Less is more: variable neighborhood search for integrated production and assembly in smart manufacturing
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Less is more: variable neighborhood search for integrated production and assembly in smart manufacturing Shaojun Lu1,3 · Jun Pei1,2,3 · Xinbao Liu1,3 · Xiaofei Qian1,3 · Nenad Mladenovic4,5 · Panos M. Pardalos2
© Springer Science+Business Media, LLC, part of Springer Nature 2019
Abstract This paper investigates an integrated production and assembly scheduling problem with the practical manufacturing features of serial batching and the effects of deteriorating and learning. The problem is divided into two stages. During the production stage, there are several semi-product manufacturers who first produce ordered product components in batches, and then these processed components are sent to an assembly manufacturer. During the assembly stage, the assembly manufacturer will further process them on multiple assembly machines, where the product components are assembled into final products. Through mathematical induction, we characterize the structures of the optimal decision rules for the scheduling problem during the production stage, and a scheme is developed to solve this scheduling problem optimally based on the structural properties. Some useful lemmas are proposed for the scheduling problem during the assembly stage, and a heuristic algorithm is developed to eliminate the inappropriate schedules and enhance the solution quality. We then prove that the investigated problem is NP-hard. Motivated by this complexity result, we present a less-is-more-approach-based variable neighborhood search heuristic to obtain the approximately optimal solution for the problem. The computational experiments indicate that our designed LIMA-VNS (less is more approach–variable neighborhood search) has an advantage over other metaheuristics in terms of converge speed, solution quality, and robustness, especially for large-scale problems. Keywords Variable neighborhood search · Less is more · Deteriorating effect · Learning effect · Serial-batching scheduling · Assembly
1 Introduction In recent years, smart manufacturing has been experiencing an explosive growth and has now become one of the
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Jun Pei [email protected] http://www.drpeijun.com Xinbao Liu [email protected]
1
School of Management, Hefei University of Technology, Hefei, China
2
Department of Industrial and Systems Engineering, Center for Applied Optimization, University of Florida, Gainesville, USA
3
Key Laboratory of Process Optimization and Intelligent Decision-Making of Ministry of Education, Hefei, China
4
Department of Industrial Engineering, Khalifa University, Abu Dhabi, UAE
5
Ural Federal University, Yekaterinburg, Russia
dominant factors for enterprises to compete in the fiercer global competition (Yang et al. 2018). The production of many products, e.g., mobile phones, aircraft, and automobiles, needs to be first processed by several semi-product manufacturers, and then it is completed by assembly manufacturers, as is shown in Fig. 1. It is of great significance to make smart decisions for semi-product manufacturers and assembly manufacturers in
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