scholarly journals Time-cost optimization model proposal for construction projects with genetic algorithm and fuzzy logic approach

2019 ◽  
Vol 18 (3) ◽  
pp. 554-567
Author(s):  
Hatice Acar Yildirim ◽  
◽  
Cemil Akcay
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Optimization Model ◽  
Construction Projects ◽  
Cost Optimization ◽  
Time Cost ◽  
Fuzzy Logic Approach ◽  
Logic Approach

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 Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Dalton Meitei Thounaojam ◽  
Thongam Khelchandra ◽  
Kh. Manglem Singh ◽  
Sudipta Roy
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Logic ◽  
Fuzzy System ◽  
Boundary Detection ◽  
Shot Boundary Detection ◽  
Detection Approach ◽  
Video Shot ◽  
Shot Boundary ◽  
Fuzzy Logic Approach ◽  
Logic Approach

This paper proposed a shot boundary detection approach using Genetic Algorithm and Fuzzy Logic. In this, the membership functions of the fuzzy system are calculated using Genetic Algorithm by taking preobserved actual values for shot boundaries. The classification of the types of shot transitions is done by the fuzzy system. Experimental results show that the accuracy of the shot boundary detection increases with the increase in iterations or generations of the GA optimization process. The proposed system is compared to latest techniques and yields better result in terms ofF1scoreparameter.

scholarly journals Optimization of Personnel Cost in Aircrew Assignment Problem using a Simple Fuzzy Logic Approach

MENDEL ◽  
2017 ◽  
Vol 23 (1) ◽  
pp. 133-140
Author(s):  
Novriana Sumarti ◽  
Ferdyanto Chandra ◽  
Jeremy Minardi
Keyword(s):  
Fuzzy Logic ◽  
Assignment Problem ◽  
Cost Optimization ◽  
Time Constraint ◽  
Optimization Process ◽  
Personnel Cost ◽  
Data Set ◽  
Final Destination ◽  
Fuzzy Logic Approach ◽  
Logic Approach

In aviation industries, the aircrew assignment problem is one of the most important factors in total operational cost optimization. This problem will be solved in two steps: flight pairing and aircrew scheduling. The constraints to be satisfied in flight pairing include having the same airport for first departure and final destination, and the limitations of flying time, duty time and transit time. The optimization process results in optimal flight pairings that minimize the number of personnel needed to serve a flight schedule over a given period of time. Further optimization is needed to obtain a schedule in which an aircrew team can serve a rotation with the largest possible number of pairings on the condition that all constraints are fulfilled. For aircrew scheduling, there are constraints on flying time, resting time, total number of takeoffs, and number of holidays and workdays. The investigated optimization process was designed to get optimal rotations along with maximum total personnel cost reduction. The data set used in this research is a one-month full flight schedule from a big airline in Indonesia. A simple fuzzy logic approach was used to find a new flying time constraint in order to optimize personnel cost and evenly distribute the assignments. The results show that the new optimal flying time constraint can reduce personnel cost up to 5.07% per month, so it can yield significant savings on a yearly basis.

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