Ant colony optimisation-based heuristic for a location-routing problem: a case study in parcel delivery services

Location and routing are the main critical problems investigated in a logistic. Location-Routing Problem (LRP) involves determining the location of facilities and vehicle routes to supply customer's demands. Determination of depots as distribution centers is one of the problems in LRP. In LRP, carbon emissions need to be considered because these problems cause global warming and climate change. In this paper, a new mathematical model for LRP considering CO2 emissions minimization is proposed. This study developed a new Mixed Integer Linear Programming (MILP) model for LRP with time windows and considered the environmental impacts. Finally, a case study was conducted in the province of Central Java, Indonesia. In this case study, there are three depot candidates. The study results indicated that using this method in existing conditions and constraints provides a more optimal solution than the company's actual route. A sensitivity analysis was also carried out in this case study.

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A Combination of Simulated Annealing and Ant Colony System for the Capacitated Location-Routing Problem

Lecture Notes in Computer Science - Knowledge-Based Intelligent Information and Engineering Systems ◽

10.1007/11892960_50 ◽

2006 ◽

pp. 409-416 ◽

Cited By ~ 20

Author(s):

Lyamine Bouhafs ◽

Amir hajjam ◽

Abder Koukam

Keyword(s):

Simulated Annealing ◽

Ant Colony ◽

Ant Colony System ◽

Location Routing Problem ◽

Routing Problem ◽

Location Routing ◽

Capacitated Location

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A hybrid ant colony algorithm for the capacitated location-routing problem

2008 3rd International Conference on Intelligent System and Knowledge Engineering ◽

10.1109/iske.2008.4730895 ◽

2008 ◽

Author(s):

Jitao Yan ◽

Qiuwen Zhang ◽

Wenshuai Zhang ◽

Tong Zhen

Keyword(s):

Ant Colony Algorithm ◽

Ant Colony ◽

Location Routing Problem ◽

Routing Problem ◽

Location Routing ◽

Capacitated Location

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Electric Vehicle Fleet Management Using Ant Colony Optimisation

International Journal of Strategic Engineering ◽

10.4018/ijose.2020010101 ◽

2020 ◽

Vol 3 (1) ◽

pp. 1-16

Author(s):

Javier Biera Muriel ◽

Abbas Fotouhi

Keyword(s):

Limited Range ◽

Ant Colony ◽

Fleet Management ◽

Ant Colony Optimisation ◽

Routing Problem ◽

Charging Time ◽

Battery Degradation ◽

Optimum Solution ◽

Advanced Version

This research is focused on implementation of the ant colony optimisation (ACO) technique to solve an advanced version of the vehicle routing problem (VRP), called the fleet management system (FMS). An optimum solution of VRP can bring benefits for the fleet operators as well as contributing to the environment. Nowadays, particular considerations and modifications are needed to be applied in the existing FMS algorithms in response to the rapid growth of electric vehicles (EVs). For example, current FMS algorithms do not consider the limited range of EVs, their charging time or battery degradation. In this study, a new ACO-based FMS algorithm is developed for a fleet of EVs. A simulation platform is built in order to evaluate performance of the proposed FMS algorithm under different simulation case-studies. The simulation results are validated against a well-established method in the literature called nearest-neighbour technique. In each case-study, the overall mileage of the fleet is considered as an index to measure the performance of the FMS algorithm.

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