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Laboratory results are essential for physicians to diagnose medical conditions. Because of the critical role of medical laboratories, an increasing number of hospitals use total laboratory automation (TLA) to improve laboratory performance. Although the benefits of TLA are well documented, systems occasionally become congested, particularly when hospitals face peak demand. This study optimizes TLA operations. Firstly, value stream mapping (VSM) is used to identify the non-value-added time. Subsequently, batch processing control and parallel scheduling rules are devised and a pull mechanism that comprises a constant work-in-process (CONWIP) is proposed. Simulation optimization is then used to optimize the design parameters and to ensure a small inventory and a shorter average cycle time (CT). For empirical illustration, this approach is applied to a real case. The proposed methodology significantly improves the efficiency of laboratory work and leads to a reduction in patient waiting times and increased service level.

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In this paper, a scatter search (SS) heuristic is proposed to solve the multidimensional knapsack problem with generalized upper bound constraints (GUBMKP). The method is organized according to the general structure of SS. We discuss the design and implementation for each of the components of SS. A greedy randomized adaptive search procedure is applied in order to diversify the initial solutions. In order to select diversified solutions to enter the reference set, we propose an algorithm based on the structure of generalized upper bound constraints. Several approaches of combining solutions are proposed to solve the problem. The computational results show the heuristic is competitive compared to the former leading method for the GUBMKP.

The manager is responsible for the operations of a distribution center (DC) and multiple retail outlets selling a seasonal product. Initially, the DC keeps the inventory, which is allocated to the outlets in the season. There are inventory holding costs at the DC and the outlets; variable shipment cost for transferring inventory from the DC; fixed ordering cost and shortage cost at an outlet. Exact demand at each outlet is a decreasing function of price. To maximize the expected profit of the season, the manager needs to determine the markdown prices for retail outlets and quantity of inventory allocated to them. The problem can be modeled as a dynamic program (DP) which takes too heavy computational effort to solve. We develop a DP-based heuristic for solving the problem. The heuristic takes light computational effort and yet has good accuracy. Insights streamlining the markdown operations are deduced from the numerical results.