Unequal Area Facility Layout Problem Research Articles

Unequal area facility layout problem considering transporters interaction– a queuing theory and machine learning approach

Unequal area facility layout problem considering transporters interaction– a queuing theory and machine learning approach

Solving Unequal Area Facility Layout Problems with Differential Evolution and Particle Swarm Optimization

The unequal area facility layout problem (UAFLP) is a problem in industrial engineering that aims to minimize material handling costs between a group of unequal-shaped facilities. In this study, Differential Evolution (DE) with continuous variables and Particle Swarm Optimization (PSO) are employed to solve the UAFLP. This study also compares the performance of the DE method (never used to solve UAFLP) and the PSO method (frequently used to solve facility layout problems). The experimental results demonstrate that PSO is effective in solving small-sized datasets, while DE works well in solving datasets with larger areas.

Learning-based simulated annealing algorithm for unequal area facility layout problem

Learning-based simulated annealing algorithm for unequal area facility layout problem

A two-stage solution approach for unequal area facility layout problem with bi-directional relaxed flexible bay structure

PurposeThe purpose of this article is to develop a bi-directional relaxed flexible bay structure (BRFBS) in the layout for the unequal area facility layout problems (UA-FLPs) and test the suitability of the proposed approach using literature data.Design/methodology/approachThis research adopts a two-stage solution approach for UA-FLPs to form BRFBS in the layout. The solution to UA-FLPs is carried out in discrete space. The proposed heuristic method optimises the layout plan for minimising the material handling cost (MHC), and also, it indirectly optimises the space utilisation by reducing the empty space in the layout. The first stage of layout design assumes that all facilities are equal in size and uses quadratic assignment problem (QAP) model. QAP is solved with a simulated annealing heuristic method. In the second stage, a heuristic method is proposed to find the optimum width for each bay and the dimension for facilities. The proposed heuristic method is tested with numerical data available in the literature. Results are compared with the results obtained by layout planning software, and with the simulated annealing algorithm for flexible bay structure (SA-FBS) heuristic procedure for continuous space UA-FLPs.FindingsThe proposed two-stage solution approach gives the BRFBS for the UA-FLPs. BRFBS helps to create proper aisle structure in the layout plan. The layout configuration and solution of the proposed method is better than the layout planning software solution and SA-FBS solution. The application of the proposed heuristic method to case data gave lesser MHC, better space utilisation and better aisle formation than the existing layout.Research limitations/implicationsThe proposed approach has the limitation that it can be applied only to UA-FLPs solved in discrete space. When the UA-FLPs are solved in continuous space, then it is not possible to make application of this approach to form bi-directional relaxed flexible bays in the layout plan.Practical implicationsMost of the modern industries are automated, and they use material handling equipment (MHE) like automated guided vehicles (AGVs). Design of layout plans that help to create proper aisle structure for AGV’s in the layout plan is a challenging to the researchers. The BRFBS configuration is more suitable in the flexible manufacturing system where AGVs are used for material transportation.Originality/valueThis paper proposes a novel two-stage heuristic method for solving the UA-FLPs in discrete space. The proposed approach generates a BRFBS in the layout plan. The BRFBS helps to create a proper aisle structure suitable for better material handling operations. Hence, this type of layout helps in easy interaction of the MHE (e.g. AGVs) with the boundaries of the facilities touching the aisle.

Unequal area facility layout problem-solving: a real case study on an air-conditioner production shop floor

Solving facility layout problems aims at identifying the optimal plan for placing a number of facilities or departments on a shop floor considering multiple optimisation criteria and constraints. Recently, the unequal area facility layout problem (UA-FLP) has drawn more attention as it is closer to real industrial scenarios. To address this problem, the typical UA-FLP in an air-conditioner production shop floor is analysed, and then a modified non-dominated sorting genetic algorithm (NSGA-II) is developed to identify the optimal layout plan considering the material handling cost (MHC) and the closeness rating score (CRS). NSGA-II is a stable algorithm for engineering applications and has an encoding structure of chromosomes that can express facility layout expediently. Besides that, the two objectives (MHC and CRS) that are conflicting might be another reason for adopting NSGA-II. During the process, the crossover and mutation operators of NSGA-II are modified based on the non-overlapping method, which reduces the time cost for eliminating unsuitable layout plans. The modified NSGA-II is compared with two related algorithms, and the results show that it has better performance on the UA-FLP with a large number of departments.

Optimization of Multi-Objective Unequal Area Facility Layout

The unequal area facility layout problem (UA-FLP) refers to the reasonable arrangement of a certain number of facilities in a given layout area. The facility layout should satisfy given layout constraints and optimize given optimization objectives as far as possible. In this paper, a method combining improved lowest horizontal line method and particle swarm optimization algorithm is proposed to solve UA-FLP, which achieves multi-objective optimization of material handling cost, the adjacent value and the utilization rate of floor shop. On the one hand, the algorithm formulates facility packing rules through the improved lowest horizontal line method, which simplifies the legalization of facility layout. On the other hand, a modified particle swarm optimization (PSO) algorithm combining objective space division method (OSD) and niche technology is used for multi-objective optimization. The proposed algorithm overcomes the shortcomings of previous facility layout methods such as complex overlapping interference process, large amount of calculation and long time for multi-objective optimization. Compared with the comparison methods, the results show that material handling cost is reduced by 1%–6% and the utilization ratio of floor shop is increased by 2%–7%.

An Application of an Unequal-Area Facilities Layout Problem with Fixed-Shape Facilities

The unequal-area facility layout problem (UA-FLP) is the problem of locating rectangular facilities on a rectangular floor space such that facilities do not overlap while optimizing some objective. The objective considered in this paper is minimizing the total distance materials travel between facilities. The UA-FLP considered in this paper considers facilities with fixed dimension and was motivated by the investigation of layout options for a production area at the Toyota Motor Manufacturing West Virginia (TMMWV) plant in Buffalo, WV, USA. This paper presents a mathematical model and a genetic algorithm for locating facilities on a continuous plant floor. More specifically, a genetic algorithm, which consists of a boundary search heuristic (BSH), a linear program, and a dual simplex method, is developed for an UA-FLP. To test the performance of the proposed technique, several test problems taken from the literature are used in the analysis. The results show that the proposed heuristic performs well with respect to solution quality and computational time.

Multi-objective unequal area pod-structured healthcare facility layout problem with daylight requirements

Outpatient clinic design can be optimized with a proposed mathematical model that formulates an Unequal Area Facility Layout Problem (UAFLP). The proposed non-linear multi-objective model maximizes natural daylight exposure and minimizes total walking distance for patients while considering constraints for modeling daylight, enabling a pod-structure, allowing for rotation of spaces, and ensuring accessibility to rooms within pods. This study identifies efficient algorithms that can tackle several inherent challenges of this NP-hard problem, including computational complexity and approximation of the area constraints for spaces with wide bounds. An adapted Genetic Algorithm for placement (GA-P) proved effective in solving the proposed multi-objective UAFLP model that estimated available daylight by using simulation–optimization. Sensitivity analysis conducted based on the experimental design indicates that the main factors affecting the algorithm’s performance are the selection mechanism for best Pareto point, number of spaces requiring lighting, and lighting dataset used. This adapted GA-P achieves better results than a Genetic Algorithm with continuous encoding of space coordinates (GA-C), Particle Swarm Optimization (PSO), and Simulated Annealing for Placement (SA-P) in terms of daylight exposure and travel distance for a multi-objective problem.

A survey on discrete space and continuous space facility layout problems

PurposeThe purpose of this paper is to review, evaluate and classify the academic research that has been published in facility layout problems (FLPs) and to analyse how researches and practices on FLPs are.Design/methodology/approachThe review is based on 166 papers published from 1953 to 2021 in international peer-reviewed journals. The literature review on FLPs is presented under broader headings of discrete space and continuous space FLPs. The important formulations of FLPs under static and dynamic environments represented in the discrete and continuous space are presented. The articles reported in the literature on various representations of facilities for the continuous space Unequal Area Facility Layout Problems (UA-FLPs) are summarized. Discussed and commented on adaptive and robust approaches for dynamic environment FLPs. Highlighted the application of meta-heuristic solution methods for FLPs of a larger size.FindingsIt is found that most of the earlier research adopted the discrete space for the formulation of FLPs. This type of space representation for FLPs mostly assumes an equal area for all facilities. UA-FLPs represented in discrete space yield irregular shape facilities. It is also observed that the recent works consider the UA-FLPs in continuous space. The solution of continuous space UA-FLPs is more accurate and realistic. Some of the recent works on UA-FLPs consider the flexible bay structure (FBS) due to its advantages over the other representations. FBS helps the proper design of aisle structure in the detailed layout plan. Further, the recent articles reported in the literature consider the dynamic environment for both equal and unequal area FLPs to cope with the changing market environment. It is also found that FLPs are Non-deterministic Polynomial-complete problems, and hence, they set the challenges to researchers to develop efficient meta-heuristic methods to solve the bigger size FLPs in a reasonable time.Research limitations/implicationsDue to the extremely large number of papers on FLPs, a few papers may have inadvertently been missed. The facility layout design research domain is extremely vast which covers other areas such as cellular layouts, pick and drop points and aisle structure design. This research review on FLPs did not consider the papers published on cellular layouts, pick and drop points and aisle structure design. Despite the possibility of not being all-inclusive, the authors firmly believe that most of the papers published on FLPs are covered and the general picture presented on various approaches and parameters of FLPs in this paper are precise and trustworthy.Originality/valueTo the best of the authors’ knowledge, this paper reviews and classifies the literature on FLPs for the first time under the broader headings of discrete space and continuous space representations. Many important formulations of FLPs under static and dynamic environments represented in the discrete and continuous space are presented. This paper also provides the observations from the literature review and identifies the prospective future directions.

Integrated framework of process mining and simulation–optimization for pod structured clinical layout design

This paper proposes a three-phase framework to leverage hospital tracking data of patient visits while designing healthcare layouts with pod structures. The first phase proposes a process mining algorithm that modifies the Probabilistic Determining Finite Automata (PDFA) with Particle Swarm Optimization (PDFA-PSO) algorithm to predict the significant patient workflows from hospital historical data. The second phase employs simulation modeling to solve a right-sizing problem to determine the optimal size of the layout pods and the frequency of flows between the different clinical locations. The final phase uses an Unequal Area Facility Layout Problem (UAFLP) to determine the layout typology. The proposed process mining and simulation model are vital steps to measure the frequency between spaces and pod areas, which are needed to solve the UAFLP for outpatient settings. The proposed framework is validated using a case study for a renovation project of a large heart and vascular clinic in the US. The research shows that process mining is an efficient tool to extract a subset of significant patient pathways among 90 pathway variants and build a more realistic simulation that reflects behavioral and operational aspects. The research shows that the PSO algorithm is efficient in estimating the PDFA parameters and improving the prediction accuracy of the extracted patient pathways. In addition, the research shows that Genetic Algorithm with Placement Staretegy is an efficient algorithm for layout automation.

A monarch butterfly optimization for an unequal area facility layout problem

A monarch butterfly optimization for an unequal area facility layout problem

A Multi-User Interactive Coral Reef Optimization Algorithm for Considering Expert Knowledge in the Unequal Area Facility Layout Problem

The problem of Unequal Area Facility Layout Planning (UA-FLP) has been addressed by a large number of approaches considering a set of quantitative criteria. Moreover, more recently, the personal qualitative preferences of an expert designer or decision-maker (DM) have been taken into account too. This article deals with capturing more than a single DM’s personal preferences to obtain a common and collaborative design including the whole set of preferences from all the DMs to obtain more complex, complete, and realistic solutions. To the best of our knowledge, this is the first time that the preferences of more than one expert designer have been considered in the UA-FLP. The new strategy has been implemented on a Coral Reef Optimization (CRO) algorithm using two techniques to acquire the DMs’ evaluations. The first one demands the simultaneous presence of all the DMs, while the second one does not. Both techniques have been tested over three well-known problem instances taken from the literature and the results show that it is possible to obtain sufficient designs capturing all the DMs’ personal preferences and maintaining low values of the quantitative fitness function.

Development of an optimisation model for unequal-area facility layout problems

Development of an optimisation model for unequal-area facility layout problems

Design of robust layout for unequal area dynamic facility layout problems with flexible bays structure

PurposeThe purpose of this paper is to develop a mathematical model for the design of robust layout for unequal area-dynamic facility layout problem with flexible bay structure (UA-DFLP with FBS) and test the suitability of generated robust layout in a dynamic environment.Design/methodology/approachThis research adopts formulation of a mathematical model for generating a single layout for unequal area facility layout problems with flexible bay structure under dynamic environment. The formulated model for the robust layout formation is solved by developing a simulated annealing algorithm. The proposed robust approach model for UA-DFLP with FBS is validated by conducting numerical experiments on standard UA-DFLPs reported in the literature. The suitability of the generated robust layout in a dynamic environment is tested with total penalty cost criteria.FindingsThe proposed model has given a better solution for some UA-DFLPs with FBS in comparison with the adaptive approach’s solution reported in the literature. The total penalty cost is within the specified limit given in the literature, for most of the layouts generated for UA-DFLPs with FBS. In the proposed model, there is no rearrangement of facilities in various periods of planning horizon and thus no disruptions in operations.Research limitations/implicationsThe present work has limitations that when the area and aspect ratio of the facilities are required to change from one period to another, then it is not possible to make application of the robust approach-based formulation to the dynamic environment facility layout problems.Practical implicationsRearrangement of facilities in adaptive approach disrupts the operations whereas in the proposed approach no disruption of production. The FBS approach is more suitable for layout planning where proper aisle structure is required. The solution of the proposed approach helps to create a proper aisle structure in the detailed layout plan. Thus, easy interaction of the material handling equipment, men and materials is possible.Originality/valueThis paper proposes a mathematical formulation for the design of robust layout for UA-FLPs with FBS in a dynamic environment and an efficient simulated annealing algorithm as its solution procedure. The proposed robust approach generates a single layout for the entire planning horizon. This approach is more useful for facilities which are difficult/sensitive to relocate in various periods of the planning horizon.

Addressing Unequal Area Facility Layout Problems with the Coral Reef Optimization algorithm with Substrate Layers

The Unequal Area Facility Layout Problem (UA-FLP) is a relevant task in industrial manufacturing, in which the disposition of a number of facilities (or departments) in a manufacturing system must be obtained, under several optimization criteria and different constraints. The UA-FLP is a hard optimization problem, in which traditional optimization techniques do not obtain good results. Thus, it has been successfully tackled with different heuristics and meta-heuristics in the last years. In this work we address the UA-FLP with a multi-method ensemble approach, the Coral Reefs Optimization algorithm with Substrate Layers (CRO-SL). It is a novel multi-method evolutionary algorithm that encourages the evolution of several searching procedures at the same time over a single population. The CRO-SL has been previously applied to very difficult optimization problems, obtaining excellent performance. In this case, we adapt the CRO-SL to the UA-FLP, by means of increasing the diversity generation within the algorithm, which is helpful to improve the exploration of the searching space, avoiding to fall into local minima. Specifically, we propose to include several reproduction mechanisms (adapted to the UA-FLP) within each substrate of the algorithm, which will highly increase the diversity generation in the CRO-SL. An exhaustive experimental study of the CRO-SL performance in a large number of UA-FLP instances is carried out, including a comparison with the state-of-the-art algorithms for this problem. We will show the ability of the CRO-SL to reach or surpass the best-known solutions in most of the tested UA-FLP cases.

The unequal area facility layout problem with shortest single-loop AGV path: how material handling method matters

This paper studies an unequal area layout problem whose objective is to find a flexible bay layout with the best shortest single loop. The problem can be used for production systems in which material handling is carried out by automated guided vehicles (AGVs) that move along a single-loop path. The designed layout is robust against future changes in material flows, which are caused by different factors such as demand fluctuations, technology advancement, and new product introduction. It is first proved that there exists a feasible single loop for any bay layout, which indicates the problem is well-defined and always has a feasible solution. Then, a powerful hybrid algorithm based on Memetic and Simulated Annealing is developed. The algorithm is tested on many randomly generated instances and well-known instances from the literature. The numerical experiment shows that the proposed heuristic is both efficient and effective. The new layout-design approach significantly improves the loop-length when compared with the traditional approach in which the layout is first determined based on rectilinear distances, and then the best single loop is found. Our study highlights the importance of adequately incorporating the material handling system into the layout design phase.

FRACTAL OFFICE LAYOUTS CONSIDERING ERGONOMICS

A number of studies in literature focus on seated workers and investigate physical workload, related back/neck pain, environmental ergonomics such as illumination, thermal comfort, cooperation, and work organization. Although, the positioning of furniture and related units may directly or indirectly influence the performance of workers, up-to best knowledge, the office layout problem is not studied in detail. Therefore, this study aims to illustrate how fractal layout concept can be adapted for cell type office layouts. The study is novel because, the constraints of a basic mathematical model that is originally defined for unequal area facility layout problem is redefined as to meet the ergonomic requirements and generate a layout. Proposed approach is used to solve the office layout problem in a university department. It is concluded that the layout obtained by considering the closeness ratings defined by office employees along with the redefined constraints outperforms the current layout.

A novel multi-objective Interactive Coral Reefs Optimization algorithm for the Unequal Area Facility Layout Problem

The Unequal Area Facility Layout Problem (UA-FLP) has been widely analyzed in the literature using several heuristics and meta-heuristics to optimize some qualitative criteria, taking into account different restrictions and constraints. Nevertheless, the subjective opinion of the designer (Decision Maker, DM) has never been considered along with the quantitative criteria and restrictions. This work proposes a novel approach for the UA-FLP based on an Interactive Coral Reefs Optimization (ICRO) algorithm, which combines the simultaneous consideration of both quantitative and qualitative (DM opinion) features. The algorithm implementation is explained in detail, including the way of jointly considering quantitative and qualitative aspects in the fitness function of the problem. The experimental part of the paper illustrates the effect of including qualitative aspects in UA-FLP problems, considering three different hard UA-FLP instances. Empirical results show that the proposed approach is able to incorporate the DM preferences in the obtained layouts, without affecting much to the quantitative part of the solutions.

Using eye-tracking into decision makers evaluation in evolutionary interactive UA-FLP algorithms

Unequal area facility layout problem is an important issue in the design of industrial plants, as well as other fields such as hospitals or schools, among others. While participating in an interactive designing process, the human user is required to evaluate a high number of proposed solutions, which produces them fatigue both mental and physical. In this paper, the use of eye-tracking to estimate user’s evaluations from gaze behavior is investigated. The results show that, after a process of training and data taking, it is possible to obtain a good enough estimation of the user’s evaluations which is independent of the problem and of the users as well. These promising results advice to use eye-tracking as a substitute for the mouse during users’ evaluations.

Un método metaheurístico para resolver el Problema de Distribución de Instalaciones de Áreas Desiguales y Dimensiones Fijas

Resumen
 Introducción: El Problema de Distribución de Instalaciones de Áreas Desiguales y Dimensiones Fijas (UA-FLP), es un problema de optimización combinatoria no lineal, bien conocido por buscar la mejor ordenación de estaciones de trabajo que poseen áreas y/o dimensiones distintas; estudios recientes muestran métodos aproximados, como metaheurísticas, para resolver este tipo de problemas, o en su defecto muestran innovación en la modelación matemática del mismo, cabe resaltar que el efecto de los decodificadores como variable del problema no había sido analizada hasta este momento.
 Objetivo: Determinar si existe diferencia significativa en la calidad de la solución ofrecida por cada una de las combinaciones Metaheurística-Decodificador.
 Metodología: Se propusieron dos metaheurísticas, un Algoritmo Genético Básico y un algoritmo llamado N2, al igual que dos decodificadores, el Decodificador en Espiral y el Decodificador en Abanico, posteriormente se realizó un experimento simple cuyo factor experimental fue la combinación Metaheurística-Decodificador y la variable dependiente fue la función objetivo del problema analizado.
 Resultados: El diseño experimental mostro que la combinación, metaheurística N2 y Decodificador en Espiral ofrecen los resultados de mejor calidad. 
 Conclusiones: Existe diferencia significativa en la combinación Metaheurística- Decodificador; En específico se puede afirmar que, para el problema en cuestión, la metaheurística N2 es más eficiente que el Algoritmo Genético Básico, añadido a esto, también se puede concluir que los decodificadores tienen gran influencia a la hora de resolver un UA-FLP.