An evolutionary algorithm approach for the constrained multi-depot vehicle routing problem

Lightner-Laws, Carin A.; Agrawal, Vikas; Lightner, Constance A.; Wagner, Neal

doi:10.1108/ijicc-06-2015-0018

Cited by 9 publications

(11 citation statements)

References 32 publications

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“…Nakandala, Lau, and Zhang [6] describe ways to reduce the cost of planning for three different road vehicles, where weight constraints prevail when transporting containers; when volume limitation dominates; when volume constraints prevail and the modeling of transport cost optimization to maintain fresh product quality. Lightner-Laws et al [7] propose to calculate The Global Vehicle Route Problem using a genetic algorithm and an automated work distribution process, reducing the total time that all jobs are delivered, using the lowest number of drivers.…”

Section: Literature Reviewmentioning

confidence: 99%

The Model of Vehicle and Route Selection for Energy Saving

2021

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The World Bank, United Nations, the Organization for Economic Cooperation and Development, and others are in line with the governments of countries that are strongly interested in the sustainable development of countries, regions, and enterprises. One of the aspects that affects the indicators and prospects of sustainable development is the efficiency of energy source use. Nationwide reductions in the greenhouse gas emissions of motor vehicles could have a direct effect on ambient temperature and reducing the effects of global warming, which can affect future environmental, societal, and economic development. Significant reductions in fuel consumption can be achieved by increasing the efficiency of use, and the performance, of current cargo vehicles. This aspect is directly related to cargo delivery systems and supply chain efficiency and effectiveness. The article solves the problem of increasing the effectiveness of cargo delivery and proposes a model that would minimize transportation costs that are directly related to fuel consumption, shortening transportation time. The model addresses the problem of a lack of models evaluating the efficiency of cargo to Lithuania that is using several different modes of transportation. For the solution to this problem, the article examines the complexity of the rational use of land and water vehicles depending on the type of cargo transported, the technical capabilities of the vehicles (loading, speed, environmental pollution, fuel consumption, etc.), and the type (cars, railways, ships). The novelty of the findings is based on the availability to select the most appropriate vehicles, on a case-by-case basis, from the available options, depending on their environmental performance and energy efficiency. This model, later in this article, is used for calculations of Lithuanian companies for selecting the most rational vehicle by identifying the most appropriate route, as well as assessing the dynamics of the economic and physical indicators. The model allows for creating dependencies between the main indicators characterizing the transport process—the cost, the time of transport, and the safety, taking into account the dynamics of economic and physical indicators, that lead to a very important issue—reducing the amount of energy required to provide products and services.

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Section: Literature Reviewmentioning

confidence: 99%

The Model of Vehicle and Route Selection for Energy Saving

2021

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“…While their approach addressed multiple variants of the classic VRP, it only marginally explored the trade-off between overall driving time and the total number of drivers. This paper further develops the solution proposed by Lightner-Laws et al (2016) to optimize a multi-objective VRP where multiple variants of the problem exist in a single problem instance. We develop a nested GA, which explores the VRP for a heterogeneous fleet of vehicles with multidepot subcontractors and constraints on pickup/delivery times and locations.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The overall total time for all route deliveries is equivalent to the sum of the job completion times for each individual driver. Both mapping software and input from field experts are used to determine the travel time between any home, pickup or delivery location (Lightner-Laws et al 2016).…”

Section: Problem Formulationmentioning

confidence: 99%

“…Specifically, we perused the literature for research about solving VRPs that involved a heterogeneous fleet of vehicles, multi-depot subcontractors (drivers), and pickup/delivery time window and location constraints. Since we were unable to find existing literature that collectively addressed the aforementioned variants in a VRP, the initial phase of our research involved developing an evolutionary algorithm to minimize the total time to complete all jobs with the fewest number of route drivers given pickup/delivery times, locations, and vehicle capacity constraints (Lightner-Laws et al 2016).…”

Section: Introduction and Background Of Bslmentioning

confidence: 99%

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A bi-criteria evolutionary algorithm for a constrained multi-depot vehicle routing problem

et al. 2016

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Most research about the vehicle routing problem (VRP) does not collectively address many of the constraints that real-world transportation companies have regarding route assignments. Consequently, our primary objective is to explore solutions for real-world VRPs with a heterogeneous fleet of vehicles, multi-depot subcontractors (drivers), and pickup/delivery time window and location constraints. We use a nested bi-criteria genetic algorithm (GA) to minimize the total time to complete all jobs with the fewest number of route drivers. Our model will explore the issue of weighting the objectives (total time vs. number of drivers) and provide Pareto front solutions that can be used to make decisions on a case-by-case basis. Three different real-world data sets were used to compare the results of our GA vs. transportation field experts' job assignments. For the three data sets, all 21 Pareto efficient solutions yielded improved overall job completion times. In 57 % (12/21) of the cases, the Pareto efficient solu- Keywords Vehicle routing problem · Bi-criteria genetic algorithm · Pareto front · Multi-depot transportation problem · Hard and soft time windows

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“…This problem can be concerned with scheduling time-dependent tasks, such as the process of scheduling manufacturing jobs. Bioinspired or metaheuristic models have been proposed for the time-dependent/ timeconstrained TSP and related problems [10][11][12]. ACO models have been also proposed for such problems [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Coordination of Pheromone Deposition Might Solve Time‐Constrained Travelling Salesman Problem

Sakiyama

Arizono

2018

Complexity

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In this study, we develop two Ant Colony Optimization (ACO) models as new metaheuristic models for solving the time-constrained Travelling Salesman Problem (TSP). Here, the time-constrained TSP means a TSP in which several cities have constraints that the agents have to visit within prescribed time limits. In our ACO models, only agents that achieved tour under certain conditions defined in respective ACO models are allowed to modulate pheromone deposition. The agents in one model are allowed to deposit pheromone only if they achieve a tour satisfying strictly the above purpose. The agents in the other model is allowed to deposit pheromone not only if they achieve a tour satisfying strictly the above purpose, but also if they achieve a tour satisfying the above purpose in some degree. We compare performance of two developed ACO models by focusing on pheromone deposition. We confirm that the later model performs well to some TSP benchmark datasets from TSPLIB in comparison to the former and the traditional AS (Ant System) models. Furthermore, the agent exhibits critical properties; i.e., the system exhibits complex behaviors. These results suggest that the agents perform adaptive travels by coordinating some complex pheromone depositions.

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An evolutionary algorithm approach for the constrained multi-depot vehicle routing problem

Cited by 9 publications

References 32 publications

The Model of Vehicle and Route Selection for Energy Saving

The Model of Vehicle and Route Selection for Energy Saving

A bi-criteria evolutionary algorithm for a constrained multi-depot vehicle routing problem

Coordination of Pheromone Deposition Might Solve Time‐Constrained Travelling Salesman Problem

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