Time dependent vehicle routing problem with a multi ant colony system

Donati, Alberto; Montemanni, Roberto; Casagrande, Norman; Rizzoli, Andrea Emilio; Gambardella, Luca Maria

doi:10.1016/j.ejor.2006.06.047

Cited by 307 publications

(181 citation statements)

References 13 publications

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“…In the same light, Ding et al [45] used ACO to solve multi objective optimization for minimizing the use of precious resources and maximizing the level of process capability in the disassembly process. Some researcher, the ACO has been employed to minimize vehicle rout in case of time and number of travels that it can minimize fossil fuel consumption and air pollution [46], [47]. And also, wastes collection facility location model has been developed by Bautista et al [48] to minimize collection cost using ACO as solution method.…”

Section: B Ant Colony Optimizationmentioning

confidence: 99%

A Review of Nature-Based Algorithms Applications in Green Supply Chain Problems

Sadrnia¹,

Soltani²,

Zulkifli³

et al. 2014

IJET

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Abstract-In the recent years, there is a significant attention among researchers and practitioners to environmental issues and green supply chain (GrSC) because of legislations and profit oriented motivations.Because of considering environmental issues GrSC besides the economic variables, the models are very difficult to find optimal. Hence, complicated green problems call for development of modern optimization methods and algorithms to solve optimization models by efficient techniques. In recent decades, meta-heuristic algorithms have been developed to overcome the problem that most of them are inspired from nature. Some of the algorithms have been inspired from natural generation, some of them inspired from swarm behavior, and others simulate natural processes. In this research we summarize the recent advance evolutionary optimization algorithms and swarm intelligence algorithms which are applied to GrSC and green logistics. Literature reviewed in this paper shows the current state of the art and discusses the potential future research trends.Index Terms-Evolutionary algorithms, green supply chain, meta-heuristic, swarm intelligence algorithms.

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Section: B Ant Colony Optimizationmentioning

confidence: 99%

A Review of Nature-Based Algorithms Applications in Green Supply Chain Problems

Sadrnia¹,

Soltani²,

Zulkifli³

et al. 2014

IJET

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“…Donati et al (2008) considered two hierarchical objective functions, which optimized the number of tours and the total travel time on selected routes. They used a meta-heuristic method based on ant colony optimization (ACO) algorithm to solve this TDVRP.…”

Section: Review Of the Literaturementioning

confidence: 99%

A bi-objective mathematical model for two-dimensional loading time-dependent vehicle routing problem

Alinaghian

Zamanlou

Sabbagh

2017

Journal of the Operational Research Society

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This paper introduces two-dimensional loading time-dependent vehicle routing problem and proposes a biobjective mathematical model. This problem assesses the process of distributing the rectangular-shaped demanded items over an urban environment; it does not, however, allow items to be loaded on top of each other. In addition to the above assumptions, the presented model also satisfies the first-in-first-out property in the timedependent vehicle routing problem. Given the NP-hard nature of the problem, a method called elitist nondominated sorting local search is developed to obtain its solutions. To evaluate the performance of the proposed algorithm, the solutions of this algorithm for small-scale problem instances are compared with the results of an exact method. For the medium-scale problem instances, results of NSGA-II and SPEA2 are used as the basis of comparison. The computational results demonstrate the good performance of the proposed method.

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“…The drawback of the proposed model is that it does not satisfy the FirstIn-First-Out (FIFO) property. Donati et al (2008) develop a model that optimizes the total travel time along with the number of routes. A solution procedure which combines the ant colony system with a local search method is developed.…”

Section: Literature Reviewmentioning

confidence: 99%