Emergency material scheduling optimization model and algorithms: A review

Hu, Hui; He, Jie; He, Xiongfei; Yang, Wanli; Nie, Jing; Ran, Bin

doi:10.1016/j.jtte.2019.07.001

Cited by 34 publications

(41 citation statements)

References 44 publications

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“…The review studies on COPs in the existing literature either discussed both single and multi-objective versions of a specific COP or only its multi-objective version along with the solution approaches. Some of the reviewed single-objective COPs by the researchers focus on specific areas like blocking flowshop scheduling problem (FSP) [10], permutation FSP [11], non-permutation FSP [12], agricultural land-use allocation [13], facility location problems [14], emergency material scheduling [15], allocation of distributed generation [16], location-routing problems [17], resource allocation for CRAN in 5G and beyond networks [18], VRP [19], cell formation problem [20] and many more.…”

Section: Combinatorial Optimization Problems (Cops)-mentioning

confidence: 99%

“…Chen et al [110] suggested pNSGA-II, a hybrid NSGA-II, to overcome the limitations of multi-objective optimization algorithms based on GA, such as premature convergence and 15 http://www.om-db.wi.tum.de/psplib/getdata_mm.html 16 http://ziyang.eecs.umich.edu/~dickrp/e3s/ non-uniformly distributed solutions for bi-objective TSP (BTSP). In their work, NSGA-II was embedded with the Physarum-inspired computational model (PCM) in the initialization phase and the hill-climbing method.…”

Section: D) Travelling Salesman Problemmentioning

confidence: 99%

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A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems

2021

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This paper provides an extensive review of the popular multi-objective optimization algorithm NSGA-II for selected combinatorial optimization problems viz. assignment problem, allocation problem, travelling salesman problem, vehicle routing problem, scheduling problem, and knapsack problem. It is identified that based on the manner in which NSGA-II has been implemented for solving the aforementioned group of problems, there can be three categories: Conventional NSGA-II, where the authors have implemented the basic version of NSGA-II, without making any changes in the operators; the second one is Modified NSGA-II, where the researchers have implemented NSGA-II after making some changes into it and finally, Hybrid NSGA-II variants, where the researchers have hybridized the conventional and modified NSGA-II with some other technique. The article analyses the modifications in NSGA-II and also discusses the various performance assessment techniques used by the researchers, i.e., test instances, performance metrics, statistical tests, case studies, benchmarking with other state-of-the-art algorithms. Additionally, the paper also provides a brief bibliometric analysis based on the work done in this study.

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Section: Combinatorial Optimization Problems (Cops)-mentioning

confidence: 99%

Section: D) Travelling Salesman Problemmentioning

confidence: 99%

A Comprehensive Review on NSGA-II for Multi-Objective Combinatorial Optimization Problems

2021

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“…By studying genetic algorithm [5,6], ant colony algorithm [7,29], particle swarm algorithm [30], and dynamic programming [18,31], transportation optimization problems can be comprehensively analyzed and solved. However, each method has some limitations in its application and needs to be improved on an original basis or construct a hybrid algorithm to solve them [8,30,[32][33][34]].…”

Section: The Solutions Of Costs and Energy Consumption Reductionmentioning

confidence: 99%

Modeling Method for Cost and Carbon Emission of Sheep Transportation Based on Path Optimization

et al. 2020

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Energy conservation, cost, and emission reduction are the research topics of most concern today. The aim of this paper is to reduce the cost and carbon emissions and improve the sustainable development of sheep transportation. Under the typical case of the “farmers–middlemen–slaughterhouses” (FMS) supply model, this paper comprehensively analyzed the factors, sources, and types of cost and carbon emissions in the process of sheep transportation, and a quantitative evaluation model was established. The genetic algorithm (GA) was proposed to search for the optimal path of sheep transportation, and then the model solving algorithm was designed based on the basic GA. The results of path optimization indicated that the optimal solution can be obtained effectively when the range of basic parameters of GA was set reasonably. The optimal solution is the optimal path and the shortest distance under the supply mode of FMS, and the route distance of the optimal path is 245.6 km less than that of random path. From the cost distribution, the fuel power cost of the vehicle, labor cost in transportation, and consumables cost account for a large proportion, while the operation and management cost of the vehicle and depreciation cost of the tires account for a small proportion. The total cost of the optimal path is 26.5% lower than that of the random path, and the total carbon emissions are 36.3% lower than that of random path. Path optimization can thus significantly reduce the cost of different types and significantly reduce the proportion of vehicle fuel power cost and consumables cost, but the degree of cost reduction of different types is different. The result of the optimal path is the key to be explored in this study, and it can be used as the best reference for sheep transportation. The quantitative evaluation model established in this paper can systematically measure the cost and carbon emissions generated in the sheep transportation, which can provide theoretical support for practical application.

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“…En losúltimos años la literatura especializada reporta investigaciones con nuevas propuestas basadas en el diseño de modelos de optimización, en particular problemas de ruteo de vehículos (VRP), con el fin de obtener rutas que deben utilizar los vehículos de transporte para lograr laóptima distribución de personas, bienes y servicios, que puedan ayudar a los damnificados de los desastres naturales [7]. Revisiones sobre modelos de optimización aplicados a problemas de desastres, se pueden encontrar en [4,8], en estas revisiones no se han encontrado aplicaciones del VRP a problemas de desastres naturales en Sudamérica.…”

Section: Figura 11: Etapas En La Gestión De Desastresunclassified