Optimization of the multi-objective green cyclical inventory routing problem using discrete multi-swarm PSO method

Rau, Hsin; Budiman, Syarif Daniel; Widyadana, I Gede Agus

doi:10.1016/j.tre.2018.10.006

Cited by 43 publications

(11 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The first case is known as the competitive MCPSO while the second is called the collaborative MCPSO. The performance of MCPSO was evaluated on six benchmarking functions and results have demonstrated that it can perform better than the SPSO [103,104].…”

Section: ) Multi-swarm Psomentioning

confidence: 99%

Particle Swarm Optimization: A Comprehensive Survey

et al. 2022

View full text Add to dashboard Cite

Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms in the literature. Although the original PSO has shown good optimization performance, it still severely suffers from premature convergence. As a result, many researchers have been modifying it resulting in a large number of PSO variants with either slightly or significantly better performance. Mainly, the standard PSO has been modified by four main strategies: modification of the PSO controlling parameters, hybridizing PSO with other well-known meta-heuristic algorithms such as genetic algorithm (GA) and differential evolution (DE), cooperation and multi-swarm techniques. This paper attempts to provide a comprehensive review of PSO, including the basic concepts of PSO, binary PSO, neighborhood topologies in PSO, recent and historical PSO variants, remarkable engineering applications of PSO, and its drawbacks. Moreover, this paper reviews recent studies that utilize PSO to solve feature selection problems. Finally, eight potential research directions that can help researchers further enhance the performance of PSO are provided.

show abstract

Section: ) Multi-swarm Psomentioning

confidence: 99%

Particle Swarm Optimization: A Comprehensive Survey

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Along with the discrete particle swarm optimization (PSO), Rau et al [47] developed a heuristic method to improve the solution quality of PSO particle to solve a multi-objective problem. Tirkolaee et al [10] solved a vehicle routing problem with time window (VRPTW) of waste collection by a simulated annealing algorithm.…”

Section: Research About Algorithmsmentioning

confidence: 99%

Optimization of Vehicle Routing for Waste Collection and Transportation

Tao

Yang

2020

IJERPH

View full text Add to dashboard Cite

For the sake of solving the optimization problem of urban waste collection and transportation in China, a priority considered green vehicle routing problem (PCGVRP) model in a waste management system is constructed in this paper, and specific algorithms are designed to solve the model. We pay particular concern to the possibility of immediate waste collection services for high-priority waste bins, e.g., those containing hospital or medical waste, because the harmful waste needs to be collected immediately. Otherwise, these may cause dangerous or negative effects. From the perspective of environmental protection, the proposed PCGVRP model considers both greenhouse gas (GHG) emission costs and conventional waste management costs. Waste filling level (WFL) is considered with the deployment of sensors on waste bins to realize dynamic routes instead of fixed routes, so that the economy and efficiency of waste collection and transportation can be improved. The optimal solution is obtained by a local search hybrid algorithm (LSHA), that is, the initial optimal solution is obtained by particle swarm optimization (PSO) and then a local search is performed on the initial optimal solution, which will be optimized by a simulated annealing (SA) algorithm by virtue of the global search capability. Several instances are selected from the database of capacitated vehicle routing problem (CVRP) so as to test and verify the effectiveness of the proposed LSHA algorithm. In addition, to obtain credible results and conclusions, a case using data about waste collection and transportation is employed to verify the PCGVRP model, and the effectiveness and practicability of the model was tested by setting a series of values of bins’ number with high priority and WFLs. The results show that (1) the proposed model can achieve a 42.3% reduction of negative effect compared with the traditional one; (2) a certain value of WFL between 60% and 80% can realize high efficiency of the waste collection and transportation; and (3) the best specific value of WFL is determined by the number of waste bins with high priority. Finally, some constructive propositions are put forward for the Environmental Protection Administration and waste management institutions based on these conclusions.

show abstract

“…Figure 5(a) shows the relationship between the taxonomies of the VRP with Environmental Considerations presented here. We can observe (yellow circle on the left) that 12 articles in the sample address the MOO-GVRP(Abad et al, 2018;Androutsopoulos & Zografos, 2017; Coelho et al, 2017; Demir et al, 2014b; Hassanzadeh & Rasti-Barzoki, 2017;Norouzi et al, 2017;Poonthalir & Nadarajan, 2018;Psychas et al, 2016;Rani & Reddy, 2017;Rao et al, 2016;Rau et al, 2018;S. Wang et al, 2018b).…”

mentioning

confidence: 91%

Multi-objective optimization for the green vehicle routing problem: A systematic literature review and future directions

2020

View full text Add to dashboard Cite

This article aims to present research conducted on the literature regarding multi-objective optimization for routing problems with environmental considerations, referred to here as Multi-objective Optimization for the Green Vehicle Routing Problem (MOOGVRP). A Brazilian database, CAPES (Coordination for the Improvement of Higher Education Personnel), was used to collect articles of general application, case studies and reviews in English starting from, since 2012. The terms "green vehicle routing problem" (GVRP), "pollution routing problem" (PRP), "vehicle routing problem in reverse logistics" (VRPRL) and "multi-objective" were used in the research protocol. Consequently, this study obtained 1,744 research results that, following the application of the filtering criterion, resulted in a sample of 76 articles from 38 journals, for which a bibliometric data (bibliometric review) survey was conducted. The originality of this article lies in how the research is presented, highlighting the results and details obtained through the survey, which may be considered of great academic importance in the sense of guiding the trends for future research.

show abstract

Optimization of the multi-objective green cyclical inventory routing problem using discrete multi-swarm PSO method

Cited by 43 publications

References 42 publications

Particle Swarm Optimization: A Comprehensive Survey

Particle Swarm Optimization: A Comprehensive Survey

Optimization of Vehicle Routing for Waste Collection and Transportation

Multi-objective optimization for the green vehicle routing problem: A systematic literature review and future directions

Contact Info

Product

Resources

About