استفاده از برنامه نویسی هدف فازی برای تصمیم گیری های مدیریت پروژه با اهداف متعدد در محیط های نامطمئن
|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|3294||2010||9 صفحه PDF||سفارش دهید||7310 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Expert Systems with Applications, Volume 37, Issue 12, December 2010, Pages 8499–8507
In real-life situations, the project manager must handle multiple conflicting goals and these conflicting goals are normally fuzzy owing to information is incomplete and unavailable. This study develops a two-phase fuzzy goal programming (FGP) method for solving the project management (PM) decision problems with multiple goals in uncertain environments. The original multi-objective linear programming (MOLP) model designed here attempts to simultaneously minimize total project costs, total completion time and total crashing costs with reference to direct costs, indirect and contractual penalty costs, duration of activities and the constraint of available budget. An industrial case is implemented to demonstrate the feasibility of applying the proposed two-phase FGP method to practical PM decisions. The contribution of this study lies in presenting a fuzzy mathematical programming methodology to fuzzy multi-objective PM decisions, and provides a systematic decision-making framework that facilitates the decision maker to interactively adjust the search direction until the preferred efficient solution is obtained.
Since the program evaluation and review technique (PERT) and the critical path method (CPM) were both developed in the 1950s, relevant project management (PM) decision issues have long attracted interest from both practitioners and academics. Numerous techniques including mathematical programming, algorithms and heuristics have also been presented to PM decisions. When any of the conventional models was used to solve PM decision problems, however, the goals and related parameters were often assumed to be deterministic/crisp (Al-Fanzine and Haouari, 2005, Davis and Patterson, 1975, Deckor and Hebert, 1989, DePorter and Ellis, 1990, Elsayed, 1982, Kotiah and Wallace, 1973, Kurtulus and Davis, 1982, Lin and Gen, 2007, MacCrimmon and Ryavec, 1964, Rabbani et al., 2007, Russell, 1986 and Wiley et al., 1998). In real-life PM decisions, model inputs and environmental coefficients, such as operating costs, activities duration, available resources and total cost budget, are typically fuzzy/imprecise owing to incomplete and unobtainable information over the project planning horizon. Conventional deterministic techniques described above obviously cannot solve practical PM decision problems in an uncertain environment. Moreover, the existing PM decision models consider only direct costs (including labor, materials, equipment and other costs directly related to projected activities), neglecting relevant indirect costs (including interest, administration, depreciation, contractual penalty and other variable overhead costs). In practical situations, a project’s total costs are the sum of direct costs and indirect costs over the project planning horizon. Generally, the real PM decisions focus on the minimization of project completion time, and/or the minimization of total project costs through crashing or shortening duration of particular activities. The aim of evaluating time-cost trade-offs is to develop a suitable PM plan that will minimize the total project costs. Thus, a project decision maker (DM) may be able to shorten project completion time, realizing savings on indirect costs, by increasing direct expenses to accelerate the project. Additionally, although various PM decision techniques have been developed to minimize project duration, most do not also minimize the total costs (Karshenas and Haber, 1990, Li, 1995 and Russell, 1986). In practice, the project DM must frequently handles conflicting goals in term of the use of organizational resources, and these conflicting goals are required to be optimized simultaneously by the DM. These goals are to minimize total costs, crashing cost, completion time, contractual penalties, and/or maximizing profits and the utilization of equipment (Al-Fanzine and Haouari, 2005, Arikan and Gungor, 2001, DePorter and Ellis, 1990, Liang, 2009, Wang and Liang, 2004, Lin and Gen, 2007, Viana and Sousa, 2000 and Yin and Wang, 2008). Particularly, it is critical that the satisfying goal values should normally be uncertain due to unit cost/time coefficients and related parameters are fuzzy/imprecise in nature. Solutions to fuzzy multi-objective PM optimization problems benefit from assessing the imprecision of the DM’s judgments, such as “the objective function of project duration should be substantially less than or equal to 200 days,” and “total costs should be substantially less than or equal to 2 millions”. Conventional deterministic PM decision techniques cannot clearly solve the fuzzy multi-objective PM programming problems. This study aims to develop a two-phase fuzzy goal programming (FGP) method for solving the PM decision problems with multiple fuzzy goals in uncertain environment. The original multi-objective linear programming (MOLP) model designed here attempts to simultaneously minimize total project costs, total completion time and total crashing costs with reference to direct costs, indirect and contractual penalty costs, duration of activities and the constraint of available budget. The remainder of this study is organized as follows. Section 2 dedicates to a review of the relevant literature. Section 3 describes the problem, details the assumptions and formulates the fuzzy multi-objective PM decision model. Subsequently, Section 4 develops the FGP method for solving the fuzzy multi-objective PM decision problems. Next, a real industrial case is used to implement the feasibility of applying the proposed method in Section 5. Finally, conclusions are drawn in Section 6.
نتیجه گیری انگلیسی
In practical PM decision problems, the project DM must simultaneously handle multiple conflicting goals that govern the use of the constrained resources, and these conflicting objectives are often fuzzy because information is incomplete and unavailable over the project planning horizon. This work aims to develop a two-phase FGP technique for solving the multi-objective PM decision problems in uncertain environments. The proposed fuzzy multi-objective PM decision model attempts to minimize total project costs, total completion time and total crashing costs with reference to direct costs, indirect and penalty costs, duration of activities and the constraint of available budget. The main advantage of the proposed method is that it provides a systematic framework that facilitates the decision-making process, enabling a DM to interactively modify the fuzzy data until a satisfactory efficient solution is obtained. An industrial case is used to demonstrate the feasibility of applying the proposed method to real PM decisions. Sensitivity analysis results for varying project duration indicate that minimizing completion time conflicts with minimizing the total costs. Overall, the main contribution of this study lies in presenting a fuzzy mathematical programming methodology to fuzzy multi-objective PM decisions, and provides a systematic decision-making framework that facilitates the decision maker to interactively adjust the search direction until the preferred efficient solution is obtained. The major limitations of the proposed method concern the certain assumptions made for each of the unit cost/time coefficients in the fuzzy objective functions and related available resources in the constraints. Hence, the proposed method must be modified make it better suited to the practical application. Furthermore, the proposed method is based on Zimmermann’s fuzzy programming technique, which implicitly assumes that the linear membership function is the proper representative fuzzy goals of the human DM for the PM decision problems. Future researchers may also apply the piecewise linear, non-linear and related membership functions to construct fuzzy multi-objective PM decision models.