Solving the open vehicle routing problem with capacity and distance constraints with a biased random key genetic algorithm

Ruiz, Efrain; Soto-Mendoza, Valeria; Barbosa, Álvaro Ernesto Ruiz; Reyes, Ricardo

doi:10.1016/j.cie.2019.05.002

Cited by 87 publications

(46 citation statements)

References 52 publications

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“…In this study, an Evolutionary Algorithm (EA) was developed to solve the VRPFIB mathematical model. Considering the findings from the conducted literature review, EA is one of the most popular solution methodologies, showing a promising performance in solving different types of vehicle routing problems [19], [20], [25], [35], [36]. Fig.…”

Section: Solution Methodologymentioning

confidence: 99%

“…Several studies have proposed a number of mathematical models and solution algorithms for different variants of the vehicle routing problem. Some studies addressed the open vehicle routing problem, where vehicles do not return to the depot after they provide service to customers [21]- [25]. Yu et al [21] formulated a mixed-integer linear mathematical model for the open vehicle routing problem with crossdocking.…”

Section: B the Vehicle Routing Problemmentioning

confidence: 99%

“…The numerical experiments showed that when 100 to 2,000 customers were served, the Iterated Local Search Algorithm provided good-quality solutions within a reasonable computational time. Ruiz et al [25] formulated the open vehicle routing problem, which aimed to minimize the total distance traveled by the vehicles. Homogenous and capacitated vehicles were used to serve the customers.…”

Section: B the Vehicle Routing Problemmentioning

confidence: 99%

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An Optimization Model and Solution Algorithms for the Vehicle Routing Problem With a “Factory-in-a-Box”

Pasha

Dulebenets

Kavoosi³

et al. 2020

IEEE Access

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The "factory-in-a-box" concept involves assembling production modules (i.e., factories) in containers and transporting the containers to different customer locations. Such a concept could be highly effective during emergencies, when there is an urgent demand for products (e.g., the COVID-19 pandemic). The "factory-in-a-box" planning problem can be divided into two sub-problems. The first sub-problem deals with the assignment of raw materials to suppliers, sub-assembly decomposition, assignment of subassembly modules to manufacturers, and assignment of tasks to manufacturers. The second sub-problem focuses on the transport of sub-assembly modules between suppliers and manufacturers by assigning vehicles to locations, deciding the order of visits for suppliers, manufacturers, and customers, and selecting the appropriate routes within the transportation network. This study addresses the second sub-problem, which resembles the vehicle routing problem, by developing an optimization model and solution algorithms in order to optimize the "factory-in-a-box" supply chain. A mixed-integer linear programming model, which aims to minimize the total cost of the "factory-in-a-box" supply chain, is presented in this study. CPLEX is used to solve the model to the global optimality, while four metaheuristic algorithms, including the Evolutionary Algorithm, Variable Neighborhood Search, Tabu Search, and Simulated Annealing, are employed to solve the model for large-scale problem instances. A set of numerical experiments, conducted for a case study of "factory-in-a-box", demonstrate that the Evolutionary Algorithm outperforms the other metaheuristic algorithms developed for the model. Some managerial insights are outlined in the numerical experiments as well. INDEX TERMS Factory-in-a-box, metaheuristics, supply chains, urgent demand, vehicle routing problem.

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Section: Solution Methodologymentioning

confidence: 99%

Section: B the Vehicle Routing Problemmentioning

confidence: 99%

Section: B the Vehicle Routing Problemmentioning

confidence: 99%

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An Optimization Model and Solution Algorithms for the Vehicle Routing Problem With a “Factory-in-a-Box”

Pasha

Dulebenets

Kavoosi³

et al. 2020

IEEE Access

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“…For testing the proposed algorithm, three sets of instances were used: group C, group O and group K. The first two sets of instances have been widely used in previous research, while the third set was only used in [42,43]. Table 1 shows the characteristics for every group of instances; n specifies the size of the instances in the group (excluding the depot), Q specifies the vehicle's capacity and L specifies the range for the maximum length allowed for the routes.…”

Section: Test Instancesmentioning

confidence: 99%

A Hybrid Grasshopper Optimization Algorithm Applied to the Open Vehicle Routing Problem

et al. 2020

Self Cite

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This paper presents a hybrid grasshopper optimization algorithm using a novel decoder and local search to solve instances of the open vehicle routing problem with capacity and distance constraints. The algorithm's decoder first defines the number of vehicles to be used and then it partitions the clients, assigning them to the available routes. The algorithm performs a local search in three neighborhoods after decoding. When a new best solution is found, every route is locally optimized by solving a traveling salesman problem, considering the depot and clients in the route. Three sets containing a total of 30 benchmark problems from the literature were used to test the algorithm. The experiments considered two cases of the problem. In the first, the primary objective is to minimize the total number of vehicles and then the total distance to be traveled. In the second case, the total distance traveled by the vehicles is minimized. The obtained results showed the algorithm's proficient performance. For the first case, the algorithm was able to improve or match the best-known solutions for 21 of the 30 benchmark problems. For the second case, the best-known solutions for 18 of the 30 benchmark problems were found or improved by the algorithm. Finally, a case study from a real-life problem is included.

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“…Therefore, extensive state-of-the-art works on VRP can be found in Laporte (2009) and Toth & Vigo (2014). Besides that, numerous resolution algorithms have been extensively tested in recent years (Nagy & Salhi 2007, Subramanian et al 2012, Neto et al 2013, Vopenka et al 2015, Vidal et al 2019, Andelmin & Bartolini 2019, Altabeeb et al 2019, Ruiz et al 2019.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Wood Supply: The Forestry Routing Optimization Model

Monti

Gomide

Oliveira

et al. 2020

An. Acad. Bras. Ciênc.

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Solving the open vehicle routing problem with capacity and distance constraints with a biased random key genetic algorithm

Cited by 87 publications

References 52 publications

An Optimization Model and Solution Algorithms for the Vehicle Routing Problem With a “Factory-in-a-Box”

An Optimization Model and Solution Algorithms for the Vehicle Routing Problem With a “Factory-in-a-Box”

A Hybrid Grasshopper Optimization Algorithm Applied to the Open Vehicle Routing Problem

Optimization of Wood Supply: The Forestry Routing Optimization Model

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