Optimal constrained non-renewable resource allocation in PERT networks with discrete activity times

AbstractIn this paper, we develop an approach to optimally allocate a limited nonrenewable resource among the activities of a project, represented by a PERT-Type Network (PTN). The project needs to be completed within some specified due date. The objective is to maximize the probability of project completion on time. The duration of each activity is an arbitrary discrete random variable and also depends on the amount of consumable resource allocated to it. On the basis of the structure of networks, they are categorized as either reducible or irreducible. For each network structure, an analytical algorithm is presented. Through some examples, the algorithms are illustrated

Project management: a simulation-based optimization method for dynamic time-cost tradeoff decisions

Project managers face difficult decisions with regard to completing projects on time and within the project budget. A successful project manager not only needs to assure that the project is completed, but also desires to make optimal use of resources and maximize the profitability of the project. The goal of this research is to address the time-cost tradeoff problem associated with selecting from among project activity alternatives under uncertainty. Specifically, activities that make up a project may have several alternatives each with an associated cost and stochastic duration. The final project cost is a result of the time and cost required to complete each activity and lateness penalties that may be assessed if the project is not completed by the specified completion time. In an effort to optimize the project time-cost tradeoff, a dynamic, simulation-based optimization method is presented. In particular, the method minimizes the expected project cost due to lateness penalties and the activity alternatives selected. The method is designed to be implemented in two phases. The first phase, referred to as the static phase, is implemented prior to the start of the project. The static phase results in the expected cost for the recommended project configuration including the alternative selected for each activity and the distributions of the project completion and total project cost. The second phase, referred to as the dynamic phase, is implemented as the project progresses. The dynamic phase allows the project manager to reevaluate the remaining project and activity alternatives to dynamically minimize the expected total project cost. The method provides an optimal solution under the assumptions of traditional crashing implementations and a heuristic solution for the generalized problem. An experimental performance evaluation shows the effectiveness of the method for making project management decisions. Finally, the method is fully implemented in computer software and integrated into a commercially available project management tool

Multi crteria decision making and its applications : a literature review

This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM