Genetic Algorithm for the Continuous Location-Routing Problem

Rybickova, Alena; Mockova, Denisa; Teichmann, Dusan

doi:10.14311/nnw.2019.29.012

Cited by 17 publications

(7 citation statements)

References 24 publications

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“…Secondly, from the perspective of algorithm characteristics, PSO based on swarm intelligence strategy seeks the optimal solution by simulating the foraging of birds within a certain range ( Chen & Tan, 2018 ), which has certain similarity with the site location problem in the kernel. However, when finding the optimal solution, GA based on evolution strategy has lots of abstract operations such as chromosome crossover and mutation for site location problem ( Alp, Erkut & Drezner, 2003 ; Rybičková, Mocková & Teichmann, 2019 ), which may affect the convergence of the algorithm. Figure 4 shows the convergence of the two algorithms after we run the RC207 example and get the value around average fitness value.…”

Section: Computational Experimentsmentioning

confidence: 99%

Optimization of carton recycling site selection using particle swarm optimization algorithm considering residents’ recycling willingness

Xi,

Tao,

Brooks

2023

PeerJ Computer Science

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With the development of the express delivery industry, how to increase the recycling rate of waste cartons has become a problem that needs to be solved. Recycling enterprises began to provide the new recycling mode, door-to-door recycling services, to residents with waste cartons. In this article, we constructed a site selection model for a carton recycling site with the aim of maximizing total profits. Considering the residents’ recycling willingness and the government subsidy earned through the contribution to carbon emission reduction, this model achieves the task of site selection and unit price fixation for carton recycling. We used the particle swarm optimization (PSO) algorithm to solve the model and compared it with the genetic algorithm (GA) for validity testing. PSO algorithm was also used to carry out sensitivity analysis in this model. The proposed model and the results of the sensitivity analysis can be used for decision-making in recycling enterprises as well as for further research on waste recycling and reverse logistics.

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Section: Computational Experimentsmentioning

confidence: 99%

Optimization of carton recycling site selection using particle swarm optimization algorithm considering residents’ recycling willingness

Xi,

Tao,

Brooks

2023

PeerJ Computer Science

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“…The efficiency and quality of the optimal solution improve when the LRP is solved as a whole. Rybickova et al [22] adopted a genetic algorithm to solve the continuous LRP. Zhang et al [23] adopted an improved particle swarm optimization algorithm to solve the dynamic multi-objective LRP in the emergency response process of major oil spill accidents at sea.…”

Section: Lrpmentioning

confidence: 99%

“…Constraint (16) means that each distribution vehicle will return to the original distribution center after the completion of the service, constraint (17) indicates that the customer's demand must be met, constraint (18) indicates that the vehicle load constraint, that is, the customer demand delivered by the vehicle on any route, must not exceed the vehicle load limit, and constraint (19) indicates that the customer demand scale does not exceed the vehicle load limit. Constraint (20) indicates that there is no distribution route between each distribution center, constraint (21) indicates that the total customer demand served by a distribution center should not exceed the capacity limit of the distribution center, and constraint (22) is the lower-level decision variable.…”

Section: The Distribution Vehicles' Dispatch Costsmentioning

confidence: 99%

A Bi-Level Programming Approach to the Location-Routing Problem with Cargo Splitting under Low-Carbon Policies

2021

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To identify the impact of low-carbon policies on the location-routing problem (LRP) with cargo splitting (LRPCS), this paper first constructs the bi-level programming model of LRPCS. On this basis, the bi-level programming models of LRPCS under four low-carbon policies are constructed, respectively. The upper-level model takes the engineering construction department as the decision-maker to decide on the distribution center’s location. The lower-level model takes the logistics and distribution department as the decision-maker to make decisions on the vehicle distribution route’s scheme. Secondly, the hybrid algorithm of Ant Colony Optimization and Tabu Search (ACO-TS) is designed, and an example is introduced to verify the model’s and algorithm’s effectiveness. Finally, multiple sets of experiments are designed to explore the impact of various low-carbon policies on the decision-making of the LRPCS. The experimental results show that the influence of the carbon tax policy is the greatest, the carbon trading and carbon offset policy have a certain impact on the decision-making of the LRPCS, and the influence of the emission cap policy is the least. Based on this, we provide the relevant low-carbon policies advice and management implications.

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“…It is necessary to consider that each run of the sequence represents solving of a hard and large combinatorial problem, which solution is obtained by time demanding inspection of a vast searching tree. To avoid this computational burden, professionals prefer a heuristic approach based on imitating the biological processes [18][19]. To obtain a good set of the non-dominated feasible solutions, an evolutionary metaheuristic seems to be a convenient tool, because an evolutionary metaheuristic is able to build up a set of the non-dominated solutions continuously in the form of an elite set [20].…”

Section: Minimize B X J J S Smentioning

confidence: 99%

Two Objective Public Service System Design Problem

Janáček¹,

Koháni²,

Grygar³

et al. 2021

Komunikácie

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The public service system serves population spread over a geographical area from a given number of service centers. One of the possible approaches to the problem with two or more simultaneously applied contradicting objectives is determination of the so-called Pareto front, i.e. set of all the feasible non-dominated solutions. The Pareto front determination represents a crucial computational deal, when a large public service system is designed using an exact method. This process complexity evoked an idea to use an evolutionary metaheuristic, which can build up a set of non-dominated solution continuously in the form of an elite set. Nevertheless, the latter approach does not assure that the resulting set of solutions represents the true Pareto front of the multi-objective problem solutions. Within this paper, authors deal with both approaches to evaluate the difference between the exact and heuristic approaches.

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Genetic Algorithm for the Continuous Location-Routing Problem

Cited by 17 publications

References 24 publications

Optimization of carton recycling site selection using particle swarm optimization algorithm considering residents’ recycling willingness

Optimization of carton recycling site selection using particle swarm optimization algorithm considering residents’ recycling willingness

A Bi-Level Programming Approach to the Location-Routing Problem with Cargo Splitting under Low-Carbon Policies

Two Objective Public Service System Design Problem

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