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Mathematical Problems in Engineering
Volume 2017, Article ID 8696910, 12 pages
https://doi.org/10.1155/2017/8696910
Research Article

Optimizing Terminal Delivery of Perishable Products considering Customer Satisfaction

1Institute of Systems Engineering, Dalian University of Technology, No. 2, Linggong Road, Dalian 116024, China
2School of Business, Dalian University of Technology, No. 2, Dagong Road, Panjin 124221, China
3College of Economic and Management, Northwest A&F University, Yangling 712100, China
4Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong

Correspondence should be addressed to Xuping Wang; nc.ude.tuld@pxw

Received 28 July 2016; Revised 20 November 2016; Accepted 14 December 2016; Published 15 February 2017

Academic Editor: Rita Gamberini

Copyright © 2017 Xuping Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Freshness of products and timeliness of delivery are two critical factors which have impact on customer satisfaction in terminal delivery of perishable products. This paper investigates how to make a cost-saving vehicle scheduling for perishable products by maximizing customer satisfaction. Customer satisfaction is defined from the two aspects of freshness and time window. Then we develop a priority function based on customer satisfaction and use the hierarchical clustering method to identify customer service priority. Based on the priority, a multiobjective vehicle scheduling optimization model for perishable products is formulated to maximize customer satisfaction and minimize total delivery costs. To solve the proposed model, a priority-based genetic algorithm (PB-GA) is designed. Numerical experiments and sensitivity analysis are performed to show the validity and advantage of our approach. Results indicate that PB-GA can achieve better solutions than traditional genetic algorithm. The improvement of customer satisfaction is higher than the decrease rate of total costs within a certain shelf life range, which reveals that the proposed method is applicable to the terminal delivery of perishable products.