The complexity of large-scale projects has led to numerous risks in their life cycle. This paper presents a new risk evaluation approach in order to rank the high risks in large-scale projects and improve the performance of these projects. It is based on the fuzzy set theory that is an effective tool to handle uncertainty. It is also based on an extended VIKOR method that is one of the well-known multiple criteria decision-making (MCDM) methods. The proposed decision-making approach integrates knowledge and experience acquired from professional experts, since they perform the risk identification and also the subjective judgments of the performance rating for high risks in terms of conflicting criteria, including probability, impact, quickness of reaction toward risk, event measure quantity and event capability criteria. The most notable difference of the proposed VIKOR method with its traditional version is just the use of fuzzy decision-matrix data to calculate the ranking index without the need to ask the experts. Finally, the proposed approach is illustrated with a real-case study in an Iranian power plant project, and the associated results are compared with two well-known decision-making methods under a fuzzy environment.

In this paper, a single facility centre location problem with a line barrier, which is uniformly distributed on a given horizontal route in the plane is proposed. The rectilinear distance metric is considered. The objective function minimizes the maximum expected barrier distance from the new facility to all demand points in the plane. An algorithm to solve the desired problem is proposed where a mixed integer nonlinear programming needs to be solved. The proposed model of this paper is solved using some already existed benchmark problem in the literature and the results are compared with other available methods.

multi-skilled project scheduling problem (MSPSP) has been generally presented to schedule a project with staff members as resources. Each activity in project network requires different skills and also staff members have different skills, too. This causes the MSPSP becomes a special type of a multi-mode resource-constrained project scheduling problem (MM-RCPSP) with a huge number of modes. Given the importance of this issue, in this paper, a mixed integer linear programming for the MSPSP is presented. Due to the complexity of the problem, a meta-heuristic algorithm is proposed in order to find near optimal solutions. To validate performance of the algorithm, results are compared against exact solutions solved by the LINGO solver. The results are promising and show that optimal or near-optimal solutions are derived for small instances and good solutions for larger instances in reasonable time.

This paper presents the decentralized supply chain with two suppliers and two competing retailers. It also investigates the sourcing and pricing strategies of two retailers in a decentralized supply chain system under a supply disruption environment. These retailers face their individual stochastic demand markets; however, they compete with each other through a two-stage price and service operation. The interactive dynamics among retailers is characterized, including the existence and uniqueness of the Nash Equilibrium in service and price games demonstrated.