Abstract
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.
Highlights
Scheduling is an integral part of advanced manufacturing systems
We proposed a hybridization of Particle Swarm Optimization (PSO) with Simulated Annealing (SA) for flow shop scheduling with a no-wait constraint
The Proposed Hybrid PSO (PHPSO) algorithm applies an evolutionary search guided by the mechanism of PSO, and it applies a local search guided by the NEH-based initial population and the mechanism of SA
Summary
Scheduling is an integral part of advanced manufacturing systems. Production scheduling is the arrangement of jobs to be processed on available machines under some constraints. A No-Wait Flow Shop Scheduling Problem (NWFSSP). Allahverdi [8] reviewed scheduling problems with the no-wait constraint with respect to different shop environments, performance measures, setup types, and optimal scheduling criteria. The solutions to solve such NP-hard problems consist of an approximate algorithm which uses constructive heuristics, local search methods, and metaheuristics. Earlier researchers [14,15,16,17,18] have developed efficient constructive heuristic algorithms for TFT minimization; these attempts are not useful for identifying near optimal solutions for larger-sized problems, as these developed algorithms usually get trapped in local optima for large-problem sizes [15]. This paper attempts to use a metaheuristic technique, viz. Particle Swarm Optimization (PSO) algorithm, and its hybridization with Simulated.
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