A Heuristic Algorithm for solving a large-scale real-world territory design problem

Lin Zhou, Lu Zhen, Roberto Baldacci, Marco Boschetti, Ying Dai and Andrew Lim

Omega, 2021, vol. 103, issue C

Abstract: In this work, we present and evaluate heuristic techniques for a real-world territory design problem of a major dairy company which produces and distributes perishable products. The problem calls for grouping customers into geographic districts, with the objective of minimising the total operational cost, computed as a function of the fixed costs of the districts and the routing costs. Two inter-connected decision levels have to be tackled: partitioning customers into districts and routing vehicles according to complex operational constraints. To solve the problem, a hybrid multi-population genetic algorithm is designed, enhanced with several evolution and search techniques. The proposed design is extensively tested on instances derived from the literature and on real-world large-scale instances, involving more than 1000 customers. The results show the effectiveness of the different components of the algorithm and the feedback from the company’s planners confirms that it produces high-quality, operational solutions. Additionally, we explore some managerial findings with respect to the adoption of alternative objectives and service requirements.

Keywords: Territory design; Periodic vehicle routing; Hybrid genetic algorithm; Dairy industry; Real-world instances (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (6)

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DOI: 10.1016/j.omega.2021.102442

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