A Particle Swarm Optimization Approach for the Earliness-Tardiness No-Wait Flowshop Scheduling Problem (original) (raw)
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Weighted earliness and tardiness Mixed integer linear programming No-wait flowshop scheduling Metaheuristic algorithms Sequence-dependent setup times Timing algorithm a b s t r a c t In this research, the no-wait flowshop sequence-dependent setup time scheduling problem with minimization of weighted earliness and tardiness penalties as the criterion, typically classified as F m jnwt; S ijk j P w 0 j E j þ w 00 j T j , is investigated. A mixed integer linear programming model for the research problem is proposed. As the problem is shown to be strongly NP-hard, several metaheuristic algorithms based on tabu search (TS) and particle swarm optimization (PSO) algorithms are developed to heuristically solve the problem. A timing algorithm is generated to find the optimal schedule and calculate the objective function value of a given sequence. In order to compare the performance of the proposed algorithms, random test problems are generated and solved by all metaheuristic algorithms. Computational results show that the PSO algorithm has better performance than TS algorithm especially for the large sized problems.
Particle Swarm Optimization Algorithms for Two-stage Hybrid Flowshop Scheduling Problem with No-wait
Universal Journal of Electrical and Electronic Engineering
Hybrid flowshop scheduling problems have attracted much attention owing to their wide applications in a variety of real-world problems. In some industries, products cannot be allowed to wait between any consecutive productions stages, which illustrates the importance of studying the no-wait constraint. One of the reasons for such a constraint is that, for some products, the waiting time could cause permanent damage. However, the no-wait constraint was neglected in many prior studies, which in some production environments may not be allowed. Minimizing the total tardiness plays a key role in making scheduling decisions to meet customers' due dates. In this study, we solve the no-wait two-stage hybrid flowshop scheduling problem with total tardiness minimization as an optimizing criterion. We formulated the problem mathematically and proposed two discrete versions of the particle swarm optimization (PSO) to solve it. Moreover, three discrete versions of PSO are adopted from previous studies and used as benchmarks to test the effectiveness of the two proposed algorithms. Compared to the benchmark algorithms, the results showed that the two newly proposed algorithms were effective and performed better than the benchmark algorithms in terms of the average relative error. The current study represents one of the few attempts to investigate the considered problem with total tardiness minimization, as well as introducing new and effective discrete versions of the PSO algorithms to solve the problem under investigation.
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.
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).
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.
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.
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...
Scheduling (FSDGS) problem, with minimization of total flow time as the criterion (F m |fmls, S plk , prmu| ∑ Cj), is proposed in this research. An encoding scheme based on Ranked Order Value (ROV) is developed, which converts the continuous position value of particles in PSO to job and group permutations. A neighborhood search strategy, called Individual Enhancement (IE), is fused to enhance the search and to balance the exploration and exploitation. The performance of the algorithm is compared with the best available meta-heuristic algorithm in literature, i.e. the Ant Colony Optimization (ACO) algorithm, based on available test problems. The results show that the proposed algorithm has a superior performance to the ACO algorithm.
Scheduling (FSDGS) problem, with minimization of total flow time as the criterion (F m |fmls, S plk , prmu| ∑ Cj), is proposed in this research. An encoding scheme based on Ranked Order Value (ROV) is developed, which converts the continuous position value of particles in PSO to job and group permutations. A neighborhood search strategy, called Individual Enhancement (IE), is fused to enhance the search and to balance the exploration and exploitation. The performance of the algorithm is compared with the best available meta-heuristic algorithm in literature, i.e. the Ant Colony Optimization (ACO) algorithm, based on available test problems. The results show that the proposed algorithm has a superior performance to the ACO algorithm.