Surgery scheduling in the presence of operating room eligibility and dedicated surgeon: an adaptive composite dispatching method

Wang, Shan; Su, Huiqiao; Wan, Guohua; Zhong, Linda L. D.

doi:10.1080/00207543.2022.2050827

Cited by 6 publications

(2 citation statements)

References 53 publications

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“…This allows for several constraints to secure a comprehensive surgery flow, source availability, and specialty supplies of human sources [8,9]. The suggested mission plays an essential role in maintaining timely medications for the patients by maintaining the stability of the hospital's source operation [10]. Current patient scheduling is necessary for operation research, such as multi-objective optimization.…”

Section: Literature Reviewmentioning

confidence: 99%

A Novel Hybrid Simulated Annealing for No-Wait Open-Shop Surgical Case Scheduling Problems

Rahimi

Hejazi

Zandieh

et al. 2023

ASI

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In this paper, the problem of finding an assignment of “n” surgeries to be presented in one of “m” identical operating rooms (ORs) or machines as the surgical case scheduling problem (SCSP) is proposed. Since ORs are among NP-hard optimization problems, mathematical and metaheuristic methods to address OR optimization problems are used. The job or surgical operation ordering in any OR is a permanent part of all sequencing and scheduling problems. The transportation times between ORs are defined based on the type of surgical operations and do not depend on distance, so there is no surgical operation waiting time for transferring. These problems are called no-wait open-shop scheduling problems (NWOSP) with transportation times. The transportation system for the problems is considered a multi-transportation system with no limitation on the number of transportation devices. Accordingly, this study modeled a novel combined no-wait open-shop surgical case scheduling problem (NWOSP-SCSP) with multi-transportation times for the first time to minimize the maximum percentile of makespan for OR as a single objective model. A mixed-integer linear program (MILP) with small-sized instances is solved. In addition to the small-sized model, a novel metaheuristic based on a hybrid simulated annealing (SA) algorithm to solve large-sized problems in an acceptable computational time is suggested, considering the comparison of the SA algorithm and a new recommended heuristic algorithm. Then, the proposed hybrid SA and SA algorithms are compared based on their performance measurement. After reaching the results with a numerical analysis in Nova Scotia health authority hospitals and health centers, the hybrid SA algorithm has generated significantly higher performance than the SA algorithm.

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Section: Literature Reviewmentioning

confidence: 99%

A Novel Hybrid Simulated Annealing for No-Wait Open-Shop Surgical Case Scheduling Problems

Rahimi

Hejazi

Zandieh

et al. 2023

ASI

View full text Add to dashboard Cite

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“…Xu et al [38] developed a heuristic template for making delayed routing decisions and three different delayed routing strategies with optimization objectives of energy efficiency and mean tardiness, the results of compared experiments demonstrate that the Genetic Programming Hyper-Heuristic (GPHH) approach with delayed routing outperforms the state-of-the-art GPHH approach. Zhang et al [39] proposed a hybrid GP algorithm to develop a DR for an intelligent job shop, and verify the effectiveness of the developed algorithm by conducting a study on two real scenarios. Jun and Lee [40] introduced a decision-tree-based approach with feature construction and tree-based learning, it can automatically extract DR from existing or wellperforming DRs.…”

Section: Introductionmentioning

confidence: 99%

Flexible Job Shop Composite Dispatching Rule Mining Approach Based on an Improved Genetic Programming Algorithm

Li,

Zhao,

Tang

et al. 2024

Tsinghua Sci. Technol.

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To obtain a suitable scheduling scheme in an effective time range, the minimum completion time is taken as the objective of Flexible Job Shop scheduling Problems (FJSP) with different scales, and Composite Dispatching Rules (CDRs) are applied to generate feasible solutions. Firstly, the binary tree coding method is adopted, and the constructed function set is normalized. Secondly, a CDR mining approach based on an Improved Genetic Programming Algorithm (IGPA) is designed. Two population initialization methods are introduced to enrich the initial population, and a superior and inferior population separation strategy is designed to improve the global search ability of the algorithm. At the same time, two individual mutation methods are introduced to improve the algorithm's local search ability, to achieve the balance between global search and local search. In addition, the effectiveness of the IGPA and the superiority of CDRs are verified through comparative analysis. Finally, Deep Reinforcement Learning (DRL) is employed to solve the FJSP by incorporating the CDRs as the action set, the selection times are counted to further verify the superiority of CDRs.

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Elective Surgery Planning in Mobile Operating Theaters Under Uncertain Demand of Emergency Patients

Li,

Shu

et al. 2024

Naval Research Logistics

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Mobile operating theaters (MOTs) emerged as a solution to cope with the serious shortage of medical resources in various European countries. In this paper, we study the multi‐MOT elective surgery planning problem. Each MOT consists of an operating room (OR) and a recovery room, that is, a post‐anesthesia care unit (PACU), with two beds, which can be used to perform surgeries and recovery care for both elective and emergency patients. The daily assignment of elective surgeries to MOTs is made in advance (e.g., a week ahead) in the face of uncertain demand of emergency patients. The problem is to determine the assignment of elective patients under uncertain surgery and recovery durations within a finite planning horizon, aiming to minimize total daily costs, which include the cost of assigning elective patients, the OR and PACU utilization cost, and the postponement cost of deferring any elective surgery to the next planning horizon. Due to the possible idle time between surgeries (recoveries, resp.) in the OR (PACU, resp.) and the possible OR blocking due to a limited PACU capacity, the daily OR and PACU utilization cost cannot be expressed in an analytical functional form directly in contrast to the existing literature on OR planning. In this paper, we simulate the surgery and recovery process in ORs and PACUs based on associated distributions, which are empirically verified by the literature, and fit the OR and PACU utilization costs as piece‐wise linear convex functions of the total surgery and recovery durations of the elective patients, respectively. We model the MOT planning problem as a set‐partitioning problem and solve it by column generation. We show that the pricing problem that arises from column generation is NP‐hard. By effectively characterizing the structural properties of the optimal solution to the continuous relaxation of the pricing problem, an efficient implementation of the branch‐and‐bound procedure can be applied to obtain the optimal integral solution to the pricing problem. This solution approach also extends to the case with general OR and PACU utilization cost functions. Computational experiments demonstrate that the proposed approach can solve moderate‐sized instances efficiently. Numerical results also show that, compared with the traditional OR planning model that (i) does not consider the daily PACU utilization cost and thereby ignores the possible blocking times between surgeries in ORs and (ii) neglects the possible idle times between surgeries in ORs caused by the random arrivals of emergency patients, our model results in a cost reduction of around 15.80% on average.

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Surgery scheduling in the presence of operating room eligibility and dedicated surgeon: an adaptive composite dispatching method

Cited by 6 publications

References 53 publications

A Novel Hybrid Simulated Annealing for No-Wait Open-Shop Surgical Case Scheduling Problems

A Novel Hybrid Simulated Annealing for No-Wait Open-Shop Surgical Case Scheduling Problems

Flexible Job Shop Composite Dispatching Rule Mining Approach Based on an Improved Genetic Programming Algorithm

Elective Surgery Planning in Mobile Operating Theaters Under Uncertain Demand of Emergency Patients

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