Adapting GA to solve a novel model for operating room scheduling problem with endogenous uncertainty

Hooshmand, F.; MirHassani, S. A.; Akhavein, A.

doi:10.1016/j.orhc.2018.02.002

Cited by 24 publications

(10 citation statements)

References 41 publications

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“…The purpose of reducing the desired costs of operating rooms and increasing their efficiency is the overlap of the obtained results from this study. In the literature [36][37][38], the main purpose of operating rooms is to reduce costs. These costs are mostly due to overtime and waiting.…”

Section: Resultsmentioning

confidence: 99%

Surgical Operation Scheduling with Goal Programming and Constraint Programming: A Case Study

2019

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The achievement of health organizations’ goals is critically important for profitability. For this purpose, their resources, materials, and equipment should be efficiently used in the services they provide. A hospital has sensitive and expensive equipment, and the use of its equipment and resources needs to be balanced. The utilization of these resources should be considered in its operating rooms, as it shares both expense expenditure and revenue generation. This study’s primary aim is the effective and balanced use of equipment and resources in hospital operating rooms. In this context, datasets from a state hospital were used via the goal programming and constraint programming methods. According to the wishes of hospital managers, three scenarios were separately modeled in both methods. According to the obtained results, schedules were compared and analyzed according to the current situation. The hospital-planning approach was positively affected, and goals such as minimization cost, staff and patient satisfaction, prevention over time, and less use were achieved.

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Section: Resultsmentioning

confidence: 99%

Surgical Operation Scheduling with Goal Programming and Constraint Programming: A Case Study

2019

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“…The problem was approximated by a sample average approximation which was solved by combining a greedy local search and Monte Carlo simulation. Hooshmand et al (2018) considered an allocation problem integrating both scheduling and rescheduling decisions. The uncertainty of surgeries was represented by a finite set of scenarios.…”

Section: Literature Reviewmentioning

confidence: 99%

Minimizing bed occupancy variance by scheduling patients under uncertainty

d’Obrenan

Ridder

Roubos³

et al. 2020

European Journal of Operational Research

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In this paper we consider the problem of scheduling patients in allocated surgery blocks in a Master Surgical Schedule. We pay attention to both the available surgery blocks and the bed occupancy in the hospital wards. More specifically, large probabilities of overtime in each surgery block are undesirable and costly, while large fluctuations in the number of used beds requires extra buffer capacity and makes the staff planning more challenging. The stochastic nature of surgery durations and length of stay on a ward hinders the use of classical techniques. Transforming the stochastic problem into a deterministic problem does not result into practically feasible solutions. In this paper we develop a technique to solve the stochastic scheduling problem, whose primary objective it to minimize variation in the necessary bed capacity, while maximizing the number of patients operated, and minimizing the maximum waiting time, and guaranteeing a small probability of overtime in surgery blocks. The method starts with solving an Integer Linear Programming (ILP) formulation of the problem, and then simulation and local search techniques are applied to guarantee small probabilities of overtime and to improve upon the ILP solution. Numerical experiments applied to a Dutch hospital show promising results.

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“…Khaligh and Mirhassani (2016) solved a single-vehicle routing problem with stochastic demands, where the actual demand of a customer is revealed only once the customer is visited. Considering the problem of hospital operation room scheduling, Hooshmand et al (2018) developed a genetic algorithm to solve the endogenous time-stochastic scheduling problem. Vayanos et al (2011) studied the production planning problem in offshore oil fields considering endogenous uncertainties.…”

Section: Endogenous Stochastic Programmingmentioning

confidence: 99%

“…The problem is NP-complete, as it is a generalisation of the travelling salesman problem, so any exact solution method is unscalable and not applicable to realistic problem instance sizes. Previous research relies on heuristics and meta-heuristics to overcome the scalability issues of exact solution methods for the TD-VRP (Gendreau et al 2015;Kok et al 2012) and multi-stage endogenous stochastic problems (Gupta and Grossmann 2011;Hooshmand and MirHassani 2016;Hooshmand et al 2018;Apap 2017). Thus, the pursuit of exact solutions for these kinds of problems is infeasible within acceptable computational time and resources.…”

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confidence: 99%

Endogenous stochastic optimisation for relief distribution assisted with unmanned aerial vehicles

et al. 2020

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Unmanned aerial vehicles (UAVs) have been increasingly viewed as useful tools to assist humanitarian response in recent years. While organisations already employ UAVs for damage assessment during relief delivery, there is a lack of research into formalising a problem that considers both aspects simultaneously. This paper presents a novel endogenous stochastic vehicle routing problem that coordinates UAV and relief vehicle deployments to minimise overall mission cost. The algorithm considers stochastic damage levels in a transport network, with UAVs surveying the network to determine the actual network damages. Ground vehicles are simultaneously routed based on the information gathered by the UAVs. A case study based on the Haiti road network is solved using a greedy solution approach and an adapted genetic algorithm. Both methods provide a significant improvement in vehicle travel time compared to a deterministic approach and a non-assisted relief delivery operation, demonstrating the benefits of UAV-assisted response.

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Adapting GA to solve a novel model for operating room scheduling problem with endogenous uncertainty

Cited by 24 publications

References 41 publications

Surgical Operation Scheduling with Goal Programming and Constraint Programming: A Case Study

Surgical Operation Scheduling with Goal Programming and Constraint Programming: A Case Study

Minimizing bed occupancy variance by scheduling patients under uncertainty

Endogenous stochastic optimisation for relief distribution assisted with unmanned aerial vehicles

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