Abstract
This paper introduces, models, and solves a rich vehicle routing problem (VRP) motivated by the case study of replenishment of automated teller machines (ATMs) in Turkey. In this practical problem, commodities can be taken from the depot, as well as from the branches to efficiently manage the inventory shortages at ATMs. This rich VRP variant concerns with the joint multiple depots, pickup and delivery, multi-trip, and homogeneous fixed vehicle fleet. We first mathematically formulate the problem as a mixed-integer linear programming model. We then apply a Geographic Information System (GIS)-based solution method, which uses a tabu search heuristic optimization method, to a real dataset of one of the major bank. Our numerical results show that we are able to obtain solutions within reasonable solution time for this new and challenging practical problem. The paper presents computational and managerial results by analyzing the trade-offs between various constraints.
Highlights
In logistics operations, fulfilling consumer demands for diverse and premium products is an important challenge [1]
The classical vehicle routing problem (VRP) aims to determine an optimal routing plan for a fleet of homogeneous vehicles to serve a set of customers, such that each vehicle route starts and ends at the depot, each customer is visited once by one vehicle, and some side constraints are satisfied
Lahyani et al [4] presented a comprehensive and relevant taxonomy for the literature devoted to Rich VRPs
Summary
In logistics operations, fulfilling consumer demands for diverse and premium products is an important challenge [1]. Contardo and Martinelli [11] developed a new exact algorithm for the multiple depots VRP under capacity and route length constraints. For further details about the multiple depots VRP and its variants, we refer the reader to the review paper of Montoya et al [12]. Many exact and heuristic methods are developed for this problem variant which is usually referred to as the pickup-and-delivery VRP. The authors used genetic algorithm for the location part, and tabu search of GIS-based solution method for the VRP part. Krichen et al [27] studied the VRP with loading and distance constraints and used a GIS solution method to solve the problem. We consider a multi-depot, multi-trip, pick-up and delivery with homogenous vehicle fleet We first define this new problem and presented a mathematical formulation.
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