Journal
APPLIED SOFT COMPUTING
Volume 66, Issue -, Pages 311-318Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.asoc.2018.02.032
Keywords
Routing; Production planning; Guided variable neighborhood descent; Skewed general variable neighborhood search
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Funding
- NSFC [71571092]
- Jiangsu Overseas Research & Training Program for University Prominent Young & Middle-aged Teachers and Presidents
- General Research Project for Humanities and Social Sciences from Chinese Ministry of Education [11YJCZH137]
- Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)
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In a production routing problem (PRP), the aim is to integrate planning of production, inventory, delivery schedules and vehicle routes in supply chains. In this work, we present a variable neighborhood search metaheuristic for the PRP. In the metaheuristic, variables of delivery and routing decisions are handled by local search based on skewed general variable neighborhood search and guided variable neighborhood descent, respectively. Binary variables for production setups and continuous variables for production quantities and the depot inventory are determined in a production-inventory subproblem with a mixed integer programming solver. The computational results show that the proposed heuristic is competitive with the state-of-the-art algorithms on the benchmark instances. Furthermore, the proposed heuristic outperforms existing heuristics on the standard and high transportation cost benchmark instances in Archetti et al. [1] and large size benchmark instances in Boudia et al. [2] within comparable computing times. (C) 2018 Elsevier B.V. All rights reserved.
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