TY - JOUR
T1 - Integrated Production Inventory Routing Planning for Intelligent Food Logistics Systems
AU - Li, Yantong
AU - Chu, Feng
AU - Feng, Chenpeng
AU - Chu, Chengbin
AU - Zhou, Meng Chu
N1 - Funding Information:
Manuscript received June 29, 2017; revised March 1, 2018; accepted May 6, 2018. Date of publication June 19, 2018; date of current version February 28, 2019. This work was supported in part by the National Natural Science Foundation of China under Grant 71571061, Grant 71431003, Grant 71601062, and Grant 71701049, and in part by the PHC Cai Yuanpei Program under Grant 34644SB. The Associate Editor for this paper was K. Boriboonsomsin. (Corresponding author: Chengbin Chu.) Y. Li is with IBISC, University of Evry, Université Paris-Saclay, 91025 Evry, France (e-mail: yantong.li@univ-evry.fr).
Publisher Copyright:
© 2000-2011 IEEE.
PY - 2019/3
Y1 - 2019/3
N2 - An intelligent logistics system is an important branch of intelligent transportation systems. It is a great challenge to develop efficient technologies and methodologies to improve its performance in meeting customer requirements while this is highly related to people's life quality. Its high efficiency can reduce food waste, improve food quality and safety, and enhance the competitiveness of food companies. In this paper, we investigate a new integrated planning problem for intelligent food logistics systems. Two objectives are considered: minimizing total production, inventory, and transportation cost and maximizing average food quality. For the problem, a bi-objective mixed integer linear programming model is formulated first. Then, a new method that combines an\epsilon-constraint-based two-phase iterative heuristic and a fuzzy logic method is developed to solve it. Computational results on a case study and on 185 randomly generated instances with up to 100 retailers and 12 periods show the effectiveness and efficiency of the proposed method.
AB - An intelligent logistics system is an important branch of intelligent transportation systems. It is a great challenge to develop efficient technologies and methodologies to improve its performance in meeting customer requirements while this is highly related to people's life quality. Its high efficiency can reduce food waste, improve food quality and safety, and enhance the competitiveness of food companies. In this paper, we investigate a new integrated planning problem for intelligent food logistics systems. Two objectives are considered: minimizing total production, inventory, and transportation cost and maximizing average food quality. For the problem, a bi-objective mixed integer linear programming model is formulated first. Then, a new method that combines an\epsilon-constraint-based two-phase iterative heuristic and a fuzzy logic method is developed to solve it. Computational results on a case study and on 185 randomly generated instances with up to 100 retailers and 12 periods show the effectiveness and efficiency of the proposed method.
KW - Bi-objective optimization
KW - food quality
KW - integrated planning
KW - intelligent food logistics system
KW - Ïμ -constraint-based two-phase iterative heuristic
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U2 - 10.1109/TITS.2018.2835145
DO - 10.1109/TITS.2018.2835145
M3 - Article
AN - SCOPUS:85059111355
SN - 1524-9050
VL - 20
SP - 867
EP - 878
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
IS - 3
M1 - 8388738
ER -