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Particle swarm optimization algorithm for the berth allocation problem

Particle swarm optimization algorithm for the berth allocation

problem

Ching-Jung Ting ?,Kun-Chih Wu,Hao Chou

Department of Industrial Engineering and Management,Yuan Ze University,Chung-Li 32003,Taiwan,ROC

a r t i c l e i n f o Keywords:

Berth allocation problem Particle swarm optimization Container port Logistics

a b s t r a c t

The berth allocation is one of the major container port optimization problems.In both port operator’s and ocean carriers’perspective,the minimization of the time a ship at the berth may be considered as an objective with respect to port operations.This paper focuses on the discrete and dynamic berth allocation problem (BAP),which assigns ships to discrete berth positions and minimizes the total waiting times and handling times for all ships.We formulate a mixed integer programming (MIP)model for the BAP.Since BAP is a NP-hard problem,exact solution approaches cannot solve the instances of realistic size optimally within reasonable time.We propose a particle swarm optimization (PSO)approach to solve the BAP.The proposed PSO is tested with two sets of benchmark instances in different sizes from the literature.Exper-imental results show that the PSO algorithm is better than the other compared algorithms in terms of solution quality and computation time.

1.Introduction

Since the introduction of the container in the 1950s,container-ships gradually become an important role in the global freight transportation.The world seaborne trade grew by an estimated 7%,taking the total of goods loaded to 8.4billion tons.World con-tainer port throughput increased by an estimated 13.3%to 531mil-lion 20-foot equivalent units (TEUs)in 2010(UNCTAD,2011).Thus,the terminal operation is an important part of the international trade of ocean shipping.

Operations in a container terminal can be broken down into three functional systems:seaside operations,yard operations,and land-side operations (Theofanis,Boile,&Golias,2009).The ?rst issue of seaside operations planning is the berth assignment to a set of vessels that have to be served within the planning hori-zon.One of the important objectives shared by the port operators and the ocean carriers is for the ships to leave the port as soon as possible.Thus,the container port authorities are forced to provide ef?cient and cost-effective services by utilizing the scarce berthing resources ef?ciently due to the ?erce competition between ports.The berth allocation problem (BAP)is to allocate berths to a set of vessels scheduled to arrive at the port within the planning hori-zon in order to minimize their time spent at the port (the sum of their waiting and handling times).Bierwirth and Meisel (2010)classi?ed the BAP according to the following spatial and temporal variations:(1)discrete versus continuous berthing space,(2)static versus dynamic vessel arrivals,(3)deterministic versus stochastic vessel handling time.The quay is divided into a set of berths,

and each berth can be used by only one vessel at a time in the discrete case.In the continuous case,a vessel can occupy any arbi-trary position along the quay as long as the safety restriction be-tween vessels is considered.Static BAP assumes that all vessels already arrived at the port for the service,while vessels can arrive at any time during the planning horizon with known future arrival information in a dynamic BAP.The main focus of this research is the discrete berth allocation problem with dynamic vessel arrivals.The discrete BAP is NP hard (Cordeau,Laporte,Legato,&Moccia,2005).Exact solution approach cannot solve large scale realistic environments,heuristics algorithms are proposed in the literature to solve the BAP.In this paper,we investigated the use of particle swarm optimization (PSO)algorithm for the BAP.PSO is a popula-tion-based random search algorithm inspired by the social behav-ior of bird ?ocks and has been applied to solve many combinatorial optimization problems.We did not ?nd any other work in the re-lated literature using PSO to tackle the BAP.Thus,it is worthwhile to evaluate the PSO for this task.The proposed PSO was tested with two sets of benchmark instances and compared with promising methods found in the literature to verify its ef?ciency.

The remainders of the paper are organized as follows.In the next section we review the related literature.Section 3presents our problem with a mixed integer programming model.The proposed particle swarm optimization algorithm to tackle the discrete and dy-namic berth allocation problems is presented in Section 4.In Sec-tion 5computational experiments are performed and the results are presented.Finally,conclusions are summarized in Section 6.2.Literature review

Berth allocation problem has attracted considerable practical and academic attention in recent years due to the needs of growing

Corresponding author.Tel.:+88634638800x2526;fax:+88634638907.

E-mail address:ietingcj@https://www.wendangku.net/doc/35444255.html,.tw (C.-J.Ting).

global supply chain.Different berth allocation models have been proposed in the literature.Steenken,Vo?,and Stahlbock(2004), Vacca et al.(2008),Stahlbock and Vo?(2008),and Bierwirth and Meisel(2010)provided a detailed review.We refer interested read-ers to the paper and references therein.In the following,we focus on the discrete and dynamic berth allocation problem.Other variants and possible extensions of the BAP will be brie?y reviewed.

Thurman(1989)proposed an optimization model for ship berthing plans at the US Naval Station https://www.wendangku.net/doc/35444255.html,ter,Brown, Lawphongpanich,and Thurman(1994)and Brown,Cormican, Lawphongpanich,and Widdis(1997)considered berth allocation models in naval ports and allowed two or more submarines to occupy a single berth position.Imai,Nagaiwa,and Chan(1997) formulated a static BAP as a nonlinear integer programming model to minimize the weighted sum of two con?icting objectives,berth performance and vessel dissatisfaction.Imai,Nishimura,and Papadimitriou(2001)introduced the dynamic BAP and solved the problem with a Lagrangian relaxation based heuristic. Nishimura,Imai,and Papadimitriou(2001)considered a dynamic BAP with multi-water depth con?guration in a public berth system and berth dependent vessel handling https://www.wendangku.net/doc/35444255.html,ter,Imai,Nishimura, and Papadimitriou(2003)considered a dynamic BAP in which different vessels have different service priorities.Genetic algorithms were developed to solve the problem in Imai et al. (2001)and Nishimura et al.(2001).

Cordeau et al.(2005)addressed a dynamic BAP with time windows in both discrete and continuous cases based upon data from a terminal in the Port of Gioia Tauro(Italy).The problem was formulated as a multiple depot vehicle routing problem with time windows(MDVRPTW),and solved by a tabu search heuristic. Monaco and Sammarra(2007)presented a compact formulation as a dynamic scheduling problem on unrelated parallel machines.The problem was solved by a Lagrangian relaxation heuristic.Imai, Nishimura,Hattori,and Papadimitriou(2007)considered a BAP in which up to two vessels can be served by the same berth simul-taneously.They formulated the problem with an integer linear programming model and solved it by genetic algorithms.Imai, Zhang,Nishimura,and Papadimitriou(2007)analyzed a two-objective berth allocation problem which minimizes service time and delay time.They used the Lagrangian relaxation with subgra-dient optimization technique and a genetic algorithm to identify the non-inferior solutions in the bi-objective model.

Cheong and Tan(2008)developed a multiple ant colony algorithm for Nishimura et al.’s(2001)model and evaluated their algorithm by simulation experiments.Hansen,Og?uz,and Mladenovic(2008)presented a minimum cost BAP based on an extension of Imai et al.’s(2003)model,and developed a variable neighborhood search(VNS)heuristic for solving it.Imai, Nishimura,and Papadimitriou(2008)studied a variant of the dynamic BAP in which an external terminal is available when there is a lack of berth capacity at the operator’s own terminal.

Mauri,Oliveira,and Lorena(2008)proposed a hybrid approach called PTA/LP,which used the population training algorithm with a linear programming model using the column generation tech-nique.Barros,Costa,Oliveira,and Lorena(2011)developed and analyzed a berth allocation model with tidal time windows,where ships can only be served during those time windows.Buhrkal, Zuglian,Ropke,Larsen,and Lusby(2011)studied several mathe-matical programming models of the dynamic BAP and formulated the problem as a generalized set partition problem(GSPP).They solved the problem with CPLEX and obtained the optimal solutions on those instances from Cordeau et al.(2005).To the best of our knowledge,their mathematical model provides the best results on benchmark instances from the literature.

de Oliveira,Mauri,and Lorena(2012b)presented an algorithm based on the clustering search method using the simulated annealing algorithm to generate solutions for the discrete BAP. Lalla-Ruiz,Melián-Batista,and Marcos Moreno-Vega(2012) developed a hybrid algorithm that combined tabu search with path relinking(T2S?+PR)to solve the BAP.They tested the instances from Cordeau et al.(2005)and newly generated data sets by them-selves.The results showed that the hybrid algorithm was compet-itive with the GSPP in small size instances.Xu,Li,and Leung(2012) considered the static and dynamic BAP that berths are limited by water depth and tidal condition.The problem was formulated as a parallel machine scheduling problem and solved with a heuristic.

Another line of berth allocation research assumes that berths along a quayside can be shared by different vessels.Lim(1998) was the?rst to study the continuous berth allocation problem. The continuous BAP was formulated as a restricted form of the two-dimensional packing problem and solved with constant handling times.Li,Cai,and Lee(1998)and Guan,Xiao,Cheung, and Li(2002)modeled berth allocation as machine scheduling problems with multiprocessor tasks,while Guan and Cheung (2004)developed ef?cient heuristics using a discrete berthing section model for batch arriving vessels.Tong,Lau,and Lim (1999)proposed an ant colony optimization(ACO)approach for the BAP addressed by Lim(1998).Park and Kim(2002)presented a mixed integer programming(MIP)model to minimize the penalty cost associated with service delays and placing a ship at a non-preferred location.A Lagrangian relaxation model with subgradient optimization technique was proposed to solve the problem.

Park and Kim(2003)integrated the berth scheduling into the quay crane assignment.The BAP was solved with an adaptation of the method from Park and Kim(2002)and the crane assignment was solved by dynamic programming.Kim and Moon(2003)for-mulated the continuous BAP as a MIP model and solved the prob-lem with a simulated annealing algorithm.Imai,Sun,Nishimura, and Papadimitriou(2005)presented a heuristic for the continuous BAP.Moorthy and Teo(2006)proposed a framework addressing the berth template design problem at the terminal on a weekly ba-sis and solved the problem with a sequence-pair-based SA algo-rithm.Wang and Lim(2007)proposed a stochastic beam search algorithm to solve the BAP in a multiple stage decision-making procedure.The improved beam search scheme and a stochastic node selection criterion were proposed.

Lee and Chen(2009)developed a candidate-based approach to handle BAP by allowing vessel shifting and considering the clear-ance distance between vessels which depends on the ship lengths and the order of berthed vessels.A three-stage neighborhood search based heuristic was proposed to solve the BAP.Tang,Li, and Liu(2009)proposed two mathematical models to minimize the total weighted service time.The authors developed an im-proved Lagrangian relaxation algorithm to solve the BAP at the raw material docks in an iron and steel complex.Cheong,Tan, Liu,and Lin(2010)considered a multiple objective BAP which in-cludes makespan,waiting time,and degree of deviation from a pre-determined priority schedule.They proposed a multi-objective evolutionary algorithm that incorporates the Pareto optimality to solve the problem.Lee,Chen,and Cao(2010)developed two ver-sions of greedy randomized adaptive search procedure(GRASP) to solve the continuous BAP.The numerical results were compared with CPLEX and stochastic beam search of Wang and Lim(2007). Raa,Dullaert,and Van Schaeren(2011)presented a MIP model for the integrated BAP and quay crane assignment taking into ac-count vessel priorities,preferred berthing locations and handling time.de Oliveira,Mauri,and Lorena(2012a)presented a clustering search(CS)method with simulated annealing heuristic to solve the continuous BAP.The computational results of the I3instances from Cordeau et al.(2005)were compared with the tabu search by Cordeau et al.(2005)and memetic algorithms by Mauri,De Andrade,and Lorena(2011).

1544 C.-J.Ting et al./Expert Systems with Applications41(2014)1543–1550

The berth allocation problem is a NP problem.Due to the computational complexity,researchers have developed heuristics to solve the BAP in the literature.These heuristic methods include the GA(Imai et al.,2001;Imai,Nishimura,Hattori,& Papadimitriou,2007;Nishimura et al.,2001),SA(Kim&Moon, 2003;Moorthy&Teo,2006),TS(Cordeau et al.,2005),ACO(Tong et al.,1999;Cheong&Tan,2008),VNS(Hansen et al.,2008)and GRASP(Lee et al.,2010).In this paper,we propose a particle swarm optimization(PSO)algorithm to solve the problem.The reasons that we use PSO to solve this problem are as follows.PSO has been applied in many different combinatorial problems and provided very ef?cient performance.It is simple and needs less control parameters comparing to other metaheuristics,such as genetic algorithms and ant colony optimization algorithms.To our knowledge,no research used PSO to solve the BAP.Details of the proposed PSO are presented in Section4.

3.Mathematical model

This section describes the mixed integer programming model for the discrete and dynamic berth allocation problem.We treat the BAP as a vehicle routing problem with time windows,where berths correspond to vehicles,ships correspond to customers and a mooring sequence at a particular berth corresponds to a vehicle route.Each vehicle must start and end at the depot.The depot is divided into two dummy nodes,o and d.Time windows can be imposed on every node.The time windows of a vehicle correspond to the availability time of the corresponding berth.

3.1.Assumptions

1.Each berth can handle one ship at a time.

2.Any ship can be handled at any berth with a given processing

time depending on both the ship and the berth.

3.All ships arrive before or after the berth becoming available

with known arrival times.

4.Once a vessel is moored,it will remain in its location until all

the required processing is done.

5.The initial status of the terminal space is ideally clean without

any ship.

3.2.Notations

a i:the arrival time of ship i

b i:the end time of time window of ship i

d:the destination of any route

e k:the end time o

f berth k availability

K:set of the berths,K={1,2,...,|K|}

M:a big number

N:set of ships that will arrive at the port,N={1,2,...,|N|}

o:the origin of any route

P k

:the processing time of ship i at berth k

S k:the start time of berth k availability

Decision variable

x k

1if ship i uses berth k immediately before ship j

0otherwise

T k

:the starting time of ship i at berth k

3.3.Model formulation

We formulate the BAP as a vehicle routing type problem,where

nodes o and d represents the origin and destination for any route. The processing time is dependent on the respective berth locations. The model has two types of decision variables:the binary assign-

ment variable x k

and the continuous variable t k

Minimize

i2N

k2K

et k

àa itp k

j2N[f d g

x k

Te1Ts:t:

i2N[f o g

x k

i2N[f d g

x k

8h2N;k2Ke2TX

k2K

j2N[f d g

x k

?18i2Ne3TX

j2N[f d g

x k

618k2Ke4T

et k

tp k

T6t k

te1àx k

TM8i;j2N[f o g[f d g;k2Ke5T

s k6t k

8k2Ke6T

t k

8k2Ke7T

a i6t k

8i2N;k2Ke8T

t k

tp k

j2N[f d g

x k

8i2N;k2Ke9T

x k

?f0;1g8i;j2N;k2Ke10T

t k

P08i2N;k2Ke11TThe objective function(1)minimizes the total service time which includes waiting time and processing time of all ships.Con-straint(2)ensures the?ow conservation for all the vessels.Con-straint(3)states that each ship must be assigned to exactly one berth k.Since berth can be left unused,constraints(4)states that berth k can only start at most once.Constraint(5)guarantees the consistency for berthing time and mooring sequence on each berth. The berth availability time is enforced by constraints(6)and(7). Constraints(9)and(10)state that the vessel must be served within the time window.Finally,constraints(10)and(11)de?ne the respective domains of the decision variables.

4.Particle swarm optimization for BAP

Particle swarm optimization(PSO),inspired by the social behavior of bird?ocking or?sh schooling,is a population-based stochastic search technique developed by Kennedy and Eberhart (1995).PSO has been applied in a wide range of combinatorial optimization problems,such as reactive power and voltage con-trol(Fukuyama&Yoshida,2001),permutation?owshop sequenc-ing problems(Liao,Tseng,&Luarn,2007),order allocation problem(Ting,Tsai,&Yeh,2007),machine scheduling(Low, Hsu,&Su,2010;Tsai&Kao,2011),production planning(Chen

& C.-J.Ting et al./Expert Systems with Applications41(2014)1543–15501545

Lin,2009),timetabling problem(Tassopoulos&Beligiannis,2012), and vehicle routing problems(Ai&Kachitvichyanukul,2009; MirHassani&Abolghasemi,2011).Compared to the genetic algo-rithm,PSO is easy to implement and there are few control param-eters to adjust.

PSO is initialized with a population of random solutions and the potential solutions,called‘‘particles’’,in PSO search through the solution space.Each particle is also assigned a randomized velocity initially.The positions of individual particles are adjusted(via changing the velocity)according to its own previous searching experience(i.e.,previous best,pBest),and other particles’searching experiences(i.e.,global best,gBest).Velocity changing is weighted by random terms,with separate random numbers being generated for acceleration toward two best solutions,pBest and gBest,in each iteration.Suppose that the search space has D-dimension, and the position and the velocity of the i th particle at the current iteration is represented by X old i=(X old i1,...,X old id,...,X old iD)and V old i=(V old i1,...,V old id,...,V old iD),respectively.The general proce-dures of PSO are as follows.

1.Initialization.Randomly generate a population(A)of potential

solutions,called particles,and each particle is assigned a ran-domized velocity.The population size is problem-dependent and suggested to be between20and40by Hu and Eberhart (2002).

2.Evaluation and update best https://www.wendangku.net/doc/35444255.html,pute the desired opti-

mization?tness function.

https://www.wendangku.net/doc/35444255.html,pare the?tness of each particle with its pBest,if the

current is better,update the pBest.

https://www.wendangku.net/doc/35444255.html,pare the pBest of each particle with the gBest,if the

pBest is better,update the gBest.

3.Velocity update.The particles are?own through hyperspace by

updating their own velocities.The velocity update of a particle is dynamically adjusted,subject to its own past best path and those of its companions.The particle updates its velocity and position with following equations(12)and(13).

V new id ?W?V old

tc1?rnd1?eP idàX old

Ttc2?rnd2

?eP gdàX old

Te12T

X new id ?X old

tV new

e13T

where V new

id is the particle new velocity,V old

is the current parti-

cle velocity,P id is the best previous position of particle i in

dimension d,P gd is the best position in dimension d found by all particles till now.W is the inertia weight,c1and c2are learn-ing factors.A larger W can prevent particles being trapped in the local optimum,while a smaller W let particles to exploit the same search space area.Eberhart and Shi(2001)suggested c1=c2=2and W=0.5+(rand()/2).X new

is the new particle

(solution)position and X old

is the current particle position in

d th dimension.rnd1and rnd2ar

e random numbers between

0and1which represent the stochastic element of the PSO.

4.Termination.Stop the algorithm if the stopping criterion is met;

otherwise go to2.In this paper,we set the stop criterion as the maximum number of iterations(G)is reached.

Particles’velocities on each dimension are clamped to a maxi-mum velocity V max,a parameter speci?ed by the user to determine the maximum change one particle can move during one iteration. If the updated velocity exceeds V max,then the velocity on that dimension is limited to V max.Eberhart and Shi(2001)suggested V max being set at about10-20%of the dynamic range of the variable on each dimension.

In PSO,only gBest gives out the information to others.It is a one-way information sharing mechanism.The evolution only looks for the best https://www.wendangku.net/doc/35444255.html,pared with the genetic algorithm,all the par-ticles tend to converge to the best solution quickly even in the local version in most cases.There are two key steps when applying PSO to optimization problems:the representation of the solution and the?tness function.One of the advantages of PSO is that PSO can take real numbers as particles.

The population size will affect the effectiveness of PSO and is problem-dependent.The number of particles most commonly used is in the range of20–40(Hu and Eberhart,2002).The dimension of particles is determined by the problem to be optimized.The range of particles is also determined by the problem to be optimized,the user can specify different ranges for different dimension of parti-cles.Learning factors,c1and c2,usually equal to2.However,other settings were also used in different papers.But usually c1equals to c2and ranges from[0,4].The stop condition is based on the max-imum number of iterations the PSO execute and the minimum er-ror requirement.

4.1.Particle representation and initial solution generation

The solution representation is the key issue when designing the PSO algorithm.It could be a string of integers or real numbers.In order to construct a direct relationship between the domain of the berth allocation problem and the PSO practices,n numbers of dimensions are presented each for one of the n vessels.Our solu-tion representation is as follows:n real numbers from a uniform distribution in the interval of(0,m)represent a solution in an array of n cells,where n is number of ships and m is the number of berths.The interval of(0,m)is used as boundary constraint to par-ticle position to guarantee that the decision variables are in the feasible region.The integer part of a real number denotes the berth that the ship is assigned to and the fractional part represents the processing order of ships on each berth.The ships are separated into groups based on the berth to which they are assigned.Based on the real number encoding,the sequence of ships on each berth can be extracted from ascending order of fractional parts.A ship with lower number will be scheduled before the ships with higher numbers.

Fig.1shows a particle representation for a berth allocation problem with six vessels and two berths.Each particle dimension is encoded as a real number between(0,2).The integer part of each value represents the berth position the vessel assigned to.Thus,the same integer part represents the vessel is in the same berth.The fractional part determines the sequence of the vessel at the berth. For example,vessels{a,d,e}will be served by berth1while vessels {b,c,f}will be served at berth2.By sorting the fractional values in ascending order,vessel d will be the?rst one moored at berth1, followed by vessel e and a.Similarly,the sequence of vessels moored at berth2is b,f,and c.

To prevent the search outside the initialized range of the feasi-ble solution space,we handle the boundary situation as follows.If the value in one cell is less than zero,we randomly generated a va-lue in(0,1)for the cell.If the value is larger than the number of berths,we randomly generated a value in(0,1)and subtract this from the number of berths.

We randomly generate all particles except one solution that is based on a simple greedy heuristic,?rst-come-?rst-served(FCFS). FCFS is practically used in the real world operations from both operator’s and carrier’s perspective.We assume that all berths be-come available at the same time and sort the ships by their arrival times in ascending order.Ships are assigned one at a time by choosing the combination of berth and ship that will?nish?rst. The time is increased when there are no ships available or all berths are busy.This continues until all ships have been assigned.

1546 C.-J.Ting et al./Expert Systems with Applications41(2014)1543–1550

4.2.Local search

In each iteration,we apply local search to improve the solution quality.Due to that the local search is a time-consuming procedure of PSO,we will only apply local search to the best particle found in this iteration.The local search technique used in this study has two procedures:swap ships in the same berth and between berths. Fig.2shows these two procedures based on the solution obtained in Fig.1.Given a list of the ships handled by berth j,the swapping compares all the possible swapping pairs within the same berth and select the best improvement to exchange their values as shown in Fig.2(a).For the swapping of two ships in two berths, two ships in two different berths(one for each berth)are randomly selected.The positions of these two ships are exchanged.The swap results are evaluated and select the best improvement to exchange as shown in Fig.2(b).

https://www.wendangku.net/doc/35444255.html,putational experiments

The proposed PSO algorithm in previous section is tested in this section.Two sets of instances,I2and I3,from Cordeau et al.(2005) were tested.The PSO algorithm was coded in Microsoft Visual Stu-dio C++2008and run on a PC with an Intel Core2Duo CPU E8400 (3.00GHz)processor and1.9GB RAM,under the Windows7oper-ating system.The effectiveness of the proposed PSO algorithm was compared with other recent algorithms for the BAP,namely tabu search(T2S)(Cordeau et al.,2005),population training algorithm with linear programming(PTA/LP)(Mauri et al.,2008),and cluster-ing search(CS)approach(de Oliveira et al.,2012b)from the liter-ature.The results of these algorithms were obtained directly from their articles.

To make the performance of our proposed PSO heuristic more robust,parameter-setting is necessary.We have performed a set of preliminary experiments in order to?nd an appropriate param-eter setting that produces overall good results across most in-stances,even if they were not the optimal settings for all instances.The control parameters of our PSO algorithm are the number of particles A,number of iterations G,inertia weight W, and learning factors C1and C2,respectively.Based on the prelimin-ary experiments undertaken,we found that the best values of the parameter setting yield to better solution quality as follows:A=20, G=200,W=0.9,C1=C2=2.Each instance is run for30times and reported both best solution and average computational time.

5.1.I2set

The I2data set including50instances were generated from the traf?c and berth-allocation data at Gioia Tauro,Italy.Five instance sizes were considered:25ships with5,7,and10berths;35ships with7and10berths.A set of10instances was generated for each size.The earliest available time is the same for every berth.The ori-ginal data set takes into account time windows.

Table1shows the results for the instances of25and35ships. We also compare the results to those obtained from the most re-cent literature.For each algorithm,we report their results and computational time.Among those results,GSPP by Buhrkal et al.(2011)solved the BAP with CPLEX and obtained the optimal solu-tions.Our PSO can?nd the optimal solutions in all50instances and provide much better results than those by Cordeau et al.(2005).

The computational times of our PSO in each instance are the average of30runs in seconds.The tabu search algorithm(T2S)pro-posed by Cordeau et al.(2005)used on average120s to solve each instance.The machine used by the GSPP was a PC with an Intel Xeon 2.66GHz,while T2S was run on a Sun workstation (900MHz).Our PSO performs better than that of T2S in terms of solution quality.We can observe that PSO?nds all the optimal solutions for all instances in I2set in short time(1.66s on average).

5.2.I3set

The I3data set that includes30instances,each with60ships and13berths,is also randomly generated by Cordeau et al. (2005)based on data from the port of Gioia Tauro.The

parameters

Table1

Comparison of results of I2set.

Instance GSPP T2S PSO

Opt.Time Best Best Time

25?5_1759 5.997597590.75

25?5_2964 3.709659640.55

25?5_3970 2.95974970 1.23

25?5_4688 2.727026880.42

25?5_5955 6.979659550.86

25?5_61129 3.10112911290.52

25??5_7835 2.318358350.52

25?5_8627 1.926296270.50

25?5_9752 4.767557520.69

25?5_101073 6.38107710730.73

25?7_1657 3.626676570.52

25?7_2662 3.156716620.44

25?7_3807 4.288238070.97

25?7_4648 3.786556480.86

25?7_5725 3.857287250.44

25?7_6794 3.607947940.52

25?7_7734 3.547407340.69

25?7_8768 3.93782768 1.05

25?7_9749 3.737597490.89

25?7_10825 3.828308250.55

25?10_1713 5.837177130.70

25?10_2727 6.997367270.75

25?10_3761 6.127647610.56

25?10_4810 5.388198100.52

25?10_5840 6.778558400.45

25?10_6689 5.576946890.44

25?10_7666 5.836736660.58

25?10_8855 5.878608550.53

25?10_9711 5.387267110.45

25?10_10801 5.968128010.47

35?7_1100012.5710191000 5.02

35?7_2119215.9311961192 4.91

35?7_312017.1612301201 4.94

35?7_4113913.5911501139 3.45

35?7_5116411.5011791164 3.36

35?7_6168629.1617031686 3.28

35?7_7117612.8911811176 4.17

35?7_8131817.5213301318 2.39

35?7_912458.4112451245 3.50

35?7_10110914.3911301109 3.89

35?10_1112419.9811281124 1.58

35?10_2118911.3711971189 4.13

35?10_39388.97953938 3.36

35?10_4122610.2812391226 2.84

35?10_5134922.3113721349 1.53

35?10_6118810.9212211188 2.44

35?10_710519.7410521051 2.17

35?10_811949.3912191194 1.28

35?10_9131129.4513151311 2.81

35?10_10118914.2811981189 2.83

Average953.668.55963.04953.66 1.66 C.-J.Ting et al./Expert Systems with Applications41(2014)1543–15501547

used for the PSO are the same as those for I2set.Table2shows the direct comparison of results with those presented in the literature. We compare our PSO with GSPP by Buhrkal et al.(2011),T2S by Cordeau et al.(2005),PTA/LP by Mauri et al.(2008),and CS by de Oliveira et al.(2012).The best solution and running time for each algorithm were obtained directly from the papers.Among these algorithms,GSPP solved the instances by CPLEX and obtained the optimal solutions.It can be seen that PTA/LP dominates T2S in terms of solution quality.Both PSO and CS can obtain the optimal solutions in all30instances and exhibit a slightly better overall 5.3.Discussion

The computational experiments show that our PSO can obtain all the optimal solutions of those80benchmark instances in short time.The average time for small instances is only1.66s while for the larger instances is8.17s.We observe that the PSO can obtain the optimal solutions within the?rst20iterations in some in-stances.The running times to reach the optimal solutions could be much shorter than those reported in Tables1and2.

To show the convergence of the PSO algorithm,we depict the

Table2

Comparison of results of I3set.

Inst.GSPP T2S PTA/LP CS PSO

Opt.Time Best Best Time Best Time Best Time

i01140917.921415140974.61140912.47140911.11 i02126115.771263126160.75126112.5912617.89 i03112913.5411391129135.45112912.6411297.48 i04130214.4813031302110.17130212.591302 6.03 i05120717.2112081207124.70120712.681207 5.84 i06126113.851262126178.34126112.5612617.67 i07127914.6012791279114.20127912.6312797.50 i08129914.211299129957.06129912.5712999.94 i09144416.511444144496.47144412.581444 4.25 i10121314.161213121399.41121312.611213 5.20 i11136814.131378136999.34136812.58136810.52 i12132515.601325132580.69132512.56132512.92 i13136013.871360136089.94136012.61136011.97 i14123315.601233123373.95123312.6712337.11 i15129513.521295129574.19129513.8012958.30 i16136413.6813751365170.36136414.4613648.48 i17128313.371283128346.58128313.731283 5.66 i18134513.511346134584.02134512.7213458.02 i19136714.5913701367123.19136713.39136711.42 i20132816.641328132882.30132812.82132812.28 i21134113.3713461341108.08134112.6813417.11 i22132615.2413321326105.38132612.6213267.94 i23126613.651266126643.72126612.6212667.25 i24126015.581261126078.91126012.641260 5.67 i25137615.801379137696.58137612.6213767.13 i26131815.3813301318101.11131812.6213187.44 i27126115.521261126182.86126112.641261 6.16 i28135916.221365136052.91135912.71135911.52 i29128015.3012821280203.36128012.6212808.11 i30134416.521351134471.02134412.5813447.13

Avg.1306.814.981309.71306.993.991306.812.791306.88.17

Fig.3.Convergence grapgh of the proposed PSO for instance i13.

1548 C.-J.Ting et al./Expert Systems with Applications41(2014)1543–1550

6.Conclusion

In this paper we have studied the discrete berth allocation problem with dynamic arrival times.The berth allocation problem is a NP-hard problem,exact solution approaches cannot solve the instances of realistic size optimally within reasonable time.We proposed a particle swarm optimization heuristic to solve the BAP and tested our algorithm with two sets of instances from the https://www.wendangku.net/doc/35444255.html,putational results have indicated that the pro-posed PSO algorithm is able to?nd all the optimal solutions for the two sets of benchmark instances from the literature in short running times.We believe that PSO is effective as an alternative to?nd good solutions for BAP by comparing our solutions with the optimal solution from CPLEX and against other recent algorithms in the literature.

In the future,we could extend our PSO to hybrid with other heuristic for improving the performance.Other metaheuristics, such as ant colony optimization algorithm,could also be consid-ered to solve the BAP.Another possible direction is to investigate the potential of applying the PSO to the continuous berth alloca-tion problem that is much closer to the real world operation. References

Ai,T.J.,&Kachitvichyanukul,V.(2009).Particle swarm optimization and two solution representations for solving the capacitated vehicle routing problem.

Computers&Industrial Engineering,56,380–387.

Barros,V.H.,Costa,T.S.,Oliveira,A.C.M.,&Lorena,L.A.N.(2011).Model and heuristic for berth allocation in tidal bulk ports with stock level constraints.

Computers and Industrial Engineering,60,606–613.

Bierwirth,C.,&Meisel,F.(2010).A survey of berth allocation and quay crane scheduling problems in container terminals.European Journal of Operational Research,202,615–627.

Brown,G.G.,Lawphongpanich,S.,&Thurman,K.P.(1994).Optimizing ship berthing.Naval Research Logistics,41,1–15.

Brown,G.G.,Cormican,K.J.,Lawphongpanich,S.,&Widdis,D.B.(1997).Optimizing submarine berthing with a persistence incentive.Naval Research Logistics,44, 301–318.

Buhrkal,K.,Zuglian,S.,Ropke,S.,Larsen,J.,&Lusby,R.(2011).Models for the discrete berth allocation problem:A computational comparison.Transportation Research Part E:Logistics and Transportation Review,47,461–473.

Chen,Y.Y.,&Lin,J.T.(2009).A modi?ed particle swarm optimization for production planning problems in the TFT Array process.Expert Systems with Applications,36,12264–12271.

Cheong,C.Y.,&Tan,K.C.(2008).A multi-objective multi-colony ant algorithm for solving the berth allocation problem.In Y.Liu,A.Sun,H.T.Loh,W.F.Lu,&E.P.

Lim(Eds.),Advances of Computational Intelligence in Industrial Systems (pp.333–350).Berlin:Springer-Verlag.

Cheong,C.Y.,Tan,K.C.,Liu,D.K.,&Lin,C.J.(2010).Multi-objective and prioritized berth allocation in container ports.Annals of Operations Research,180,63–103. Cordeau,J.F.,Laporte,G.,Legato,P.,&Moccia,L.(2005).Models and tabu search heuristics for the berth-allocation problem.Transportation Science,39,526–538. de Oliveira,R.M.,Mauri,G.R.,&Lorena,L.A.N.(2012a).Clustering search heuristics for solving a continuous berth allocation problem.Lecture Notes in Computer Science,7245,49–62.

de Oliveira,R.M.,Mauri,G.R.,&Lorena,L.A.N.(2012b).Clustering search for the berth allocation problem.Expert Systems with Applications,39,5499–5505. Eberhart,R.&Shi,Y.(2001).Tracking and optimizing dynamic system with particle swarms.In Proceedings of congress on evolutionary computation(pp.94–97).

Seoul,Korea.

Fukuyama,Y.,&Yoshida,H.(2001).A particle swarm optimization for reactive power and voltage control in electric power systems.Proceedings of Congress on Evolutionary Computation,1,87–93.

Guan,Y.,&Cheung,R.K.(2004).The berth allocation problem:Models and solution methods.OR Spectrum,26,75–92.

Guan,Y.,Xiao,W.Q.,Cheung,R.K.,&Li, C.L.(2002).A multiprocessor task scheduling model for berth allocation:Heuristic and worst-case analysis.

Operations Research Letters,30,343–350.

Hansen,P.,Og?uz,C.,&Mladenovic,N.(2008).Variable neighborhood search for minimum cost berth allocation.European Journal of Operational Research,191, 636–649.

Hu,X.&Eberhart,R.(2002).Multiobjective optimization using dynamic neighborhood particle swarm optimization.In Proceedings of the Congress on Evolutionary Computation,Honolulu,USA(pp.1677–1681).

Imai,A.,Nagaiwa,K.,&Chan,W.T.(1997).Ef?cient planning of berth allocation for container terminals in Asia.Journal of Advanced Transportation,31,75–94. Imai,A.,Nishimura,E.,&Papadimitriou,S.(2001).The dynamic berth allocation problem for a container port.Transportation Research Part B:Methodological,35, 401–417.Imai, A.,Nishimura, E.,&Papadimitriou,S.(2003).Berth allocation with service priority.Transportation Research Part B:Methodological,37, 437–457.

Imai,A.,Nishimura,E.,Hattori,M.,&Papadimitriou,S.(2007).Berth allocation at indented berths for mega-containerships.European Journal of Operational Research,179,579–593.

Imai,A.,Nishimura,E.,&Papadimitriou,S.(2008).Berthing ships at a multi-user container terminal with a limited quay capacity.Transportation Research Part E: Logistics and Transportation Review,44,136–151.

Imai,A.,Sun,X.,Nishimura,E.,&Papadimitriou,S.(2005).Berth allocation in a container port:Using a continuous location space approach.Transportation Research Part B:Methodological,39,199–221.

Imai,A.,Zhang,J.-T.,Nishimura,E.,&Papadimitriou,S.(2007).The berth allocation problem with service time and delay time objectives.Maritime Economics and Logistics,9,269–290.

Kennedy,J.&Eberhart,R.C.(1995).Particle swarm optimization.In Proceedings of IEEE International Conference on Neural Networks(pp.1942–1948).Piscataway, NJ,USA

Kim,K.H.,&Moon,K. C.(2003).Berth scheduling by simulated annealing.

Transportation Research Part B:Methodological,37,541–560.

Lalla-Ruiz, E.,Melián-Batista, B.,&Marcos Moreno-Vega,J.(2012).Arti?cial intelligence hybrid heuristic based on tabu search for the dynamic berth allocation problem.Engineering Applications of Arti?cial Intelligence,25, 1132–1141.

Lee,D.H.,Chen,J.H.,&Cao,J.X.(2010).The continuous berth allocation problem:A greedy randomized adaptive search solution.Transportation Research Part E: Logistics and Transportation Review,46,101–1029.

Lee,Y.,&Chen,C.Y.(2009).An optimization heuristic for the berth scheduling problem.European Journal of Operational Research,196,500–508.

Li,C.L.,Cai,X.,&Lee,C.Y.(1998).Scheduling with multiple-job-on-one-processor pattern.IIE Transactions,30,433–445.

Liao,C.J.,Tseng,C.T.,&Luarn,P.(2007).A discrete version of particle swarm optimization for?owshop scheduling https://www.wendangku.net/doc/35444255.html,puters&Operations Research,34,3099–3111.

Lim, A.(1998).The berth planning problem.Operations Research Letters,22, 105–110.

Low,C.Y.,Hsu,C.J.,&Su,C.T.(2010).A modi?ed particle swarm optimization algorithm for a single-machine scheduling problem with periodic maintenance.

Expert Systems with Applications,37,6429–6434.

Mauri,G.R.,Oliveira,A.C.M.,&Lorena,L.A.N.(2008).A hybrid column generation approach for the berth allocation problem.Lecture Notes in Computer Science, 4972,110–122.

Mauri,G.R.,De Andrade,L.N.&Lorena,L.A.N.(2011).A Memetic Algorithm for a continuous case of the Berth allocation problem.In Proceedings of the international conference on evolutionary computation theory and applications (pp.105–113).Paris,France.

MirHassani,S. A.,&Abolghasemi,N.(2011).A particle swarm optimization algorithm for open vehicle routing problem.Expert Systems with Applications, 38,11547–11551.

Monaco,M.F.,&Sammarra,M.(2007).The berth allocation problem:A strong formulation solved by a Lagrangian approach.Transportation Science,41, 265–280.

Moorthy,R.,&Teo,C.P.(2006).Berth management in container terminal:The template design problem.OR Spectrum,28,495–518.

Nishimura,E.,Imai,A.,&Papadimitriou,S.(2001).Berth allocation planning in the public berth system by genetic algorithms.European Journal of Operational Research,131,282–292.

Park,K.T.,&Kim,K.H.(2002).Berth scheduling for container terminals by using a subgradient optimization technique.Journal of the Operational Research Society, 53,1054–1062.

Park,Y.M.,&Kim,K.H.(2003).A scheduling method for berth and quay cranes.OR Spectrum,25,1–23.

Raa,B.,Dullaert,W.,&Van Schaeren,R.(2011).Expert Systems with Applications,38, 1413–1417.

Stahlbock,R.,&Vo?,S.(2008).Operations research at container terminals:A literature update.OR Spectrum,30,1–52.

Steenken,D.,Vo?,S.,&Stahlbock,R.(2004).Container terminal operation and operations research a classi?cation and literature review.OR Spectrum,26, 3–49.

Tang,L.,Li,S.,&Liu,J.(2009).Dynamically scheduling ships to multiple continuous berth spaces in an iron and steel complex.International Transactions in Operational Research,16,87–107.

Tassopoulos,I.X.,&Beligiannis,G.N.(2012).Solving effectively the school timetabling problem using particle swarm optimization.Expert Systems with Applications,39,6029–6040.

Theofanis,S.,Boile,M.,&Golias,M.M.(2009).Container terminal berth planning:Critical review of research approaches and practical challenges.

Transportation Research Record:Journal of the Transportation Research Board, 2100,22–28.

Thurman,K.P.(1989).Optimal ship berthing plans.Master’s thesis,Operations Research,Naval Postgraduate School,Monterey,California–EUA.

Ting,C.J.,Tsai,C.Y.,&Yeh,L.W.(2007).The use of particle swarm optimization for order allocation under multiple capacitated sourcing and quantity discounts.

Industrial Engineering and Management Systems,6,136–145.

Tong,C.J.,Lau,H.C.,&Lim,A.(1999).Ant colony optimization for the ship berthing problem.Lecture Notes in Computer Science,1742,359–370.

C.-J.Ting et al./Expert Systems with Applications41(2014)1543–15501549

Tsai,C.Y.,&Kao,I.W.(2011).Particle swarm optimization with selective particle regeneration for data clustering.Expert Systems with Applications,38, 6565–6576.

UNCTAD.(2011).Review of maritime transportation.United Nations Conference on Trade and Development.

Vacca,I.,Bierlaire,M.&Salani,M.(2008).Optimization at container terminals: Status,trends and perspectives.Report TRANSP-OR080528.Transport and

Mobility Laboratory,Ecole Polytechnique Fédérale de Lausanne, Switzerland.

Wang, F.,&Lim, A.(2007).A stochastic beam search for the berth allocation problem.Decision Support Systems,42,2186–2196.

Xu,D.S.,Li,C.L.,&Leung,J.Y.T.(2012).Berth allocation with time-dependent physical limitations on vessels.European Journal of Operational Research,216, 47–56.

1550 C.-J.Ting et al./Expert Systems with Applications41(2014)1543–1550

标准物质与标准样品

1 概述在国际上标准物质和标准样品英文名称均为“Reference Materials”，由 ISO/REMCO组织负责这一工作。在我国计量系统将“Reference Materials”叫为“标准物质”，而标准化系统叫为“标准样品”。实际上二者有很多相同之处，同时也有一些微小差异（见表1）。 2 标准物质的分类与分级 2.1 标准物质的分类根据中华人民共和国计量法的子法—标准物质管理办法（1987年7月10日国家计量局发布）中第二条之规定：用于统一量值的标准物质，包括化学成分标准物质、物理特性与物理化学特性测量标准物质和工程技术特性测量标准物质。按其标准物质的属性和应用领域可分成十三大类，即：

l 钢铁成分分析标准物质； l 有色金属及金属中气体成分分析标准物质； l 建材成分分析标准物质； l 核材料成分分析与放射性测量标准物质； l 高分子材料特性测量标准物质； l 化工产品成分分析标准物质； l 地质矿产成分分析标准物质； l 环境化学分析标准物质； l 临床化学分析与药品成分分析标准物质； l 食品成分分析标准物质； l 煤炭石油成分分析和物理特性测量标准物质； l 工程技术特性测量标准物质； l 物理特性与物理化学特性测量标准物质。 2.2 标准物质的分级我国将标准物质分为一级与二级，它们都符合“有证标准物质”的定义。 2.2.1 一级标准物质一级标准物质是用绝对测量法或两种以上不同原理的准确可靠的方法定值，若只有一种定值方法可采取多个实验室合作定值。它的不确定度具有国内最高水平，均匀性良好，在不确定度范围之内，并且稳定性在一年以上，具有符合标准物质技术规范要求的包装形式。一级标准物质由国务院计量行政部门批准、颁布并授权生产，它的代号是以国家级标准物质的汉语拼音中“Guo”“Biao”“Wu”三个字的字头“GBW”表示。 2.2.2 二级标准物质二级标准物质是用与一级标准物质进行比较测量的方法或一级标准物质的定值方

安全的系统发展生命周期(SSDLC)介绍

安全的系統發展生命週期 (SSDLC)介紹
賴溪松講師 TWISC@NCKU 中心主任兼召集人成大資通安全研發中心主任成功大學電機系特聘教授
1

課程大綱
系統發展生命週期
本章說明系統發展生命週期中，各階段的意義與要點。
生命週期各階段之安全工作要求
本章說明系統發展生命週期中，不同階段的安全需求與執行要項。
應用系統安全實例介紹
本章以實例的方式，點出各個階段的執行內容。
結論
安全的系統發展生命週期(SSDLC)重點結論說明。
2

第一章系統發展生命週期
? 1-1 系統發展生命週期簡介 ? 1-2 系統發展生命週期的異同
3

1-1 系統發展生命週期簡介
4

系統發展生命週期簡介
? 系統發展生命週期 (System Development Life Cycle, SDLC)意指發展一套系統的順序，用以開發完善的資訊系統。一般來說，根據各階段的定義，主要可分為：
– 分析設計 (Define)
? 著重需求定義，以符合業務內容及使用者需求為目的。
需求分析需求分析
5

系統發展生命週期簡介（續）
– 架構設計 (Design)
? 根據需求分析結果，進行包含系統任務目標、功能關聯、邊界範圍、各階層使用者的角色等內外部使用的規劃。
– 程式實作 (Develop)
? 落實既有之規劃，符合委託者或使用者的需要，將操作介面、資料處理、功能運作等完整的實現。
需求分析需求分析
6

试运用生态系统的承载能力(生命支持系统的承载能力)等理

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国际标准物质介绍

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