A location-inventory supply chain network model using two heuristic algorithms for perishable products with fuzzy constraints

Zhuo Dai, Faisal Aqlan, Xiaoting Zheng, Kuo Gao

Research output: Contribution to journalArticle

16 Scopus citations


Supply chain network is very important to the development of industries. This paper integrates a location-inventory problem into a supply chain network and develops an optimization model for perishable products with fuzzy capacity and carbon emissions constraints. This model is formulated a mixed integer nonlinear programming model. In order to solve this model, hybrid genetic algorithm (HGA) and hybrid harmony search (HHS) are put forward to minimize the total costs. Instances under different situations are calculated using these two algorithms and Lindo (optimization solver). The impacts of some factors such as the number of facilities, intact rates, and demand on the total costs are investigated. The results of numerical experiments demonstrate that the proposed algorithms can effectively deal with problems under different conditions and these two algorithms have their own advantages. Specially, the quality of HHS's solution is higher than that of HGA's solution, whereas HGA is faster than HHS.

Original languageEnglish (US)
Pages (from-to)338-352
Number of pages15
JournalComputers and Industrial Engineering
StatePublished - May 2018


All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Engineering(all)

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