Operating rooms are considered a significant revenue source, as well as the main source of waste and cost, among the hospital's departments. Any cost savings in operating rooms will have a broad financial impact. Over the last decades, many researchers and practitioners have conducted studies to deal with the issue of managing surgical supplies and instruments, which are highly affected by surgeons' preferences. The purpose of this article is to present an up-to-date review of research in the field of inventory management of surgical supplies and instruments. We have analysed the literature in a systematic manner and organised the identified papers into two groups: the papers that were published by scientific researchers and developed optimisation techniques and the papers that were published by practitioners and reported their observations of the current issues in the operating room. We also identify the future research directions leading to operating room inventory cost reduction.

Patient no-shows and late cancellations for an appointment are common problems in healthcare, which adversely affect the financial performance and quality of service of healthcare organizations. A high rate of patient no-show and late cancellation in a clinic can significantly limit access to healthcare. In general, hospitals create predictive models to assess risk of no-show, and then assign overbooking appointments utilizing those risks. In this paper, by incorporating machine learning and optimization techniques, we proposed a predictive model to assist with the overbooking decision. The model consists of two phases. First, we utilized a metaheuristic optimization technique to explore the best subset of featuresknown as feature selection problemthat can significantly contribute to the prediction outcomes. Second, using the output of the first stage, we proposed a stacking model to improve the prediction performances further. Our extensive computations and comparisons across different classifiers show that formulating the feature selection problem as a multi-objective problem instead of a single-objective problem using random forest classifier yields better results. The proposed model will improve the overbooking at clinics, by increasing the patient access to care. We introduced important new features to the literature that can describe the no-show and late cancellation behavior.

Product allocation is one of the most important duties in warehousing operations. Based on the time and the number of products to put away and retrieve, storage allocation can directly affect the efficiency of a warehouse in terms of both response and operation time. Traditionally, the product storage allocation is done based on the less distance of shipment but focusing on distance may decrease the efficiency of a warehouse in terms of efficient utilization of resources such as warehouse inbound shipping fleet. This paper proposes a storage space allocation model that considers the availability of forklift fleet in a warehouse instead of product shipping distance. The numerical example shows that storing the products based on the proposed model can reduce the volume of forklift fleet idle hours on days with fewer volume of receiving and shipping outs. The proposed model also reduces the overtime working hours on days with higher volume of receiving and shipping out products.

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