A genetic algorithm for permutation flowshop scheduling under practical make-to-order production system. Issue 1 (6th June 2016)

• Record Type: Journal Article
• Authors: Rahman, Humyun Fuad; Sarker, Ruhul; Essam, Daryl
• Is Part Of: AI EDAM. Volume 31:Issue 1(2017)
• Page Start: 87
• Page End: 103
• Publication Date: 2016-06-06
• Subjects: Flowshop Scheduling, -- Genetic Algorithm, -- Memetic Algorithm, -- Order Acceptance, -- Practical Make-to-Order System; Engineering design -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; 620.00420285
• DOI: 10.1017/S0890060416000196
• Languages: English
• ISSNs: 0890-0604

Abstract

Abstract: The aim of this work is to bridge the gap between the theory and actual practice of production scheduling by studying a problem from a real-life production environment. This paper considers a practical Sanitaryware production system as a number of make-to-order permutation flowshop problems. Due to the wide range of variation in its products, real-time arrival of customer orders, dynamic batch adjustments, and time for machine setup, Sanitaryware production system is complex and also time sensitive. In practice, many such companies run with suboptimal solutions. To tackle this problem, in this paper, a memetic algorithm based real-time approach has been proposed. Numerical experiments based on real data are also been presented in this paper.