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Han Lai, Huchang Liao, Yilu Long, Edmundas Kazimieras Zavadskas
No abstract is available for this record.
Harold Tiemessen, Maria Fleischmann, Geert‐Jan van Houtum, J.A.E.E. van Nunen · 5 authors
No abstract is available for this record.
Adetokunbo A. Oyetunji, Stuart D. Anderson
Project delivery systems define the roles and responsibilities of the parties involved in a project. They also establish an execution framework in terms of sequencing of design, procurement, and construction. The decision made in the selection of a project delivery system for a project impacts all phases of execution of the project and greatly impacts the efficiency of project execution. Such decisions should be facilitated by thorough analysis. Structured, quantitative decision analysis processes have been shown to have several benefits over the simplistic, holistic, and informal processes that typically characterize subjective evaluations. However, a dearth of quantitative values of project delivery systems established and validated through research has invariably left project managers with no alternative than to make project delivery selection decisions on the basis of subjective evaluations. Development of the needed quantitative values for application in a decision analysis process would greatly enhance the quality of the decision-making process and provide a defensible rationale for selection of project delivery systems for capital projects. This paper presents research findings that provide the needed quantitative values in this area. Based on the quantitative values defined here, interested parties can develop and implement quantitative evaluation of project delivery alternatives to identify the optimal solution for a given project. Multicriteria decision analysis was found to be the suitable approach for a quantitative, analytical evaluation of project delivery systems. Consequently, the quantitative values presented in this paper were developed in accordance with the requirements of the multicriteria decision analysis technique known as simple multiattribute rating technique with swing weights (SMARTS). Utilizing the quantitative values presented here and applying the analysis technique of SMARTS, a decision support tool has been developed and validated for the Construction Industry Institute. The decision support tool is presently being utilized by member companies of the Construction Industry Institute that were privy to its development. With the presentation of the quantitative values in this paper, other parties interested in developing similar tools would benefit from the research results presented here.
Kiyotaka Shimizu, Eitaro Aiyoshi, Tetsuo Ueno
We are concerned with a class of organizations composed of a coordinating central system and plural semi-autonomous subsystems, such that each of them has a decision-making unit. Such a problem is regarded as that of a decentralized two-level optimization. The basic principle of planning for this organization is that the central system allocates resources so as to optimize its own objective, while the subsystems optimize their own objectives using the given resources.Within this framework of decision making, we consider a transportation problem in which N transport agents transport their own commodity. Each transport agent n, n=1, …, N, finds optimal flow patterns of the associated commodity n so that the transportation cost is minimized based on its own objective function under the arc capacity restriction imposed by the central agent. The coordinating central agent governs the transport agents through the way of allocating the arc capacity so that the optimality of whole network system is achieved. Here, the lower level problem is composed of a set of single commodity minimum cost flow problems of the transport agents, each of which can be easily solved separately by the subsystem.The decentralized optimization problem is solved in principle by a parametric approach. A feasible direction algorithm using directional derivative and application of a constraint simplex method are proposed to solve the formulated network flow problem.