An Improved Genetic Algorithm for the Optimal Distribution of Fresh Products under Uncertain Demand

Hao Zhang, Yan Cui, Hepu Deng, Shuxian Cui and Huijia Mu
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Hao Zhang: School of Business, Beijing Technology and Business University, Beijing 100048, China
Yan Cui: School of Business, Beijing Technology and Business University, Beijing 100048, China
Hepu Deng: School of Accounting, Information Systems and Supply Chain, RMIT University, Melbourne, VIC 3149, Australia
Shuxian Cui: School of Business, Beijing Technology and Business University, Beijing 100048, China
Huijia Mu: School of Economics and Management, University of Science and Technology Beijing, Beijing 100083, China

Mathematics, 2021, vol. 9, issue 18, 1-18

Abstract: There are increasing challenges for optimally distributing fresh products while adequately considering the uncertain demand of customers and maintaining the freshness of products. Taking the nature of fresh products and the characteristics of urban logistics systems into consideration, this paper proposes an improved genetic algorithm for effectively solving this problem in a computationally efficient manner. Such an algorithm can adequately account for the uncertain demand of customers to select the optimal distribution route to ensure the freshness of the product while minimizing the total distribution cost. Iterative optimization procedures are utilized for determining the optimal route by reducing the complexity of the computation in the search for an optimal solution. An illustrative example is presented that shows the improved algorithm is more effective with respect to the distribution cost, the distribution efficiency, and the distribution system’s reliability in optimally distributing fresh products.

Keywords: fresh product distribution; optimization; uncertain demand; genetic algorithms; decision making (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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