Non-dominated Sorting Genetic Algorithm-II to solve bi-objective multi-mode resource-constrained project scheduling problem

Damak, N.; Jarboui, Bassem; Loukil, Taïcir

doi:10.1109/codit.2013.6689652

Cited by 7 publications

(6 citation statements)

References 18 publications

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“…Khalilzadeh et al (2012) also regarded a type of resource investment problem, in which tardiness was allowed for renewable resources and the total costs of consuming non-renewable resources and penalty cost caused by tardiness of renewable resources were minimized in the objective function. Damak et al (2013) analyzed a type of MRCPSP, which helped the minimization of the project completion time and cost of non-renewable resources.…”

Section: Resource-based Objective Functionsmentioning

confidence: 99%

Resource-constrained project scheduling problem: review of past and recent developments

Habibi¹,

Barzinpour²,

Sadjadi³

2018

10.5267/j.jpm

105

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The project scheduling problem is both practically and theoretically of paramount importance. From the practical perspective, improvement of project scheduling as a critical part of project management process can lead to successful project completion and significantly decrease of the relevant costs. From the theoretical perspective, project scheduling is regarded as one of the interesting optimization issues, which has attracted the attention of many researchers in the area of operations research. Therefore, the project scheduling issue has been significantly evaluated over time and has been developed from various aspects. In this research, the topics related to Resource-Constrained Project Scheduling Problem (RCPSP) are reviewed, recent developments in this field are evaluated, and the results are presented for future studies. In this regard, first, the standard problem of RCPSP is expressed and related developments are presented from four aspects of resources, characteristics of activities, type of objective functions, and availability level of information. Following that, details about 216 articles conducted on RCPSP during 1980-2017 are expressed. At the end, in line with the statistics obtained from the evaluation of previous articles, suggestions are made for the future studies in order to help the development of new issues in this area.

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Section: Resource-based Objective Functionsmentioning

confidence: 99%

Resource-constrained project scheduling problem: review of past and recent developments

Habibi¹,

Barzinpour²,

Sadjadi³

2018

10.5267/j.jpm

105

View full text Add to dashboard Cite

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“…Lastly, the scheduling problems which is the most discussed category of MOCOPs consists of open shop scheduling problem [125], [126], job shop scheduling problem (JSSP) [127], [128], [129], [130], [131], [132], FSP [133], [134], [135], [136], [137], [138], project scheduling problem (PSP) [139], resource constrained PSP (RCPSP) [140], [141], [142], [143], [144], [145], timetabling problem [146], cross-docking scheduling problem [147], task scheduling problem [148], [149], [150], [151], [152], [153], [154], machine scheduling problem [155], [156], [157], [158], [159], [160], [161], satellite range scheduling problem [162], multi-objective satellite data transmission scheduling problem [163], satellite scheduling of large areal tasks [164], operating room scheduling [165], [166], harvest scheduling problem [167], energy-efficiency scheduling problem…”

Section: A Nsga-ii For Mocopsmentioning

confidence: 99%

“…Wang et al [143] suggested NSGA-II for RCPSP with multiple activity performance modes that were tested using an agricultural example. Damak et al [145] designed NSGA-II to solve bi-objective multi-mode RCPSP with two objectives of minimizing makespan and nonrenewable resource cost.…”

Section: E) Scheduling Problemmentioning

confidence: 99%

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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“…26 In addition, the solution of OPF problem considering RESs is solved by a hybrid algorithm, 27 grey wolf optimizer algorithm, 28 particle swarm optimization (PSO) method, 29 and flower pollination algorithm (FPA), 30 as well as differential evolution (DE), FPA, and MBF. 31 Moreover, the works presented in previous studies [32][33][34] make use of certain type of specific heuristic or metaheuristic, such as PSO or DE optimization. Adewuyi et al proposed to solve the multiobjective problem, which is considered as mix generation planning considering utility-scale solar PV system on Nigerian system.…”

Section: Introductionmentioning

confidence: 99%

Optimal power flow of power systems with controllable wind‐photovoltaic energy systems via differential evolutionary particle swarm optimization

Duman

Rivera

et al. 2019

Int Trans Electr Energ Syst

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Summary The produced energy from varied sources in modern power systems is to be optimally planned for planning and operating of power system under the determined limit conditions. Recently, the rising overall people population of the world, the increasing of people requirements, improvements of technology, and ecosystem and global climate changes have caused with the increasing of electric energy demand. One of the most important solution methods to meet this energy demand is considered as utilization of renewable energy sources (RESs) in power systems. The structure of power systems has become with the usage of RESs more complex. The optimal power flow (OPF) from planning and operation problems has converted to difficult problem with RESs integrated into modern power systems. This paper presents the OPF problem of power systems with a high penetration of controllable renewable sources. These kinds of sources are able to inject a determined power since they have a back‐up unit (storage). Uncertain solar irradiance and wind speed are simulated via log‐normal and Rayleigh probability distributions, respectively. The proposed OPF problem with controllable renewable sources is solved by the differential evolutionary particle swarm optimization (DEEPSO) algorithm. Simulations conducted on various test systems illustrate the effectiveness and efficiency of DEEPSO as compared with other algorithms including moth swarm algorithm, backtracking search algorithm, and differential search algorithm. In addition, the Wilcoxon signed‐rank test is applied to show the supremacy, effectiveness, and robustness of DEEPSO algorithm.

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Non-dominated Sorting Genetic Algorithm-II to solve bi-objective multi-mode resource-constrained project scheduling problem

Cited by 7 publications

References 18 publications

Resource-constrained project scheduling problem: review of past and recent developments

Resource-constrained project scheduling problem: review of past and recent developments

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

Optimal power flow of power systems with controllable wind‐photovoltaic energy systems via differential evolutionary particle swarm optimization

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