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Less is more: variable neighborhood search for integrated production and assembly in smart manufacturing

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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.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 71871080, 71601065, 71231004, 71690235, 71501058, 71601060, 71922009), and Innovative Research Groups of the National Natural Science Foundation of China (71521001), the Humanities and Social Sciences Foundation of the Chinese Ministry of Education (No. 15YJC630097), and Base of Introducing Talents of Discipline to Universities for Optimization and Decision-making in the Manufacturing Process of Complex Product (111 project).

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Authors and Affiliations

  1. School of Management, Hefei University of Technology, Hefei, China

    Shaojun Lu, Jun Pei, Xinbao Liu & Xiaofei Qian

  2. Department of Industrial and Systems Engineering, Center for Applied Optimization, University of Florida, Gainesville, USA

    Jun Pei & Panos M. Pardalos

  3. Key Laboratory of Process Optimization and Intelligent Decision-Making of Ministry of Education, Hefei, China

    Shaojun Lu, Jun Pei, Xinbao Liu & Xiaofei Qian

  4. Department of Industrial Engineering, Khalifa University, Abu Dhabi, UAE

    Nenad Mladenovic

  5. Ural Federal University, Yekaterinburg, Russia

    Nenad Mladenovic

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  1. Shaojun Lu
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  2. Jun Pei
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  5. Nenad Mladenovic
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  6. Panos M. Pardalos
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Correspondence to Jun Pei or Xinbao Liu.

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Lu, S., Pei, J., Liu, X. et al. Less is more: variable neighborhood search for integrated production and assembly in smart manufacturing. J Sched 23, 649–664 (2020). https://doi.org/10.1007/s10951-019-00619-5

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