CN114154823A - Robust berth shore bridge joint distribution method based on improved particle swarm optimization - Google Patents

Robust berth shore bridge joint distribution method based on improved particle swarm optimization Download PDF

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CN114154823A
CN114154823A CN202111391031.0A CN202111391031A CN114154823A CN 114154823 A CN114154823 A CN 114154823A CN 202111391031 A CN202111391031 A CN 202111391031A CN 114154823 A CN114154823 A CN 114154823A
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杨玉
马金慧
戴红伟
戴迎春
贾东宝
李存华
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Abstract

The invention discloses a robust berth shore bridge joint distribution method based on an improved particle swarm algorithm, which comprises the following steps of S1: initializing a population; s2: calculating the earliest berthing time; s3: calculating the latest berthing time; s4: inserting a buffer area; s5: self-adaptive variation of particles; s6: calculating an objective function value of the sample; namely the delay departure time of the ship; s7: updating the individual optimal value and the global optimal value of the particle; s8: updating the speed and position of the particles; s9: let TN be TN +1, judge whether it meets the termination condition of reaching maximum iteration number TN, if yes, terminate iteration and output the optimal solution, otherwise return to step S2 to continue iteration; the invention reduces the negative influence of uncontrollable factors such as delay of arrival of ships or prolongation of cargo loading and unloading time of ships caused by weather or equipment faults on the overall operation efficiency of the port, reduces economic cost and time cost, can quickly and effectively solve the berth shore bridge scheduling problem, and further improves the overall operation efficiency of the container terminal.

Description

Robust berth shore bridge joint distribution method based on improved particle swarm optimization
Technical Field
The invention relates to the field of transportation decision of sea-land combined transportation, in particular to a robust berth shore bridge combined distribution method based on an improved particle swarm algorithm.
Background
The berth and the shore bridge are used as important resources of the wharf, and have important influence on the production efficiency of the wharf. The berth is the area that the pier is used for boats and ships to berth along the bank line, and the bank bridge is the important instrument of container on the loading and unloading boats and ships. Under the influence of uncertain factors such as severe weather and wharf facility faults, the arrival time, loading and unloading working time and the like of a ship can have certain uncertainty, and the uncertain factors often make a berth shore bridge scheduling plan in a tight coupling mode difficult to execute, so that the original berth shore bridge scheduling plan can be delayed or even interrupted, and the overall efficiency of a port is seriously influenced.
In the prior art, the influence of the shore bridge fault on the ship shore bridge scheduling scheme is considered, a model is constructed to obtain the optimal shore bridge re-scheduling scheme at each rolling stage decision point, but the influence of the accumulation of the shore bridge fault and the site bridge fault on the ship loading and unloading progress is not considered, and only the shore bridge scheduling scheme of a single ship is researched.
In the prior art, a two-stage model of berth and shore bridge joint scheduling considering berth preference and shore bridge movement frequency is established. The first stage model adopts a variable arrival time strategy of ships, and establishes a mixed integer programming model with minimum cost as a target. In the second stage model, interference constraint of the shore bridge is considered, an integer planning model with the smallest shore bridge movement frequency as a target is established, and a berth-shore bridge combined scheduling plan in a given planning period can be obtained, however, the situation that in the actual wharf operation process, when the planning period is longer, the actual working conditions of berths and the shore bridge come in and go out with the plan is not considered. Although many models and algorithms for solving the problem of the berth shore bridge allocation are provided at present, and certain improvement is provided in the aspect of optimizing precision of the models and algorithms, the models and the methods for solving the problem of the berth shore bridge still need to be further improved in the case of large scale, long planning period and possible change of the actual environment.
In an actual berthing shore bridge operation scene, uncontrollable factors such as delay of ships arriving at a port or prolonging of cargo loading and unloading time of the ships may occur due to weather or equipment faults, and the occurrence of the unexpected factors can cause extra economic cost, time cost and the like, so that a robust berthing shore bridge scheduling scheme which can better cope with the influence of the uncertain factors is very important for relieving the negative influence of the uncertain factors on the overall operation efficiency of the port; therefore, the robust berth shore bridge joint distribution method based on the improved particle swarm optimization is provided.
Disclosure of Invention
The invention aims to provide a robust berth shore bridge joint distribution method based on an improved particle swarm algorithm aiming at the defects of the prior art so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: a robust berth shore bridge joint distribution method based on an improved particle swarm algorithm comprises the following specific steps:
s1: initializing a population; the method specifically comprises the steps of population scale, iteration times, upper and lower boundaries of particle positions and speeds, inertia weight and learning factors; taking 6 ships as an example, the coding length is 6: the ship number is: 1. 2, 3, 4, 5, 6; docking sequence OjComprises the following steps: 1. 3, 5, 2, 4, 6; berthing xjComprises the following steps: 2. 1,2, 3, 4; number y of quay bridgesjComprises the following steps: 3. 5, 2, 4, 1, 2;
s2: calculate the earliest berthing time tbj(ES): for vessel j, calculate tbjThe formula (ES) is as follows:
Figure BDA0003368817030000021
wherein s isijkIndicating that the k-th berthing of the ship j at the berth i is 1, otherwise, the k-th berthing is 0, tajAnd to show vesselsTime to arrival, td of jj'Represents the departure time of vessel j' at berth i;
s3: calculating the latest berthing time tbj(LS): if there is a ship j' that is berthed at the same berth and is berthed next to it, let t be tbj'(LS) if no such ship exists, let t be tdjCalculating tbjThe formula for (LS) is as follows:
Figure BDA0003368817030000022
wherein twjRepresents the operating time of vessel j;
s4: inserting a buffer area;
s5: self-adaptive variation of particles; the PSO algorithm has strong global search capability and memory, has strong optimization capability in the early stage of iteration, is difficult to jump out of a local optimal solution to find a global optimal solution because of inevitable dropping into a local trap in the later stage of iteration, and is improved by designing the following adaptive variation strategy aiming at the characteristics of the PSO algorithm and a constructed berth shore bridge problem model, so that the updating formula of the adaptive variation probability P is as follows:
Figure BDA0003368817030000031
wherein TN is the current iteration frequency, and TN is the total iteration frequency; for the particle i, randomly generating a random number between 0 and 1, and if the random number is greater than the variation probability of the iteration, performing variation operation on the particle;
s6: calculating an objective function value of the sample; namely the delay departure time of the ship, the formula is as follows:
Figure BDA0003368817030000032
s7: updating the individual optimal value and the global optimal value of the particle; for each particle, comparing the fitness value obtained by the iteration with the optimal fitness value pbest, if the fitness value is smaller than pbest, updating, and otherwise, keeping pbest unchanged; comparing pbest with the global optimum value gbest, and if the pbest is smaller than the gbest, replacing the gbest with the pbest;
s8: and updating the speed and the position of the particle, wherein the speed updating formula of the ith particle is as follows:
vij(tn+1)＝wvij(tn)+c1r1j(pij-xij(tn))+c2r2j(gj-xij(tn)),j∈V
the location update formula is: x is the number ofij(tn+1)＝xij(tn)+vij(tn+1),j∈V
Wherein v isij(tn +1) represents the j-dimension velocity value of the tn +1 th iteration particle i, w is a weight coefficient, c1And c2Dimensional learning factor, r1jAnd r2jIs a chaotic variable based on Logistic chaotic sequence, pijIs the j-th dimension position value, g, of the optimal solution of the particle ijPosition value of j dimension, x, being global optimum solutionij(tn) represents a j-dimensional position value of the particle i at the tn-th iteration; for the particles with updated speed and positions, carrying out boundary processing on the particles so that the codes of the particles meet the constraint;
s9: and (5) making TN be TN +1, judging whether a termination condition reaching the maximum iteration time TN is met, if so, terminating iteration and outputting an optimal solution, otherwise, returning to the step S2 to continue iteration.
As a preferable embodiment of the present invention, O in S1 isjRepresenting the berthing sequence of the ship, and randomly arranging integers between 1 and n; x is the number ofjRepresenting a berthing of the vessel; y isjRepresenting the number of shore bridges allocated to the vessel, is randomly generated between the minimum and maximum required number of shore bridges for the vessel, e.g. vessel 1 is first berthed at berth # 2 and is allocated 3 shore bridges.
As a preferred technical solution of the present invention, the specific steps of inserting the buffer area in S4 are as follows:
s41: updating the weighting factor wj(ii) a The weighting factor represents the service priority of the ship, for the ship j, if the weighting factor exists, the weighting factor represents the service priority of the shipTb is calculatedj(ES) and tbjTime and space conflicting vessels j' in (LS) schemes, i.e. { xj＝xj',tbj'(ES)＜tbj(ES)＜tbj'(LS)+twj'If j, j' is equal to V }, then wjSet to 1, otherwise set to 0, where the set V ═ {1,2, …, n };
s42: calculating the accumulated weight; calculating two key parameters alphaj、βjThe cumulative weight α of the ship j in the same berth and the ship in the previous portjAnd the cumulative weight beta of the ships at the port of the berth behindj；
In the berth shore bridge scheme, not only the front and rear ships in transition but also other ships sharing the same wharf space need to be considered; thus, the definition set Fa (j) records the ships that are berthed at the same berth as the ship j and are berthed ahead of the ship, the definition set Fb (j) records the ships that are berthed at the same berth as the ship j and are berthed behind the ship, and the definition W is used as the total weight of all the ships, then
Figure BDA0003368817030000041
Calculating alphajAnd betajThe formula of (1) is as follows:
Figure BDA0003368817030000042
s43: obtaining a more robust berth shore bridge scheme; finally obtaining the berthing time tb of the ship inserted into the buffer areajFrom tbj(ES)、tbj(LS)、αj、βjObtaining:
Figure BDA0003368817030000051
as a preferred technical solution of the present invention, the mutation strategy in S5 is designed with two types of exchange and reverse order, specifically, the exchange is as follows: randomly selecting two columns for exchange; and (3) reversing: two columns are randomly selected from the sample, and the parts including the selected two columns and between the two columns are arranged in the reverse order.
The invention has the beneficial effects that: the invention reduces the negative influence of uncontrollable factors such as delay of arrival of ships or prolongation of cargo loading and unloading time of ships caused by weather or equipment faults on the overall operation efficiency of the port, reduces economic cost and time cost, can quickly and effectively solve the berth shore bridge scheduling problem, and further improves the overall operation efficiency of the container terminal.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a comparison graph of index 1 and index 2 for different numbers of ships according to the present invention;
FIG. 3 is a schematic diagram of the mutation strategy of the present invention.
Detailed Description
The following detailed description of the preferred embodiments of the present invention, taken in conjunction with the accompanying drawings, will make the advantages and features of the invention more readily understood by those skilled in the art, and thus will more clearly and distinctly define the scope of the invention.
Referring to fig. 1, the present invention provides a technical solution: a robust berth shore bridge joint distribution method based on an improved particle swarm algorithm comprises the following specific steps:
s1: initializing a population; the method specifically comprises the steps of population scale, iteration times, upper and lower boundaries of particle positions and speeds, inertia weight and learning factors; taking 6 ships as an example, the coding length is 6: the ship number is: 1. 2, 3, 4, 5, 6; docking sequence OjComprises the following steps: 1. 3, 5, 2, 4, 6; berthing xjComprises the following steps: 2. 1,2, 3, 4; number y of quay bridgesjComprises the following steps: 3. 5, 2, 4, 1, 2;
Ojrepresenting the berthing sequence of the ship, and randomly arranging integers between 1 and n; x is the number ofjRepresenting a berthing of the vessel; y isjRepresenting the number of shore bridges allocated to the ship, randomly generated between the minimum and the maximum required number of shore bridges of the ship, such as the first berth of the ship 1 at No. 2, and 3 shore bridges allocated to the ship;
s2: calculate the earliest berthing time tbj(ES): for vessel j, calculate tbj(ES) AThe formula is as follows:
Figure BDA0003368817030000061
wherein s isijkIndicating that the k-th berthing of the ship j at the berth i is 1, otherwise, the k-th berthing is 0, tajAnd represents the arrival time, td, of the ship jj'Represents the departure time of vessel j' at berth i;
s3: calculating the latest berthing time tbj(LS): if there is a ship j' that is berthed at the same berth and is berthed next to it, let t be tbj'(LS) if no such ship exists, let t be tdjCalculating tbjThe formula for (LS) is as follows:
Figure BDA0003368817030000062
wherein twjRepresents the operating time of vessel j;
s4: inserting a buffer area;
s41: updating the weighting factor wj(ii) a The weighting factor represents the service priority of the ship, and if the weight coefficient exists, the calculated tb is used for jj(ES) and tbjTime and space conflicting vessels j' in (LS) schemes, i.e. { xj＝xj',tbj'(ES)＜tbj(ES)＜tbj'(LS)+twj'If j, j' is equal to V }, then wjSet to 1, otherwise set to 0, where the set V ═ {1,2, …, n };
s42: calculating the accumulated weight; calculating two key parameters alphaj、βjThe cumulative weight α of the ship j in the same berth and the ship in the previous portjAnd the cumulative weight beta of the ships at the port of the berth behindj；
In the berth shore bridge scheme, not only the front and rear ships in transition but also other ships sharing the same wharf space need to be considered; thus, the set Fa (j) of definitions records the ships that are berthed at the same berth as and ahead of ship j,the definition set Fb (j) records the ships berthing at the same berth as the ship j and berthing behind the ship j, and the definition W is used as the total weight of all the ships, then
Figure BDA0003368817030000071
Calculating alphajAnd betajThe formula of (1) is as follows:
Figure BDA0003368817030000072
s43: obtaining a more robust berth shore bridge scheme; finally obtaining the berthing time tb of the ship inserted into the buffer areajFrom tbj(ES)、tbj(LS)、αj、βjObtaining:
Figure BDA0003368817030000073
s5: self-adaptive variation of particles; the PSO algorithm has strong global search capability and memory, has strong optimization capability in the early stage of iteration, but inevitably falls into a local trap in the later stage of iteration, and is difficult to jump out of a local optimal solution to find a global optimal solution, so that aiming at the characteristics of the PSO algorithm and a constructed berth shore bridge problem model, the following adaptive variation strategy is designed to improve the traditional PSO algorithm, and the updating formula of the adaptive variation probability P is as follows:
Figure BDA0003368817030000074
wherein TN is the current iteration frequency, and TN is the total iteration frequency; for the particle i, randomly generating a random number between 0 and 1, and if the random number is greater than the variation probability of the iteration, performing variation operation on the particle;
s51: the mutation strategy is designed with two types of exchange and reverse order respectively, specifically as follows: randomly selecting two columns for exchange; and (3) reversing: randomly selecting two columns from the sample, and arranging the parts including the two selected columns and the parts between the two selected columns in a reverse order, as shown in FIG. 2;
s6: calculating an objective function value of the sample; namely the delay departure time of the ship, the formula is as follows:
Figure BDA0003368817030000075
s7: updating the individual optimal value and the global optimal value of the particle; for each particle, comparing the fitness value obtained by the iteration with the optimal fitness value pbest, if the fitness value is smaller than pbest, updating, and otherwise, keeping pbest unchanged; comparing pbest with the global optimum value gbest, and if the pbest is smaller than the gbest, replacing the gbest with the pbest;
s8: and updating the speed and the position of the particle, wherein the speed updating formula of the ith particle is as follows:
vij(tn+1)＝wvij(tn)+c1r1j(pij-xij(tn))+c2r2j(gj-xij(tn)),j∈V
the location update formula is: x is the number ofij(tn+1)＝xij(tn)+vij(tn+1),j∈V
Wherein v isij(tn +1) represents the j-dimension velocity value of the tn +1 th iteration particle i, w is a weight coefficient, c1And c2Dimensional learning factor, r1jAnd r2jIs a chaotic variable based on Logistic chaotic sequence, pijIs the j-th dimension position value, g, of the optimal solution of the particle ijPosition value of j dimension, x, being global optimum solutionij(tn) represents a j-dimensional position value of the particle i at the tn-th iteration; for the particles with updated speed and positions, carrying out boundary processing on the particles so that the codes of the particles meet the constraint;
s9: and (5) making TN be TN +1, judging whether a termination condition reaching the maximum iteration time TN is met, if so, terminating iteration and outputting an optimal solution, otherwise, returning to the step S2 to continue iteration.
Example 1: considering the actual working condition of the container terminal, the method formulates a berth shore bridge distribution scheme in advance according to the ship information (such as the number of ships, estimated arrival time, estimated departure time, box carrying capacity and the like) planned to berth at a port and the terminal equipment information (such as the number of berths, the berth length and the total number of shore bridges); for each ship, the following three arrangements should be made: the first is berthing time, the second is berthing, and the third is the number of allocated shore bridges.
Set the following sets, parameters and variables:
b: a set of berths of the wharf along a shoreline, i ∈ B ═ 1,2,. said, m };
v: a set of ships arriving at port, i ∈ V ═ 1, 2.
VLj: safe length of vessel j (including safe distance);
t: planning period, t being unit time index
TN: the iteration times of the cross entropy algorithm, tn is the index of times
QLi: the length of dock i;
r: the working efficiency of the shore bridge;
q: the number of shore bridges on the shore line;
Pj: a preferred berth for ship j;
taj: estimated time to port for ship j;
tdej: estimated departure time for ship j;
Nj: the number of containers carried by ship j;
Figure BDA0003368817030000091
the minimum required number of shore bridges for ship j;
Figure BDA0003368817030000092
the number of shore bridges required by the ship j at most;
Nj: the number of containers carried by ship j;
xj: berthing of the ship j;
yj: the number of shore bridges allocated to ship j;
Oj: berthing sequence of vessel j;
tbj: berthing time of ship j;
tdj: the departure time of ship j;
twj: the operating time of vessel j;
sijk: if the ship j is berthed on the kth berth i, the berth is 1, and if not, the berth is 0;
sjt: if the ship j is served at the time t, the ship j is 1, otherwise the ship j is 0;
the invention aims at minimizing the delay departure time of the ship, and the established optimization model is as follows:
Figure BDA0003368817030000093
constraint conditions are as follows:
Figure BDA0003368817030000101
Figure BDA0003368817030000102
Figure BDA0003368817030000103
Figure BDA0003368817030000104
Figure BDA0003368817030000105
Figure BDA0003368817030000106
Figure BDA0003368817030000107
Figure BDA0003368817030000108
Figure BDA0003368817030000109
Figure BDA00033688170300001010
Figure BDA00033688170300001011
Figure BDA00033688170300001012
the constraint condition (2) indicates that the ship can only berth after arriving at port, the constraint condition (3) indicates that the ship can only berth at a berth larger than the ship length, the constraint condition (4) indicates that the ship has only one berthing opportunity, the constraint condition (5) indicates that one berth serves at most one ship at the same time, the constraint condition (6) indicates that the number of shore bridges allocated to the ship is within a given range, the constraint condition (7) ensures the continuity of the operation of the ship, the constraint condition (8) defines the operating time of the ship, the constraint condition (9) defines the departure time of the ship, and the constraint condition (10) indicates that the number of the shore bridges operated at any time can not exceed the total number of the shore bridges. The constraints (11) - (13) define the value ranges of variables relevant to the decision variables. According to the technical scheme, the berth shore bridge allocation problem is solved, and a final allocation scheduling scheme can be obtained.
And (3) experimental verification:
in order to verify the effectiveness and robustness of the IPSO method for solving the berth shore bridge distribution problem, the length of a shore line is 1200m, 4 berths are arranged, the lengths of 1-4 berths are respectively 200m, 300m, 300m and 400m, the unit length is 20m, the unit time t is 5min, 12 shore bridges are arranged, the working efficiency of the shore bridges is 3.33TEU/t, the estimated arrival time ta is randomly generated in [0,288], the ship length is randomly generated between [6 and 20], the parameter design of the ship is shown in the following table, the working time is recorded as tw, and the estimated departure time is randomly generated between [ ta + tw +60 ].
TABLE 1 Ship parameter settings
Class of ship Captain of ship Number of containers Range of shore bridge
Small boat [6,10] [800,1500] [1,2]
Middle ship [11,15] [1500,2500] [2,4]
Large ship [16,20] [2500,4000] [3,6]
The berth shore bridge allocation scheme required by the traditional method is recorded as S1,the berthing time corresponding to the scheme S1 is recorded as tbj(ES), the robust berth shore bridge allocation scheme after the buffer is inserted is recorded as S2, and the berthing time corresponding to the scheme S2 is recorded as tbj. For simulating a real scene, the robustness of the method is tested, and the actual working time is enabled to be [ tw,1.1tw]Internally randomly generating, if a ship can not work by berthing due to the delay of the departure time of the previous ship at the expected berthing time, waiting to the departure time of the previous ship and having enough shore bridges to ensure the berthing of the ship after the work, wherein the actual berthing time is tba。
The invention generates 10 ships with different scales, and compares and obtains the performance of two robustness indexes, namely ship berthing delay time and delay quantity, under 200 random scenes, wherein the specific indexes are as follows:
index 1: sum of delay time of berthing of ship:
Figure BDA0003368817030000111
Figure BDA0003368817030000112
Figure BDA0003368817030000113
wherein Δ t (S1) represents the sum of S1 delayed berthing times, Δ t (S2) represents the sum of S2 delayed berthing times, and tIR is the improvement rate of index 1.
Index 2: number of berthing delays of the ship:
Figure BDA0003368817030000121
Figure BDA0003368817030000122
Figure BDA0003368817030000123
wherein Δ n (S1) is the sum of the S1 delayed berth numbers, and Δ n (S2) is the sum of the S2 delayed berth numbers; the improvement rate of index 2 is nIR.
At 10 scales, 200 scenes were randomly generated at each scale, the experimental results are shown in table 2, and fig. 2 shows the comparison of index 1 and index 2 at 10 scales:
TABLE 2 results of the experiment
Number of ships n Δt(S1) Δt(S2) tIR Δn(S1) Δn(S2) nIR
8 63.70 29.94 53.00 5.02 1.91 61.95
9 79.43 34.42 56.67 6.23 2.01 67.74
10 102.82 48.96 52.38 7.09 2.85 59.80
11 136.03 64.20 52.80 8.09 2.93 63.78
12 162.77 73.39 54.91 9.21 3.23 64.93
13 196.08 102.81 47.57 10.09 3.93 61.05
14 233.29 125.24 46.32 11.12 4.28 61.51
15 271.49 129.76 52.20 11.94 4.37 63.40
16 333.53 176.68 47.03 13.06 5.42 58.50
17 347.56 168.54 51.51 14.11 5.42 61.59
As can be seen from table 2, under different calculation examples of the number of ships, Δ t (S2) is always significantly better than Δ t (S1) in index 1, and as can be seen from fig. 2(a) and (b), as the number of ships increases, Δ t (S1), Δ t (S2), Δ n (S1), and Δ n (S2) all show an upward trend, and as can be seen from fig. 2(c) and (d), as the number of ships increases, tIR and nIR values show a slightly decreasing trend, because as the number of ships increases, the time for delaying berthing and the number of ships increase inevitably, but the improvement rates of both indexes are higher, so the improved particle swarm algorithm provided by the present invention can effectively reduce the delay berthing time and the number of actual berthing bridge schemes.
The above examples only show some embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention.

Claims (4)

1. A robust berth shore bridge joint distribution method based on an improved particle swarm optimization is characterized in that: the method comprises the following specific steps:
s1: initializing a population; the method specifically comprises the steps of population scale, iteration times, upper and lower boundaries of particle positions and speeds, inertia weight and learning factors; taking 6 ships as an example, the coding length is 6: the ship number is: 1. 2, 3, 4, 5, 6; docking sequence OjComprises the following steps: 1. 3, 5, 2, 4, 6; berthing xjComprises the following steps: 2. 1,2, 3, 4; number y of quay bridgesjComprises the following steps: 3. 5, 2, 4, 1, 2;
s2: calculate the earliest berthing time tbj(ES): for vessel j, calculate tbjThe formula (ES) is as follows:
Figure FDA0003368817020000011
wherein s isijkIndicating that the k-th berthing of the ship j at the berth i is 1, otherwise, the k-th berthing is 0, tajAnd represents the arrival time, td, of the ship jj'Representing vessels j' at berth iDeparture time;
s3: calculating the latest berthing time tbj(LS): if there is a ship j' that is berthed at the same berth and is berthed next to it, let t be tbj'(LS) if no such ship exists, let t be tdjCalculating tbjThe formula for (LS) is as follows:
Figure FDA0003368817020000012
wherein twjRepresents the operating time of vessel j;
s4: inserting a buffer area;
s5: self-adaptive variation of particles; the PSO algorithm has strong global search capability and memory, has strong optimization capability in the early stage of iteration, is difficult to jump out of a local optimal solution to find a global optimal solution because of inevitable dropping into a local trap in the later stage of iteration, and is improved by designing the following adaptive variation strategy aiming at the characteristics of the PSO algorithm and a constructed berth shore bridge problem model, so that the updating formula of the adaptive variation probability P is as follows:
Figure FDA0003368817020000021
wherein TN is the current iteration frequency, and TN is the total iteration frequency; for the particle i, randomly generating a random number between 0 and 1, and if the random number is greater than the variation probability of the iteration, performing variation operation on the particle;
s6: calculating an objective function value of the sample; namely the delay departure time of the ship, the formula is as follows:
Figure FDA0003368817020000022
s7: updating the individual optimal value and the global optimal value of the particle; for each particle, comparing the fitness value obtained by the iteration with the optimal fitness value pbest, if the fitness value is smaller than pbest, updating, and otherwise, keeping pbest unchanged; comparing pbest with the global optimum value gbest, and if the pbest is smaller than the gbest, replacing the gbest with the pbest;
s8: and updating the speed and the position of the particle, wherein the speed updating formula of the ith particle is as follows:
vij(tn+1)＝wvij(tn)+c1r1j(pij-xij(tn))+c2r2j(gj-xij(tn)),j∈V
the location update formula is: x is the number ofij(tn+1)＝xij(tn)+vij(tn+1),j∈V
Wherein v isij(tn +1) represents the j-dimension velocity value of the tn +1 th iteration particle i, w is a weight coefficient, c1And c2Dimensional learning factor, r1jAnd r2jIs a chaotic variable based on Logistic chaotic sequence, pijIs the j-th dimension position value, g, of the optimal solution of the particle ijPosition value of j dimension, x, being global optimum solutionij(tn) represents a j-dimensional position value of the particle i at the tn-th iteration; for the particles with updated speed and positions, carrying out boundary processing on the particles so that the codes of the particles meet the constraint;
s9: and (5) making TN be TN +1, judging whether a termination condition reaching the maximum iteration time TN is met, if so, terminating iteration and outputting an optimal solution, otherwise, returning to the step S2 to continue iteration.
2. The robust berth shore bridge joint distribution method based on the improved particle swarm optimization as claimed in claim 1, wherein: o in said S1jRepresenting the berthing sequence of the ship, and randomly arranging integers between 1 and n; x is the number ofjRepresenting a berthing of the vessel; y isjRepresenting the number of shore bridges allocated to the vessel, is randomly generated between the minimum and maximum required number of shore bridges for the vessel, e.g. vessel 1 is first berthed at berth # 2 and is allocated 3 shore bridges.
3. The robust berth shore bridge joint distribution method based on the improved particle swarm optimization as claimed in claim 1, wherein: the specific steps of inserting the buffer zone in S4 are as follows:
s41: updating the weighting factor wj(ii) a The weighting factor represents the service priority of the ship, and if the weight coefficient exists, the calculated tb is used for jj(ES) and tbjTime and space conflicting vessels j' in (LS) schemes, i.e. { xj＝xj',tbj'(ES)＜tbj(ES)＜tbj'(LS)+twj'If j, j' is equal to V }, then wjSet to 1, otherwise set to 0, where the set V ═ {1,2, …, n };
s42: calculating the accumulated weight; calculating two key parameters alphaj、βjThe cumulative weight α of the ship j in the same berth and the ship in the previous portjAnd the cumulative weight beta of the ships at the port of the berth behindj；
In the berth shore bridge scheme, not only the front and rear ships in transition but also other ships sharing the same wharf space need to be considered; thus, the definition set Fa (j) records the ships that are berthed at the same berth as the ship j and are berthed ahead of the ship, the definition set Fb (j) records the ships that are berthed at the same berth as the ship j and are berthed behind the ship, and the definition W is used as the total weight of all the ships, then
Figure FDA0003368817020000031
Calculating alphajAnd betajThe formula of (1) is as follows:
Figure FDA0003368817020000032
s43: obtaining a more robust berth shore bridge scheme; finally obtaining the berthing time tb of the ship inserted into the buffer areajFrom tbj(ES)、tbj(LS)、αj、βjObtaining:
Figure FDA0003368817020000033
4. the robust berth shore bridge joint distribution method based on the improved particle swarm optimization as claimed in claim 1, wherein: the mutation strategy in S5 is designed with two types of switching and reverse order, specifically, switching: randomly selecting two columns for exchange; and (3) reversing: two columns are randomly selected from the sample, and the parts including the selected two columns and between the two columns are arranged in the reverse order.
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