Soft Computing Based on an Interval Type-2 Fuzzy Decision Model for Project-Critical Path Selection Problem

Soft Computing Based on an Interval Type-2 Fuzzy Decision Model for Project-Critical Path Selection Problem

Y. Dorfeshan, S. Meysam Mousavi
Copyright: © 2018 |Pages: 24
DOI: 10.4018/IJAIE.2018010101
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This article describes how project managers are faced with the conflicting criteria to make their decisions. In many real-world conditions, it may be difficult to get certain information about activities attributes, including time, cost, risk, and quality. In this case, interval type-2 fuzzy sets (IT2FSs) which consider more uncertainty than type-1 fuzzy sets (T1FSs) are used. In this article, a new group multi-criteria analysis model is expressed based on new compromise solution and relative preference relation (RPR) concept under IT2FSs environment. Also, a new version of the evaluation on distance from average solution (EDAS) method is introduced to specify the weight of each expert under IT2FSs. Furthermore, the RPR is more reasonable than the defuzzification approach. In fact, the RPR not only can provide preference degree between two fuzzy numbers but also can keep some information. Finally, an application from literature is adopted and solved to demonstrate the applicability of proposed method.
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1. Introduction

Multi-criteria decision-making method plays a crucial role in project management problems. In recent years, many extended MCDM methods have been applied to these problems (Gitinavard et al., 2016b; Lin et al., 2016). VIKOR method is categorized as one of the multi-criteria decision-making techniques which use for ranking of the alternatives and specified the solution, called compromise, that is the nearest to the ideal. Liu et al. (2015) presented an approach for failure mode and effects analysis by means of fuzzy VIKOR method. Opricovic and Miloradov (2016) expressed an MCDM method based on VIKOR for selection of municipal waste treatment system. Lin et al. (2016) introduced a method for improving project risk management by VIKOR, DANP, and DEMATEL methods.

The critical path selection problem was considered as an MCDM problem for the first time of the literature by Zammori et al. (2009). They considered efficient criteria such as duration variability, costs, shared resources, risk of major design revisions and external risks. Then, Amiri and golozari (2011) obtained the critical path project by time, cost, quality, and risk criteria. Moreover, Cristobal (2012) presented a PROMETHE method for specifying the critical path of the project by using the time, cost, quality and safety criteria. Finally, Mehlawat and Gupta (2016) introduced an MCDM method based on the strength and weakness concepts by means of time, cost, risk, and quality criteria. In this paper, a set of efficient criteria, such as time, cost, risk, and quality criteria, are considered to determine the critical path of the project in real-world situations.

In group decision-making problem, determining the weight of decision makers (DMs) is an important issue. In order to specify weights of DMs, many studies were recently done. Yue (2011) developed the TOPSIS method for determining weights of the DMs based on the closeness to the ideal average solution concept. Moreover, Gitinavard et al. (2016a) introduced an extended method for specifying the weight of each DM. One of the new MCDM method in recent years is an evaluation based on distance from average solution (EDAS) method. EDAS method was applied in many MCDM problems. This method was presented by Keshavarz Ghorabaee et al. (2015) for multi-criteria inventory classifications. Peng and Liu (2017) introduced a decision-making method based on the EDAS method. Keshavaz Ghorabaee et al. (2017) developed the EDAS method under IT2FSs for multi-criteria group decision making. In this paper, to use advantages of EDAS method, it is applied to determine weights of the DMs for the first time in the literature.

In the classical MCDM approaches, ratings and weights of the criteria are known precisely. In other words, the problem is considered under certain environment. While in real-projects, the decision environment is not certain; it has vagueness and ambiguity (Mousavi et al., 2015). To address uncertainty, evaluation ratings and criteria weights in fuzzy MCDM (FMCDM) problems are expressed by imprecision and vagueness. Furthermore, experts and DMs can utilize linguistic variables by their knowledge and experience. With this approach, they can provide more realistic and reasonable judgments and feelings. Sanayei et al. (2010) used fuzzy VIKOR to supplier selection by group decision-making process. Shemshadi et al. (2011) presented a fuzzy VIKOR method to supplier selection based on entropy measure for objective weights. Yücenur and Demirel (2012) expressed an extension of VIKOR method under a fuzzy environment for group decision process to insurance company selection problem. Vahdani et al. (2010) developed an interval-valued fuzzy VIKOR (IVF-VIKOR) to solve MCDM problems in which the performance rating values as well as the weights of criteria are linguistics terms which could be taken in interval-valued fuzzy numbers (IVFNs).

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