The multi-depot open location routing problem with a heterogeneous fixed fleet

Abstract

This paper introduces the multi-depot open location routing problem (MD-OLRP) with a heterogeneous fixed fleet. The problem is inspired by the collection problem of a company which collects raw materials from different suppliers coordinating several carriers. Each carrier has a heterogeneous fixed fleet. Moreover, there is a fixed cost for contracting each vehicle and a variable cost associated with the distance traveled. The empty haul return to the vehicles depot is not considered in the cost. The raw materials collected are delivered to a single delivery point. The problem is modeled as a Mixed Integer Linear Program (MILP) that minimizes the total cost, selecting the carriers to be contracted, the vehicles to be used from each contracted carrier and the collection routes. For small instances, the model can be solved to optimality. However, approximate procedures are necessary to handle larger instances. In this sense, in the present work we propose an intelligent metaheuristic which incorporates problem specific knowledge to solve it. The computational results show that the solution method is computationally efficient and provides high quality solutions. In particular, the new solution obtained for the case of study generates savings of 30.86% to the company.The main contributions of the paper are the new problem statement that was not found in the literature, its association to the real problem of a company and the intelligent metaheuristic proposed to solve it. Additional experimentation used the model proposed to solve a simpler problem obtaining new best solutions compared to those reported in the recent literature.

Metaheuristic Algorithms Based on Compromise Programming for Multi-Objective Urban Shipment Problem

“…Many researchers designed heuristic, metaheuristic, and combinations to the VRP and its variant. Samuel Nucamendi-Guillén (2021) [16] developed a metaheuristic procedure to find a solution by improving the initial solution using local search algorithms. Zhiwei Liu (2017) [28] proposed a method that combines Tabu with mem-brane computing to find the solution for VRPTW.…”

Designing a locating-routing three-echelon supply chain network under uncertainty

“…The heterogeneous fleet of vehicles, which means that rates of vehicle loading change, is one of the restrictions introduced to LRPs (Nucamendi-Guillén et al , 2021). Vehicle velocities in various LRPs fluctuate on various days and are not always identical (Chang, 2005).…”

A hybrid approach to multi-depot multiple traveling salesman problem based on firefly algorithm and ant colony optimization

“…where 𝜌 represents the evaporation of trail, and ∆𝜏 𝑖𝑗 𝑘 is the addition of pheromones produced by ant-k on the edge (𝑖, 𝑗) is calculated by (7),…”

“…Unlike TSP which has been extensively researched recently years, the MmTSP is not intensively studied. In fact, many applications which are related to routing problems with multi-depot can be modeled as MmTSP, such as the logistic distribution problem [3], vehicle routing problem [4], vehicle scheduling problem [5], split-delivery vehicle routing problem [6], and the location routing problem [7], [8]. Since TSP is NP-hard and the MmTSP includes the TSP as a particular case, so it has a higher complexity than TSP [9].…”