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ABSTRACTIn this paper, a multi-mode capital-constrained project payment scheduling problem with bonus-penalty structure is developed where activities can be performed under one of several possible modes and a bonus-penalty structure exists at project deadline. The objective is to assign activity modes and progress payments so as to maximize the net present value (NPV) of the contractor under the constraint of capital availability. The event-based method is employed to construct the mathematical model and Tabu search (TS) is used to solve the strongly NP-hard problem. Using a randomly generated dataset, we compare the performance of our solution with benchmarks generated by Simulated annealing (SA), Random sampling (RS), and Multi-start iterative improvement (MSII) methods, and demonstrate that the proposed TS is superior. Moreover, the effects of several key parameters on the contractor’s NPV are investigated. We show that the contractor’s NPV grows with the increase in the contractor’s initial capital availability, the payment number, and the rewarding rate in bonus-penalty structure.

Due to the effect of stochastic factors such as failure of machines on competitive power of manufacturing organisations in the competitive global marketplace, importance of production planning is doubled. In order to deal with the uncertainty conditions in manufacturing systems, failure-prone manufacturing systems have arisen. In this paper, a network of failure-prone manufacturing machines are considered and final product is perishable product. In previous studies, perishable product to network of failure-prone manufacturing machines has not been studied. The purpose of this study is to find the optimum rate of machines production based on hedging point policy such that the average system costs are minimal. Because of uncertainty in such systems, in this paper discrete-event simulation with the help of ARENA software for estimating system costs is used. Taguchi method is employed to determine the optimal values of decision variables. A numerical example will show the efficiency of the proposed approach.

Healthcare issues have a special role in the people's lives in most societies. Hospitals are the most important centres of healthcare delivery services. The emergency department (ED) of a hospital is a critical unit as it is related to the life and death of the patients where time plays vital role in patients' health. Also, nowadays the cost of healthcare services is exponentially rising. These are leading hospital managers to offer patients faster services with better quality with concern to hospital limitations. Nurse scheduling has an important effect on time and quality of services. It is usually written by hand by the supervisor of the department. This thesis is proposing mathematical method for nurse scheduling that fulfil the hospital limitations and nurse preferences while minimising the patients' waiting queue time. In order to reach the aforementioned objective, the emergency department of a governmental hospital is simulated by arena and simulated annealing algorithm is applied to find the appropriate schedule. Results show that the waiting time of achieved schedule from proposed algorithm is 18% less than existing schedule.