Research Trends in the Optimization of the Master Surgery Scheduling Problem

Abstract

The Master Surgery Scheduling Problem (MSSP) allocates operating theatre time to surgery groups such as medical specialities or surgeons, which is essential for daily operational planners. Many researchers have highlighted issues in the optimization of surgical scheduling problems. However, most recent reviews limited the issues at the operational level and excluded the problem characteristics such as surgery group type and schedule cyclicity. This study aims to review the state-of-the-art of MSSP and identify new trends in optimization strategies (problem characteristics and objective function), uncertainty scheduling factors (types and approaches), and solution and evaluation methods. These aspects are the key components in developing an effective MSSP optimization model. This paper reviews articles published between 2000 and 2021 that addressed MSSP, concentrating on papers between 2016 and 2021. We underlined the popularity of medical specialities as the surgery group, one-week horizon length, a cyclic schedule, multi-objective optimization and evaluation by benchmarking. The analysis shows that surgery duration is the most prominent uncertainty type, while strategies for handling this are stochastic programming, robust optimization, and fuzzy programming. We highlighted the role of heuristic approaches in addressing the MSSP's computational complexity. This review's trends, challenges, and potential solutions are essential for future researchers in developing optimization models for MSSP.

An Improved Saturation Degree-Based Constructive Heuristic for Master Surgery Scheduling Problem: Case Study

“…When constructing solutions for the MSSP, the key component of optimization is the objectives. The objectives can be classified into eight categories: throughput, waiting measure, patient score, OT utilization, emergency capacity, costs, schedule assignment, and upstream and downstream resources [18]. Throughput objectives include maximizing the number of surgeries performed [8][9][10]27], minimizing surgical cancellations [8,28], and minimizing surgical postponements [29].…”

“…The constraints can be divided into capacity allocation (12)(13)(14)(15) and MSSP (16)(17)(18)(19)(20). Constraint (12) states that each surgical unit must be allocated at least one slot in the planning horizon.…”

“…Constraint (17) forces unit 𝑔 to be assigned to a slot with an available surgery team on day 𝑝 of the week 𝑤 [9,31]. Constraint (18) ensures unit 𝑔 is only assigned to suitable or predefined OTs [17]. For each day, the number of slots assigned to units of subspecialty 𝑗 must not exceed its maximum parallel slots, as expressed in constraint (19) [31].…”

“…However, these models do not consider preferences for scheduling a surgery group on two consecutive days. In addition, while existing models have included objectives related to various resources such as OTs and upstream and downstream resources [18], limitations in resource sharing that constrain the daily assignment of surgery groups based on equipment availability have not been incorporated as objectives in any MSSP model.…”

Enhanced memetic search for reducing energy consumption in fuzzy flexible job shops

“…Scheduling [1,2] is a classical manufacturing problem that consists in organizing the execution of a set of tasks or operations to make the best use of the available resources under some constraints in order to fulfill some objectives. Solving scheduling problems is essential not only in industrial applications [3,4], in particular in Industry 4.0 and 5.0 [5], but also in healthcare [6,7], computing infrastructures [8] or education [9]. Besides its undeniable applicability, scheduling also poses a computational challenge, since many of its problems are NP-Hard [10].…”