a b s t r a c tAn assembly line is a flow-oriented production system in which the productive units performing the operations, referred to as stations, are aligned in a serial manner. The simple assembly line balancing problem (SALBP) is a fundamental version of the general problem which has attracted attention of researchers and practitioners of operations research for almost half a century. With respect to the objective function, the SALBP is further classified into SALBP-1, SALBP-2, SALBP-E and finally SALBP-F. The types of SALBP may be complemented by a secondary objective which consists of smoothing station loads. This objective guarantees a better flow of material.Although there is a great deal of research addressing this problem, most of them consider smoothing station loads as a primary objective. In this paper, a differential evolution algorithm is developed to minimize workload smoothness index in SALBP-2. Also, the algorithm parameters are optimized using the Taguchi method. To validate the proposed algorithm, the results are compared with those of a published heuristic. This comparison indicates effectiveness of the proposed algorithm.

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In this research, the Master Surgical Scheduling (MSS) problem at the tactical level of hospital planning and scheduling is studied. Before constructing the MSS, a strategic level problem, i.e., Case Mix Planning Problem (CMPP), shall be solved to allocate the capacity of Operating Room (OR) to each surgical specialty. In order to make an e ective coordination between CMPP and MSS, the results obtained from solving the CMPP are used as an input for the respective MSS. In the MSS, frequently performed elective surgeries are planned in a cyclic manner for a pre-de ned planning period. As a part of the planning process, it is required to adjust downstream limited resources, such as Intensive Care Unit (ICU) and ward beds, with patient ow. In this study, a mathematical model is developed to construct an MSS. The proposed model is based on a lexicographic goal programming approach, which is aimed at minimizing the OR spare time while considering the results of the CMPP. In this paper, the data required to solve MSS are collected from a medium-sized Iranian hospital. Hence, a robust estimation method is applied to reduce the e ect of outliers on the decision-making process. The results show the performance of the proposed method against the solution put in practice in the hospital.