + total horizontal transport time). In this embodiment, the improved savings value reflects the "optimization benefit of the objective function per unit of intermodal container volume." This change transforms the selection logic of the greedy destruction operator from "prioritizing the deletion of nodes with the largest total savings value" to "prioritizing the deletion of nodes with the largest savings value per unit of container volume."

[0157] For vessels with small intermodal container volumes but high savings (such as riverboats that only transship a small number of containers but have long waiting times), the savings from the improvement may be even greater, and they will be prioritized for deletion in this embodiment. The small container volume of such nodes has little impact on the overall intermodal transport, but deletion can significantly reduce the objective function and make them more cost-effective.

[0158] For vessels with large intermodal container volumes but moderate savings (such as river vessels undertaking major transshipment tasks), the savings from improvements may be small, and their priority for deletion will decrease. This avoids disrupting the transportation plans of a large number of containers due to the deletion of high-volume nodes, thus ensuring the stability of the core intermodal transport links.

[0159] 2) Solution space exploration focuses more on "unit container volume efficiency", balancing local optimization and global benefits.

[0160] The savings value may cause the algorithm to overemphasize nodes with "large total savings but small container volume" (e.g., deleting a small container volume riverboat can reduce the target value by 10 hours), while ignoring nodes with "medium total savings but large container volume" (e.g., deleting a large container volume riverboat only reduces the target value by 8 hours, but involves 500 TEU container volume). In this embodiment, it is more inclined to retain high container volume nodes, even if their total savings value is slightly lower, because the unit container volume efficiency of high container volume nodes is low. After deletion, the objective function loss per unit container volume is greater, which is not conducive to the overall intermodal transport efficiency.

[0161] Promoting the exploration of solutions towards "stable high-volume nodes and flexible adjustment of low-volume nodes" is more in line with the actual needs of the ship-to-ship direct transfer mode to "prioritize the core intermodal transport links".

[0162] 3) The optimization targets are more closely correlated with intermodal container volume, improving the feasibility of actual scheduling.

[0163] The core objectives of this application are to "minimize the total time in port" and "minimize the total horizontal transport time". The total horizontal transport time is directly related to the intermodal container volume. In this embodiment, the selection of the greedy destruction operator will indirectly reduce the destruction probability of high container volume nodes, reduce the fluctuation of horizontal transport time caused by changes in large container transshipment plans, and make the optimization result closer to the actual operational goal of "lowest cost per unit container volume", rather than simply pursuing the minimum total time. Especially when port resources are limited (such as quay cranes and berths are tight), prioritizing the efficiency of high container volume nodes can improve the input-output ratio of the overall intermodal transport.

[0164] In addition, repair operators include random repair operators, greedy repair operators, and regret repair operators;

[0165] Random Repair Operator: Randomly selects a ship node and inserts it into the ship order of the available berths;

[0166] Greedy Repair Operator: Based on the idea of ​​a greedy strategy, this operator inserts the selected ship nodes into their optimal berths, that is, selects the berth position and insertion order with the smallest objective function increment, until all removed ship nodes are re-inserted.

[0167] Regret Repair Operator: Select the ship node with the highest regret value and its insertion index. The selection process follows the formula below:

[0168]

[0169] In the formula, RP is the node removal pool, and Δf i τ Represent the τ-th optimal insertion position of ship i and allocate the insertion cost of the quay crane;

[0170] (3) Operations that damage or modify: such as Figure 3 The diagram illustrates an example of a damage and repair operation involving 8 vessels across 3 berths. Vessels 2 and 5 are removed from berth 1, leaving vessels 1 and 7; vessel 4 is removed from berth 3, leaving vessel 8; vessels 3 and 6 at berth 2 remain undamaged. After repair, vessels 1, 4, and 7 remain at berth 1, vessels 2, 5, and 8 remain at berth 3, and vessels 3 and 6 at berth 2 require no repair. In other words, vessels 2, 4, and 5 from berths 1 to 3 are removed, and after the repair operation, the three removed vessels are reassigned to available berths, forming a new vessel sequence.

[0171] The random repair operator, greedy repair operator, and regret repair operator are each configured with operator scores. operator weight ω i At the beginning of each iteration segment, both the operator weights and operator scores are initialized to 1.

[0172] In each iteration, the update mechanism for operator weights and operator scores is as follows:

[0173]

[0174] Among them, r1, r2, and r3 are adjustment parameters.

[0175] Step S5: Calculate the objective function value of the new solution.

[0176] Determine whether the objective function value of the new solution is better than that of the optimal solution. If so, assign the new solution to the optimal solution.

[0177] Determine whether the objective function value of the new solution is better than that of the current solution. If it is, then assign the current solution as the new solution. Otherwise, determine whether the new solution is accepted with the first probability. If the new solution is accepted, then assign the current solution as the new solution.

[0178] The first probability is:

[0179] e -(new_obj-obj) / TK

[0180] Where: obj is the objective function value of the current neighborhood solution, new_obj is the objective function value of the test solution, TK is the temperature, initially set to TK0, and during the iteration process, the temperature decreases proportionally to c. TK ·TK, 0 < c TK <1.

[0181] Step S6: Repeat steps S4 and S5 until the termination condition is met, allocate berths to each vessel according to the optimal solution, and generate a quay crane plan.

[0182] like Figure 4 As shown, the basic information of a certain port's dredged basin is as follows:

[0183] The port adopts a discrete berth layout with a 1,000-meter-long seaside shoreline, including two seaside berths, each equipped with four double-trolley quay cranes. The seaside berths S1# and S2# are both 400 meters long. The dredged harbor basin has a 1,000-meter-long shoreline on both sides of the river, equipped with five riverside berths and eight single-trolley quay cranes. The riverside berths R1# to R5# and R6# to R10# are all 200 meters long.

[0184] Table 1 shows the vessel matching plan and vessel parameter information for a certain port's river-sea intermodal transport:

[0185] Table 1

[0186]

[0187]

[0188] After the ship berths, it needs to be loaded and unloaded by quay cranes. In this embodiment, an automated double-trolley quay crane is used as the sea-side quay crane, with a loading and unloading efficiency of 45 TEU / h and a maximum of 4 quay cranes can be allocated. On the river side, a single-trolley quay crane is used, with a loading and unloading efficiency of 30 TEU / h and a maximum of 3 quay cranes can be allocated.

[0189] Table 2 shows the horizontal transport schedule between river-sea intermodal berths at a certain port.

[0190] Table 2

[0191]

[0192] Table 3 shows the settings for the relevant parameters of the ALNS algorithm:

[0193]

[0194]

[0195] After setting all parameters, objective functions, and constraints within the river-sea intermodal berth quay crane collaborative scheduling model, the model is run to obtain the ALNS algorithm iteration process, see [link / reference]. Figure 6 As shown, the optimal solution for the coordinated scheduling of berths and quay cranes in river-sea intermodal transport was obtained, along with the target values ​​for each objective.

[0196] As shown in Tables 4 and 5, the results after the operation are summarized to obtain the optimal solution for the coordinated scheduling of berths and quay cranes in the river-sea intermodal transport operation, along with the target values. Table 4 shows the berth and quay crane allocation plan, and Table 5 shows the coordinated scheduling results. This embodiment has two operation batches: A and B, where each seagoing vessel needs to complete container unloading and multiple river vessels will undertake the transfer tasks.

[0197] Table 4

[0198]

[0199]

[0200] As shown in Table 4, seagoing vessel A1 was assigned to berth S1# and berthed at 0:14, while the corresponding five river vessels were assigned to berths R1#–R3#. Seagoing vessel B1 berthed at berth S2#, and river vessels B1-6 to B1-9 were assigned to berths R6#–R8#, forming a complete intermodal transport chain. The number of quay cranes at each berth was relatively evenly distributed. A1 and B1 were each assigned 4 automated double-trolley quay cranes (DQC), while the river-side berths were assigned 2 or 3 quay cranes (SQC) to accommodate the operational needs of different vessels. This allocation scheme ensured operational needs while avoiding waste or shortage of quay crane resources. The berthing times of seagoing and river vessels were as closely linked as possible to reduce waiting time for river vessels and improve operational continuity. For example, in operation batch A, after seagoing vessel A1 berthed, river vessels A1-1, A1-2, etc., could simultaneously commence operations, accelerating cargo transfer.

[0201] Table 5

[0202]

[0203] As can be seen from Table 5:

[0204] From the perspective of operational time, it is evident that the berthing and loading / unloading operations of seagoing vessels are closely coordinated with those of river vessels. Taking the seagoing vessel A1 as an example, its loading / unloading operations lasted 5.13 hours. After berthing, the river vessel A1-1 immediately commenced loading / unloading operations, taking 2.33 hours. This also reflects the potential for optimization in the time coordination between the two. The waiting time for the river vessel A1-1 to berth was 0.23 hours. If the arrival time of seagoing vessels can be accurately predicted when formulating the river-sea intermodal vessel matching plan, the arrival time of river vessels can be adjusted in advance and berthing can be planned, potentially reducing the idle waiting time of river vessels.

[0205] As can be seen from the horizontal transport process, the river vessels that arrive later (such as A1-4 and A1-5) berth at berths closer to seagoing vessel berth S1#, which shortens the horizontal transport time of containers (for example, the transport time of A1-4 is only 7.5 hours). This strategy effectively improves the efficiency of container transshipment.

[0206] like Figure 7 As shown, to more intuitively represent the berthing time, berthing position, and quay crane allocation of each vessel in the river-sea intermodal transport plan, a two-dimensional coordinate graph of the location-time of the joint allocation plan of river-sea intermodal berths and quay cranes is drawn, taking river-sea intermodal transport operation plan A as an example, in conjunction with Table 4.

[0207] The river-sea intermodal transport operation plan A successfully completed the transshipment of containers from seagoing vessels to river vessels within 6 hours. In terms of time coordination, the berthing times of each vessel were relatively compact, with an average waiting time of 0.078 hours for river vessels and a maximum waiting time of 0.23 hours. Furthermore, the relative berthing positions of river vessels and seagoing vessels, as well as the allocation of quay cranes, were relatively reasonable. Overall, this operation plan effectively controlled vessel dwell time in port, optimized berth scheduling and horizontal transport time, and achieved efficient transshipment of river-sea intermodal containers while meeting berthing time constraints.

[0208] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for optimizing the coordinated scheduling of quay cranes at river-sea intermodal transport berths under a ship-to-ship direct transfer mode, characterized in that, include: Step S1: Obtain information on the vessel to be moored and the berth information; Step S2: Generate an initial solution. Any solution is a ship sequence. The elements in the ship sequence are tuples consisting of ship number and ship berth number. The ship numbers of all elements in the ship sequence cover all ships to be berthed, and the ship numbers of any two elements are different. Step S3: Assign the current solution and the optimal solution the initial solution, and calculate the objective function value of the initial solution; Step S4: Use the destruction operator to delete multiple elements from the current solution to obtain a fragmented ship sequence. Use the repair operator to rematch the berths of the ships corresponding to the deleted elements. Insert the generated tuple consisting of the ship number and the ship berth number into the fragmented ship sequence to obtain a new solution. Step S5: Calculate the objective function value of the new solution. Determine whether the objective function value of the new solution is better than that of the optimal solution. If so, assign the new solution to the optimal solution. Determine whether the objective function value of the new solution is better than that of the current solution. If it is, then assign the current solution as the new solution. Otherwise, determine whether the new solution is accepted with the first probability. If the new solution is accepted, then assign the current solution as the new solution. Step S6: Repeat steps S4 and S5 until the termination condition is met, allocate berths to each vessel according to the optimal solution, and generate a quay crane plan.

2. The method for optimizing the coordinated scheduling of quay cranes at river-sea intermodal transport berths under direct ship-to-ship transfer mode as described in claim 1, characterized in that, The process of calculating the objective function value of the initial solution in step S3 includes: Step S3-1: Based on the initial solution and under the constraints, allocate berths to each vessel and generate a quay crane plan; Step S3-2: Calculate the objective function value based on the generated berth allocation results and the quay crane calculation.

3. The method for optimizing the coordinated scheduling of quay cranes at river-sea intermodal transport berths under direct ship-to-ship transfer mode as described in claim 1, characterized in that, The objective function value is optimized by decreasing it, and its mathematical expression is: f = βf1 + (1-β)f2 f1=λΣ i∈V1 (e i -a i )+S j∈V2 (e j -a j ) Where: f is the objective function, β is the parent weight coefficient, f1 is the first sub-objective, representing minimizing the total port time of river-sea intermodal vessels, f2 is the second sub-objective, representing minimizing the total horizontal transport time of river-sea intermodal containers, λ is the weight coefficient for seagoing vessel port time, and e i Let a be the loading / unloading completion time of vessel i in the seagoing vessel pool. i Let V1 be the estimated arrival time of vessel i in the seagoing vessel group, V2 be the river vessel group, and e be the estimated arrival time of vessel i in the river vessel group. j Let a be the loading and unloading completion time of vessel j in the riverboat assembly. j B1 represents the estimated arrival time of vessel j in the riverboat assembly, B2 represents the assembly of seagoing vessels at berths, and C represents the assembly of riverboat vessels at berths. ij The volume of intermodal containers transferred directly from vessel i in the ocean-going vessel pool to vessel j in the river vessel pool via horizontal transport. The average time for a single container to be transported / round-trip from berth b1 in the ocean-going vessel berth cluster to berth b2 in the river vessel berth cluster using an intelligent guided transport vehicle. This is a binary variable; it is 1 when ship i first berths at berth b1 at time t, and 0 otherwise. T is the time interval. It is a binary variable, which is 1 when ship i first berths at berth b2 at time t, and 0 otherwise.

4. The method for optimizing the coordinated scheduling of quay cranes at river-sea intermodal transport berths under direct ship-to-ship transfer mode as described in claim 3, characterized in that, The destruction operators include random destruction operators and greedy destruction operators. Greedy destruction operators are used in the early stages of iteration, and random destruction operators are used when local destruction is achieved. The random destruction operator removes multiple elements from the current solution to obtain a fragmented ship sequence. The greedy destruction operator sequentially deletes multiple elements with the largest saving value to obtain a fragmented ship sequence, wherein the saving value is the difference between the objective function value of retaining the element and the objective function value of removing the element.

5. The method for optimizing the coordinated scheduling of quay cranes at river-sea intermodal transport berths under direct ship-to-ship transfer mode as described in claim 4, characterized in that, The savings value is the improved savings value, which is the ratio of the savings value to the intermodal container volume of the corresponding vessel.

6. The method for optimizing the coordinated scheduling of quay cranes at river-sea intermodal transport berths under ship-to-ship direct transfer mode as described in claim 3, characterized in that, The repair operators include random repair operators, greedy repair operators, and regret repair operators; The random repair operator, greedy repair operator, and regret repair operator are each configured with operator scores. operator weight ω i At the beginning of each iteration segment, both the operator weights and operator scores are initialized to 1. In each iteration, the update mechanism for operator weights and operator scores is as follows: Among them, r1, r2, and r3 are adjustment parameters.

7. The method for optimizing the coordinated scheduling of quay cranes at river-sea intermodal transport berths under direct ship-to-ship transfer mode as described in claim 1, characterized in that, The first probability is: e -(new_obj-obj) / TK Where: obj is the objective function value of the current neighborhood solution, new_obj is the objective function value of the test solution, TK is the temperature, initially set to TK0, and during the iteration process, the temperature decreases proportionally to c. TK ·TK, 0 < c TK <1.

8. The method for optimizing the coordinated scheduling of quay cranes at river-sea intermodal transport berths under ship-to-ship direct transfer mode as described in claim 3, characterized in that, The constraints include: Time constraints include berthing time constraints and arrival time constraints for ships. Spatial constraints; Equipment constraints include the number of quay cranes allocated to each vessel being limited by the total number of existing quay cranes at the terminal, quay crane safety distance constraints, and loading / unloading efficiency constraints. The constraints of river-sea intermodal transport include the order of berthing times for river-sea intermodal vessels and the interrelationships or exclusions between berthing locations.

9. A collaborative scheduling optimization device for river-sea intermodal berths and quay cranes under ship-to-ship direct transfer mode, comprising a memory, a processor, and a program stored in the memory, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1-8.

10. A storage medium having a program stored thereon, characterized in that, When the program is executed, it implements the method as described in any one of claims 1-8.

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