An Efficient Batch Scheduling Model for Hospital Sterilization Services Using Genetic Algorithm

Shubin Xu and John Wang
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Shubin Xu: College of Business and Management, Northeastern Illinois University, Chicago, USA
John Wang: School of Business, Montclair State University, Upper Montclair, USA

International Journal of Strategic Decision Sciences (IJSDS), 2018, vol. 9, issue 1, 1-17

Abstract: A major challenge faced by hospitals is to provide efficient medical services. The problem studied in this article is motivated by the hospital sterilization services where the washing step generally constitutes a bottleneck in the sterilization services. Therefore, an efficient scheduling of the washing operations to reduce flow time and work-in-process inventories is of great concern to management. In the washing step, different sets of reusable medical devices may be washed together as long as the washer capacity is not exceeded. Thus, the washing step is modeled as a batch scheduling problem where washers have nonidentical capacities and reusable medical device sets have different sizes and different ready times. The objective is to minimize the sum of completion times for washing operations. The problem is first formulated as a nonlinear integer programming model. Given that this problem is NP-hard, a genetic algorithm is then proposed to heuristically solve the problem. Computational experiments show that the proposed algorithm is capable of consistently obtaining high-quality solutions in short computation times.

Date: 2018
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