CN114048996A - U-shaped automatic wharf green integrated scheduling method considering uncertainty of arrival of external hub card - Google Patents

U-shaped automatic wharf green integrated scheduling method considering uncertainty of arrival of external hub card Download PDF

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CN114048996A
CN114048996A CN202111324029.1A CN202111324029A CN114048996A CN 114048996 A CN114048996 A CN 114048996A CN 202111324029 A CN202111324029 A CN 202111324029A CN 114048996 A CN114048996 A CN 114048996A
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许波桅
接德培
李军军
杨勇生
吴华锋
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Shanghai Maritime University
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Abstract

The invention discloses a green integrated dispatching method for a U-shaped automatic wharf considering uncertainty of arrival of an external hub card, which comprises the following steps: s1, enabling the iteration number iter to be 0, and carrying out chromosome coding in a task allocation mode to generate an initial population; s2, integrating calculation and selection operations of the fitness value of the green waiting strategy integrated scheduling scheme, and recording the current optimal solution; s3, performing intersection operation based on the Levy flight; s4, carrying out random self-adaptive variation; s5, updating the current optimal solution, wherein iter is iter + 1; and S6, when the termination condition is met, outputting the green integrated scheduling scheme corresponding to the maximum fitness value f as the optimal scheduling scheme. Its advantage does: the method aims at the loading and unloading process of the U-shaped automatic wharf, considers the uncertainty of the arrival of the external container truck, establishes a multi-target green integrated scheduling model with the minimum completion time of all tasks and the minimum carbon emission of loading and unloading equipment, can improve the loading and unloading efficiency of the U-shaped automatic wharf, and promotes the development of green ports.

Description

U-shaped automatic wharf green integrated scheduling method considering uncertainty of arrival of external hub card
Technical Field
The invention relates to the field of production scheduling of an automatic wharf, in particular to a green integrated scheduling method of a U-shaped automatic wharf considering uncertainty of arrival of an external truck.
Background
The U-shaped automatic container terminal (called as the U-shaped automatic terminal for short) is efficient and economical, and is the direction for transforming the future automatic terminal. The double-trolley shore bridge (short for shore bridge), the AGV, the double-cantilever rail crane (short for rail crane) and the outer truck are main loading and unloading equipment of the U-shaped automatic wharf, and the four are mutually associated and mutually influenced, so that how to enable the shore bridge, the AGV, the rail crane and the outer truck to efficiently and cooperatively operate is guaranteed, all loading and unloading tasks are completed in the least time, and the key scientific problem to be solved urgently is solved. With the acceleration of the construction of the automated wharf, how to reduce the energy consumption of the operation of large-scale automated equipment is another important scientific problem.
The problem of integrated scheduling of the automated wharf is studied by numerous scholars at home and abroad. However, most of the existing automated wharf integrated scheduling research is established under certain ideal conditions, and the external hub cards are less considered; most researches on energy conservation and emission reduction of container terminals are concentrated on the traditional wharf, and the research on energy conservation and emission reduction under the condition of integrated dispatching of the automatic wharf is lacked, so that the progress of high-quality transformation development of container terminals in China is greatly limited.
Disclosure of Invention
The invention aims to provide a U-shaped automatic wharf green integrated scheduling method considering uncertainty of arrival of an external hub, which aims at a loading and unloading process of a U-shaped automatic wharf 'double-trolley quay crane + Automatic Guided Vehicle (AGV) + double-cantilever rail crane + external hub', establishes a multi-target green integrated scheduling model with minimum completion time of all tasks and minimum carbon emission of loading and unloading equipment by considering uncertainty of arrival of the external hub, designs an improved hybrid genetic cuckoo algorithm for optimal solution, effectively improves loading and unloading efficiency of the U-shaped automatic wharf and promotes development of a green port.
In order to achieve the purpose, the invention is realized by the following technical scheme:
a green integrated dispatching method for a U-shaped automatic wharf considering uncertainty of arrival of an outer hub card comprises the following steps:
s1, enabling the iteration number iter to be 0, and carrying out chromosome coding in a task allocation mode to generate an initial population;
s2, integrating calculation and selection operations of the fitness value of the green waiting strategy integrated scheduling scheme, and recording the current optimal solution;
s3, performing intersection operation based on the Levy flight;
s4, carrying out random self-adaptive variation;
s5, updating the current optimal solution, wherein iter is iter + 1;
s6, meeting a termination condition, and outputting a green integrated scheduling scheme corresponding to the maximum fitness value f as an optimal scheduling scheme when the evolution algebra iter is equal to a preset maximum evolution algebra maxim;
in the step S6, when the evolution algebra is smaller than the preset maximum evolution algebra, the process goes to step S2 until the optimal scheduling scheme is obtained.
Preferably, the chromosome coding in the form of task allocation in step S1 specifically includes:
aiming at the integrated scheduling of four devices, namely a double-trolley shore bridge, an Automatic Guided Vehicle (AGV), a double-cantilever rail crane and an outer container truck, in a U-shaped automatic wharf, the established model needs to distinguish tasks of the shore bridge, the AGV, the rail crane and the outer container truck, so that a task allocation form is adopted for chromosome coding. In order to distinguish ship unloading and ship loading tasks, a number "0" is added to the chromosome to distinguish, the left side of the "0" is ship unloading task, and the right side is ship loading task.
Preferably, the integrated scheduling scheme fitness value calculation and selection operation of fusing the green waiting policy in step S2 specifically includes:
and a green waiting strategy, namely when the berths of the external truck task and the ship task in the yard are consistent, the AGV transporting the ship task waits for the external truck to arrive, and then the track crane immediately loads and unloads the ship task after completing the loading and unloading of the external truck task, so that the running distance and the parking times of the track crane can be fully reduced, and the carbon emission of the track crane is reduced.
According to a fitness function, i.e.
Figure BDA0003346305750000021
And calculating the adaptability value of each integrated scheduling scheme of the U-shaped automatic wharf. Wherein f is1Minimum time to complete all tasks, f2Shows that the carbon emission of three kinds of equipment, namely a shore bridge, an AGV and a track crane, is minimum,
Figure BDA0003346305750000022
is f1The weight coefficient of (a) is,
Figure BDA0003346305750000023
is f2The weight coefficient of (2). The selection is made using a roulette mechanism, and 5% of the bad individuals are retained in the selection operation in order to prevent the algorithm from falling into a locally optimal solution.
Preferably, the intersection operation based on the levy flight in step S3 specifically includes:
introducing a Levy flight theory into the cross part, numbering the tasks of the first layer of the chromosome, respectively carrying out the following operations on the ship unloading task and the ship loading task in the chromosome, and firstly carrying out the Lavy flight formula on each gene
Figure BDA0003346305750000031
And
Figure BDA0003346305750000032
updating is carried out, wherein U to N (0, sigma)2),V～(0,1),
Figure BDA0003346305750000033
XtFor the current optimal solution, Xt+1For the updated optimal solution, αmaxIs the maximum step size, αminFor the minimum step size, iter is the evolutionary algebra, Maxiter is the maximum evolutionary algebra, λ ∈ [1,2 ]]The random number of (2). The result of this operation will produce an infeasible solution, which is then restored to a feasible solution, specifically the operation is to round the elements in each locus, set the same elements to 0, and finally select the one with the best fitness in the populationComparing the individuals with the updated individuals, and replacing the elements which are contained in the randomly selected individuals but not contained in the updated individuals with the '0' elements in the latter in turn. For chromosome second, fourth and fifth layers, chromosomes are crossed at a single point, crossover points are randomly selected, and all elements before or after the crossover point of the two chromosomes are exchanged. For the third layer of chromosomes, the AGV can perform any tasks, so there is no need to operate the third layer.
Preferably, the random adaptive mutation in step S4 is specifically:
the random self-adaptive mutation adopts the reverse order operation, namely, two points are randomly selected on the chromosome, and the tasks between the two points are arranged in the reverse order. The mutation probability adopts self-adaptive mutation probability and can be automatically adjusted according to evolution algebra, namely
Figure BDA0003346305750000034
Wherein p ismaxTo the maximum mutation probability, pminIs the minimum mutation probability.
Preferably, the optimal green integrated scheduling scheme is as follows: and under the condition that the arrival time of the external container truck is uncertain, the total time for reasonably allocating the operation tasks to realize integrated scheduling among the shore bridge, the AGV, the track crane and the external container truck is the minimum, and the carbon emission of the three loading and unloading devices is the minimum.
Compared with the prior art, the invention has the following advantages:
(1) according to the green integrated dispatching method for the U-shaped automatic wharf considering the uncertainty of arrival of the external hub trucks, an improved hybrid genetic cuckoo algorithm is formed by improving a genetic algorithm and a cuckoo algorithm, and chromosome coding is performed in a task allocation mode, so that the solving result and efficiency of the algorithm are improved;
(2) the method provides a green waiting strategy, when the task scale is small, the result of the strategy which is not waited is good, and the difference value between the two is gradually reduced along with the increase of the task amount; when the task scale is large, the green waiting strategy has a good result;
(3) the method considers that the total time for realizing integrated scheduling among a shore bridge, an AGV, a track crane and an external container truck is minimum by reasonably distributing operation tasks under the condition that the arrival time of the external container truck is uncertain, and the carbon emission of three loading and unloading devices is minimum;
(4) the solving quality of the method is obviously superior to that of a genetic algorithm, a self-adaptive genetic algorithm and a wolf optimization algorithm, and particularly when a large-scale problem is solved, the calculating time is greatly improved.
Drawings
Fig. 1 is a layout view of a U-shaped automated container terminal according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating a green integrated dispatching method for a U-type automated terminal considering uncertainty of arrival of an outer truck according to the present invention;
FIG. 3 is a schematic illustration of chromosome coding in an embodiment of the present invention;
FIG. 4 is a schematic diagram of chromosome crossing in an embodiment of the present invention;
FIG. 5 is a graph comparing 15 example performance trends for four algorithms;
FIG. 6 is a comparison of the average run times(s) of 15 example CPU sets for four algorithms;
FIG. 7 is a comparison of small-scale calculation results for two strategies;
FIG. 8 is a comparison of results of large-scale calculation of two strategies.
Detailed Description
The present invention will now be further described by way of the following detailed description of a preferred embodiment thereof, taken in conjunction with the accompanying drawings.
In this embodiment, a ship is loaded and unloaded in a U-shaped automated dock at a port, and the layout of the dock is shown in fig. 1. The horizontal transportation area of the wharf is 300m long and 120m wide, the length of one berth of the storage yard is 15m, and each storage yard area has 40 berths. The constant-speed running speed of the AGV is 5m/s, and the constant-speed running speed of the track crane is 2 m/s. The time that the front trolley of the shore bridge takes the boxes from the ship or puts the boxes on the transfer platform on the ship is subject to the uniform distribution of (20s, 30s), the time that the rear trolley of the shore bridge puts the containers on or takes the boxes from the AGV is 20s, and the time that the double-cantilever rail crane takes the boxes from the AGV to the appointed stockpiling point or puts the boxes on the AGV from the appointed stockpiling point is subject to the uniform distribution of (30s, 50 s).
The integrated scheduling of a U-shaped automatic wharf double-trolley shore bridge and an Automatic Guided Vehicle (AGV), a double-cantilever rail crane and an outer collecting card is unfolded under a side loading and side unloading mode, namely the AGV completes a container loading task and then completes an unloading task, and then completes a loading task after completing an unloading task. In the U-shaped automatic wharf, the outer container truck can enter a storage yard to interact with the rail crane, so that tasks of the outer container truck can be inserted into AGV tasks, waiting time of the outer container truck is reduced, namely when the outer container truck arrives, the rail crane immediately executes the tasks of the outer container truck after finishing the loading and unloading tasks of the current AGV.
As shown in fig. 2, a U-type automated green integrated dispatching method for wharf considering uncertainty of arrival of alien trucks of the present invention includes the following steps:
s1, let iter be 0, perform chromosome coding in the form of task assignment, and generate an initial population.
The chromosome coding in the step S1 in the form of task allocation specifically includes:
aiming at the integrated scheduling of four devices, namely a double-trolley shore bridge, an Automatic Guided Vehicle (AGV), a double-cantilever rail crane and an outer container truck, in a U-shaped automatic wharf, the established model needs to distinguish tasks of the shore bridge, the AGV, the rail crane and the outer container truck, so that a task allocation form is adopted for chromosome coding. Suppose that there are 4 ship loading tasks and 4 ship unloading tasks, 2 double-trolley shore bridges, 2 AGVs, 2 yard areas, each of which is equipped with 2 double-cantilever rail cranes, and a schematic diagram of chromosome coding is shown in fig. 3. In order to distinguish ship unloading and ship loading tasks, a number "0" is added to the chromosome to distinguish, the left side of the "0" is ship unloading task, and the right side is ship loading task. The first line represents the serial number of a container task, the second line represents the serial number of a shore bridge, the third line represents the serial number of an AGV, the AGV with the odd number firstly carries out a ship loading task and then carries out a ship loading task, the AGV with the even number firstly carries out the ship loading task and then carries out the ship unloading task, the fourth line represents the serial number of a yard, the fifth line represents that an outer container truck arrives randomly, wherein the number of '1' represents that an outer container truck arrives, and '0' represents that no outer container truck arrives.
And (3) decoding the chromosome, wherein the AGV path No. 1 is as follows: shore bridge 2 → yard 2 (ship unloading task 3) → yard 2 → shore bridge 1 (ship loading task 1) → shore bridge 1 → yard 2 (ship unloading task 2) → yard 1 → shore bridge 2 (ship loading task 3), the route of No. 2 AGV being: yard 1 → shore bridge 2 (shipment task 4) → shore bridge 1 → yard 1 (ship unloading task 4) → yard 1 → shore bridge 2 (shipment task 2) → shore bridge 1 → yard 2 (ship unloading task 1). For the external truck-collecting task, the rail crane immediately executes the external truck-collecting task after the current task is executed, for example, when the external truck-collecting randomly reaches "1", the rail crane in the storage yard 2 immediately executes the external truck-collecting task after the ship-unloading task 3 is executed, and when the external truck-collecting randomly reaches "0", the rail crane continues to execute the next AGV task after the current task is executed.
And S2, integrating the calculation and selection operation of the green waiting strategy integrated scheduling scheme adaptability value, and recording the current optimal solution.
The integrated scheduling scheme fitness value calculation and selection operation of the green waiting policy fused in step S2 specifically includes:
the invention considers the uncertainty of the arrival of the external hub, applies the related theory of mixed flow shop scheduling, and provides a green waiting strategy, namely when the external hub task is consistent with the berth of the ship task in a yard, an AGV transporting the ship task waits for the arrival of the external hub, and then the track crane immediately loads and unloads the ship task after completing the loading and unloading of the external hub task, thereby fully reducing the running distance and the parking times of the track crane, and reducing the carbon emission of the track crane.
A multi-objective green integrated scheduling model with the minimum completion time of all tasks and the minimum carbon emission of loading and unloading equipment is established.
Figure BDA0003346305750000061
f2＝min{Esq+Esy+EAGV} (2)
Figure BDA0003346305750000062
Equation (1) is the first goal, and the dock equipment has the least time to complete all tasks. Equation (2) is the second objective, minimizing carbon emissions from the three devices. Formula (3) is an objective function, wherein
Figure BDA0003346305750000063
Is f1The weight coefficient of (a) is,
Figure BDA0003346305750000064
is f2The weight coefficient of (2).
Figure BDA0003346305750000065
Figure BDA0003346305750000066
Figure BDA0003346305750000067
Figure BDA0003346305750000068
Figure BDA0003346305750000069
Equation (4) represents the relationship between the start time of the ship unloading task and the arrival time of the AGV at the shore bridge. Equation (5) represents the relationship between the time when the AGV arrives at the quay crane and the time when the quay crane places the container onto the AGV. Equation (6) shows the relationship between the departure time of the AGV from the shore bridge and the arrival time of the AGV at the designated loading bay. Equation (7) shows the relationship between the time when the rail crane starts unloading the container on the AGV and the task end time. Equation (8) represents the relationship between the end time of the last ship unloading task and the time when the AGV reaches the yard specific parking stall of the next ship loading task.
Figure BDA0003346305750000071
Figure BDA0003346305750000072
Figure BDA0003346305750000073
Figure BDA0003346305750000074
Figure BDA0003346305750000075
Equation (9) represents the start time of the shipment task. Equation (10) shows the relationship between the start time of the shipment task and the arrival time of the AGV at the shore bridge. Equation (11) represents the relationship between the time the AGV arrives at the shore bridge and the time the shore bridge removes the container from the AGV. Equation (12) shows the relationship between the time the quay crane takes a container from the AGV and the end of the loading mission. Equation (13) shows the relationship between the time the AGV completed the last ship loading task and the time it reached the shore bridge for the next ship unloading task.
Figure BDA0003346305750000076
Figure BDA0003346305750000077
Figure BDA0003346305750000078
Figure BDA0003346305750000079
Figure BDA00033463057500000710
Figure BDA00033463057500000711
Figure BDA00033463057500000712
Equation (14) represents the relationship between the time when the AGV executing the ship task having the same target bay as the external truck task reaches the target bay and the external truck arrival time. Equation (15) represents the time when the gantry crane reaches the target berm of the mission. Equation (16) shows that the same AGV can only complete the loading task of one shore bridge after completing the unloading task of one shore bridge. The formula (17) shows that the same AGV can only complete the ship unloading task of one shore bridge after completing the ship loading task of one shore bridge. Equation (18) indicates that the trolley can only perform one outer truck task at a time. Equation (19) indicates that a container can only be transported by one AGV. Equation (20) indicates that a container can only be loaded and unloaded by one shore bridge.
Figure BDA0003346305750000081
Figure BDA0003346305750000082
Equation (21) represents the total carbon emissions of the shore bridge after all the tasks have been completed. Equation (22) represents the total carbon emissions of the gantry crane after all tasks have been completed.
EAGV＝C1·(E1+E2+E3+E4) (23)
Figure BDA0003346305750000083
Figure BDA0003346305750000084
Figure BDA0003346305750000085
Figure BDA0003346305750000086
Equation (23) represents the total carbon emissions after the AGV has completed all tasks. Equation (24) represents the energy consumption of the AGV to transport the container from the shore bridge to the yard. Equation (25) represents the energy consumption of the AGV to travel from the yard to the next mission yard. Equation (26) represents the energy consumption of the AGV to transport the container from the yard to below the shore bridge. Equation (27) represents the energy consumption of the AGV traveling from the shore bridge to the next mission shore bridge.
Figure BDA0003346305750000087
Figure BDA0003346305750000088
Equations (28) to (29) represent the ranges of values of the partial parameters and the decision variables.
Description of the symbols
(1) And (3) gathering: u is an import container set;Lan export container set;Wan outer hub task set; v is an AGV set;Qa quay crane set;Ya double-cantilever rail crane set; set of all tasks, i.e. N ═U∪L∪W；OsA virtual start bridge; o isFA virtual ending bridge;Bthe task is at the bay.
(2) Parameters are as follows: e.g. of the typelThe average energy consumption per unit time when the AGV runs is obtained; etaqThe average unit energy consumption of one quay crane;
Figure BDA0003346305750000091
average unit energy consumption during movement of one track crane;
Figure BDA0003346305750000092
the unit energy consumption of one track crane in each parking is averaged; c1Carbon emission per unit energy consumption of AGV; c2Carbon emission per unit energy consumption of quayside container; c3Carbon emission per unit energy consumption of the rail crane; c4The carbon emission of the single time of the track crane parking; v' is the running speed of the rail crane;Ma very large positive integer; t is1The time required for the bank side trolley to unload the container onto the AGV or the time for the container on the AGV to be unloaded onto the transfer platform is represented;
Figure BDA0003346305750000093
time for loading and unloading the outer truck task w of the bridge k to an outer truck or a stockpiling point;
Figure BDA0003346305750000094
the time for unloading the ship task i of the bridge k to a specified stockpiling point or the time for loading the container of the stockpiling point on the AGV is represented;
Figure BDA0003346305750000095
when the sea side trolley unloads the ship task i of the field bridge k to the shore bridge transfer platform or loads the container on the transfer platform to the ship;
Figure BDA0003346305750000096
AGV is the time required from equipment node m to equipment node n.
(3) Is not 0-1 variable:
Figure BDA0003346305750000097
beginning time of task i of track crane k;
Figure BDA0003346305750000098
ending time of a task i of the track crane k;
Figure BDA0003346305750000099
when an AGV processing a task i of a track crane k reaches the lower part of a quayside crane;
Figure BDA00033463057500000910
the moment when the bank side trolley of the bank bridge takes the task i of the track crane k from the AGV or the moment when the container is put on the AGV;
Figure BDA00033463057500000911
when the AGV transports the task i of the track crane k to the designated scallop position or reaches the designated scallop position of the boxing task;
Figure BDA00033463057500000912
representing the position of the outer truck collection task w processed by the track crane k;
Figure BDA00033463057500000913
the place where the ship task i processed by the track crane k is located is a berth;
Figure BDA00033463057500000914
when the track crane k reaches the beige position of the task i;
Figure BDA00033463057500000915
the time when the outer truck collection task w of the track crane k reaches the designated scallop is defined; TN is the number of times of stopping of all track cranes in the loading and unloading process;E sqtotal carbon emissions of the quay crane;E sytotal carbon emissions of the gantry crane;E AGVtotal carbon emissions for AGVs.
(4) Variable 0-1：xikjlAfter the same AGV finishes the task i of the track crane k, the task j of executing the track crane l is 1, otherwise, the task j is 0;
Figure BDA00033463057500000916
if the quayside crane q executes the task i of the track crane k, the task i is 1, otherwise, the task i is 0;
Figure BDA00033463057500000917
if the AGV v executes the task i of the track crane k, the task i is 1, and if not, the task i is 0;
Figure BDA00033463057500000918
after completing the task i, the track crane k executes the outer truck collecting task w to be 1, otherwise, the track crane k is 0;
Figure BDA00033463057500000919
the destinations of the outer truck task w and the ship task i of the track crane k are in the same berth, and are 1, otherwise, the destinations are 0.
According to a fitness function, i.e.
Figure BDA00033463057500000920
And calculating the adaptability value of each integrated scheduling scheme of the U-shaped automatic wharf. The selection is made using a roulette mechanism, and 5% of the bad individuals are retained in the selection operation in order to prevent the algorithm from falling into a locally optimal solution.
And S3, performing intersection operation based on the Levy flight.
The intersection operation based on the levy flight in step S3 is specifically:
the Laiwei flight theory is introduced into the cross section, and the ship unloading task and the ship loading task in the chromosome are respectively carried out according to the first-layer task number of the chromosome, as shown in a figure 4. Firstly, according to the Laivy flight formula for each gene
Figure BDA0003346305750000101
And
Figure BDA0003346305750000102
updating is carried out, wherein U to N (0, sigma)2),V～(0,1),
Figure BDA0003346305750000103
XtFor the current optimal solution, Xt+1For the updated optimal solution, αmaxIs the maximum step size, αminFor the minimum step size, iter is the evolutionary algebra, and maxim is the maximum evolutionary algebra. The result of the operation can generate an infeasible solution, then the infeasible solution is restored into a feasible solution, the specific operation is to round and round the elements in each gene position, the same elements are set to be 0, finally the individual with the best fitness in the population is selected to be compared with the updated individual, and the elements contained in the randomly selected individual but not contained in the updated individual are sequentially replaced by the '0' elements in the latter. And aiming at the second, fourth and fifth layers of chromosomes, respectively carrying out single-point crossing on the chromosome ship unloading and the loading and unloading task shore bridge, randomly selecting crossing points, and exchanging all elements before or after the crossing points of the two chromosomes. For the third layer of chromosomes, the AGV can perform any tasks, so there is no need to operate the third layer.
S4, random adaptive mutation
The random adaptive mutation in step S4 is specifically:
the random self-adaptive mutation adopts the reverse order operation, namely, two points are randomly selected on the chromosome, and the tasks between the two points are arranged in the reverse order. The mutation probability adopts self-adaptive mutation probability and can be automatically adjusted according to evolution algebra, namely
Figure BDA0003346305750000104
Wherein p ismaxTo the maximum mutation probability, pminIs the minimum mutation probability.
Preferably, the optimal green integrated scheduling scheme is as follows: and under the condition that the arrival time of the external container truck is uncertain, the total time for reasonably allocating the operation tasks to realize integrated scheduling among the shore bridge, the AGV, the track crane and the external container truck is the minimum, and the carbon emission of the three loading and unloading devices is the minimum.
Comparative experiment of model and algorithm effectiveness
In order to verify the effectiveness of the model and the algorithm, a 15-group comparison experiment is carried out by respectively adopting an Adaptive Genetic Algorithm (AGA), a Genetic Algorithm (GA), a wolf optimization algorithm (GWO) and an improved hybrid genetic cuckoo algorithm (HGACS). In an objective function
Figure BDA0003346305750000111
And
Figure BDA0003346305750000112
push button
Figure BDA0003346305750000113
And (4) setting. Relevant parameters such as operation energy consumption of a shore bridge, an AGV and a track crane are set as follows: e.g. of the typel/[ kw.h (h.table)-1]＝21，ηq/[ kw.h (h.table)-1]＝91.24，
Figure BDA0003346305750000114
Regarding the calculation of carbon emission, the electric power energy consumption of the loading and unloading equipment is firstly calculated, then the energy consumption is converted into the carbon emission according to the carbon emission factor, wherein the carbon emission factor selects a carbon emission factor EF of a power grid reference line in southern ChinagridOM, y 0.8042 tons CO 2/kwh. The algorithm parameters are set as follows: population size 50, maximum 500, alphamin＝0.05；αmax＝0.5，λ＝1.5，pc＝0.5，pmax＝0.8，pmin0.1. The experiments were solved 30 times for each group, and the comparison with the target value as the final result is shown in fig. 5, and the comparison with the average running time(s) of the CPU is shown in fig. 6. Fig. 5 shows the optimal target values of the 4 algorithms when calculating different numbers of tasks and different numbers of AGVs, where the comparison result shows that when the number of tasks is small, the optimal values of the algorithms are similar, and as the number of tasks increases, the difference between the target function values of the other 3 algorithms and the target values of the HGACS algorithm becomes larger. As can be seen from fig. 6: the improved hybrid genetic cuckoo algorithm can stably obtain an approximate optimal solution of a large-scale container calculation problem in the solving process. Such as in figures 6, examples 12 and 15, at the container taskAt 800 AGVs, 40, the CPU computation time is about 15 minutes, and it can be seen in fig. 5 that the target value is 12739. When the container task is 2000 and the AGV number is 50, the CPU computation time is about 21 minutes, the target value is 32210, and the time and energy consumption of the current automatic wharf operation system scheduling are basically met.
Comparison experiment of two scheduling strategies at different scales
The result pairs for both the small-scale and large-scale example scheduling strategies are shown in fig. 7 and 8. As can be seen from fig. 7, when the task size is smaller, the result of the non-waiting strategy is better, and as the task amount increases, the difference between the two gradually decreases; as can be seen from fig. 8, when the task size is larger, the result of adopting the waiting strategy is better, and the difference between the two gradually increases as the task amount increases. The reason for this is that when the task size is small, the utilization amount of dock equipment resources is small, the carbon emission amount is small, the waiting strategy is adopted to reduce the operating efficiency of the AGV and prolong the total completion time of the task, and the objective function includes both the total completion time and the energy consumption, at this time, the total completion time accounts for a relatively large proportion, and finally, the objective function value is increased. When the task scale is large, the utilization amount of wharf resources is large, the carbon emission amount is large, the running distance and the parking times of the rail crane can be fully reduced by adopting a waiting strategy, the carbon emission amount of the wharf is reduced, the energy consumption ratio is large due to the fact that the carbon emission amount is increased, and finally the objective function value is reduced.
In conclusion, the green integrated scheduling method for the U-shaped automatic wharf considering the uncertainty of the arrival time of the external hub card reasonably allocates the operation tasks under the condition of considering the uncertainty of the arrival time of the external hub card to realize the minimum total time of integrated scheduling among the shore bridge, the AGV, the track crane and the external hub card and the minimum carbon emission of three kinds of loading and unloading equipment, establishes a multi-target green integrated scheduling model integrating green waiting strategies, adopts the improved hybrid genetic cuckoo algorithm to carry out optimal solution, can improve the loading and unloading efficiency of the U-shaped automatic wharf while obtaining the optimal scheduling scheme, and promotes the development of the green wharf. Meanwhile, the algorithm adopts a task allocation form to carry out chromosome coding, so that the solving result and the efficiency of the algorithm are improved.
While the present invention has been described in detail with reference to the preferred embodiments, it should be understood that the above description should not be taken as limiting the invention. Various modifications and alterations to this invention will become apparent to those skilled in the art upon reading the foregoing description. Accordingly, the scope of the invention should be determined from the following claims.

Claims (6)

1. A green integrated dispatching method for a U-shaped automatic wharf considering uncertainty of arrival of an outer hub card is characterized by comprising the following steps:
s1, enabling the iteration number iter to be 0, and carrying out chromosome coding in a task allocation mode to generate an initial population;
s2, integrating calculation and selection operations of the fitness value of the green waiting strategy integrated scheduling scheme, and recording the current optimal solution;
s3, performing intersection operation based on the Levy flight;
s4, carrying out random self-adaptive variation;
s5, updating the current optimal solution, wherein iter is iter + 1;
s6, meeting a termination condition, and outputting a green integrated scheduling scheme corresponding to the maximum fitness value f as an optimal scheduling scheme when the evolution algebra iter is equal to a preset maximum evolution algebra maxim;
in the step S6, when the evolution algebra is smaller than the preset maximum evolution algebra, the process goes to step S2 until the optimal scheduling scheme is obtained.
2. The method for automated wharf green integrated dispatching considering uncertainty of arrival of outsourced cards according to claim 1, wherein the chromosome coding in the form of task allocation in the step S1 is specifically:
aiming at the integrated scheduling of four devices, namely a double-trolley shore bridge, an Automatic Guided Vehicle (AGV), a double-cantilever rail crane and an outer container truck, in a U-shaped automatic wharf, the established model needs to distinguish tasks of the shore bridge, the AGV, the rail crane and the outer container truck, so that a task allocation form is adopted for chromosome coding. In order to distinguish ship unloading and ship loading tasks, a number "0" is added to the chromosome to distinguish, the left side of the "0" is ship unloading task, and the right side is ship loading task.
3. The method for automated wharf green integrated scheduling considering uncertainty of arrival of outsourced cards according to claim 2, wherein the operation of calculating and selecting the adaptation value of the integrated scheduling scheme fusing the green waiting policy in step S2 is specifically as follows:
and a green waiting strategy is provided, namely when the external truck task is consistent with the berth of the ship task in a storage yard, an AGV transporting the ship task waits for the external truck to arrive, and then the track crane immediately loads and unloads the ship task after completing the loading and unloading of the external truck task, so that the running distance and the parking times of the track crane can be fully reduced, and the carbon emission of the track crane is reduced.
According to a fitness function, i.e.
Figure FDA0003346305740000021
And calculating the adaptability value of each integrated scheduling scheme of the U-shaped automatic wharf. Wherein f is1Minimum time to complete all tasks, f2Shows that the carbon emission of three kinds of equipment, namely a shore bridge, an AGV and a track crane, is minimum,
Figure FDA0003346305740000022
is f1The weight coefficient of (a) is,
Figure FDA0003346305740000023
is f2The weight coefficient of (2). The selection is made using a roulette mechanism, and 5% of the bad individuals are retained in the selection operation in order to prevent the algorithm from falling into a locally optimal solution.
4. The method for automated green wharf integrated dispatching considering uncertainty of arrival of external hub trucks at a U-shaped dock according to claim 3, wherein the step S3 of the lave flight-based crossover operation is specifically as follows:
introducing a Levy flight theory into the cross part, numbering the tasks of the first layer of the chromosome, respectively carrying out the following operations on the ship unloading task and the ship loading task in the chromosome, and firstly carrying out the Lavy flight formula on each gene
Figure FDA0003346305740000024
And
Figure FDA0003346305740000025
is updated, wherein
Figure FDA0003346305740000026
XtFor the current optimal solution, Xt+1For the updated optimal solution, αmaxIs the maximum step size, αminFor the minimum step size, iter is the evolutionary algebra, Maxiter is the maximum evolutionary algebra, λ ∈ [1,2 ]]The random number of (2). The result of the operation can generate an infeasible solution, then the infeasible solution is restored into a feasible solution, the specific operation is to round and round the elements in each gene position, the same elements are set to be 0, finally the individual with the best fitness in the population is selected to be compared with the updated individual, and the elements contained in the randomly selected individual but not contained in the updated individual are sequentially replaced by the '0' elements in the latter. For chromosome second, fourth and fifth layers, chromosomes are crossed at a single point, crossover points are randomly selected, and all elements before or after the crossover point of the two chromosomes are exchanged. For the third layer of chromosomes, the AGV can perform any tasks, so there is no need to operate the third layer.
5. The method for automated quay green integrated dispatching considering uncertainty of arrival of outsourced cards at U-shaped terminals according to claim 4, wherein the random adaptive mutation in the step S4 is specifically:
the random self-adaptive mutation adopts the reverse order operation, namely two points are randomly selected on the chromosome, and the task between the two points is carried outAnd (4) arranging in a reverse order. The mutation probability adopts self-adaptive mutation probability and can be automatically adjusted according to evolution algebra, namely
Figure FDA0003346305740000031
Wherein p ismaxTo the maximum mutation probability, pminIs the minimum mutation probability.
6. The method of automated quay green integration scheduling of a U-type that takes into account outlier card arrival uncertainty of claim 5,
the optimal green integrated scheduling scheme is as follows: and under the condition that the arrival time of the external container truck is uncertain, the total time for reasonably allocating the operation tasks to realize integrated scheduling among the shore bridge, the AGV, the track crane and the external container truck is the minimum, and the carbon emission of the three loading and unloading devices is the minimum.
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