scholarly journals Fuzzy Multi-Objective Genetic Algorithm Based Resource Constrained Time-Cost Trade-Off Model under Uncertain Environment

2021 ◽  
Vol 9 (8) ◽  
pp. 1126-1133
Author(s):  
Ashish Sharma
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Construction Projects ◽  
Risk Level ◽  
Pareto Optimal ◽  
Time Cost ◽  
Trade Off ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

Abstract: In every construction project, the time and cost are the two most important objectives/factors to be considered. Clients and contractors should strive to optimize the project time and cost to maximize the return. Resources are also one of the major constraints of the construction projects. In recent years, several studies have been conducted to optimize the time and cost of project under constraint conditions of resources. Since most studies assume the time and cost as deterministic parameters, uncertainties should be considered in estimating the time and cost of the project's activities when minimizing the duration and cost of the project. For this purpose, this paper embeds the fuzzy logic to handle the uncertainties in estimating the time and cost. Besides, the multi-objective genetic algorithm (MOGA) is used to develop the resourceconstrained time-cost trade-off model. Alpha-cut approach is utilized to define the accepted risk level of decision maker. The efficiency of the proposed model is demonstrated through solvinga case study project of highway construction. The results of case study project provide a set of Pareto-optimal solutions. The developed model encourage the decision making process by choosing specified risk levels and utilizing the related Pareto-front. Keywords: Construction projects, time-cost trade-off, uncertainties, fuzzy logic, MOGA,Pareto-optimal solution.

scholarly journals A new approach to solving the time-cost trade-off problem based on the genetic algorithm and fuzzy theory

2012 ◽  
Vol 1 (3) ◽  
pp. 238
Author(s):  
Nasser Shahsavari Pour ◽  
Arman Ghamginzadeh ◽  
Mansoor Pour Kheradmand
Keyword(s):  
Genetic Algorithm ◽  
Taguchi Method ◽  
Fuzzy Theory ◽  
New Method ◽  
Time Cost ◽  
New Approach ◽  
Trade Off ◽  
Uncertainty Condition ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

Time and cost are two important and controllable objectives in project structures which are considerably dependent on each other. Recently, beneficiaries demands for cost and time reduction in completing a project have been increased. This study proposes a new method for time-cost trade off problem (TCTP) in uncertainty condition. To solve the model a multi objective genetic algorithm has been integrated with fuzzy theory. Efficiency of this algorithm is demonstrated through an existing case example from the literature. Finally to make the algorithm more efficient the existing parameters in the model have been set through Taguchi method.

scholarly journals Analisis Perbandingan Aggregat Of Function (AOF) dengan Non-Dominated Sorting Genetic Algorithm (NSGA-II) dalam Menentukan Optimasi Multi-Objective pada Penjadwalan Mesin Produksi Flow Shop

2018 ◽  
Vol 17 (2) ◽  
pp. 158
Author(s):  
Fifin Sonata ◽  
Dede Prabowo Wiguna
Keyword(s):  
Genetic Algorithm ◽  
Flow Shop ◽  
Total Tardiness ◽  
Pareto Optimal ◽  
Nsga Ii ◽  
Trade Off ◽  
Multi Objective

Penjadwalan mesin produksi dalam dunia industri memiliki peranan penting sebagai bentuk pengambilan keputusan. Salah satu jenis sistem penjadwalan mesin produksi adalah sistem penjadwalan mesin produksi tipe flow shop. Dalam penjadwalan flow shop, terdapat sejumlah pekerjaan (job) yang tiap-tiap job memiliki urutan pekerjaan mesin yang sama. Optimasi penjadwalan mesin produksi flow shop berkaitan dengan penyusunan penjadwalan mesin yang mempertimbangkan 2 objek yaitu makespan dan total tardiness. Optimasi kedua permasalahan tersebut merupakan optimasi yang bertolak belakang sehingga diperlukan model yang mengintegrasikan permasalahan tersebut dengan optimasi multi-objective A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimazitaion : NSGA-II. Dalam penelitian ini akan dibandingkan 2 buah metode yaitu Aggregat Of Function (AOF) dengan NSGA-II agar dapat terlihat nilai solusinya. Penyelesaian penjadwalan mesin produksi flow shop dengan algoritma NSGA-II untuk membangun jadwal dengan meminimalkan makespan dan total tardiness.Tujuan yang ingin dicapai adalah mengetahui bahwa model yang dikembangkan akan memberikan solusi penjadwalan mesin produksi flow shop yang efisien berupa solusi pareto optimal yang dapat memberikan sekumpulan solusi alternatif bagi pengambil keputusan dalam membuat penjadwalan mesin produksi yang diharapkan. Solusi pareto optimal yang dihasilkan merupakan solusi optimasi multi-objective yang optimal dengan trade-off terhadap seluruh objek, sehingga seluruh solusi pareto optimal sama baiknya.

Broiler management using fuzzy multi-objective genetic algorithm: A case study

2020 ◽  
Vol 233 ◽  
pp. 103941
Author(s):  
Erfan Khosravani Moghadam ◽  
Mohammad Sharifi ◽  
Shahin Rafiee ◽  
Claus Aage Grøn Sørensen
Keyword(s):  
Genetic Algorithm ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

scholarly journals A Pareto Optimal Solution for Environmental/Economic Power Dispatch using Multi-objective Genetic Algorithm

2019 ◽  
Vol 11 (4) ◽  
Author(s):  
Jawad Talaq
Keyword(s):  
Genetic Algorithm ◽  
Environmental Economic ◽  
Economic Power ◽  
Pareto Optimal ◽  
Fuel Cost ◽  
Trade Off ◽  
Pareto Optimal Front ◽  
Multi Objective ◽  
Power Dispatch ◽  
Economic Power Dispatch

The aim of this paper is to apply genetic algorithm (GA) to the solution of the environmental economic power dispatch problem. The environmental economic power dispatch is a multi-objective optimization problem. Fuel cost is considered as one of the objectives. The other objective is emissions such as SO2 or NOx or a combination of both. A trade-off relation between fuel cost and emissions can be formed through a pareto optimal front. Valve point opening and prohibited operating zones add non-smoothness and non-convexities to the objective functions. Evolutionary algorithms can efficiently solve such non-smooth and non-convex problems. Solutions need to be diversified and distributed among the whole range of the pareto optimal front. This allows operators to trade-off between fuel cost and emissions in feasible optimal regions. Applying genetic algorithm with diversity enhancement proves its effectiveness. Application of the algorithm on three and six unit systems is demonstrated

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