arrow
Return

MULTI-OBJECTIVE TIME-COST OPTIMIZATION USING COBB-DOUGLAS PRODUCTION FUNCTION AND HYBRID GENETIC ALGORITHM

delete2015-08-24
delete7
delete
OA
AI
Z
Zhigang Shen *
A
Ashkan Hassani
施
施骞 (Qian Shi)
DOI:10.3846/13923730.2014.897966delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
Existing research on construction time-cost tradeoff issues rarely explore the origin of the crashing cost. Crashing cost function was either assumed without much justification, or came from historical data of some real projects. As a result the conclusions of the papers can hardly be used to guide allocations of labor and equipment resources respectively. The authors believe Cobb-Douglas function provides a much-needed piece to modeling the cost functions in the construction time-cost tradeoff problem during the crashing process. We believe this new perspective fills a gap of existing time-cost tradeoff research by considering project duration, labor and equipment cost as parameters of the CobbDouglas production function. A case study was presented to show how the proposed framework works. Our conclusion is that introducing Cobb-Douglas function into time-cost tradeoff problem provides us extra capacity to further identify the optimal allocations of labor and equipment resources during crashing.
Keywords:
Cobb-Douglas
production function
crashing
time-cost tradeoff
hybrid genetic algorithm

Journal

J
Journal of Management in Engineering
IF:
7
Papers:
3.1K
Citations:
6.8K

Organization

N
New York University
Scholars:
4.4W
Papers: 3.9W
Citations: 5.8W
U
University of Nebraska Lincoln
Scholars:
7.8K
Papers: 6.5K
Citations: 2.0W
University of Nebraska System cover
University of Nebraska System
Scholars:
2.7W
Papers: 2.3W
Citations: 58
researcher View more organizations
Cited Papers

Cited Papers

APPLYING METAHEURISTIC STRATEGIES IN CONSTRUCTION PROJECTS MANAGEMENT
err2012-09-28
err28
errOAAI
errBozejko, Wojciech; Hejducki, Zdzislaw; Wodecki, Mieczyslaw
errShare
errSave
Style-based quantum generative adversarial networks for Monte Carlo events
err2022-08-17
err0
errOAAI
errCarlos Bravo-Prieto; Julien Baglio; Marco Cè; Anthony Francis; Dorota M. Grabowska; Stefano Carrazza
errShare
errSave
errShare
errSave
errShare
errSave
researcher View more