A Constraint-Programming-Based Branch-and-Price-and-Cut Approach for Operating Room Planning and Scheduling

Doulabi, Seyed Hossein Hashemi; Rousseau, Louis-Martin; Pesant, Gilles

doi:10.1287/ijoc.2015.0686

Cited by 72 publications

(26 citation statements)

References 29 publications

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“…2010, Hashemi Doulabi et al. 2016). We first formulate GORPS using MIP and CP models with a continuous overtime function for each member of each resource.…”

Section: Problem Description and Mathematical Modelingmentioning

confidence: 98%

“…2020), time‐based (Hashemi Doulabi et al. 2016), and constraint programming (Wang et al. 2015) modeling paradigms.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Constraint programming (CP) is another paradigm for modeling surgery sequencing, and it has been shown to be relatively effective for solving integrated allocation‐sequencing OR scheduling problems for realistically sized instances of the problem (Hashemi Doulabi et al. 2016). We show, however, that CP can be incorporated effectively into our decomposition techniques to quickly ascertain surgical schedule feasibility.…”

Section: Literature Reviewmentioning

confidence: 99%

“…2006), or concurrently, using exact techniques such as mathematical programming models solved via existing optimization solvers (Hashemi Doulabi et al. 2016, Marques et al. 2012, Pham and Klinkert 2008, Roshanaei et al.…”

Section: Literature Reviewmentioning

confidence: 99%

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Increased Surgical Capacity without Additional Resources: Generalized Operating Room Planning and Scheduling

Naderi

Roshanaei

Begen

et al. 2021

Production and Operations Management

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We study a generalized operating room planning and scheduling (GORPS) problem at the Toronto General Hospital (TGH) in Ontario, Canada. GORPS allocates elective patients and resources (i.e., operating rooms, surgeons, anesthetists) to days, assigns resources to patients, and sequences patients in each day. We consider patients’ due‐date, resource eligibility, heterogeneous performances of resources, downstream unit requirements, and lag times between resources. The goal is to create a weekly surgery schedule that minimizes fixed‐ and over‐time costs. We model GORPS using mixed‐integer and constraint programming models. To efficiently and effectively solve these models, we develop new‘ multi‐featured logic‐based Benders decomposition approaches. Using data from TGH, we demonstrate that our best algorithm solves GORPS with an average optimality gap of 2.71% which allows us to provide our practical recommendations. First, we can increase daily OR utilization to reach 80%—25% higher than the status quo in TGH. Second, we do not require to optimize for the daily selection of anesthetists—this finding allows for the development of effective dominance rules that significantly mitigate intractability. Third, solving GORPS without downstream capacities (like many papers in literature) makes GORPS easier to solve, but such OR schedules are only feasible in 24% of instances. Finally, with existing ORs’ safety capacities, TGH can manage 40% increase in its surgical volumes. We provide recommendations on how TGH must adjust its downstream capacities for varying levels of surgical volume increases (e.g., current urgent need for more capacity due to the current Covid‐19 pandemic).

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“…2010, Hashemi Doulabi et al. 2016). We first formulate GORPS using MIP and CP models with a continuous overtime function for each member of each resource.…”

Section: Problem Description and Mathematical Modelingmentioning

confidence: 98%

“…2020), time‐based (Hashemi Doulabi et al. 2016), and constraint programming (Wang et al. 2015) modeling paradigms.…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Increased Surgical Capacity without Additional Resources: Generalized Operating Room Planning and Scheduling

Naderi

Roshanaei

Begen

et al. 2021

Production and Operations Management

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show abstract

“…Finally, constraint (14) demands that one and only one surgeon from each group can operate. Constraints (15)(16)(17)(18)(19)(20) denote positive and binary decision variables.…”

Section: Setsmentioning

confidence: 99%

Multiple Resource Surgical Case Scheduling Problem: Ant Colony System Approach

Behmanesh¹,

Zandieh²,

Molana³

2020

ECECSR

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In this paper, we address the multiple-resource surgical case scheduling(MRSCS) problem in multi-operating theatre to minimize makespan. The constraints of no-wait multiple resource flexible job shop problem (FJSP) are considered for formulating MRSCS problem because the environment of the operating room is similar to the FJSP. Minimization of makespan forMRSCS is NP-hard combinatorial optimization problem; hence we employ the ant colony optimization algorithm so as to tackle this problem. These proposed approaches are illustrated by three test cases include small, medium, and large dataset. Consequently, the results of the experiments indicate that ant colony system and elitism ant system outperform traditional ant system since, the mean, the standard deviation, the best, and the worst solutions of proposed algorithms are better than the results of the traditional algorithm.

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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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A Constraint-Programming-Based Branch-and-Price-and-Cut Approach for Operating Room Planning and Scheduling

Cited by 72 publications

References 29 publications

Increased Surgical Capacity without Additional Resources: Generalized Operating Room Planning and Scheduling

Increased Surgical Capacity without Additional Resources: Generalized Operating Room Planning and Scheduling

Multiple Resource Surgical Case Scheduling Problem: Ant Colony System Approach

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

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