Discrete Self-Organising Migrating Algorithm for Flow Shop Scheduling With No Wait Makespan

Abstract

This paper introduces a novel discrete Self Organising Migrating Algorithm for the task of flowshop schedul¬ing with no-wait makespan. The new algorithm is tested with the small and medium Taillard benchmark problems and the obtained results are competitive with the best performing heuristics in literature.

Emerging Biology-based CI Algorithms

“…There are several variants and applications can be found in the literature (Nolle et al 2005;Senkerik et al 2010;Zelinka et al 2009;Davendra and Zelinka 2009;Davendra et al 2013). Two evolutionary operators (i.e., mutation and crossover) are employed in SOMA to maintain the perturbation of individuals and movement of an element.…”

“…The population is generated via Eqs. 17.140 and 17.141, respectively (Davendra et al 2013): where b is the number of individuals, x t i;j represents the element in each individual, and N is the dimension of the problem.…”

“…Two evolutionary operators (i.e., mutation and crossover) are employed in SOMA to maintain the perturbation of individuals and movement of an element. The main steps of SOMA are outlined as follows (Davendra et al 2013):…”

A Survey of Recent Research on Optimization Models and Algorithms for Operations Management from the Process View

“…(1) Economic criteria observed are as follows: makespan, that is, completion time of the last job (as discussed by Davendra et al [5]; Engin and Günaydin [6]; Zhang and Chen [7]); completion, including total completion time (as discussed by Framinan et al [8]; Li et al [9]; Nikjo and Rezaeian [10]; Shahvari et al [11]; Sabouni and Logendran [12]) and total weighted completion time (as discussed by Bozorgirad and Logendran [13]; Correa et al [14]); flow time, or named production time in some publications (as discussed by Sabouni and Logendran [12]; Ying et al [15]; Lu and Logendran [16]); setup cost, including intracell movement time [17], energy cost [18,19], and other costs that may result in augmentation of the operation cost, such as tardiness penalty (as discussed by Le and Pang [18]). …”

An effective co-evolutionary quantum genetic algorithm for the no-wait flow shop scheduling problem

“…12 In recent years, the particle swarm optimization was introduced for the problem and yielded outstanding results. 13,14 Besides, the problem was also solved by an improved iterated greedy algorithm, 15 a discrete differential evolution (DDE), 16 and a novel discrete selforganizing migrating algorithm 17 for the makespan minimization. In 2015, Ding et al 18 proposed a tabumechanism improved iterated greedy algorithm for the problem, which is a modification of the iterated greedy algorithm using a tabu-based reconstruction strategy.…”