{
"title": "Finding Pareto Optimal Front for the Multi- Mode Time, Cost Quality Trade-off in Project Scheduling",
"authors": "H. Iranmanesh, M. R. Skandari, M. Allahverdiloo",
"country": null,
"institution": null,
"volume": "16",
"journal": "International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering",
"pagesStart": 397,
"pagesEnd": 402,
"ISSN": "1307-6892",
"URL": "http:\/\/waset.org\/publications\/9985",
"abstract": "Project managers are the ultimate responsible for the\r\noverall characteristics of a project, i.e. they should deliver the project\r\non time with minimum cost and with maximum quality. It is vital for\r\nany manager to decide a trade-off between these conflicting\r\nobjectives and they will be benefited of any scientific decision\r\nsupport tool. Our work will try to determine optimal solutions (rather\r\nthan a single optimal solution) from which the project manager will\r\nselect his desirable choice to run the project. In this paper, the\r\nproblem in project scheduling notated as\r\n(1,T|cpm,disc,mu|curve:quality,time,cost) will be studied. The\r\nproblem is multi-objective and the purpose is finding the Pareto\r\noptimal front of time, cost and quality of a project\r\n(curve:quality,time,cost), whose activities belong to a start to finish\r\nactivity relationship network (cpm) and they can be done in different\r\npossible modes (mu) which are non-continuous or discrete (disc), and\r\neach mode has a different cost, time and quality . The project is\r\nconstrained to a non-renewable resource i.e. money (1,T). Because\r\nthe problem is NP-Hard, to solve the problem, a meta-heuristic is\r\ndeveloped based on a version of genetic algorithm specially adapted\r\nto solve multi-objective problems namely FastPGA. A sample project\r\nwith 30 activities is generated and then solved by the proposed\r\nmethod.",
"references": null,
"publisher": "World Academy of Science, Engineering and Technology",
"index": "International Science Index 16, 2008"
}