CN109784547B - Wharf shore bridge and yard bridge collaborative optimization scheduling method - Google Patents
Wharf shore bridge and yard bridge collaborative optimization scheduling method Download PDFInfo
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Abstract
The invention discloses a method for collaboratively and optimally scheduling a wharf shore bridge and a field bridge, which comprises the following steps of: the shore bridge and the field bridge are taken as scheduling objects, a container stack is taken as a minimum loading and unloading unit, a ship schedule and the positions of the containers in the ship and the storage yard are combined, any minimum time spent on loading and unloading all the containers on one ship and any short moving distance of the shore bridge and the field bridge are taken as scheduling targets of the shore bridge and the field bridge, a mathematical model of the same-shell synchronous loading and unloading operation of the import and export containers is constructed, and an optimal shore bridge and field bridge scheduling scheme under a given loading and unloading task of the import and export containers is obtained. According to the optimal scheduling method for the quay crane and the yard crane, an entropy evaluation system is designed, and an improved genetic algorithm based on an entropy matching principle is designed, so that the solving capability of the algorithm is greatly improved, and the cooperative scheduling capability of the quay crane and the yard crane is improved.
Description
Technical Field
The invention belongs to the field of a wharf loading and unloading equipment collaborative optimization scheduling method, and particularly relates to a wharf shore bridge and site bridge collaborative optimization scheduling method.
Background
With the rapid development of the world economy integration, container shipping plays an extremely important role in international logistics. The container transportation is developed for nearly 30 years till now, the container throughput keeps steadily increasing except for the global economic crisis period of 2009, wherein the increasing rate of the first 20 years is about 10%, and the increasing rate of the last 10 years is still about 5% although the increasing rate is slowed down. The rapid development of container transportation has driven the intense competition between container terminals, which can only be competitive if the operation time of the terminals is shortened and the operation cost of container ships at the terminals is reduced. The synchronous operation mode of the same shellfish is adopted, and the improvement of the operation capacity of wharf loading and unloading equipment becomes the development target of the wharf.
The quayside container loading and unloading synchronous operation mode is firstly proposed in 2003, and the container loading and unloading operation efficiency is greatly improved after the quay crane synchronous operation mode is applied to a wharf. The loading and unloading efficiency of the synchronous operation mode of the same shell is not only related to the self operation capability of the shore bridge, but also influenced by the cooperative operation degree of the internal container truck and the field bridge. Therefore, in order to match with the synchronous loading and unloading operation mode of the same shell, the strategy of separately stacking the import and export boxes of the storage yard is also replaced by the strategy of mixing the piles of the storage yard. The yard mixing improves the utilization rate of the yard and the heavy load rate of the yard bridge and the container truck, but also increases the difficulty of the cooperative scheduling of the shore bridge and the yard storage operation.
Disclosure of Invention
The invention provides a method for collaboratively and optimally scheduling a wharf shore bridge and a yard bridge, aiming at solving the problem that the collaborative scheduling of the wharf shore bridge and the yard bridge and the storage operation of the yard are difficult.
The invention is realized by the following technical scheme:
a dock shore bridge and yard bridge collaborative optimization scheduling method comprises the following steps:
step 1: defining a shore bridge and a field bridge as scheduling objects, combining a ship schedule and positions of containers in ships and storage yards to finish the minimum time spent by loading and unloading all containers on one ship as the scheduling target of the shore bridge and the field bridge, and constructing a mathematical model of the synchronous loading and unloading operation of the imported and exported containers and the shellfish;
step 2: expressing the container storage state of ships and storage yards by adopting an entropy evaluation system, coding the container loading and unloading sequence by adopting double-layer chromosomes, designing an improved genetic algorithm based on the entropy values of the ships and the storage yards, solving a mathematical model of the container one-shell synchronous loading and unloading operation, and obtaining an optimal shore bridge and field bridge cooperative scheduling scheme under the condition of a given container loading and unloading task;
further, in the above technical solution, the container loading and unloading sequence adopts a double-layer real number coding.
Further, in the above technical solution, the entropy evaluation system is characterized in that: regarding the container as the particle Jz of the open system, wherein the combination of the container to be loaded and unloaded and the container to be turned over is set as the energy Efx of each particle in the fx state, the probability P of the stockpiling system in a certain state (Jz, fx) can be knownjz,fxIs composed of
Heap system entropy H of
Wherein: k is a proportionality coefficient; alpha is the particle motion rate; β is the energy dissipation rate; d is an entropy factor.
Deducing a stockpiling system entropy evaluation index H according to the formula (15) and the formula (16)
Further, in the above technical solution, the shore bridge and yard bridge co-scheduling mathematical model is represented as:
the objective function is:
the constraint conditions are as follows:
(ltcg'(b',z',c')＞0|ltcg'(b',z',c'+1)＞0)b'＝1,2,...,B'，z'＝1,2,...,Z'，c'＝1,2,...,C'-1 (2)
b, Z and C respectively represent the number of shelfs, the number of stacks and the stacking height of a storage yard; b ', Z ' and C ' respectively represent the number of shelfs, the number of stacks and the stacking height of the ship; o represents the number of container customers that need to be unloaded; WXoRepresents the number of containers for the O-th customer, O1, 2.., O; ZLxz，ZLzzRespectively representing the total number of containers that need to be unloaded and loaded,
g' represents the total number of stages, G ═ ZLxz+ZLzz(ii) a qc, yc respectively represent the shore bridge and the field bridge number, qc is 1,2, yc is 1, 2;
respectively representing the time required by single trip heavy load and no-load of the shore bridge;
representing the time required by the shore bridge to move one shellfish;
the average time required by one-time turnover of the shore bridge is represented;
respectively representing the time required by single-trip overload and no-load of the field bridge;
representing the time required by the field bridge to move one shellfish;
representing the average time required by turning over the box once for the bridge; ltc0(b ', z ', c ') represents initial ship stockpiling information,
ltcg'(b ', z', c ') represents ship stocking information at the G' stage, G '∈ G';
respectively, denote the beta number, stack number and layer number of the ith container placement unloaded by the quayside crane qc, qc ═ 1,2, i ═ 1,2, …, XZqc；
Respectively indicate the beige number, stack number, layer number, yc ═ 1,2, i ═ 1,2, …, ZZ 'of the ith container placement of the yc load of the bridge'yc；
Respectively represents the start time and the end time of loading the ith container by the quayside crane qc, qc is 1,2, i is 1,2, …, ZZqc；
Respectively representing the start time and the end time of unloading the ith container by the quayside crane qc, qc is 1,2, i is 1,2, …, XZqc；
Respectively represents the start time and the end time of the ith container loaded by the yc bridge, and yc is 1,2, i is 1,2, …, ZZ'yc；
Respectively representing the start time and the end time of unloading the ith container by the bridge yc, qc is 1,2, i is 1,2, …, XZqc；
Respectively representing the position of the shore bridge qc and the field bridge yc at time t,
respectively, denote the beta number, stack number and layer number of the ith container placement loaded on the quayside crane qc, qc is 1,2, i is 1,2, …, ZZqc；
Let us denote the beta number, stack number and layer number, respectively, of the ith container placement of the bye yc unloading, yc ═ 1,2, i ═ 1,2, …, XZ'yc；ZZqc，XZqcRespectively representing the total loading and unloading amount of the container of the quayside container qc, and obtaining an operation sequence [1,2, …, ZZ ] according to the sequenceqc]，[1,2,…,XZqc]，qc＝1,2；ZZ'yc，XZ'ycRespectively representing the total loading and unloading amount of the container of the bridge yc, and obtaining the operation sequence [1,2, …, ZZ'yc]，[1,2,…,XZ'yc]，yc＝1,2。
Further, in the above technical solution, the step 2 specifically includes the following steps:
step (1): carrying out initial coding on all container loading and unloading tasks to generate a scheduling scheme of a shore bridge and a field bridge;
setting the chromosome scale as ch, the crossing rate as jc, the variation rate as by and the iteration times as ge generation;
and coding all container tasks according to stack numbers by adopting two layers of arrays, wherein each group of codes represents a scheduling scheme of a field bridge and a shore bridge, the first layer is an export container task, namely the scheduling scheme of the field bridge, and the second layer is an import container task, namely the scheduling scheme of the shore bridge. When in coding: the first two digits of each group represent the shell number of the container task, the subsequent digits represent each stack number in the shell number by taking two digits as a small group, and the sequence of the two digits in the chromosome represents the sequence of loading and unloading. And so on until all container tasks are arranged and coded; the total ch chromosomes adopt the coding mode.
Step (2): matching an import and export stack of the container according to the principle of highest matching degree of entropy values;
in the generated ch chromosomes, the upper layer and the lower layer of each chromosome respectively determine the operation sequence of an export container and an import container, and random numbers are generated to judge whether the operation sequence is crossed or varied; if so, performing intragroup crossing on the chromosomes according to an entropy matching principle, and obtaining ch chromosomes; if the stack number is varied, the stack number is randomly generated to replace the existing stack number.
And (3): and (3) generating a new coding group aiming at the ch new chromosomes generated in the step (2), respectively calculating the time required by each stack container according to the loading and unloading operation sequence, and taking the maximum time point of all tasks and the travel path length of the shore bridge and the field bridge as final solutions.
And (4): and judging whether the value of each set of chromosome solution is the current optimal solution, comparing the minimum value of the current algebraic solution with the minimum value of the previous solution, if the current solution is more optimal, taking the minimum value of the current solution as the optimal solution, otherwise, taking the optimal solution as the minimum value of the previous solution.
And (5): sorting the target function values corresponding to the solutions of each group from small to large, directly entering the first 10% of chromosomes into the next generation, removing the last 10% of chromosomes, and generating 10% of chromosomes again by the method in the step (1); under the condition of ensuring that the total chromosome amount is not changed, the whole population is ensured not to fall into local optimum easily.
And (6): generating random numbers to judge whether to cross or mutate; if so, performing intragroup crossing on the chromosomes according to an entropy matching principle, and obtaining ch chromosomes; if the stack number is varied, the stack number is randomly generated to replace the existing stack number.
And (7): judging whether the current generation reaches a termination condition, if so, terminating the algorithm; if not, the step (2) is entered.
And (4) performing intragroup crossing by taking shellfish as a minimum unit, and determining the gene bit length of the crossed fragment.
When the task amount of the import container is inconsistent with the task amount of the export container, the length of the chromosome is determined by the larger chromosome, and the subsequent length of the chromosome of the other layer is less than 0.
In the crossing process, the entropy value matching principle takes the entropy value of each shell as a first matching principle, and when the entropy values of the import container task shell and the export container task shell are relatively close, the absolute positions of the import container task shell and the export container task shell in chromosomes of respective layers are kept consistent; and taking the entropy values of the stacks as a second matching principle, and keeping the absolute positions of the imported container task stack and the exported container task stack in the chromosome segments of the shellfish to be consistent when the entropy values of the imported container task stack and the exported container task stack are relatively close.
Further, in the above technical solution, the chromosome number ch is 100, and the crossover probability jc is 0. And 8, wherein the mutation probability by is 0.05, and the maximum iteration number ge is 1000.
Further, the first 10% of chromosomes in step (4) are not performed in step (6), and the newly generated 10% of chromosomes are not performed in step (6).
The invention has the beneficial effects that:
(1) the invention provides a wharf shore bridge and field bridge cooperative optimization scheduling method, which considers the container truck waiting time, and under the condition of a plurality of practical constraints, distributes an import container operation task and an export container operation task to a field bridge and a shore bridge, and simultaneously distributes landing points of import and export containers on ships and storage yards, so that the loading and unloading operation utilization rate of the wharf is highest, the container truck waiting time is shortest, the berthing time of the ships on the wharf is further reduced, and the operation cost of the wharf is reduced.
(2) According to the wharf shore bridge and yard bridge cooperative optimization scheduling method, the container stockpiling state of a ship and a yard is represented by designing an entropy evaluation system, an improved genetic algorithm is designed, the solving capability of the algorithm is greatly improved, and the quay bridge and yard bridge cooperative scheduling capability is improved.
Drawings
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
FIG. 1 is a schematic flow chart of a method for collaboratively optimizing and scheduling a quay crane and a yard bridge according to the present invention;
FIG. 2 is a schematic diagram of an entropy evaluation system according to the present invention;
FIGS. 3 a-3 b are iteration diagrams of the improved genetic algorithm of the present invention, wherein FIG. 3a is an iteration diagram of the optimal solution of the algorithm, and FIG. 3b is an iteration diagram of the average value of the optimal solution of the algorithm;
fig. 4 a-4 b are moving path diagrams of a shore bridge and a bridge, wherein fig. 4a is a moving path diagram of the shore bridge in a certain optimal solution, and fig. 4b is a moving path diagram of the bridge in a certain optimal solution;
FIG. 5 is a difference diagram of the optimal solution of the improved genetic algorithm based on the principle of entropy matching and the traditional genetic algorithm of the present invention;
FIG. 6 is an optimal solution diagram of the ship and the yard under different scale and different absolute values of entropy values;
detailed description of the invention
The technical scheme of the invention is completely described in the following by combining the attached drawings and the embodiment.
Example 1
As shown in fig. 1, a method for collaboratively and optimally scheduling a quay crane and a yard bridge includes the following steps:
step 1: defining a shore bridge and a field bridge as scheduling objects, combining a ship schedule and positions of containers in ships and storage yards to finish the minimum time spent by loading and unloading all containers on one ship as the scheduling target of the shore bridge and the field bridge, and constructing a mathematical model of the synchronous loading and unloading operation of the imported and exported containers and the shellfish;
step 2: expressing the container storage state of ships and storage yards by adopting an entropy evaluation system, coding the container loading and unloading sequence by adopting double-layer chromosomes, designing an improved genetic algorithm based on the entropy values of the ships and the storage yards, solving a mathematical model of the container one-shell synchronous loading and unloading operation, and obtaining an optimal shore bridge and field bridge cooperative scheduling scheme under the condition of a given container loading and unloading task;
further, in the above technical solution, the container loading and unloading sequence adopts a double-layer real number coding.
Further, in the above technical solution, the entropy evaluation system is characterized in that: regarding the container as the particle Jz of the open system, wherein the combination of the container to be loaded and unloaded and the container to be turned over is set as the energy Efx of each particle in the fx state, the probability P of the stockpiling system in a certain state (Jz, fx) can be knownjz,fxIs composed of
Heap system entropy H of
Wherein: k is a proportionality coefficient; alpha is the particle motion rate; β is the energy dissipation rate; d is an entropy factor. Deducing a stockpiling system entropy evaluation index H according to the formula (15) and the formula (16)
Further, in the above technical solution, the shore bridge and yard bridge co-scheduling mathematical model is represented as:
the objective function is:
the constraint conditions are as follows:
(ltcg'(b',z',c')＞0|ltcg'(b',z',c'+1)＞0)b'＝1,2,...,B'，z'＝1,2,...,Z'，c'＝1,2,...,C'-1 (2)
b, Z and C respectively represent the number of shelfs, the number of stacks and the stacking height of a storage yard; b ', Z ' and C ' respectively represent the number of shelfs, the number of stacks and the stacking height of the ship; o represents the number of container customers that need to be unloaded; WXoRepresents the number of containers for the O-th customer, O1, 2.., O; ZLxz，ZLzzRespectively representing the total number of containers that need to be unloaded and loaded,
g' represents the total number of stages, G ═ ZLxz+ZLzz(ii) a qc, yc respectively represent the shore bridge and the field bridge number, qc is 1,2, yc is 1, 2;
respectively representing the time required by single trip heavy load and no-load of the shore bridge;
representing the time required by the shore bridge to move one shellfish;
the average time required by one-time turnover of the shore bridge is represented;
respectively representing the time required by single-trip overload and no-load of the field bridge;
representing the time required by the field bridge to move one shellfish;
representing the average time required by turning over the box once for the bridge; ltc0(b ', z ', c ') represents initial ship stockpiling information,
ltcg' (b ', z ', c ') indicates ship stocking information at the G ' th stage, G ' ∈ G ';
respectively, denote the beta number, stack number and layer number of the ith container placement unloaded by the quayside crane qc, qc ═ 1,2, i ═ 1,2, …, XZqc；
Respectively indicate the beige number, stack number, layer number, yc ═ 1,2, i ═ 1,2, …, ZZ 'of the ith container placement of the yc load of the bridge'yc；
Respectively represents the start time and the end time of loading the ith container by the quayside crane qc, qc is 1,2, i is 1,2, …, ZZqc；
Respectively representing the start time and the end time of unloading the ith container by the quayside crane qc, qc is 1,2, i is 1,2, …, XZqc；
Respectively represents the start time and the end time of the ith container loaded by the yc bridge, and yc is 1,2, i is 1,2, …, ZZ'yc；
Respectively representing the start time and the end time of unloading the ith container by the bridge yc, qc is 1,2, i is 1,2, …, XZqc；
Respectively representing the position of the shore bridge qc and the field bridge yc at time t,
respectively, denote the beta number, stack number and layer number of the ith container placement loaded on the quayside crane qc, qc is 1,2, i is 1,2, …, ZZqc；
Let us denote the beta number, stack number and layer number, respectively, of the ith container placement of the bye yc unloading, yc ═ 1,2, i ═ 1,2, …, XZ'yc；ZZqc，XZqcRespectively representing the total loading and unloading amount of the container of the quayside container qc, and obtaining an operation sequence [1,2, …, ZZ ] according to the sequenceqc]，[1,2,…,XZqc]，qc＝1,2；ZZ'yc，XZ'ycRespectively representing the total loading and unloading amount of the container of the bridge yc, and obtaining the operation sequence [1,2, …, ZZ'yc]，[1,2,…,XZ'yc]，yc＝1,2。
The method comprises the following steps that a formula (1) is an objective function, in order to determine an optimal scheduling scheme by solving minimum ship berthing time and moving distances of a shore bridge and a field bridge, various scheduling schemes are formulated, the maximum completion time after each scheduling scheme executes all container tasks and the moving distances of the field bridge and the shore bridge are respectively calculated, the minimum value is taken and used as the optimal scheduling scheme;
the formula (2) ensures that the container is not placed in the air;
the formulas (3) and (4) ensure the time logic sequence of loading and unloading the containers;
the formulas (5) and (6) ensure that only one container can be placed in one container position;
the safe distance between the shore bridge and the site bridge is ensured by the formulas (7) and (8);
the formula (9) ensures that only one container can be operated by one shore bridge at the same time;
the formula (10) ensures that only one container can be operated by one field bridge at the same time;
the formulas (11) to (14) ensure that the stockpiling information of each stage can be updated in time;
further, in the above technical solution, the step 2 specifically includes the following steps:
in the method for the cooperative optimization scheduling of the shore bridge and the yard bridge, the import container task is that the import container needs to be unloaded from a container ship through the shore bridge and is loaded onto an inner container truck, then the import container is transported to the side of a storage yard through the inner container truck, and a container is stored into a container position in the storage yard through the yard bridge, so that the unloading operation of an import container is completed;
the export container task is that the export container needs to be taken down from a storage container position of a container yard through a yard bridge and is loaded onto an inner container truck, then the export container is transported to a shoreline side through the inner container truck, and the container is stored into a container position in a ship through the shore bridge, so that the unloading operation of an export container is completed;
the synchronous loading and unloading operation of the same shellfish means that after a field bridge finishes a box taking task of an export container, a box placing task of an import container is followed by a task.
The synchronous loading and unloading operation of the same shellfish means that after a shore bridge finishes the loading and unloading task of an export container, the loading and unloading task of an import container is followed by the task.
Step (1): carrying out initial coding on all container loading and unloading tasks to generate a scheduling scheme of a shore bridge and a field bridge; setting the chromosome scale as ch, the crossing rate as jc, the variation rate as by and the iteration times as ge generation; and coding all container tasks according to stack numbers by adopting two layers of arrays, wherein the first layer is an export container task, and the second layer is an import container task. When in coding: the first two digits of each group represent the shell number of the container task, the subsequent digits represent each stack number in the shell number by taking two digits as a small group, and the sequence of the two digits in the chromosome represents the sequence of loading and unloading. And so on until all container tasks are arranged and coded;
each set of chromosome codes generated in this embodiment represents a scheduling scheme, and since synchronous loading and unloading operations are adopted, containers in each stack are loaded and unloaded in the order from top to bottom, and cannot be changed.
Step (2): matching an import and export stack of the container according to the principle of highest matching degree of entropy values;
in the generated ch chromosomes, the upper layer and the lower layer of each chromosome respectively determine the operation sequence of an export container and an import container, and random numbers are generated to judge whether the operation sequence is crossed or varied; if so, performing intragroup crossing on the chromosomes according to an entropy matching principle, and obtaining ch chromosomes; if the stack number is varied, the stack number is randomly generated to replace the existing stack number.
The entropy matching principle means that when the shore bridge and the field bridge perform the same-shell synchronous loading and unloading operation, the import container and the export container are matched with each other in pairs by taking a stack as a unit to form a group of import and export container loading and unloading operation groups, so that the waiting time of the inner container card can be shortened as much as possible during the loading and unloading operation.
The yard and the ship are composed of a plurality of groups of container stacks, various overall entropy values are generated after different combinations are carried out on the container stacks according to the front-back sequence, and the overall waiting time of the inner container card during loading and unloading operation is shortest on the basis of the principle that the overall entropy value matching degree of the operation sequence of the ship and the yard is the highest.
And (3): and (3) generating a new coding group aiming at the ch new chromosomes generated in the step (2), respectively calculating the time required by each stack container according to the loading and unloading operation sequence, and taking the maximum time point of all tasks and the travel path length of the shore bridge and the field bridge as final solutions.
And (4): and judging whether the value of each set of chromosome solution is the current optimal solution, comparing the minimum value of the current algebraic solution with the minimum value of the previous solution, if the current solution is more optimal, taking the minimum value of the current solution as the optimal solution, otherwise, taking the optimal solution as the minimum value of the previous solution.
And (5): sorting the target function values corresponding to the solutions of each group from small to large, then removing the last 10% of chromosomes, and generating 10% of chromosomes again by the method in the step (1); under the condition of ensuring that the total chromosome amount is not changed, the whole population is ensured not to fall into local optimum easily.
And (6): generating random numbers to judge whether to cross or mutate; if so, performing intragroup crossing on the chromosomes according to an entropy matching principle, and obtaining ch chromosomes; if the stack number is varied, the stack number is randomly generated to replace the existing stack number.
And (7): judging whether the current generation reaches a termination condition, if so, terminating the algorithm; if not, the step (2) is entered.
And (4) performing intragroup crossing by taking the number of the shell as a minimum unit, and determining the gene bit length of a crossed fragment.
When the task amount of the import container is inconsistent with the task amount of the export container, the length of the chromosome is determined by the larger chromosome, and the subsequent length of the chromosome of the other layer is less than 0.
The entropy value matching principle takes the entropy values of all the shells as a first matching principle, and when the entropy values of the import container task shell and the export container task shell are relatively close, the absolute positions of the import container task shell and the export container task shell in chromosomes of respective layers are kept consistent; and taking the entropy values of the stacks as a second matching principle, and keeping the absolute positions of the imported container task stack and the exported container task stack in the chromosome segments of the shellfish to be consistent when the entropy values of the imported container task stack and the exported container task stack are relatively close.
The application stipulates that the shore bridge, the container truck and the yard bridge can only carry one container at a time, and each container position of the ship and the yard can only stack one container. The total amount of the containers at the inlet and the outlet is within the containable range of ships and storage yards.
Setting the number qc of quays to 2, the number yc of bridges to 2, the number B of storage yards to 10, the number Z of stacks to 10, the layer height C to 5, the remaining parameters β to 0.9 to 0.05,
as can be seen from the optimal value broken line in fig. 3(a) and the average optimal value convergence curve in fig. 3(b), when the entropy matching principle is used, the optimal value can be converged significantly, and the convergence rate in the first 100 generations is fast, and the convergence rate in the later 900 generations is slow because the convergence rate is already close to the optimal value. Mean values for the chromosome population can also converge significantly.
As can be seen from the shore bridge movement path diagram in fig. 4(a) and the yard bridge movement path diagram in fig. 4(b), the distance between the two shore bridges and the two yard bridges is always not less than 2 in the whole loading and unloading process, and therefore, the method belongs to the safe operation range, and the solution is a feasible solution.
The scale original parameters of each example are shown in table 1, the improved genetic algorithm adopting the principle of entropy matching and the genetic algorithm without considering entropy matching are respectively adopted for solving, and GAPs of the two solving algorithms are shown in fig. 5.
TABLE 1 original parameters of each scale
Through the original parameters in table 1 and the results in fig. 5, it can be seen that the improved genetic algorithm based on the entropy matching principle of the present invention has a good effect when being used for solving the shore bridge and yard bridge co-scheduling optimization model, compared with the common genetic algorithm, the optimal solution effect can be improved by 8% to 10%, and the optimization effect is continuously improved along with the problem scale enlargement, so the solution algorithm designed by the present application is effective.
Adjusting the container stock original state of a ship and a stock dump, adjusting the overall entropy value from one level to six levels according to an entropy value evaluation system, respectively applying to the examples of each scale in table 1, and solving by adopting a solving algorithm designed by the application, wherein the result is shown in fig. 6.
As can be seen from fig. 6, the solution algorithm designed in the present application can effectively suppress the increase of the optimal solution value due to the increase of the entropy, and the problem scale determines the lower bound of the optimal value of the model, and as the problem scale increases, the optimal solution value significantly increases.
The algorithm has high solving quality and solving efficiency, can solve large-scale problems, can solve the problem of wharf loading and unloading operation with high entropy, and meets the requirement of actual scheduling under various conditions of a wharf.
The following describes, with reference to a specific example, a part of the operation process of step 2 in the technical solution of the present invention, according to the shore bridge and yard bridge collaborative optimization scheduling model constructed in step 1:
step (1): performing initial coding on all import and export container stacks to generate a scheduling scheme of a shore bridge and a field bridge system;
in this example, the container loading and unloading tasks of 3 stacks in 1 bei are set to carry out initial coding, and two groups of generated codes are as follows, namely two scheduling schemes are generated,
| |
1 | 2 | 3 |
| |
1 | 2 | 3 |
| |
1 | 2 | 3 |
| |
3 | 1 | 2 |
Step (2): matching container inlet and outlet stacks according to principle of highest matching degree of entropy values
Since the container stack needs to be in a principle of from top to bottom when loading and unloading, in the first group of codes, the container operation sequence of the bridge is 101011011011111001, and the container operation sequence of the shore bridge is 101011111001011011; in the second group of codes, the container operation sequence of the yard bridge is 101011011011111001, and the container operation sequence of the shore bridge is 011011101011111001. Wherein, 1 represents a container to be operated, and 0 represents a container needing to be turned over.
It can be seen that the entropy matching degree is low for both loading and unloading orders. When the entropy matching principle is adopted, when the stack operation sequence of the export container is 123, the stack operation sequence of the import container is 132, that is, the operation sequences of both containers are 101011011011111001.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (2)
1. A dock shore bridge and yard bridge collaborative optimization scheduling method is characterized by comprising the following steps: the method comprises the following steps:
step 1: defining a shore bridge and a field bridge as scheduling objects, combining a ship schedule and positions of containers in ships and storage yards to finish the minimum time spent by loading and unloading all containers on one ship as the scheduling target of the shore bridge and the field bridge, and constructing a mathematical model of the synchronous loading and unloading operation of the imported and exported containers and the shellfish; the shore bridge and the field bridge cooperative scheduling mathematical model is as follows:
the objective function is:
the constraint conditions are as follows:
(ltcg'(b',z',c')>0|ltcg'(b',z',c'+1)>0) b'＝1,2,…,B'，z'＝1,2,…,Z'，c'＝1,2,…,C'-1 (2)
b, Z and C respectively represent the number of shelfs, the number of stacks and the stacking height of a storage yard; b ', Z ' and C ' respectively represent the number of shelfs, the number of stacks and the stacking height of the ship; o represents the number of container customers that need to be unloaded; WXoIndicates the number of containers for the O-th customer, O-1, 2, …, O; ZLxz，ZLzzRespectively representing the total number of containers that need to be unloaded and loaded,
g' represents the total number of stages, G ═ ZLxz+ZLzz(ii) a qc, yc respectively represent the shore bridge and the field bridge number, qc is 1,2, yc is 1, 2;
respectively representing the time required by single trip heavy load and no-load of the shore bridge;
representing the time required by the shore bridge to move one shellfish;
the average time required by one-time turnover of the shore bridge is represented;
respectively representing the time required by single-trip overload and no-load of the field bridge;
representing the time required by the field bridge to move one shellfish;
representing the average time required by turning over the box once for the bridge; ltc0(b ', z ', c ') represents initial ship stockpiling information,
ltcg'(b ', z', c ') represents ship stocking information at the G' stage, G '∈ G';
respectively, denote the beta number, stack number and layer number of the ith container placement unloaded by the quayside crane qc, qc ═ 1,2, i ═ 1,2, …, XZqc；
Respectively indicate the beige number, stack number, layer number, yc ═ 1,2, i ═ 1,2, …, ZZ 'of the ith container placement of the yc load of the bridge'yc；
Respectively represents the start time and the end time of loading the ith container by the quayside crane qc, qc is 1,2, i is 1,2, …, ZZqc；
Respectively representing the start time and the end time of unloading the ith container by the quayside crane qc, qc is 1,2, i is 1,2, …, XZqc；
Respectively represents the start time and the end time of the ith container loaded by the yc bridge, and yc is 1,2, i is 1,2, …, ZZ'yc；
Respectively representing the start time and the end time of unloading the ith container by the bridge yc, qc is 1,2, i is 1,2, …, XZqc；
Respectively representing the position of the shore bridge qc and the field bridge yc at time t,
respectively, denote the beta number, stack number and layer number of the ith container placement loaded on the quayside crane qc, qc is 1,2, i is 1,2, …, ZZqc；
Let us denote the beta number, stack number and layer number, respectively, of the ith container placement of the bye yc unloading, yc ═ 1,2, i ═ 1,2, …, XZ'yc；ZZqc，XZqcRespectively representing the total loading and unloading amount of the container of the quayside container qc, and obtaining an operation sequence [1,2, …, ZZ ] according to the sequenceqc]，[1,2,…,XZqc]，qc＝1,2；ZZ'yc，XZ'ycRespectively representing the total loading and unloading amount of the container of the bridge yc, and obtaining the operation sequence [1,2, …, ZZ'yc]，[1,2,…,XZ'yc]，yc＝1,2；
Step 2: the container storage state of ships and storage yards is represented by an entropy evaluation system, the containers are regarded as particles Jz of an open system by the entropy evaluation system, the combination of the containers to be loaded and unloaded and the containers to be turned is set as the energy Efx of each particle in the fx state, and the probability P of the storage system in a certain state (Jz, fx) can be knownjz,fxComprises the following steps:
the heap system entropy H is:
wherein: k is a proportionality coefficient; alpha is the particle motion rate; β is the energy dissipation rate; d is an entropy factor;
deducing a stockpiling system entropy evaluation index H according to the formula (15) and the formula (16)
The loading and unloading sequence of the container is coded by adopting a double-layer chromosome, an improved genetic algorithm is designed based on entropy values of ships and stacks of a storage yard, and a mathematical model of synchronous loading and unloading operation of the same shell of the container is solved; obtaining an optimal shore bridge and site bridge cooperative scheduling scheme under the condition of a given container loading and unloading task; the method specifically comprises the following steps:
step (1): carrying out initial coding on all container loading and unloading tasks to generate a scheduling scheme of a shore bridge and a field bridge;
setting the chromosome scale as ch, the crossing rate as jc, the variation rate as by and the iteration times as ge generation;
adopt two-layer array to encode all container tasks according to stack number, each group of code represents the scheduling scheme of a kind of field bridge and bank bridge, and wherein the first layer is export container task, the scheduling scheme of field bridge promptly, and the second floor is import container task, the scheduling scheme of bank bridge promptly, during the code: the first two digits of each group represent the shell number of the container task, the subsequent digits take two digits as a small group to represent each stack number in the shell number, the sequence of the two digits in the chromosome represents the sequence during loading and unloading, and the process is repeated until all the container tasks are arranged and coded; the total ch chromosomes adopt the coding mode, when the task amount of the import container is inconsistent with the task amount of the export container, the length of the chromosome is determined by the larger chromosome, and the subsequent length of the chromosome on the other layer is short of 0;
step (2): matching an import and export stack of the container according to the principle of highest matching degree of entropy values;
the loading and unloading sequence of the container adopts double-layer real number coding; in the generated ch chromosomes, the upper layer and the lower layer of each chromosome respectively determine the operation sequence of an export container and an import container, and random numbers are generated to judge whether the operation sequence is crossed or varied; if so, performing intragroup crossing on the chromosome according to an entropy matching principle to obtain ch chromosomes, wherein the intragroup crossing is performed according to the minimum unit of shellfish, and the length of the gene bit of a cross segment is determined; if the stack number is varied, the stack number is randomly generated to replace the existing stack number;
and (3): generating a new coding group aiming at the ch new chromosomes generated in the step (2), respectively calculating the time required by each stack container according to the loading and unloading operation sequence, and taking the maximum time point of complete completion of all tasks and the travel path length of the shore bridge and the field bridge as final solutions;
and (4): judging whether the value of each set of chromosome solution is the current optimal solution, comparing the minimum value of the current algebraic solution with the minimum value of the previous solution, if the current algebraic solution is more optimal, taking the minimum value of the current algebraic solution as the optimal solution, otherwise, taking the optimal solution as the minimum value of the previous solution;
and (5): sorting the target function values corresponding to the solutions of each group from small to large, then directly entering the first 10% of chromosomes into the next generation, removing the last 10% of chromosomes, and generating 10% of chromosomes again by the method in the step (1); under the condition of ensuring that the total amount of chromosomes is not changed, the whole population is ensured not to fall into local optimum easily;
and (6): generating random numbers to judge whether to cross or mutate; if so, performing intragroup crossing on the chromosomes according to an entropy matching principle, and obtaining ch chromosomes; if the stack number is varied, the stack number is randomly generated to replace the existing stack number;
and (7): judging whether the current generation reaches a termination condition, if so, terminating the algorithm; if not, the step (2) is entered.
2. The quay crane and yard bridge cooperative optimization scheduling method according to claim 1, wherein: in the crossing process, the entropy value matching principle takes the entropy value of each shell as a first matching principle, and when the entropy values of the import container task shell and the export container task shell are relatively close, the absolute positions of the import container task shell and the export container task shell in chromosomes of respective layers are kept consistent; and taking the entropy values of the stacks as a second matching principle, and keeping the absolute positions of the imported container task stack and the exported container task stack in the chromosome segments of the shellfish to be consistent when the entropy values of the imported container task stack and the exported container task stack are relatively close.
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| CN112862269B (en) * | 2021-01-20 | 2022-03-18 | 复旦大学 | Efficient optimal scheduling method for shore bridge |
| CN112836974B (en) * | 2021-02-05 | 2024-01-23 | 上海海事大学 | Dynamic scheduling method for multiple field bridges between boxes based on DQN and MCTS |
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