Facility Layout Problem (FLP) is gaining increasing attention among researchers; it is a term relating to the poor layout of facilities as a significant contributing factor of poor performance. FLP is of paramount importance when determining inefficiencies in large room layouts, such as a library building, since the building’s layout closely influences air distribution and impacts on human comfort. Thus, this can lead to unnecessary high energy use to mitigate any inefficiencies. This problem is classified as an NP-hard problem (non-deterministic polynomial-time hardness), considering the various factors influencing thermal factors and layout design. However, previous research shows a lack of consideration of FLP for large rooms. It is identified that various types of constraints are considered in the layout problem literature, and penalty-based constraints are often being prioritised by mere human judgement and intuition. Hence, the accuracy of the objective decision-making is questionable. Therefore, this study proposes a multi-stage methodology to determine the weight of each constraint for FLP by using a multi-criteria decision making (MCDM) method specific to a library building as a case study exemplar. This study’s main focus is to determine penalty based constraints in meta-heuristic approaches for the effective use of FLP. This study concludes by advocating that the proposed methodological approach can be used to identify the most significant constraints in FLP.
Whale Optimization Algorithm (WOA) is considered as one of the newest metaheuristic algorithms to be used for solving a type of NP-hard problems. WOA is known of having slow convergence and at the same time, the computation of the algorithm will also be increased exponentially with multiple objectives and huge request from n users. The current constraints surely limit for solving and optimizing the quality of Demand Side Management (DSM) case, such as the energy consumption of indoor comfort index parameters which consist of thermal comfort, air quality, humidity and vision comfort. To address these issues, this proposed work will firstly justify and validate the constraints related to the appliances scheduling problem, and later proposes a new model of the Cluster based Multi-Objective WOA with multiple restart strategy. In order to achieve the objectives, different initialization strategy and cluster-based approaches will be used for tuning the main parameter of WOA under different MapReduce application which helps to control exploration and exploitation, and the proposed model will be tested on a set of well-known test functions and finally, will be applied on a real case project i.e. appliances scheduling problem. It is anticipating that the approach can expedite the convergence of meta-heuristic technique with quality solution.
Facility Layout Problem (FLP) can be considered as a classical problem in quantitative studies. However, the literature in FLP are largely neglected the thermal comfort as part of the objective function. Today, energy savings for buildings are a major concern in the world as they cover a big portion of energy use. The public room consumes high energy use because of its ability to occupy many people at one time. Issues arise when each person has a dissimilar thermal satisfaction rate, while each area in a room provides a different temperature. There are many factors that influence the people dispersion in the room including the facility layout. However, it is really testing to handle an air-conditioning control () system by considering the mention factors to ensure the thermal satisfaction is increased and energy is reduced. Since lack of report on thermal factors in Facility Layout Problem (FLP) area, this work aims to optimize the temperature setting of an system at the best point and achieving the finest plan for the facility layout in a room. Further, our ultimate goals to maximize the thermal comfort level and reduce energy consumption are able to accomplish. A non-linear mathematical model is utilized to optimize the thermal satisfaction rate () and room layout. At the end of the article, we proposed an Evolutionary Algorithm (EA) to find a quality solution or near optimal since it is hard to solve this problem in a reasonable time.
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