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

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

Shubin Xu (College of Business and Management, Northeastern Illinois University, Chicago, USA) and John Wang (School of Business, Montclair State University, Upper Montclair, USA)
Copyright: © 2018 |Pages: 17
DOI: 10.4018/IJSDS.2018010101

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.
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1. Introduction

Nowadays hospitals are under ever more pressure to establish core competencies needed to thrive in a fast-changing healthcare market. A major challenge for hospitals is to provide efficient medical services. This paper focuses on the sterilization services in hospitals. These services, which have received little attention in the literature, have a great impact on the overall efficiency of the healthcare system (Di Mascolo & Gouin, 2013).

Sterilization refers to a process that destroys or eliminates all forms of microbial life conveyed by contaminated medical devices and is carried out in healthcare facilities by physical or chemical methods (Rutala et al., 2008). A medical device is an instrument, apparatus, machine, or other similar or related article, which is used for medical purposes on patients in diagnosis, therapy, or surgery (U.S. Food and Drug Administration, 2015). There are two types of medical devices: single-use medical devices and reusable medical devices (RMDs). This study, however, is concerned with the sterilization process of RMDs. Examples of RMDs include surgical forceps, endoscopes, and stethoscopes. After each use, sterilization guarantees the desired hygiene level of RMDs for subsequent uses in operating rooms and other aseptic procedures.

A typical sterilization service consists of the following steps (Di Mascolo & Gouin, 2013): pre-disinfection, rinsing, washing, inspection, packing, sterilization, and storage. Pre-disinfection is a manual step in which soiled RMDs are soaked in a chemical substance for a period of time. This step softens and loosens much of the soil contaminated on the devices. Complete brushing is then followed to remove most or all of the visible soil from the RMDs. Rinsing can be performed manually or automatically in washers. In the washing step, RMDs are thoroughly cleaned in automatic washers, which offer a wide range of temperature settings to speed up cleaning and provide some disinfection. After washing, each RMD is inspected for function and cleanliness. RMDs are then packed into sterile packages (e.g., pouches, wraps, or containers), which serve to maintain the sterility of processed RMDs and allow for aseptic opening at point of use. Several RMDs can be placed in the same sterile package. Finally, they are sterilized in autoclaves, before being transferred to the storage area in the vicinity of operating rooms for reuse.

Among all the steps in the sterilization services, the washing step is generally the bottleneck (Ozturk, Begen, & Zaric, 2014), and thus is the focus of this study. All RMDs used in a surgical operation constitute the RMD set for this surgery. In a typical hospital, there can be a large number of RMD sets. As each surgery may require different number and type of RMDs, RMD sets may be of different sizes. After a surgery, all RMDs used in the surgery are sent to the sterilization service. Due to various reasons, e.g., each surgery may have a different start and finish time, and the pre-disinfection step may take different amount of time, RMD sets are ready for washing at different times. The duration of the washing cycle, however, is the same for all RMD sets.

In the washing step, multiple automatic washers are operated in parallel. These washers may have different capacities, and each can handle more than one RMD set simultaneously as long as its capacity is not exceeded. All the RMD sets washed together in the same washer constitute a batch, and all the RMD sets in a batch start and finish washing at the same time. The decisions are then which RMD sets to put together to form a batch and when to start a washing cycle.

As the washing step constitutes a bottleneck in the sterilization services, efficient scheduling of the washing operations to reduce flow time and work-in-process inventories is of great concern to management. This motivates the authors to adopt the measure of total completion time (i.e., sum of the washing completion times for all the RMD sets) to evaluate the performance of the scheduling algorithms. Minimizing total completion time is equivalent to minimizing the average time spent in the washing step by a RMD set, and as such will increase throughput while reducing work-in-process inventories.

To remain consistent with the scheduling literature, automatic washers are referred to as batch processing machines (BPMs), and RMD sets as jobs. Thus, this paper addresses the problem of minimizing total completion time on parallel nonidentical BPMs (i.e., parallel BPMs with different capacities) with nonidentical job sizes, different ready times, and equal processing times.

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