Particle swarm optimization with cocktail decoding method for hybrid flow shop scheduling problems with multiprocessor tasks (original) (raw)
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Algorithms, 2017
The no-wait flow shop is a flowshop in which the scheduling of jobs is continuous and simultaneous through all machines without waiting for any consecutive machines. The scheduling of a no-wait flow shop requires finding an appropriate sequence of jobs for scheduling, which in turn reduces total processing time. The classical brute force method for finding the probabilities of scheduling for improving the utilization of resources may become trapped in local optima, and this problem can hence be observed as a typical NP-hard combinatorial optimization problem that requires finding a near optimal solution with heuristic and metaheuristic techniques. This paper proposes an effective hybrid Particle Swarm Optimization (PSO) metaheuristic algorithm for solving no-wait flow shop scheduling problems with the objective of minimizing the total flow time of jobs. This Proposed Hybrid Particle Swarm Optimization (PHPSO) algorithm presents a solution by the random key representation rule for converting the continuous position information values of particles to a discrete job permutation. The proposed algorithm initializes population efficiently with the Nawaz-Enscore-Ham (NEH) heuristic technique and uses an evolutionary search guided by the mechanism of PSO, as well as simulated annealing based on a local neighborhood search to avoid getting stuck in local optima and to provide the appropriate balance of global exploration and local exploitation. Extensive computational experiments are carried out based on Taillard's benchmark suite. Computational results and comparisons with existing metaheuristics show that the PHPSO algorithm outperforms the existing methods in terms of quality search and robustness for the problem considered. The improvement in solution quality is confirmed by statistical tests of significance.
Chaos Solitons & Fractals, 2008
It is well known that the flow-shop scheduling problem (FSSP) is a branch of production scheduling and is NP-hard. Now, many different approaches have been applied for permutation flow-shop scheduling to minimize makespan, but current algorithms even for moderate size problems cannot be solved to guarantee optimality. Some literatures searching PSO for continuous optimization problems are reported, but papers searching PSO for discrete scheduling problems are few. In this paper, according to the discrete characteristic of FSSP, a novel particle swarm optimization (NPSO) algorithm is presented and successfully applied to permutation flow-shop scheduling to minimize makespan. Computation experiments of seven representative instances (Taillard) based on practical data were made, and comparing the NPSO with standard GA, we obtain that the NPSO is clearly more efficacious than standard GA for FSSP to minimize makespan.
2018
A flow shop with no-wait schedules jobs continuously through all machines without any wait at consecutive machines. This scheduling problem is combinatorial optimization problem and observed as NP-hard as appropriate sequence of jobs for scheduling from all possible combination of sequences is to be determined for reducing total completion time (makespan). This paper presents an effective hybrid Particle Swarm Optimization algorithm for solving no wait flow shop scheduling problem with the objective of minimization of makespan. This Proposed Hybrid Particle Swarm Optimization Makespan (PHPSOM) algorithm represents discrete job permutation by converting the continuous position information values of particles with random key representation rule. The proposed algorithm balances global exploration and local exploitation with evolutionary search guided by the mechanism of PSO, and local search by the mechanism of Simulated Annealing (SA) along with efficient population initialization wit...
An effective hybrid PSO-based algorithm for flow shop scheduling with limited buffers
In this paper, an effective hybrid algorithm based on particle swarm optimization (HPSO) is proposed for permutation flow shop scheduling problem (PFSSP) with the limited buffers between consecutive machines to minimize the maximum completion time (i.e., makespan). First, a novel encoding scheme based on random key representation is developed, which converts the continuous position values of particles in PSO to job permutations. Second, an efficient population initialization based on the famous Nawaz-Enscore-Ham (NEH) heuristic is proposed to generate an initial population with certain quality and diversity. Third, a local search strategy based on the generalization of the block elimination properties, named block-based local search, is probabilistically applied to some good particles. Moreover, simulated annealing (SA) with multi-neighborhood guided by an adaptive meta-Lamarckian learning strategy is designed to prevent the premature convergence and concentrate computing effort on promising solutions. Simulation results and comparisons demonstrate the effectiveness of the proposed HPSO. Furthermore, the effects of some parameters are discussed. ᭧
The International Journal of Advanced Manufacturing Technology, 2008
This paper proposes a novel hybrid discrete particle swarm optimization (HDPSO) algorithm to solve the no-wait flow shop scheduling problems with the criterion to minimize the maximum completion time (makespan). Firstly, a simple approach is presented in the paper to calculate the makespan of a job permutation. Secondly, a speed-up method is proposed to evaluate the similar insert neighborhood solution. Thirdly, a discrete particle swarm optimization (DPSO) algorithm based on permutation representation and a local search algorithm based on the insert neighborhood are fused to enhance the searching ability and to balance the exploration and exploitation. Then, computational simulation results based on the well-known benchmarks and statistical performance comparisons are provided. It is concluded that the proposed HDPSO algorithm is superior to both the single DPSO algorithm and the existing hybrid particle swarm optimization (HPSO) algorithm from literature in terms of searching quality, robustness and efficiency.
A similar particle swarm optimization algorithm for job-shop scheduling to minimize makespan
Applied Mathematics and Computation, 2006
The flow-shop scheduling problem (FSSP) is a branch of production scheduling, which is among the hardest combinatorial optimization problems. It is well known that this problem with current algorithms even moderately sized problems cannot be solved to guaranteed optimality. Many different approaches have been applied for permutation flowshop scheduling to minimize makespan, but these methods are not satisfying. Particle swarm optimization (PSO) has been developing rapidly and has been applied widely since it was introduced, as it is easily understood and realized. This paper through the improvement of the option modes of gBest and pBest of PSO algorithm, a similar particle swarm optimization algorithm (SPSOA) applied for permutation 0096-3003/$ -see front matter Ó (B. Jiao).
A swarm optimization approach for flexible flow shop scheduling with multiprocessor tasks
The International Journal of Advanced …, 2012
In simple flow shop problems, each machine operation center includes just one machine. If at least one machine center includes more than one machine, the scheduling problem becomes a flexible flow shop problem (FFSP). Flexible flow shops are thus generalization of simple flow shops. Flexible flow shop scheduling problems have a special structure combining some elements of both the flow shop and the parallel machine scheduling problems. FFSP can be stated as finding a schedule for a general task graph to execute on a multiprocessor system so that the schedule length can be minimized. FFSP is known to be NP-hard. In this study, we present a particle swarm optimization (PSO) algorithm to solve FFSP. PSO is an effective algorithm which gives quality solutions in a reasonable computational time and consists of less numbers parameters as compared to the other evolutionary metaheuristics. Mutation, a commonly used operator in genetic algorithm, has been introduced in PSO so that trapping of solutions at local minima or premature convergence can be avoided. Logistic mapping is used to generate chaotic numbers in this paper. Use of chaotic numbers makes the algorithm converge fast towards near-optimal solution and hence reduce computational efforts further.
IOP Conference Series: Materials Science and Engineering, 2018
This paper proposes a discrete Particle Swam Optimization (PSO) to solve limitedwait hybrid flowshop scheduing problem with multi objectives. Flow shop schedulimg represents the condition when several machines are arranged in series and each job must be processed at each machine with same sequence. The objective functions are minimizing completion time (makespan), total tardiness time, and total machine idle time. Flow shop scheduling model always grows to cope with the real production system accurately. Since flow shop scheduling is a NP-Hard problem then the most suitable method to solve is metaheuristics. One of metaheuristics algorithm is Particle Swarm Optimization (PSO), an algorithm which is based on the behavior of a swarm. Originally, PSO was intended to solve continuous optimization problems. Since flow shop scheduling is a discrete optimization problem, then, we need to modify PSO to fit the problem. The modification is done by using probability transition matrix mechanism. While to handle multi objectives problem, we use Pareto Optimal (MPSO). The results of MPSO is better than the PSO because the MPSO solution set produced higher probability to find the optimal solution. Besides the MPSO solution set is closer to the optimal solution
Applied Mathematical Sciences, 2015
In this paper, an algorithm based on particle swarm optimization is proposed, for hybrid flow shop scheduling problem, to minimize the makespan. First an effective new approach using two decisions based on parallel priority dispatching rules is applied. Next we develop an hybridizing DPSO, that presents new components to updating velocity and position using genetic operators, with an adaptive neighborhood procedure based on insert-interchange mutation. The performance of the proposed algorithm was tested on benchmark problems of Carlier and Néron [8].
Applied Mathematics and Computation, 2006
The flow-shop scheduling problem (FSSP) is a branch of production scheduling, which is among the hardest combinatorial optimization problems. It is well known that this problem with current algorithms even moderately sized problems cannot be solved to guaranteed optimality. Many different approaches have been applied for permutation flowshop scheduling to minimize makespan, but these methods are not satisfying. Particle swarm optimization (PSO) has been developing rapidly and has been applied widely since it was introduced, as it is easily understood and realized. This paper through the improvement of the option modes of gBest and pBest of PSO algorithm, a similar particle swarm optimization algorithm (SPSOA) applied for permutation 0096-3003/$ -see front matter Ó (B. Jiao).