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This paper presents a comprehensive and novel mathematical model to address the scheduling problem of inpatient surgeries. The interaction quality and compatibility level of surgical team members can have a significant impact on the quality and safety of a surgery. The first aim of this paper is to incorporate the decision-making styles of the surgical team members (as a personality indicator) in an operating room scheduling problem to improve the compatibility level within the surgical teams. The second aim of this paper is to provide a more effective and realistic solution for the problem by considering several practical factors. These factors include the availability of material resources (i.e., operating rooms, post-anesthesia beds, and equipment), priorities of patients, and availability, skills, and competencies of the surgical personnel. To cope with the computational complexity of the problem, two metaheuristics (i.e., NSGA-II and MOPSO) are developed to find Pareto solutions. Furthermore, the PROMETHEE-II method is used to select the best among the obtained Pareto solutions. Finally, a real case study is provided to show the applicability of the developed approach.

The ongoing revolutions in e-business and progresses of IT and communication, has resulted in the increasing number of companies with formal virtual project teams. In such situation, Uncertainty analysis gains more importance, being conducted within risk management framework. In this paper, based on the six phases of risk management procedure in PMBOK methodology, a risk management process in virtual projects is introduced. In qualitative analysis phase (of PMBOK methodology), the most effective factors of project management in virtual project teams are prioritized. In quantitative analysis phase, for the very first time, “Fuzzy Linear Programming Model” is employed to assess project risks based on project life cycle. Also given time and budget constraints, a method for developing appropriate strategies of reacting to each risk factor is introduced. We use GAMS (General Algebraic Modeling System) to select these strategies. Finally, we test our model in a numerical example, as evidence.

In this paper, a multi-objective scenario based mathematical model is presented for the capacitated hub location problem in public transportation considering economic and investment aspects. Three objective functions are regarded in the presented mathematical model. The first one aims to minimise the total costs considering the possibility of investing the unused budget at each period. The second one aims to minimise the total processing time in the hub network at each period. The last one aims to minimise the maximum distance between each pair of origin-destination nodes in the network. To solve the model, three multi-objective meta-heuristic algorithms are developed, namely S metric selection evolutionary multi-objective optimisation algorithm (SMS-EMOA), multi-objective imperialist competitive algorithm (MOICA) and non-dominated sorting genetic algorithm (NSGA-II). Finally, developed algorithms are compared to each other based on several comparison measures using a relatively novel statistical approach.