An adaptive learning approach for no-wait flowshop scheduling problems to minimize make-span

Orhan Engin,Cengiz Gunaydin

doi:10.2991/ijcis.2011.4.4.11

Abstract

An adaptive learning approach for no-wait flowshop scheduling problems to minimize make-span

Highlights

In the permutation flowshop, n different jobs have to be processed on m machines
Aldowaisan and Allahverdi[2] presented two new heuristics that are based on simulated annealing and genetic algorithm techniques for no-wait flowshops to minimize makespan by incorporating a modified Nawaz-Enscore-Ham (NEH) heuristic, which were shown to outperform those of Gangadharan and Rajendran[11] and Rajendran[12]
The aim of this paper is to suggest an adaptive learning approach for No-wait flowshop scheduling problem (NW-FSSP) with makespan criteria

Summary

Introduction

N different jobs have to be processed on m machines. Each job has one operation on each machine and all jobs have the same ordering sequencing on each machine. Aldowaisan[14] developed a new heuristic method for two-machine no-wait flowshop problem with separate setup times from processing times and sequence independent. Aldowaisan and Allahverdi[2] presented two new heuristics that are based on simulated annealing and genetic algorithm techniques for no-wait flowshops to minimize makespan by incorporating a modified Nawaz-Enscore-Ham (NEH) heuristic (see Nawaz et al.15), which were shown to outperform those of Gangadharan and Rajendran[11] and Rajendran[12]. Aldowaisan and Allahverdi[6] presented several new heuristics for the m-machine nowait flowshop with total completion time as the criterion. TavakkoliMoghaddam et al.[21] proposed an immune algorithm for a multi-objective NW-FSSP by minimizing the weighted mean completion time and weighted mean tardiness simultaneously.

Adaptive Learning Approach

Genetic Heuristic Approach

Computational Results

Conclusions