Heuristic based genetic algorithms for the re-entrant total completion time flowshop scheduling with learning consideration

Abstract

Recently, both the learning effect scheduling and re-entrant scheduling have received more attention separately in research community. However, the learning effect concept has not been introduced into re-entrant scheduling in the environment setting. To fill this research gap, we investigate re-entrant permutation flowshop scheduling with a position-based learning effect to minimize the total completion time. Because the same problem without learning or re-entrant has been proved NP-hard, we thus develop some heuristics and a genetic algorithm (GA) to search for approximate solutions. To solve this problem, we first adopt four existed heuristics for the problem; we then apply the same four methods combined with three local searches to solve the proposed problem; in the last stage we develop a heuristic-based genetic algorithm seeded with four good different initials obtained from the second stage for finding a good quality of solutions. Finally, we conduct experimental tests to evaluate the behaviours of all the proposed algorithms when the number of re-entrant times or machine number or learning effect or job size changes.

Population-based Tabu search with evolutionary strategies for permutation flow shop scheduling problems under effects of position-dependent learning and linear deterioration

“…Shiau et al 2015proposed a branch-and-bound algorithm and several GA algorithms in order to obtain feasible solutions for a two-agent scheduling problem in a two-machine permutation flow shop with learning effects. Xu et al (2016) investigated re-entrant permutation flow shop scheduling with a position-based learning effect to minimize the total completion time. They developed some heuristics and a GA to search for approximate solutions.…”

Minimization of total energy consumption in an m-machine flow shop with an exponential time-dependent learning effect

“…They provided a polynomial-time scheduling rule for the optimization of each single factory and proposed an efficient BA-VNS algorithm to solve the complex coordinate problem. Wu and Lee (2009), Rudek (2011), Kuo et al (2012), Sun et al (2013), Wang et al (2013), Xu et al (2016), and Gao et al (2017) conducted recent studies on FSSLE.…”

A new mathematical formulation and a hybrid evolutionary algorithm for re-entrant flow-shop problem with release date

“…The authors have modeled the problem to minimize makespan and only difference between their works is applied in the solution method. Besides, there are other studies [23][24][25]20] in whose, the objectives such as total completion time, total weighted completion time, total tardiness, and total weighted tardiness are minimized to achieve an optimized schedule. As it is considered, a simple model of RFSS is tackled by using various algorithms to minimize different single objective.…”

“…Although, there are many researches employing exact methods such as branch and bound algorithm to tackle the RFSS [3,18,5,23,29,4] however, solving these problems have been attempted using evolutionary, and intelligence algorithms in order to reduce the computational time of algorithms [36,37]. For instance, genetic and hybrid evolutionary algorithms [26,19,16,31,21,24,32,25,38], hybrid tabu search with evolutionary algorithm [39], particle swarm optimization and hybrid with genetic [40], hybrid simulated annealing [17,13] and hybrid ant colony optimization [27] have been employed in the literature for solving RFSS or MORFSS problems. As it is obvious in the literature, GA and hybrid of this algorithm with other approaches have been applied more than other meta-heuristic algorithms to solve the problem.…”