Location‐routing problem: a classification of recent research

Abstract

The location-routing problem (LRP) and its variants have been studied extensively over the past few years. The number of articles has grown more than ever, since the last surveys. Therefore, this study aims to perform a review of recent LRP research from 222 academic literature published from 2014 to 2019. The analysis of publication intensity, problem characteristics, solution methods, and application areas was executed to draw the state-of-the-art of LRP research. As the main contributions to this study, a new taxonomy was proposed to capture some recently emerging issues in LRP and some suggestions for future research were listed and discussed.

Unbiased decision making in location-routing problems with uncertain customer demands

“…To date, the metaheuristic algorithms represent the most popular option to solve an LRP model. Most studies examined by Mara et al (2021) (i.e. 67.12%) are based on metaheuristic schemes.…”

“…67.12%) are based on metaheuristic schemes. In particular, Mara et al (2021) specify that the variants of simulated annealing and genetic algorithm are favoured for single-objective LRP, while non-dominated sorting genetic algorithm II and multi-objective particle swarm optimisation are the preferred paradigms for multi-objective LRP. An interesting comparison among algorithms based on different paradigms is proposed by Lopes et al (2013) with respect to three widely used sets of benchmark instances; for these algorithms the authors report the average computing time and the average percentage gap between the obtained results and the bestknown lower bound.…”

“…Specifically, we refer to a logistics problem known as location-routing problem (LRP) and highlight the importance of considering uncertainty adequately. The LRP aims at determining the best facility locations among a set of potential facility sites to minimise a total logistics cost which also includes the transport cost paid to serve the customers from the activated facilities (Mara et al 2021). This problem arises in case of less-than-full-load shipments, when the same vehicle can serve multiple customers; as a result, the transport cost depends on the order in which the customers to be served are visited and, consequently, the optimal facility locations depend on the vehicle routes.…”

A variable neighborhood search simheuristic algorithm for reliability optimization of smart grids under uncertainty

“…Simheuristics (Juan et al., 2015; Chica et al., 2020), as a simulation‐optimization approach, combine simulation with metaheuristics to solve stochastic combinatorial optimization problems. Application areas of simheuristics include transportation and logistics (Juan et al., 2014; Reyes‐Rubiano et al., 2017; Juan et al., 2018, 2019; Gruler et al., 2020; Raba et al., 2020; Mara et al., 2021; Villarinho et al., 2021), finance (Panadero et al., 2020; Saiz et al., 2021), healthcare (Fikar et al., 2016), waste collection (Gruler et al., 2017b, 2017a), and cloud computing (Mazza et al., 2018). For real‐worlds complex stochastic optimization problems, simheuristics should be considered as a “first‐resort” method (Chica et al., 2020), as it can handle reality in uncertain problems by simulation modeling, it can assess risk with ease, and a post‐run simulation output analysis can be made.…”

Toward Baggage-Free Airport Terminals: A Case Study of London City Airport

“…The location-routing problem (LRP) is one of the main tasks of the BFAT, identifying the optimal location of logistic hubs and planning the optimal vehicle routes for baggage collection and delivery. For a comprehensive literature review of recent research on LRP we refer to [3]. Since the location of logistic hubs will be specified in our study, we focus on solving the vehicle routing problem (VRP).…”