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The Scientific World Journal / 2014 / Article
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Research and Development of Advanced Computing Technologies

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Research Article | Open Access

Volume 2014 |Article ID 630280 | 15 pages | https://doi.org/10.1155/2014/630280

Discrete Bat Algorithm for Optimal Problem of Permutation Flow Shop Scheduling

Academic Editor: Shifei Ding
Received22 Jun 2014
Accepted30 Jul 2014
Published27 Aug 2014

Abstract

A discrete bat algorithm (DBA) is proposed for optimal permutation flow shop scheduling problem (PFSP). Firstly, the discrete bat algorithm is constructed based on the idea of basic bat algorithm, which divide whole scheduling problem into many subscheduling problems and then NEH heuristic be introduced to solve subscheduling problem. Secondly, some subsequences are operated with certain probability in the pulse emission and loudness phases. An intensive virtual population neighborhood search is integrated into the discrete bat algorithm to further improve the performance. Finally, the experimental results show the suitability and efficiency of the present discrete bat algorithm for optimal permutation flow shop scheduling problem.

1. Introduction

Scheduling problems are taking the very important effect in both manufacturing systems and industrial process for improving the utilization efficiency of resources [1], such as, aircraft landing scheduling problem, job shop scheduling problem, and flow shop scheduling problem. In the past several decades, scheduling problems are widely researched. Permutation flow shop scheduling problem (PFSP) is one of best known production scheduling problems, which can be viewed as a simplified version of the flow shop problem and has been proved that non-deterministic polynomial (NP) time [2]. Due to its significance in both academic and engineering applications, the permutation flow shop with the criterion of minimizing the makespan, maximum lateness of jobs, or minimizing total flow time, a great diversity of methods have been proposed to solve PFSP and some achievements were obtained.

So far, there are many methods that have been introduced for solving PFSP with the objective of minimizing the makespan. To sum up, these methods can be classified into three categories: exact methods, constructive heuristic methods, and metaheuristic algorithms based on the constructive operation and neighborhood search. Exact methods include branch and bound method [3], integer linear programming method [4], and so on. Constructive heuristic methods which build some rule to construct a feasible scheduling, such as,

Johnson method, Rajendran NEH can be viewed as the typical cases [5]. Among them, the NEH is one of the most successful constructive methods and can provide comparable results with metaheuristics. The metaheuristics mainly include genetic algorithm (GA) [6], particle swarm optimization algorithm (PSO) [7], differential evolution (DE) [8], and bat algorithm (BA) [9] and so on. Many metaheuristic algorithms are used to solve flow shop scheduling based on the constructive operation and neighborhood search in the past few years. In [6], Wang and Zheng proposed a SGA to solve flow shop scheduling, which used the well-known NEH combined with GA to generate the initial population and applied multicrossover operators to enhance the exploring potential. In [10], Tasgetiren et al. applied the PSO algorithm to solve PFSP for makespan and total flow time minimization by using the smallest position value rule borrowed from the random key representation of GA, and the proposed algorithm was combined with the variable neighborhood-based local search, as called PSO_VNS. Liu et al., in [11], proposed an efficient particle swarm optimization based mimetic algorithm (MA) for PFSP to minimize the maximum completion time. In [12], two effective heuristics are used during the local search to improve all generated chromosomes in every generation. Yagmahan and Yenisey have proposed a multiobjective ant colony system algorithm to simultaneously minimize objectives of makespan and total flow time [13]. Tasgetiren et al. present a discrete artificial bee colony algorithm hybridized with a variant of iterated greedy algorithms to find the permutation that gives the smallest total flow time [14]. In [15], a novel mechanism is employed in initializing the pheromone trails based on an initial sequence, and the pheromone trail intensities are limited between lower and upper bounds which change dynamically. Moreover, a local search is performed to improve the performance quality of the solution. In [16], Li and Yin applied a differential evolution based memetic algorithm, named ODDE, to solve PFSP by combining with NEH heuristic initialization, opposition-based learning, pairwise local search, and fast local search in ODDE. In [17], Liu et al. a multipopulation PSO based memetic algorithm for permutation flow shop scheduling is proposed. In [18], Mirabi proposed a novel hybrid genetic algorithm to solve the sequence-dependent permutation flow shop scheduling problem. In [19], Victor and Framinan use on insertion tie-breaking rules in heuristics for the permutation flow shop scheduling problem.

In recent years, a bat algorithm (BA) as a new metaheuristic optimization algorithm is proposed [9]. BA is inspired by the intelligent echolocation behavior of microbats when their foraging. After the bat algorithm is proposed by Yang in 2010, bat algorithm is used to solve various optimization problems. For example, Gandomi et al. focus on solving constrained optimization tasks [20]. Yang and Gandomi apply bat algorithm to solve many global engineering optimizations [21]. Mishra et al. present a model for classification using bat algorithm to update the weights of a functional link artificial neural network (FLANN) classifier [22]. Meanwhile, there are improved bat algorithms that are applied to various optimization problems; Xie et al. proposed a DLBA bat algorithm based on differential operator and Lévy flights trajectory to solve function optimization and nonlinear equations [23]. Wang et al. proposed a new bat algorithm with mutation (BAM) to solve the uninhabited combat air vehicle (UCAV) path planning problem [24]. In this paper, we propose a discrete bat algorithm (DBA) to solve PFSP. Here, the DBA is constructed based on the idea of continuous bat algorithm, which divide whole scheduling problem into many subscheduling problems, then NEH heuristic was introduced to solve subscheduling problem. Moreover, some subsequences are operated with certain probability in the pulse emission and loudness phases. An intensive virtual population neighborhood search is integrated into the DBA to further improve the performance. Finally, the experimental results show the effectiveness of the discrete bat algorithm for PFSP.

2. Problem Descriptions and Bat Algorithm

2.1. Permutation Flow Shop Scheduling Problem

The permutation flow shop scheduling problem (PFSP) in the paper consists of a set of jobs on a set of machines with the objective of minimizing the makespan. In PFSP, jobs are to be processed on a series of machines, sequentially. All jobs are processed in the same permutation; meanwhile, every job is processed in one machine only once and each machine can only process one job at a time, and all jobs are processed in an identical processing order on all machines.

The permutation flow shop scheduling problems are often denoted by the symbols , where represents the number of jobs; is the number of machines; denotes the type of flow shop scheduling problem; and is the makespan. Let be the times of job processed on machine , assuming preparation time for each job is zero or is included in the processing time ; is a scheduling permutation of all jobs. is set of all scheduling permutation. is completion time of job on machine , and every job will be processed on machine 1 to machine orderly. The completion time of the permutation flow shop scheduling problem according to the processing sequence is shown as follows: where is the most suitable arrangement which is the goal of the permutation flow shop problem to find is the minimal makespan.

2.2. Bat Algorithm (BA)

The bat algorithm (BA) is an evolutionary algorithm first introduced by Yang in 2010 [9]. In simulations of BA, under several ideal rules, the updated rules of their positions and velocities in a D-dimensional search space are defined. The new solutions and velocities at generation are given by where is a random vector drawn from a uniform distribution, denotes frequency of each bat, and the frequency . Here is the current global best location (solution) which is located after comparing all the solutions among all the bats.

After the position updating of bat, a random number is generated; if the random number is greater than the pulse emission rate , a new position will be generated around the current best solutions, and it can be represented by where is a random number, while is the average loudness of all the bats at current generation .

Furthermore, the loudness and the pulse emission rate will be updated and a solution will be accepted if a random number is less than loudness and . and are updated by where , are constants and is fitness function. The algorithm repeats until the termination criterion is reached. The basic steps of the bat algorithm (BA) can be described in Algorithm 1.

Begin
                            Initialization. Set the generation counter ; Initialize the population of bats
                             randomly and each bat corresponding to a potential solution to the given problems;
                            define loudnes , pulse frequency and the initial velocities   ;
                            set pulse rate .
           While the termination criterion is not satisfied or   do
                             Generate new solutions by adjusting frequency, and updating velocities and location
                             Solutions (2),
                            if (rand ) then
                                  Select a solution among the best solutions;
                                  Generate a location solution around the selected best solution
                            endif
                                  Generate a new solution by flying randomly
                            if (rand &&
                                  Accept the new solution
                                  Increase and reduce
                           endif
                                  Rank the bats and the find the current best
                                   ;
           endwhile
                                  Post-processing the results and visualization.
end.

3. Discrete Bat Algorithm for PFSP

Since standard BA is a continuous optimization algorithm, the standard continuous encoding scheme of BA cannot be used to solve PFSP directly. Meanwhile, many combinational optimization problems are discrete problem, and PFSP is a typical case. In order to apply BA to PFSP, there are two methods: the first method is to solve PFSP using continuous BA, however, this method needs to construct a direct mapping relationship between the job sequence and the vector of individuals in BA; the second method is to construct a discrete BA for PFSP. Therefore, in this paper, a discrete bat algorithm is proposed to solve PFSP with minimal makespan.

In addition, for PFSP, some neighborhood search methods always are used to enhance the quality of the solution, and the performance is remarkable. In this paper, four neighborhood search methods, that is, insert, swap, inverse, and crossover, will be employed. These neighborhood operations are shown in Figure 1. The details of these neighborhoods are as follows.

Swap. Choose two different positions from a job permutation randomly and swap them.

Insert. Choose two different positions from a job permutation randomly and insert the back one before the front.

Inverse. Inverse the subsequence between two different random positions of a job permutation.

Crossover. Choose a subsequence in a random interval from another random job permutation and replace the corresponding part of subsequence.

3.1. Solution Representation in DBA

In original BA, the position of each virtual bat is viewed as a candidate solution of problem; these bat individuals adjust the flight speed by randomly selecting frequency of sonic wave which they emitted and then update the position of bats. Furthermore, the pulse emission rate and loudness are used to control the intensive local search that is process to generate a new individual around the current global best solution. In DBA, in general, the position of individual denotes a scheduling plan on th iteration, where represents the scheduling plan including jobs. The is also viewed as a . For example, if , which represents the processing order of all jobs on all machines, is , this permutation represents the position of first bat individual in second generation. The velocity consists of a part of scheduling plan or whole scheduling plan on th iteration, where .

3.2. Population Initialization

In this paper, the DBA is applied to explore the new search space. Initial swarm is often generated randomly, and, in DBA, this initial strategy is adopted. Meanwhile, recent studies have confirmed the superiority of NEH over the most recent constructive heuristic [5]. Many metaheuristic algorithms in order to generate an initial population with certain quality and diversity take advantage of the NEH heuristic to generate some individuals and the rest of the individuals are initialized with random values [16]. In this paper, this kind of initialization strategy is not including in DBA, but NEH is used in position updating of bat. However, a discrete bat algorithm with NEH initialization strategy is experimented. By experiments, we find that the combination of NEH initialization strategy and succeeding operation always deteriorates the population diversity, by tracking offspring, the results showed that all the individuals in the final population were similar.

In [25], NEH heuristic is regarded as the best heuristic for the PFSP. The NEH algorithm is based on the idea that the high processing time on all machines should be scheduled as early in the sequence as possible. The NEH heuristic has two phases.(1)The jobs are sorted in nonincreasing sums of their processing time.(2)A job sequence is established by evaluating the partial schedules based on the initial order of the first phase. The standard NEH and a variant of standard NEH (NEH1) can be described as shown in Algorithm 2; the only difference of two NEH is that the inserted position of new job in partial schedules is different: NEH1 have only two possibilities of inserting.

    Compute the total processing time for each job on m machine;
    Generate a sequence by sorting the jobs in non-increasing order according to
    the total processing time;
    The first job is taken. ;
for   : 
          /*  The  implemented operations of NEH and NEH1 is different, the NEH insert a job into all possible
                 positions of , but the NEH1 only insert a job into the front and rear of . The other operations are
                consistent both NEH and NEH1.  */
         Take job form   and insert into all possible positions of ;//  Operation of NEH
         Take job form   and insert into the front and rear of ;//  Operation of NEH1
         Evaluate the new sequence ;
        Select the with lowest objective value;
endfor
    return ;

3.3. Position Updating of Bat

Scheduling problem with many jobs can be viewed as a combination of many subscheduling problems; as we all know, we can apply dynamic programming to solve this problem. However, in this paper, the idea of partition is adopted, a complete scheduling sequence is divided into many segments, and each subscheduling problem is solved by superior NEH.

In continuous BA, the bat individual randomly selects a certain range of frequency, and its speed is updated according to their selected frequency; at last, a new position is generated using its speed and its own position. In DBA, for each individual, firstly, a frequency is selected in the range of frequency ; frequency denotes the number of subsequences, where , are two integers in the range of job amount , where denotes rounded down function. Secondly, frequency decides the starting location and ending location of each subsequence, and the position is divided into subsegments; these subsequences are viewed as the velocity of bat individual, where . Thirdly, these velocities are updated by NEH; the new velocity is called . At last, the corresponding part of is replaced by . In order to facilitate understanding, there is a simple instance: , , ; , , ; , , ; , so .

3.4. Pulse Emission Rate Local Operation

In original BA, the pulse emission rate and loudness are used to control the intensive local search, that is to generate a new individual around the current global best individual . In DBA, each individual has its own pulse emission rate . The initial pulse emission rate is a positive and smaller number; with the increase of iteration, pulse emission rate will increase to 1. The updating of using Figure 2 presents an example of updating curve of pulse emission rate under maximal iterations is 100, pulse emission rate has a value ranging from 0 to 1. Using this updating formula, the algorithm can not only quickly exploit near the current optimal position in the early iteration, so that speed up the convergence rate, but also can mainly concentrate in diversity in later search and can avoid to fall into local optima.

The pulse emission rate will control the subsegment local operation. For each individual, randomly generate a random number; if this random number is larger than its , this position of bat individual will be updated by random swap two segments defined by frequency ; otherwise, the updating operation will be implement by random inserting operation; the pseudo code can be described as shown in Algorithm 3.

for  each individual
            Compute pulse emission rate by (6);
        if  rand >
                    /* sub-sequence swap  */
                    Randomly select two sub-sequences defined by frequency on ;
                    Swap the two sub-sequences to generate a new position;
             else
                    /* sub-sequence inserting  */
                    Randomly select one sub-sequence defined by frequency on ;
                    Insert this sub-sequence into a random location in remainder sequence;
        endif
endfor

3.5. Loudness Local Operation

In DBA, the loudness of bat individual is relative to its own fitness ; the better fitness, the less loudness. The loudness can be described by where is the fitness of individual and and are the minimum and maximum fitness in current population, respectively. In DBA, the loudness reflects the quality of individual. In this subsection, there are two kinds of local search embedded into algorithm, random subsequence inverse and random subsequence inserting. Note that, where inserting operation is different from inserting operation in Section 3.4.

In this part, for each individual, randomly generate a random number; if this random number is larger than its , a random length of subsequence is randomly selected in range of ; this position of bat individual will be updated by inserting operation with random subsequence; otherwise, the updating operation will be implement by random subsequence inverse operation. Note that the subsequence is a portion of the current best position ; however, the corresponding replacement portion is the individual in bat population, and the pseudo code can be described as show in Algorithm 4.

for   each individual
                   Compute loudness by (7);
             if  rand >
                          /*  random sub-sequence inserting  */
                          Randomly select a length of sub-sequence;
                          Randomly determine the sub-sequence with selected length in ;
                          Insert this sub-sequence into a random location in remainder sequence;
             else
                          /*  random sub-sequence inverse  */
                          Randomly select a length of sub-sequence;
                          Randomly determine the sub-sequence with selected length in ;
                          Perform inverse operation on selected sub-sequence;
                          Replace original sub-sequence with inverted sub-sequence
             end if
end for

Although this inserting and inverse operation may generate invalid scheduling sequence,those invalid scheduling sequences need to adjust to a feasible solution. The adjustment of the pseudo code can be described as show in Algorithm 5.

for  each individual
                   Find out all jobs and their position in current scheduling sequence;
                 1 : , where   denotes the number of jobs in current scheduling problem;
                 1 : ;
                 Generate an insert order   randomly;
                 Select a job in   according to   and insert into ;
end for

In order to facilitate understanding, process of adjustment , , , , , , and .

3.6. Intensive Virtual Population Neighborhood Search

In this paper, an intensive virtual population neighborhood search with same population size is easily embedded in DBA for solving PFSP. The purpose of the virtual population neighborhood search is to find a better solution from the neighborhood of the current global best solution. In this part, three neighborhoods, that is, insert, swap, and single-point move backward operate, are employed. These operations are used to improve the diversity of population and enhance the quality of the solution.

In order to enhance the local search ability and get a better solution, a new population is generated based on the current global best solution, and the population size is not less than original bat population; the new population is called virtual population. The new population size , is real number.

Firstly, the virtual population is generated by randomly selecting two jobs to perform swap operation. Secondly, the virtual population is generated by randomly selecting a job and insert into another random location. At last, the single-point move backward operation is performed also based on current global best individual . In the simulation, first of all, a job position is chosen randomly in ; the selected job is inserted into the back of job , orderly, until the population size is reached. For example, the population size , random job position , and ; the virtual population is generated as follows:

3.7. Discrete Bat Algorithm (DBA)

In DBA, all individuals once the update either in bat population or in virtual population, these individuals will be evaluated and one solution be accepted as the current global best if the objective fitness of it is better than the fitness of the last . The algorithm terminates until the stopping criterion is reached; the DBA algorithm for PFSP can be described in Algorithm 6.

Begin
                  Initialize the population , , other parameters and bat population
                  Evaluate fitness for each individual and find out and
           while (the termination condition does not satisfy)
                    /*  Position Updating of Bat  */
                    for   : 
                                Generate frequency ;
                                Obtain velocity ;
                                Determine by NEH method;
                                Update using ;
                                Evaluate fitness of individual and update ;
                                Perform Pulse Emission Rate Local Operation ;//  Algorithm 2
                                Evaluate fitness of individual and update
                                Perform Loudness Local Operation;//  Algorithm 3
                                 = adjustment();//  Algorithm 4
                                Evaluate fitness of individual and update ;
                    endfor
                                Find out current global best position ;
                                /*  Intensive Virtual Population Neighborhood Search  */
                    for   : 
                                Execute swap operation based on
                    endfor
                                Evaluate fitness for each individual and find out
                    for   : 
                                Execute insert operation based on 
                    endfor
                                Evaluate fitness for each individual and find out
                    for   : 
                                Execute single-point move backward operation based on
                    endfor
                                Evaluate fitness for each individual and find out
                 ;
           endwhile
                 Output result and plot;
end

4. Numerical Simulation Results and Comparisons

To test the performance of the proposed DBA for the permutation flow shop scheduling, computational simulations are carried out with some well-studied problems taken from the OR-Library (http://people.brunel.ac.uk/~mastjjb/jeb/info.html). In this paper, 29 problems from two classes of PFFSP test problems are selected. The first eight problems are instances Car1, Car2 through to Car8 designed by Carlier [26]. The second 21 problems are instances Rec01, Rec03 through to Rec41 designed by Reeves and Yamada [27]. So far, these problems have been widely used as benchmarks to certify the performance of algorithms by many researchers.

The DBA is coded in MATLAB 2012a, and in our simulation, numerical experiments are performed on a PC with AMD Athlon(tm) II X4 640 Processor 3.0 GHz and 2.0 GB memory. In the experiment, the termination criterion is set as  ms maximum computation time. Setting the time limitation in this way allows the much computation time as the job number or the machine number increases. And, this method is also adopted by many researchers, such as Jarboui et al. [28], Ruiz and Stützle [29]. Each instance is independently run 15 times for every algorithm for comparison.

The comparison method adopts , , and to measure the quality of solution by the percentage difference from ; these expressions as follows: where is the optimal makespan or upper bound value known so far, the makespan of an obtained solution in DBA is , represents the best relative error to , denotes the average relative error to , and represents the worst relative error to . denotes the standard deviation of the makespan. These performance measures are employed in our experiments; these results are rounded to the nearest number which contains 2 or 3 digits after the decimal point.

4.1. Parameter Analysis

In the subsection, parameters of DBA are determined by experiments, and the impact of each parameter is analyzed. In DBA, parameters , are tested. is population size, A small may lead insufficient information provided, and the diversity cannot guarantee. On the other side, a large one indicates diversity is sufficient, but the computing time will increase. determines the size of virtual population; the large one can perform large single point neighborhood search, which may achieve a better solution, especially, the current best solution extraordinarily approximated the exact solution; however, an oversize will increase the computing time, and the precision of optimal solution may have lesser improvement. In order to evaluate the sensitivity of parameters, Car5 and Rec11 are chosen to run 15 times and the results are shown in Figures 3 and 4.

Figures 3 and 4 represent the relative error of test case Car5 and Rec11 after 15 times independent running, which showed the sensitivity of parameters and . when test parameter , and when test parameter . From the two test cases, for Car5, the performance is better and better while parameter gradually increases. But for Rec11, equal to 40 or 50 can achieve exact solution, but the performances do not follow the laws of Car5. In DBA, the parameter takes a compromise values, . Similarly, parameter equal to 2 is optimal for Car5; however, is optimal for Rec11. In order to balance all test cases, the parameter is set as 1 while .

4.2. Comparisons of DBA, DBA_NEH1, and DBA-IVPNS

In order to evaluate the performance of each strategy, two variants of DBA are compared, whose abbreviations are as follows.(1)DBA: DBA with NEH.(2)DBA_NEH1: DBA with NEH1.(3)DBA-IVPNS: DBA without intensive virtual population neighborhood search.

At this group experiment, the parameter setting is , , termination criterion is set as  ms maximum computation time, and the algorithm is run 15 times independently. The statistical performances of DBA, DBA _NEH1, and DBA-IVPNS are shown in Table 1.


ProblemDBADBA_NEH1DBA-IVPNS
BREAREWREStdBREAREWREStdBREAREWREStd

Car17038000000000000
Car2716600.195 2.931 54.22 00.391 2.931 73.89 0000
Car3731200.476 1.190 44.12 00.635 1.190 44.93 00.397 1.190 42.45
Car48003000000000000
Car5772000.246 1.308 35.70 00.664 1.360 45.95 00.352 1.308 40.88
Car68505000000000000
Car76590000000000000
Car88366000000000000
Rec112470.160 0.209 0.722 1.84 0.160 0.241 1.043 2.85 0.160 0.545 1.925 7.92
Rec311090.090 0.481 2.164 7.09 00.499 1.803 6.09 0.180 0.385 1.713 4.51
Rec512420.242 0.623 2.174 9.49 0.242 0.768 2.496 10.72 0.242 1.100 2.496 10.69
Rec715661.149 1.443 3.831 11.41 1.149 2.048 3.831 18.33 1.149 1.537 3.831 13.54
Rec9153702.420 3.709 13.44 02.065 3.318 15.98 1.041 2.728 4.815 13.13
Rec1114310.559 1.975 3.564 13.56 02.241 7.617 29.69 01.859 4.403 16.64
Rec1319300.415 2.394 3.938 19.84 0.933 2.525 4.819 19.30 1.762 2.694 4.352 16.29
Rec1519500.154 2.178 4.615 23.43 0.821 2.410 4.615 24.23 1.231 2.903 4.256 19.21
Rec1719020.946 2.685 4.206 19.27 0.894 3.582 5.941 25.20 1.577 5.065 6.730 25.65
Rec1920930.573 2.621 4.252 21.38 1.386 2.599 4.730 19.58 2.484 3.883 5.542 20.28
Rec2120171.438 2.310 4.412 19.89 1.636 2.568 5.702 24.05 1.785 3.543 5.255 19.50
Rec2320110.945 3.216 5.868 23.88 1.591 3.090 4.923 19.22 3.282 4.422 6.266 18.48
Rec2525132.348 3.520 5.213 20.71 1.870 3.489 5.133 23.77 3.780 5.428 6.805 19.57
Rec2723732.402 3.638 5.057 19.03 1.728 3.217 5.900 23.66 2.023 4.374 5.942 23.93
Rec2922871.530 4.323 7.084 33.64 2.186 3.615 5.597 24.60 4.766 6.046 7.521 19.20
Rec3130453.284 4.917 6.502 30.44 3.153 4.926 6.765 38.21 5.353 6.192 7.783 21.95
Rec3331140.835 1.916 4.143 29.20 1.317 2.338 4.528 26.65 1.927 2.899 4.689 25.99
Rec3532770.092 0.484 2.014 18.73 0.092 1.082 3.021 36.89 0.244 1.107 2.563 20.99
Rec3749515.615 7.172 8.140 39.66 5.918 7.387 8.826 37.75 8.503 9.156 10.261 21.68
Rec3950873.696 5.578 6.408 41.52 4.914 6.083 7.529 35.22 6.979 7.629 8.374 23.50
Rec4149606.129 7.435 8.952 33.55 6.573 7.589 8.952 29.22 8.105 9.319 10.726 37.80

Average1.124 2.154 3.531 20.17 1.261 2.278 3.882 22.62 1.951 2.881 4.095 16.68

From Table 1, we can find out that the average performance of DBA is better than the other two variants of DBA; for benchmarks Car1 to Car8, the DBA-IVPNS is better; the reason may be that the IVPNS implementation is single-point operation on the current global best individual ; this operation may improve the quality of solution, bur this needs much computing time, so the DBA-IVPNS have more time to explore of more new position. However, from Rec1 to Rec41, the DBA is much better than other variants. For DBA_NEH, only it has a difference that the position updating of bat by NEH1. The NEH1 has lesser computational complexity than NEH. From experiment results, we can find out that DBA_NEH1 can find better solutions for several benchmarks. In general, the DBA is better than DBA_NEH1 for all benchmarks.

In addition, in order to demonstrate the effect of each strategy in the specific scheduling problem, the frequency of finding a new best solution by applying these moves in DBA is recorded; it can show the contribution of each strategy. The Car1 to Car8 and Rec1 to Rec15 16 benchmarks are chosen to tested. Each problem was run 10 times; each time a new best solution was found by the algorithm; the move resulting in this improvement was recorded. Figure 5 demonstrates the percentage of contribution.

4.3. Comparisons of DBA, PSOMA, PSOVNS, OSA

In order to show the effectiveness of DBA, we carry out a simulation to compare our DBA with other state-of-art algorithms, that is, PSOMA proposed by Liu et al. [11], PSOVNS proposed by Tasgetiren et al., and experimental results reference [5], and SA is a simulated annealing, the experimental results reference [16]. The population size is 50 and the termination criterion is set as  ms maximum computation time. The experimental results are listed in Table 2.


ProblemDBAPSOVNS PSOMA OSA
BREAREWREStdBREAREWREBREAREWREBREAREStd

Car170380000000000000
Car271660000000000000
Car3731200.397 1.190 42.45 00.420 1.189 00000.625 47.19
Car480030000000000000
Car57720000000.039 0.389 00.018 0.375 00.801 50.73
Car68505000000.076 0.764 00.114 0.764 02.093 274.71
Car76590000000000001.483 114.21
Car88366000000000002.297 254.63
Rec1124700.080 0.160 0.85 0.160 0.168 0.321 00.144 0.160 0.160 0.160 0
Rec3110900.081 0.180 0.88 00.158 0.180 00.189 0.721 00.189 1.85
Rec512450.242 0.242 0.242 00.242 0.249 0.420 0.242 0.249 0.402 0.242 0.588 4.62
Rec7156600.575 1.149 9.40 0.702 1.095 1.405 00.986 1.149 00.434 11.59
Rec9153700.638 2.407 15.00 00.651 1.366 00.621 1.691 00.690 12.39
Rec11143101.167 2.655 11.17 0.071 1.153 2.656 00.129 0.978 02.215 37.60
Rec1319380.415 1.461 3.782 19.01 1.036 1.790 2.643 0.259 0.893 1.502 0.311 1.793 14.69
Rec1519530.154 1.226 2.103 7.97 0.769 1.487 2.256 0.051 0.628 1.076 0.718 1.569 16.07
Rec1719090.368 1.277 2.154 41.65 0.999 2.453 3.365 01.330 2.155 1.840 3.796 36.72
Rec1921050.573 0.929 2.023 33.06 1.529 2.099 2.532 0.430 1.313 2.102 0.287 0.803 9.48
Rec2120461.438 1.671 2.231 4.04 1.487 1.671 2.033 1.437 1.596 1.636 1.438 1.477 1.69
Rec2320270.796 1.173 2.381 39.27 1.343 2.106 2.884 0.596 1.310 2.038 0.497 0.854 10.82
Rec2525541.632 2.921 3.940 18.96 2.388 3.166 3.780 0.835 2.085 3.233 1.194 1.938 15.06
Rec2723971.011 1.419 2.298 21.35 1.728 2.463 3.203 1.348 1.605 2.402 0.843 1.845 21.06
Rec2923111.049 2.580 3.935 22.84 1.968 3.109 4.067 1.442 1.888 2.492 0.612 2.882 38.83
Rec3131152.299 3.392 4.532 23.66 2.594 3.232 4.237 1.510 2.254 2.692 0.296 1.333 30.39
Rec3331330.610 0.728 1.734 39.40 0.835 1.007 1.477 00.645 0.834 0.128 0.732 7.32
Rec35327700.037 0.092 1.52 00.038 0.092 000000
Rec3751183.373 4.872 5.979 40.31 4.383 4.949 5.736 2.101 3.537 4.039 2.000 2.751 25.43
Rec3952032.280 3.851 5.347 45.97 2.850 3.371 5.585 1.553 2.426 2.830 0.767 1.240 12.31
Rec4151493.810 5.095 6.532 42.89 4.173 4.867 5.585 2.641 3.684 4.052 1.734 2.726 39.38

From Table 2, for the Car problems, the DBA, PSOVNS, PSOMA, and OSA all can find the exact solution, and DBA is better than the other algorithm on ARE. For the Rec problems, DBA also can find better solutions. Compared with DBA, PSOVNS, PSOMA, and OSA, the DBA achieved 14 exact solutions; several optimal job permutations are shown in Table 3. PSOVNS achieved 11 exact solutions, PSOMA achieved 16 exact solutions, and OSA achieved 13 exact solutions. For all test problems, obtained solutions of DBA are not better than the PSOMA and OSA, but the performance is similar to PSOMA and OSA.


Problem

Car170388, 1, 3, 11, 5, 9, 4, 10, 7, 2, 6
Car271667, 3, 4, 11, 9, 1, 8, 12, 5, 2, 13, 10, 6
Car3731211, 6, 5, 10, 12, 9, 3, 2, 4, 7, 8, 1
Car480034, 12, 13, 14, 5, 7, 6, 1, 9, 10, 11, 8, 2, 3
Car577205, 4, 2, 1, 3, 8, 6, 10, 9, 7
Car685057, 1, 5, 6, 8, 3, 4, 2
Car765905, 4, 2, 6, 7, 3, 1
Car883667, 3, 8, 5, 2, 1, 6, 4
Rec112476, 9, 2, 20, 12, 14, 17, 15, 13, 7, 1, 18, 3, 4, 11, 5, 8, 10, 19, 16
Rec311096, 14, 7, 1, 2, 3, 11, 8, 9, 17, 15, 5, 19, 4, 16, 10, 12, 13, 18, 20
Rec7156617, 13, 18, 12, 9, 1, 6, 3, 8, 4, 5, 2, 7, 15, 10, 19, 11, 16, 14, 20
Rec915374, 19, 17, 12, 18, 14, 7, 16, 5, 13, 2, 10, 9, 11, 8, 20, 1, 15, 3, 6
Rec11143116, 2, 14, 9, 12, 4, 20, 13, 10, 19, 8, 11, 3, 5, 15, 17, 1, 18, 7, 6
Rec35327713, 14, 40, 39, 50, 36, 46, 35, 37, 26, 2, 18, 19, 8, 41, 10, 25, 20, 38, 29, 33, 15, 27, 9, 21, 17, 42, 22, 32, 3, 1, 23, 4, 12, 5, 49, 11, 45, 43, 16, 34, 6, 44, 30, 7, 48, 47, 28, 24, 31

In order to compare each norm (BRE, ARE, WRE, and Std) of corresponding algorithms, for all benchmarks, each norm is scored among corresponding algorithms. The first is score 4, the second is score 3, the third is score 2, the fourth is score 1, and the last is score 0, if several results are same, they have same score. The statistical results are listed in Table 4. From Table 4, for Car problems, the DBA is best on ARE, the DBA and PSOMA are identical on WRE, DBA has better Std compared with OSA. For Rec problems, the OSA and PSOMA have better BRE, the DBA is better than PSOVNS, the DBA is better than PSOVNS, OSA, the DBA is also better than PSOVNS on WRE among DBA, PSOVNS, and PSOMA, but the Std is not better than OSA. The DBA is best on ARE for Car1 to Rec29 among DBA, PSOVNS, PSOMA, and OSA, and the Std is better than OSA. On the whole, the achieved solutions of DBA have better quality. For large-scale scheduling problems, the DBA still have the room for improvement; it also is our further work.


BenchmarkDBAPSOVNS PSOMA SGA + NEH OSA
BREAREWREStdBREAREWREBREAREWREBREAREBREAREStd

Car1–Car8323130323227283229303016321927
Rec1–Rec4160586270403756736678204735777
Car1–Rec29787878836354678575844719825481
Car1–Rec4192899210272648410595108502010576104

The DBA achieved 14 exact solutions, due to the fact that Rec35 have 10 machines and 50 jobs, the margin of paper is restricted, the Gantt chart of an optimal schedule for Rec35 cannot display on this paper, and the Gantt chart of Car5, Car6, Rec7, and Rec11 is selected as instance. These Gantt charts of an optimal schedule are shown in Figures 6, 7, 8, and 9.

Figures 10, 11, 12, and 13 show the convergence curves of Car5, Car6 Rec7, and Rec11. From Figures 10 to 13, the convergence rate of DBA is fast, and the precision of solution is prominent. The performance of DBA is similar to PSOMA; however, the convergence rate of DBA is faster than PSOMA in the early phase of iteration. The precision of solution is not as good as PSOMA while the scale of scheduling problems is increasing. The DBA is better than SGA + NEH [5], PSOBNS, and OSA in some aspects.

5. Conclusions

In this paper, we construct a direct relationship between the job sequence and the vector of individuals in DBA; a DBA is proposed to solve PFSP. In order to evaluate the performance of the proposed DBA, we compare DBA with several PFSP algorithms with benchmark problems of PFSP. Experimental results have shown that our algorithm is pretty effective, the performance of each strategy is evaluated, and sensitivity of parameters is analyzed. Moreover, our further work is to study the theoretical aspects as well as the performance of the technique.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

This work is supported by National Science Foundation of China under Grant no. 61165015, Key Project of Guangxi Science Foundation under Grant no. 2012GXNSFDA053028, and Key Project of Guangxi High School Science Foundation under Grant nos. 20121ZD008, 201203YB072.

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Copyright © 2014 Qifang Luo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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