Flow shop scheduling with no-wait flexible lot streaming using adaptive genetic algorithm

Abstract

In this paper, we propose a flow shop scheduling problem with no-wait flexible lot streaming. The problem involves the splitting of order quantities of different products into sublots and the consideration of alternative machines with different processing times. Sublots of a particular product are not allowed to intermingle, that is sublots of different products must be no-preemptive. The objective of the problem is the minimization of makespan. An adaptive genetic algorithm is proposed which is composed of three main steps: first step is a position-based crossover of products and four kinds of local search-based mutations to generate better generations. Second step is an iterative hill-climbing to improve the current generation. The last step is the adaptive regulation of crossover and mutation rates. Experimental results are presented for various sizes of problems to describe the performance of the proposed four local search-based mutations in adaptive algorithm