An NSGA-II Algorithm with Adaptive Local Search for a New Double-Row Model Solution to a Multi-Floor Hospital Facility Layout Problem

Research on equipment layout of multi-layer circular manufacturing cell based on NSGA III

This paper investigates the layout optimization of multi-layer circular manufacturing cells (MCMC), a topic that has garnered limited attention compared to single-layer circular manufacturing cells (SCMC). With the continuous advancement of global intelligent manufacturing, MCMC has emerged as a viable solution, with several smart factories already implementing this model. Existing literature predominantly utilizes the NSGA II algorithm for SCMC layouts due to their relatively few objectives. However, the layout problem for MCMC encompasses a significantly larger number of objectives, rendering NSGA II inadequate. This study aims to fill this research gap by proposing an innovative approach using NSGA III, specifically designed for complex multi-objective optimization. A multi-dimensional target mathematical model for MCMC is established, facilitating the systematic examination of layout configurations. The methodology employs NSGA III to effectively tackle the challenges posed by MCMC layouts. To validate the effectiveness of NSGA III, empirical data from a smart factory in Zhejiang, China, is utilized. The findings demonstrate that NSGA III significantly outperforms traditional algorithms, yielding superior solutions for MCMC layout problems. This research not only challenges the conventional SCMC layout paradigm but also expands the options available for facility layouts in smart factories. Ultimately, it addresses the pressing engineering needs of smart factory construction and contributes valuable insights to the field of MCMC research, establishing a robust methodology for future studies.

Enhancing healthcare operations: a systematic literature review on approaches for hospital facility layout planning

PURPOSE

The appropriate physical layout of hospital services can help resolve management problems by streamlining the work of medical teams, improving the flow of patients between specific areas and the medical support environment. Nevertheless, the academic literature lacks structured research into how the physical layout of hospitals might be improved. Our study aims to fill this research gap, providing information for researchers and professionals who intend to guide the hospital facility layout planning (HFLP) from the steps and prescribed approaches found in the literature.

DESIGN/METHODOLOGY/APPROACH

This study analyzes the current literature status and concerning approaches that support HFLP and identifies their strengths and weaknesses. The literature was classified using the following criteria: approaches for layout generation, approaches for layout evaluation and healthcare facility layout outcomes.

FINDINGS

The hospital facility layout outcomes achieved for each phase served as a basis for identifying a list of strengths and weaknesses for the hospital layout facility generation and evaluation approaches. Readers can refer to this paper to identify the approach that best fits the desired goal and the HFLP step.

PRACTICAL IMPLICATIONS

This is a contribution to current studies into HFLP, and it provides guidelines for selecting the approach to be utilized based on the desired outcome.

ORIGINALITY/VALUE

The paper describes how to conduct an HFLP and lists the strengths and weaknesses of each approach. The research may be used as a strategy for determining which tool is most suited based on the practitioner's target purpose.

Hospital reconversion in response to the COVID-19 pandemic using simulation and multi-objective genetic algorithms

With the outbreak of the novel coronavirus SARS-CoV2, many countries have faced problems because of their available hospital capacity. Health systems must be prepared to restructure their facilities and meet the requirements of the pandemic while keeping their services and specialties active. This process, known as hospital reconversion, contributes to minimizing the risk of contagion between hospital staff and patients and optimizing the efficient treatment and disposal of healthcare wastes that represent a risk of nosocomial infection contagion. A methodology based upon simulation and mathematical optimization with genetic algorithms is proposed to address the hospital reconversion problem. Firstly, a discrete event simulation model is developed to study the flow of patients within the hospital system. Subsequently, the hospital reconversion problem is formulated through a mathematical model seeking to maximize the proximity relationships between departments and minimize the costs due to the flow of agents within the system. Finally, the results obtained from the optimization process are evaluated through the simulation model. The proposed framework is validated by assessing the hospital reconversion process in a COVID-19 Hospital in Mexico. The results show the mathematical model's effectiveness by incorporating the medical personnel's expertise in decisions regarding the use of elevators, departments' location, structural dimensions, use of corridors, and the floors to which the departments are assigned when facing a pandemic. The contribution of this approach can be replicated during the hospital reconversion process in other hospitals with different characteristics.

Dynamic Layout Design Optimization to Improve Patient Flow in Outpatient Clinics Using Genetic Algorithms

Evolutionary algorithms, such as genetic algorithms have been used in various optimization problems. In this paper, we propose to apply this algorithm to obtain the layout design/redesign in order to improve the patient flow in an outpatient clinic. Layout designs are planned considering long-term requirements whereas the layout keeps modifying as per short-term demands. Over a period of time, the layout often does not remain efficient. Therefore, there is a need for such a model that helps in decision making on layout redesigns, and it must also optimize workflow by incorporating the flow constraints. In this study, we propose to minimize the waiting times by obtaining optimal and sub-optimal layout designs. A genetic algorithm is implemented to redesign the layouts based on the changing dynamics of patient demand, clinical pathways and services offered. The workflow is simulated with current layout and optimized layouts, and the results in terms of waiting time and cycle time are compared. The study shows that when layout design or redesign incorporate the workflow and pathways along with associated constraints, improves waiting time and cycle time of patients in the outpatient clinic. The distance between the departments/locations is translated to travelling time and overall travel distance/time is minimized by rearranging the allocations of departments to the location through genetic algorithms.