Alternative Heuristic Algorithm for Flow Shop Scheduling Problem

Abstract

In this chapter an alternative heuristic algorithm is proposed that is assumed for a deterministic flow shop scheduling problem. The algorithm is addressed to an m-machine and n-job permutation flow shop scheduling problem for the objective of minimizing the make-span when idle time is allowed on machines. This chapter is composed in a way that the different scheduling approaches to solve flow shop scheduling problems are benchmarked. In order to compare the proposed algorithm against the benchmarked, selected heuristic techniques and genetic algorithm have been used. In realistic situation, the proposed algorithm can be used as it is without any modification and come out with acceptable results.

Hybrid GA-based metaheuristics for production planning and scheduling optimization in intelligent flow-shop manufacturing systems

“…Palmer adopted his idea and proposed the slope index to be utilized for the m-machine n-job permutation flow shop sequencing problem in [8]. A simple heuristic extension of Johnson's rule to m-machine flow shop problem has been introduced in [16]. Another method to obtain an optimized schedule is presented in [17].…”

Elaboration of portfolio of orders for machine enterprises to reduce the risk of missed profit

“…Огляд сучасних робіт з управління діяльність ма шинобудівних підприємств показує, що автори [1][2][3][4][5][6][7] з дев'яностих років визначають проблеми виконання замовлень як процес, коли замовлення вже прийняте.…”

“…Аналогічні проблеми розглядаються в роботах [6,7]. Але нічого не говориться про проблеми вибору замовлень.…”

A shuffled complex evolution algorithm with opposition-based learning for a permutation flow shop scheduling problem

“…These types of algorithms can be broadly classified into three categories: constructive heuristic algorithms, improved heuristic algorithms and hybrid heuristic algorithms (Gupta et al 2002;Zobolas, Tarantilis, and Ioannou 2009;. The constructive heuristics generally began with simple heuristics and then built a feasible schedule from scratch (Framinan and Leisten 2003;Laha and Chakravorty 2011;Modrak, Semanco, and Knuth 2012;Pan and Wang 2012) and could typically obtain an almost optimal solution in a reasonable computational time; however, the quality of the solutions was not satisfactory. The NEH heuristics (Nawaz, Enscore, and Ham 1983) was one of the most successful of the constructive heuristics and could provide comparable results to those of the metaheuristics.…”