Simulation-optimization approach for the stochastic location-routing problem

Herazo-Padilla, Nilson; Montoya‐Torres, Jairo R.; Isaza, Santiago Nieto; Alvarado, Jaime

doi:10.1057/jos.2015.15

Cited by 18 publications

(10 citation statements)

References 53 publications

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“…In particular, they have shown their greatest advantage when applied to highly dynamic and uncertain problems. Where only local information is available, such as the supply chain management under demand uncertainty (Jung et al, 2004), the stochastic location-routing problem (Herazo-Padilla et al, 2015) and the container yard design problem under uncertainty (Zhou et al, 2016). In this section we propose a new Simulation Optimization based ant colony heuristic to solve the uncertain QCSP with the aim to minimize the expected value of the compilation date of the last task in the vessel (makespan).…”

Section: Solution Methodologymentioning

confidence: 99%

Simulation optimization based ant colony algorithm for the uncertain quay crane scheduling problem

Rouky¹,

Abourraja²,

Boukachour³

et al. 2019

10.5267/j.ijiec

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This work is devoted to the study of the Uncertain Quay Crane Scheduling Problem (QCSP), where the loading /unloading times of containers and travel time of quay cranes are considered uncertain. The problem is solved with a Simulation Optimization approach which takes advantage of the great possibilities offered by the simulation to model the real details of the problem and the capacity of the optimization to find solutions with good quality. An Ant Colony Optimization (ACO) meta-heuristic hybridized with a Variable Neighborhood Descent (VND) local search is proposed to determine the assignments of tasks to quay cranes and the sequences of executions of tasks on each crane. Simulation is used inside the optimization algorithm to generate scenarios in agreement with the probabilities of the distributions of the uncertain parameters, thus, we carry out stochastic evaluations of the solutions found by each ant. The proposed optimization algorithm is tested first for the deterministic case on several well-known benchmark instances. Then, in the stochastic case, since no other work studied exactly the same problem with the same assumptions, the Simulation Optimization approach is compared with the deterministic version. The experimental results show that the optimization algorithm is competitive as compared to the existing methods and that the solutions found by the Simulation Optimization approach are more robust than those found by the optimization algorithm.

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Section: Solution Methodologymentioning

confidence: 99%

Simulation optimization based ant colony algorithm for the uncertain quay crane scheduling problem

Rouky¹,

Abourraja²,

Boukachour³

et al. 2019

10.5267/j.ijiec

View full text Add to dashboard Cite

show abstract

“…Simulation technique is also deployed by a number of works (Nadizadeh and Nasab, 2014;Wei et al, 2014;Herazo-Padilla et al, 2015;Xu et al, 2016;Zhang et al, 2018;Quintero-Araujo et al, 2019b;Rabbani et al, 2019). All of these works employed simulation techniques to handle the uncertain values of some variables, and integrated them into a metaheuristic framework, resulting in a simheuristic technique (Juan et al, 2015).…”

Section: Solution Approachmentioning

confidence: 99%

“…For instances, Rabbani et al (2019) hybridized the NSGA-II with the classical Monte Carlo simulation and implemented it for handling the demand uncertainty of a hazardous waste management case. Meanwhile, Herazo-Padilla et al (2015) integrated a discrete-event simulation with ant colony optimization for the LRP with stochastic travel speed and transportation cost.…”

Section: Simheuristicsmentioning

confidence: 99%

Location‐routing problem: a classification of recent research

Mara

Kuo

Asih

2021

Int Trans Operational Res

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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.

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“…The search algorithm for vehicle routes is the particle swarm [52]. Secondly [27] present the problem of location and routing of vehicles with transportation costs stochastic displacement speeds. The authors propose a hybrid solution procedure based on the optimization of ant colonies (ACO) and discrete event simulation (DES).…”

Section: Brief Description Of the Vehicle Routing Problemmentioning

confidence: 99%

The Vehicle Routing Problem Complex Network Analysis

Chípuli¹,

Mota²,

Reyna³

et al. 2018

JAMC

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One of the main characteristics of transportation problems is not only the hard design model process; but also, in the characterization of the elements that make up the supply chain. A more detailed analysis will grant undoubtedly a greater knowledge of the system. However, the use of regular networks for the development of models can eliminate key characteristics for correct decision making. Faced with this fact, the characterization of transport models is proposed through the conceptualization of complex networks. This new proposal will allow to apply metrics and perform more detailed analysis to the distribution network; such as vulnerability analysis, fault propagation, element grouping identification, among other metrics. The objective of this research is to show a methodology to build the Vehicle Routing Problem models and analyze the distribution network using the corresponding metrics of complex networks.

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Simulation-optimization approach for the stochastic location-routing problem

Cited by 18 publications

References 53 publications

Simulation optimization based ant colony algorithm for the uncertain quay crane scheduling problem

Simulation optimization based ant colony algorithm for the uncertain quay crane scheduling problem

Location‐routing problem: a classification of recent research

The Vehicle Routing Problem Complex Network Analysis

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