Effective heuristics and metaheuristics to minimise total tardiness for the distributed permutation flowshop scheduling problem

ABSTRACT

During recent years, the distributed permutation flowshop scheduling problem (DPFSP) has become a very active area of research. However, minimising total tardiness in DPFSP, a very essential and relevant objective for today's customer-orientated market, has not been studied much. In this paper, we address the DPFSP with the total tardiness criterion. We present a mixed-integer linear programming model, two heuristics, hybrid discrete Harris hawks optimisation and an enhanced variant of iterated greedy algorithm to solve the considered problem. Problem-specific knowledge is explored and effective technologies, such as path relinking and random sub-sequence/single-point local search, are employed to improve the presented algorithms. The operators and parameters of the algorithms are analysed and calibrated using the design of experiments. To evaluate the performance, the well-known benchmark problem set of Naderi and Ruiz for DPFSP is extended with due dates. We compare the presented algorithms against seven other well-known meta-heuristics from the literature. Statistically sound results demonstrate the effectiveness of the presented algorithms for the considered problem.

KEYWORDS:

• Distributed scheduling
• total tardiness
• mixed integer linear programming
• Harris hawks optimisation algorithm
• iterated greedy algorithm
• path relinking

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Ankit Khare

Ankit Khare is a Ph.D. student in the Department of Mechanical Engineering, PDPM, Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, India. He received his B.E. degree in Production and Industrial Engineering from Jabalpur Engineering College, India, in 2012 and M.Tech degree in Computer Aided Manufacturing from Motilal Nehru National Institute of Technology, Prayagraj, India, in 2015. His current research interest includes computational intelligence with applications in scheduling, transportation, and manufacturing domains.

Sunil Agrawal

Sunil Agrawal is an associate professor at Indian Institute of Information Technology, Design and Manufacturing Jabalpur, India. He received his PhD in Industrial and Management Engineering from Indian Institute of Technology Kanpur, India in 2008. His research interests are in the areas of Supply Chain Management, Inventory Management, and Manufacturing Systems. His research works are Bullwhip Effect in Supply Chains, Inventory Order Crossover, Agriculture Supply Chain Management, Flow Shop Scheduling, and Two-sided Assembly Line Balancing Problems. Currently he is working in the domain of use of cyber physical systems in managing Agriculture Supply Chains. His research results were disseminated in 15 international journals and more than 35 national and international conferences.