Abstract

This paper presents multiple readings to solve a vehicle routing problem with pickup and delivery (VRPPD) based on a real-life case study. Compared to theoretical problems, real-life ones are more difficult to address due to their richness and complexity. To handle multiple points of view in modeling our problem, we developed three different Mixed Integer Linear Programming (MILP) models, where each model covers particular constraints. The suggested models are designed for a mega poultry company in Tunisia, called CHAHIA. Our mission was to develop a prototype for CHAHIA that helps decision-makers find the best path for simultaneously delivering the company’s products and collecting the empty boxes. Based on data provided by CHAHIA, we conducted computational experiments, which have shown interesting and promising results.

Highlights

  • Transportation studies can be considered as an intersection between several domains, methods, and techniques to propose, solve, and develop solutions for real-life problems.Artificial intelligence [1,2,3,4] and optimization [5,6,7] stand behind the majority of developed solutions

  • This paper develops three different Mixed Integer Linear Programming models (MILP)

  • Three different vehicle routing problem with pickup and delivery (VRPPD) models have been developed based on Mixed Integer Linear Programming approaches

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Summary

Introduction

Transportation studies can be considered as an intersection between several domains, methods, and techniques to propose, solve, and develop solutions for real-life problems. Artificial intelligence [1,2,3,4] and optimization [5,6,7] stand behind the majority of developed solutions. Among transportation and mobility problems, we recall the Vehicle Routing. VRP is a well-known problem studied in Operational Research and Computational and Decision sciences. It consists of routing products from depots to customers by drawing adequate distribution plans. The routing plans, called circuits or tours, need to respect multiple constraints, such as vehicle loading capacities and customer demands

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