Article: A scatter search algorithm for scheduling optimisation of job shop problems Journal: International Journal of Product Development (IJPD) 2010 Vol.10 No.1/2/3 pp.259 - 272 Abstract: The Job Shop Scheduling (JSS) problem is one of the most difficult NP-hard combinatorial optimisation problems. it is very difficult to solve by conventional optimisation techniques. There has been increasing interest in applying metaheuristic methods to solve such hard optimisation problems. In this work, a novel metaheuristic approach called Scatter Search (SS) is applied for the JSS problem, an NP-hard sequencing problem. The approach is used to find a schedule to minimise the makespan (C<SUB align="right">max</SUB>), that is, the time required to complete all jobs. SS contrasts with other evolutionary procedures by providing a wide exploration of the search space through intensification and diversification. In addition, it has a unifying principle for joining solutions. It exploits the adaptive memory principle to avoid generating or incorporating duplicate solutions at various stages of the problem. The main aim of this research is to explore the potential of SS for scheduling JSS problems. SS provides unifying principles for joining solutions based on generalised path constructions and by utilising strategic designs where other approaches resort to randomisation. In this paper, 11 benchmark problems are taken from the literature. The results available for the various existing metaheuristic methods for the selected benchmark problems are compared with results obtained by the SS method. The proposed framework achieves better results for all 11 problems. Inderscience Publishers - linking academia, business and industry through research

Title: A scatter search algorithm for scheduling optimisation of job shop problems

Authors: M. Saravanan, A. Noorul Haq

Addresses: Department of Mechanical Engineering, RVS College of Engineering & Technology, Dindigul, Tamil Nadu 624 005, India. ' Department of Production Engineering, National Institute of Technology (Deemed University), Tiruchirappalli, Tamil Nadu 620 015, India

Abstract: The Job Shop Scheduling (JSS) problem is one of the most difficult NP-hard combinatorial optimisation problems. it is very difficult to solve by conventional optimisation techniques. There has been increasing interest in applying metaheuristic methods to solve such hard optimisation problems. In this work, a novel metaheuristic approach called Scatter Search (SS) is applied for the JSS problem, an NP-hard sequencing problem. The approach is used to find a schedule to minimise the makespan (C_max), that is, the time required to complete all jobs. SS contrasts with other evolutionary procedures by providing a wide exploration of the search space through intensification and diversification. In addition, it has a unifying principle for joining solutions. It exploits the adaptive memory principle to avoid generating or incorporating duplicate solutions at various stages of the problem. The main aim of this research is to explore the potential of SS for scheduling JSS problems. SS provides unifying principles for joining solutions based on generalised path constructions and by utilising strategic designs where other approaches resort to randomisation. In this paper, 11 benchmark problems are taken from the literature. The results available for the various existing metaheuristic methods for the selected benchmark problems are compared with results obtained by the SS method. The proposed framework achieves better results for all 11 problems.

Keywords: job shop scheduling; JSS; makespan; metaheuristics; scatter search algorithm; RefSet; heuristics; NP-hard combinatorial optimisation; nondeterministic polynomial time; adaptive memory principle; strategic design; production management; advanced production systems; production design.

DOI: 10.1504/IJPD.2010.029996

International Journal of Product Development, 2010 Vol.10 No.1/2/3, pp.259 - 272

Published online: 03 Dec 2009 *

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Title: A scatter search algorithm for scheduling optimisation of job shop problems

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