Academic journal article Iranian Journal of Management Studies

A Multi-Objective Resource-Constrained Optimization of Time-Cost Trade-Off Problems in Scheduling Project

Academic journal article Iranian Journal of Management Studies

A Multi-Objective Resource-Constrained Optimization of Time-Cost Trade-Off Problems in Scheduling Project

Article excerpt

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Resource-constraint project scheduling problem (RCPSP) is a class of project scheduling that one's activities should be scheduled subject to precedence and resource constraints and it is proven to be NP-hard (Aboutalebi1 et al., 2012). Minimization of project duration is often used as an objective of a general project scheduling problem while other objectives such as Maximizing of net present value of cash flows, and leveling of resource usage are also considered. Resources involved in a project can be single or multiple varieties, and can be renewable or nonrenewable (Ritwik & Paul, 2013). The time-cost tradeoff problem in project management originates when activity time can be reduced with some extra direct cost (Jongyul et al., 2012). Time Cost Trade off analysis is the compression of the project schedule to achieve a more favorable outcome in terms of project duration, cost, and projected revenues. The objectives of the Time Cost Trade off analysis are compressing the project to the optimum duration which minimizes the total project cost (Rifat & Önder Halis, 2012).

Another important type of objective emerges if cash flows occur while the project is carried out. Cash outflows are induced by the execution of activities and the usage of resources. On the other hand, cash inflows result from payments due to the completion of specified parts of the project. Typically, discount rates are also included. Note that cash flows related to activity j might occur at several points in time during execution of j. However, they can easily be compounded to a single cash flow at the beginning or the end of j. These considerations result in problems with the objective to maximize the net present value (NPV) of the project which subject to the standard RCPSP constraints (Shu-Shun & Chang-Jung, 2008). Project payment scheduling problem involves how to schedule progress payments effectively including the amount, time or spots (i.e. the key activities or events associated with payments), and so on of payments in the project so as to maximize the profits of the contractor or/and the client.

In real life situations, there are at least two parties involved in the project: the client, who is the owner of the project, and the contractor, whose job is to execute the project. They have to agree with the method of payment transferring from the client to the contractor for the execution of the project. The ideal situation for the client would be a single payment at the end of the project. The contractor, on the other hand, would like to receive the whole payment at the beginning of the project (Zhengwen & Yu, 2008). Time-Cost optimization (TCO) problem has been extensively examined by a number of research studies. Various approaches have been proposed for optimizing construction time and cost including (1) heuristic methods (Moselhi, 1993; Siemens, 1971); (2) mathematical programming (Liu et al., 1995; Moussourakis & Haksever, 2004); and (3) meta-heuristic methods. Mathematical programming such as linear programming is suitable for problems with linear time-cost relationships, but they often fail to solve the problem with discrete time-cost relationships (Feng et al., 1997). Moreover, it requires a lot of computational efforts to solve a large scale project network. Heuristic methods are able to overcome such limitation of a large scale problem, but fail to guarantee optimal solutions. Therefore, many research studies have focused on utilizing meta-heuristic methods in time-cost tradeoff analysis to overcome the limitation of heuristic methods and mathematical programming.

Liu et al. (1995) have developed optimization model using a hybrid method that integrates linear and integer programming. Linear programming was used to find lower bounds of the solutions, and then integer programming was used to obtain the exact solution. The integer programming was then used to minimize total project cost with the constraints of activity precedence and the selection of a single resource utilization option for each activity. …

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