Local search and lower bounds for the patient admission scheduling problem

Ceschia, Sara; Schaerf, Andrea

doi:10.1016/j.cor.2011.01.007

Cited by 78 publications

(50 citation statements)

References 31 publications

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“…Furthermore, previous attempts at solving even just the smaller instances using mathematical programming techniques have proven futile. For comparative purposes, we will compare with the best known bounds, running times, and solutions obtained by Ceschia and Schaerf [2011]. In order to make this comparison we use the standard penalties described by Demeester et al [2010].…”

Section: Computational Resultsmentioning

confidence: 99%

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A column generation approach for solving the patient admission scheduling problem

Range

Lusby

Larsen

2014

European Journal of Operational Research

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This paper addresses the Patient Admission Scheduling (PAS) problem. The PAS problem deals with assigning elective patients to beds, satisfying a number of soft and hard constraints. The problem can be seen as part of the functions of hospital management at an operational level. There exists a small number of different variants on this problem. We propose an optimization-based heuristic building on branch-andbound, column generation, and dynamic constraint aggregation for one of the variants. We achieve tighter bounds than previously reported in the literature, and in addition we are able to produce new best solutions for five out of six instances from a publicly available repository.

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Section: Computational Resultsmentioning

confidence: 99%

“…The remaining work in this area, Demeester et al [2010], Bilgin et al [2012], Ceschia and Schaerf [2011], all consider the same version of PAS. This definition of the PAS problem was originally presented in Demeester et al [2010].…”

Section: Introductionmentioning

confidence: 99%

A column generation approach for solving the patient admission scheduling problem

Range

Lusby

Larsen

2014

European Journal of Operational Research

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“…Schaerf [7,11], Range et al [9]. Metaheuristic approaches are therefore an attractive alternative as they can overcome such tractability issues.…”

Section: Solution Approachmentioning

confidence: 99%

“…Schaerf [7]. Recall that we do not intend to solve this formulation directly by mixed integer linear…”

Section: A Mathematical Model For the Static Pasmentioning

confidence: 99%

“…The authors proposed a hybrid tabu search algorithm to solve this problem. The same instances have subsequently been considered by Ceschia and Schaerf [7], Bilgin et al [8], and Range et al [9]. The first two contributions also consider local search based heuristics, while the last describes a column generation based heuristic approach combined with constraint aggregation.…”

Section: Introductionmentioning

confidence: 99%

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An adaptive large neighborhood search procedure applied to the dynamic patient admission scheduling problem

Lusby

Schwierz²,

Range

et al. 2016

Artificial Intelligence in Medicine

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Objective: The aim of this paper is to provide an improved method for solving the so-called dynamic patient admission scheduling (DPAS) problem. This is a complex scheduling problem that involves assigning a set of patients to hospital beds over a given time horizon in such a way that several quality measures reflecting patient comfort and treatment efficiency are maximized.Consideration must be given to uncertainty in the length of stays of patients as well as the possibility of emergency patients. Method:We develop an adaptive large neighborhood search (ALNS) procedure to solve the problem. This procedure utilizes a Simulated Annealing framework. Results: We thoroughly test the performance of the proposed ALNS approach on a set of 450 publicly available problem instances. A comparison with the current state-of-the-art indicates that the proposed methodology provides solutions that are of comparable quality for small and medium sized instances (up to 1000 patients); the two approaches provide solutions that differ in quality by approximately 1% on average. The ALNS procedure does, however, provide solutions in a much shorter time frame. On larger instances (between 1000-4000 patients) the improvement in solution quality by the ALNS procedure is substantial, approximately 3-14% on average, and as much as 22% on a single instance. The time taken to find such results is, however, in the worst case, a factor 12 longer on average than the time limit which is granted to the current state-of-the-art. Conclusion:The proposed ALNS procedure is an efficient and flexible method for solving the DPAS problem.

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