Optimizing Multi-Echelon Delivery Routes for Perishable Goods with Time Constraints

Manqiong Sun, Yang Xu, Feng Xiao (), Hao Ji, Bing Su and Fei Bu
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Manqiong Sun: School of Economics and Management, Xi’an Technological University, Xi’an 710021, China
Yang Xu: School of Economics and Management, Xi’an Technological University, Xi’an 710021, China
Feng Xiao: International Business School, Shaanxi Normal University, Xi’an 710119, China
Hao Ji: School of Economics and Management, Xi’an Technological University, Xi’an 710021, China
Bing Su: School of Economics and Management, Xi’an Technological University, Xi’an 710021, China
Fei Bu: School of Economics and Management, Xi’an Technological University, Xi’an 710021, China

Mathematics, 2024, vol. 12, issue 23, 1-24

Abstract: As the logistics industry modernizes, living standards improve, and consumption patterns shift, the demand for fresh food continues to grow, making cold chain logistics for perishable goods a critical component in ensuring food quality and safety. However, the presence of both soft and hard time windows among demand nodes can complicate the single-network distribution of perishable goods. In response to these challenges, this paper proposes an optimization model for multi-distribution center perishable goods delivery, considering both one-echelon and two-echelon network joint distributions. The model aims to minimize total costs, including transportation, fixed, refrigeration, goods damage, and penalty costs, while measuring customer satisfaction by the start time of service at each demand node. A two-stage heuristic algorithm is designed to solve the model. In the first stage, an initial solution is constructed using a greedy approach based on the principles of the k-medoids clustering algorithm, which considers both spatial and temporal distances. In the second stage, the initial routing solution is optimized using a linear programming approach from the Ortools solver combined with an Improved Adaptive Large Neighborhood Search (IALNS) algorithm. The effectiveness of the proposed model and algorithm is validated through a case study analysis. The results demonstrate that the initial solutions obtained through the k-medoids clustering algorithm based on spatio-temporal distance improved the overall cost optimization by 1.85% and 4.74% compared to the other two algorithms. Among the three two-stage heuristic algorithms, the Ortools-IALNS proposed here showed enhancements in the overall cost optimization over the IALNS, with improvements of 3.24%, 1.12%, and 0.41%, respectively. The two-stage heuristic algorithm designed in this study also converged faster than the other two heuristic algorithms, with overall optimization improvements of 1.55% and 1.28%, further validating the superior performance of the proposed heuristic algorithm.

Keywords: joint distribution networks; time windows; perishable goods; two-stage heuristic algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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