Static and Dynamic Layout Problems with Varying Areas

Lacksonen, Thomas A.

doi:10.1038/sj/jors/0450107

Cited by 9 publications

(14 citation statements)

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“…, the discretised polyhedral outer-approximation of the area constraint is used, as shown in constraint (A4). Unlike Lacksonen (1994), constraint (A4) provides a purely linear approximation of the area constraint without involving binary variables. The Δ tangential supports to the curve l…”

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confidence: 99%

Improving patient timeliness of care through efficient outpatient clinic layout design using data-driven simulation and optimisation

et al. 2019

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With greater demand for outpatient services, the importance of patient-centric clinic layout design that improves timeliness of patient care has become more elucidated. In this paper, a novel simulation-optimisation (SO) framework is proposed focusing on the physical and process flows of patients in the design of a paediatric orthopaedic outpatient clinic. A discrete-event simulation model is used to estimate the frequency of movements between clinic units. The resulting information is utilised as input to a mixed integer programming (MIP) model, optimising the clinic layout design. In order to solve the MIP model, Particle Swarm Optimisation (PSO), a metaheuristic approach enhanced with several heuristics is utilised. Finally, the optimisation model outputs are evaluated with the simulation model. The results demonstrate that improvements to the quality of the patient experience can be achieved through incorporating SO methods into the clinic layout design process.

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confidence: 99%

Improving patient timeliness of care through efficient outpatient clinic layout design using data-driven simulation and optimisation

et al. 2019

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“…Conway and Venkataraman (1994) used a genetic search algorithm to solve the constrained dynamic layout problem (CDLP) and found it to be effective in solving the sample problems presented in Rosenblatt (1986). Lacksonen (1994) extended the analysis of dynamic layout problems by developing heuristic procedures for the QAP applicable to problems where workcenters have varying areas.…”

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confidence: 99%

Monte-Carlo-Based Robust Procedure for Dynamic Line Layout Problems

Chan¹,

Malmborg²

2013

Theory and Applications of Monte Carlo Simulations

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Carlo simulation techniques can be applied to map the distribution of volume distance values in the solution space corresponding to deterministic line layout problems is exploited to define stopping criteria for robust procedures. A line layout algorithm is proposed which uses these sampling procedures to first construct a mapping of the volume distance solution space for each parameter set, and then identify layouts meeting minimum performance standards for all potential operating scenarios.The next section describes uncertainty in the information base associated with line layout problems and describes previous work associated with solving stochastic problems and the empirical mapping of volume distance distributions. The third section describes a robust algorithm for solving the stochastic and/or dynamic line layout problem. In the fourth section, two test problems are introduced and computational experience with the procedure is described. The fifth section extends the study by allowing nonlinear material handling costs. The final section offers a summary and conclusions.

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“…Conway and Venkataramanan (1994) propose the use of genetic algorithms (GA) for the DPLP in 1994 while Kaku and Mazzola (1997) use Tabu Search. Research on dynamic layout when the sizes of departments are unequal have been carried out by among others, Lacksonen (1994), and Montreuil and Venkatadri (1990). Formulations and detailed reviews of these and other approaches to the DPLP can be found in Balakrishnan and Cheng (1998).…”

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confidence: 99%

The dynamic plant layout problem: Incorporating rolling horizons and forecast uncertainty☆

Balakrishnan

Cheng

2009

Omega

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To the best of our knowledge, research in the Dynamic Plant Layout Problem (DPLP) assumes that the planning horizon is fixed and that material flows are known with certainty. But in practice, many companies use rolling planning horizons. Further, they have to deal with the effect of uncertainty in material flow forecasts. This paper investigates the performance of algorithms under fixed and rolling horizons, under different shifting costs and flow variability, and under forecast uncertainty. Nearly 1800 problems were run using different algorithms. The results show that algorithms that dominated under fixed horizons may not work as well under rolling horizons. Also it is difficult to identify an algorithm that performs well under all situations. Thus the development of efficient and effective heuristics might be useful in solving the rolling horizon problem. It also appears that increasing the planning horizon under rolling plans does not offer any advantage. Further forecast uncertainty may not significantly affect the performance of algorithms and in some cases may be beneficial.

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Static and Dynamic Layout Problems with Varying Areas

Cited by 9 publications

References 0 publications

Improving patient timeliness of care through efficient outpatient clinic layout design using data-driven simulation and optimisation

Improving patient timeliness of care through efficient outpatient clinic layout design using data-driven simulation and optimisation

Monte-Carlo-Based Robust Procedure for Dynamic Line Layout Problems

The dynamic plant layout problem: Incorporating rolling horizons and forecast uncertainty☆

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