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.

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One of the well-known problems in single machine scheduling context is the Job Sequencing and Tool Switching Problem (SSP). The SSP is optimally sequencing a finite set of jobs and loading restricted subset of tools to a magazine with the aim of minimizing the total number of tool switches. It has been proved in the literature that the SSP can be reduced to the Job Sequencing Problem (JSeP). In the JSeP, the number of tool switches from the currently processed job to the next job depends on the sequencing of all predecessors. In this paper, the JSeP is modeled as a Traveling Salesman Problem of Second Order (2-TSP). We call the induced JSeP by 2-TSP as the Job Sequencing Problem of Second Order (2-JSeP) with a different objective function formulation from JSeP and prove that 2-JSeP is NP-hard. Then the Assignment Problem of Second Order (2-AP) and Karp-Steele patching heuristic are incorporated to solve 2-JSeP. The obtained solution, however, does not guarantee the optimal sequence and are used to seed a Dynamic Q-learning-based Genetic Algorithm (DQGA) to improve the solution quality. Q-learning, which is a kind of reinforcement learning method, is used to learn from the experience of selecting the order of mutation and crossover operators in each generation of the genetic algorithm. The computational results on 320 benchmark instances show that the proposed DQGA is comparable to the state-of-the-art methods in the literature. The DQGA even outperforms the existing methods for some instances, as could improve the reported "best-known solutions" in notably less time. Finally, through the statistical analysis, the performance of DQGA is compared with those of non-learning genetic algorithms.