Abstract

In this paper, we compare different formulations of the multi-depot fleet size and mix vehicle routing problem (MDFSMVRP). This problem extends the multi-depot vehicle routing problem and the fleet size and mix vehicle routing problem, two logistics problems that have been extensively studied for many decades. This difficult vehicle routing problem combines complex assignment and routing decisions under the objective of minimizing fixed vehicle costs and variable routing costs. We first propose five distinct formulations to model the MDFSMVRP. We introduce a three-index formulation with an explicit vehicle index and a two-index formulation in which only vehicle types are identified. Other formulations are obtained by defining aggregated and disaggregated loading variables. The last formulation makes use of capacity-indexed variables. For each formulation, we summarize known and propose new valid inequalities, including symmetry breaking, lexicographic ordering, routing, and rounded capacity cuts. We then implement branch-and-cut and branch-and-bound algorithms for these formulations, and we fed them into a general purpose solver. We compare the bounds provided by the formulations on a commonly used set of instances in the MDFSMVRP literature, containing up to nine depots and 360 customers, and on newly generated instances. Our in-depth analysis of the five formulations shows which formulations tend to perform better on each type of instance. Moreover, our results have considerably improved available lower bounds on all instances and significantly improved quality of upper bounds that can be obtained by means of currently available methods.

Highlights

  • Distribution problems are central to the performance of many industries

  • We model and solve the multi-depot fleet size and mix vehicle routing problem (MDFSMVRP)

  • Vehicle symmetry breaking constraints and lexicographic ordering constraints no longer hold for this formulation because they require distinguishing between vehicle index and not vehicle types

Read more

Summary

Introduction

Distribution problems are central to the performance of many industries. The area of transportation has been widely studied, notably the vehicle routing problem (VRP) (Toth and Vigo 2014) which has attracted the interest of many researchers for more than 50 years (Laporte 2009) and is still among the most prominent and widely studied combinatorial optimization problems. We model and solve the multi-depot fleet size and mix vehicle routing problem (MDFSMVRP). This problem is a direct generalization of the classical VRP by considering multiple depots and different types of vehicles to serve a set of customers with known demands. The authors propose a multi-level composite heuristic based on integrating and modifying efficient heuristics designed for the single depot fleet size and mix vehicle routing problem (FSMVRP). Their method relies on switching to a more powerful and expensive neighborhood when moving to a superior level.

Problem description and mathematical formulations
Explicit formulation
Implicit vehicle index formulation
Compact formulation with loading variables
Compact formulation with disaggregated loading variables
Capacity-indexed formulation
Theoretical insights
Solution algorithms
Computational experiments
Implementation details
Description of the instances
Linear programming relaxation
Comparison of upper and lower bounds
Comparison against the best known solutions
Effect of valid inequalities
Findings
Conclusions
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call