Facility layout problem: Bibliometric and benchmarking analysis

Facility layout problem is related to the location of departments in a facility area, with the aim of determining the most effective configuration. Researches based on different approaches have been published in the last six decades and, to prove the effectiveness of the results obtained, several instances have been developed. This paper presents a general overview on the extant literature on facility layout problems in order to identify the main research trends and propose future research questions. Firstly, in order to give the reader an overview of the literature, a bibliometric analysis is presented. Then, a clusterization of the papers referred to the main instances reported in literature was carried out in order to create a database that can be a useful tool in the benchmarking procedure for researchers that would approach this kind of problems

A novel Island Model based on Coral Reefs Optimization algorithm for solving the unequal area facility layout problem

This paper proposes a novel approach to address the Unequal Area Facility Layout Problem (UA-FLP), based on the combination of both an Island Model and a Coral Reefs Optimization (CRO) algorithm. Two different versions of this Island Model based on Coral Reefs Optimization Algorithm (IMCRO) are proposed and applied to the UA-FLP. The structure of flexible bays has been selected as effective encoding to represent the facility layouts within the algorithm. The two versions of the proposed approach have been tested in 22 UA-FLP cases, considering small, medium and large size categories. The empirical results obtained are compared with previous state of the art algorithms, in order to show the performance of the IMCRO. From this comparison, it can be extracted that both versions of the proposed IMCRO algorithm show an excellent performance, accurately solving the UA-FLP instances in all the size categories

The aperiodic facility layout problem with time-varying demands and an optimal master-slave solution approach

In many seasonal industries, customer demands are constantly changing over time, and accordingly the facility layout should be re-optimized in a timely manner to adapt to changing material handling patterns among manufacturing departments. This paper investigates the aperiodic facility layout problem (AFLP) that involves arranging facilities layout and re-layout aperiodically in a dynamic manufacturing environment during a given planning horizon. The AFLP is decomposed into a master problem and a combination set of static facility layout problems (FLPs, the slave problems) without loss of optimality, and all problems are formulated as mixed-integer linear programming (MILP) models that can be solved by MIP solvers for small-sized problems. An exact backward dynamic programming (BDP) algorithm with a computational complexity of O(n 2) is developed for the master problem, and an improved linear programming based problem evolution algorithm (PEA-LP) is developed for the traditional static FLP. Computational experiments are conducted on two new problems and twelve well-known benchmark problems from the literature, and the experimental results show that the proposed solution approach is promising for solving the AFLP with practical sizes of problem instances. In addition, the improved PEA-LP found new best solutions for five benchmark problems

Evolutionary Computation Strategies applied to the UA-FLP

En la presente tesis doctoral se desarrollan dos aproximaciones distintas al problema de distribución en planta de áreas desiguales (UA-FLP). En primer lugar, se trata de incorporar el conocimiento del diseñador experto a los algoritmos clásicos de optimización, de forma que, además de buscar buenas soluciones desde el punto de vista cuantitativo, por ejemplo minimizando el flujo de materiales, se introduzca la posibilidad de que el diseñador aporte su experiencia y preferencias personales. Para facilitar la intervención humana en el proceso de búsqueda de soluciones, se ha utilizado un procedimiento de clustering, el cual permite clasificar las soluciones subyacentes en el conjunto de búsqueda, de forma que se presente al diseñador un número suficientemente representativo y, a la vez, evitándole una fatiga innecesaria. Además, en esta primera propuesta se han implementado dos técnicas de niching, denominadas Deterministic Crowding y Restricted Tournament Selection. Estas técnicas tienen la capacidad de mantener ciertas propiedades dentro de la población de soluciones, preservar múltiples nichos con soluciones cercanas a los óptimos locales, y reducir la probabilidad de quedar atrapado en ellos. De esta manera el algoritmo se enfoca simultáneamente en más de una región (nicho) en el espacio de búsqueda, lo cual es esencial para descubrir varios óptimos en una sola ejecución. Por otro lado, en la segunda aproximación al problema, se ha implementado una estrategia evolutiva paralela, muy útil para los problemas de alta complejidad en los que el tiempo de ejecución con un enfoque evolutivo secuencial es prohibitivo. La propuesta desarrollada, denominada IMGA, está basada en un algoritmo genético paralelo de grano grueso con múltiples poblaciones o islas. Este enfoque se caracteriza por evolucionar varias subpoblaciones independientemente, entre las que se intercambian individuos, haciendo posible explorar diferentes regiones del espacio de búsqueda, al mismo tiempo que se mantiene la diversidad de la población, permitiendo la obtención de buenas y diversas soluciones. Con ambas propuestas se han realizado experimentos que han arrojado resultados muy satisfactorios, encontrando buenas soluciones para un conjunto de problemas bien conocidos en la bibliografía. Estos buenos resultados han permitido la publicación de dos artículos indexados en el primer decil del ranking JCR (Journal Citation Reports).The present doctoral thesis develops two different approaches to the Unequal Area Facility Layout Problem (UA-FLP). The first approach encompasses the designer’s knowledge on classic optimization of algorithms in pursuance of good quantitative solutions (e.g. minimizing the materials flow) and also opens the possibility to include the contribution of the designer by means of his expertise and personal preferences. A clustering procedure has been used to facilitate human intervention in the process of finding solutions. This allows the underlying solutions to be classified in the search in order to present the designer with sufficiently representative solutions and, at the same time, avoiding unnecessary fatigue. In addition, two niching techniques have been implemented, called Deterministic Crowding and Restricted Tournament Selection. These techniques have the ability to maintain certain properties within the solutions space, preserve multiple niches with solutions close by local optimums, and reduce the probability of being trapped in them. In this way, the algorithm focuses simultaneously on more than one region (niche) in the search space, which is essential to discover several optimums in a single execution. The second approach to the problem comprises the implementation of a parallel evolutionary strategy. This method is useful for problems of high complexity in which the execution time using a sequential evolutionary approach is prohibitive. The proposal developed, called IMGA (Island Model Genetic Algorithm), is based on a parallel genetic algorithm of multiple-population coarse-grained. This is characterized by evolving several subpopulations independently among which individuals are exchanged. Different regions of the search space can be explored while the diversity of the population is maintained. Satisfactory and diverse solutions have been obtained as a result of this method. Experiments with both proposals have been carried out with satisfactory results, providing good solutions for a set of problems well known in the literature. These results were already published in two papers indexed in the first decile of the JCR (Journal Citation Reports) ranking

Topology Network Optimization of Facility Planning and Design Problems

The research attempts to provide a graphical theory-based approach to solve the facility layout problem. Which has generally been approached using Quadratic Assignment Problem (QAP) in the past, an algebraic method. It is a very complex problem and is part of the NP-Hard optimization class, because of the nonlinear quadratic objective function and (0,1) binary variables. The research is divided into three phases which together provide an optimal facility layout, block plan solution consisting of MHS (material handling solution) projected onto the block plan. In phase one, we solve for the position of departments in a facility based on flow and utility factor (weight for location). The position of all the departments is identified on the vertices of MPG (maximal planar graph), which maximizes the possibility of flow. We use named MPG produced in literature, throughout the research. The grouping of the department is achieved through GMAFLAD, a QSP (quadratic set packing) based optimizer. In Phase 2, the dual for the MPG’s is solved consisting of department location as per phase 1, to generate Voronoi graphs. These graphs are then, expanded by an ingenious parameter optimization formulation to achieve area fitting for individual cases. Optimization modeling software, Lingo17.0 is used for solving the parameter optimization for generating coordinates of the block plan. The plotting of coordinates for the block plan graphics is done via Autodesk inventor 2019. In phase 3, the solution for MHS is achieved using an RSMT (Rectilinear Steiner minimal tree) graph approach. The Voronoi seed coordinates produced through phase 2 results are computed by GeoSteiner package to generated the RSMT graph for projection onto the block plan (Also, done by Inventor 2019). The graphical method employed in this research, itself has complex and NP-hard problem segments in it, which have been relaxed to polynomial time complexity by fragmenting into groups and solving them in sections. Solving for MPG & RSMT are a class of NP-Hard problem, which have been restricted to N=32 here. Finally, to validate the research and its methodology a real-life case study of a shipyard building for the data set of PDVSA, Venezuela is performed and verified

Facility layout problem: Bibliometric and benchmarking analysis

Facility layout problem is related to the location of departments in a facility area, with the aim of determining the most effective configuration. Researches based on different approaches have been published in the last six decades and, to prove the effectiveness of the results obtained, several instances have been developed. This paper presents a general overview on the extant literature on facility layout problems in order to identify the main research trends and propose future research questions. Firstly, in order to give the reader an overview of the literature, a bibliometric analysis is presented. Then, a clusterization of the papers referred to the main instances reported in literature was carried out in order to create a database that can be a useful tool in the benchmarking procedure for researchers that would approach this kind of problems

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

Hybrid metaheuristics for the accessibility windows assembly line balancing problem level 2 (AWALBP-L2)

This paper addresses an assembly line balancing problem in which the length of the workpieces is larger than the width of the workstations. The problem differs from traditional variants of assembly line balancing in the sense that only a portion of the workpiece, or portions of two consecutive workpieces, can be reached from any workstation. Consequently, at any stationary stage of the cycle, each workstation can only process a portion of the tasks, namely, those which are inside the area of a workpiece that is reachable from the workstation. The objective is to find a (cyclic) movement scheme of the workpieces along the line and a task assignment to stationary stages of the production process, while minimizing the cycle time. We propose three hybrid approaches of metaheuristics and mathematical programming - one based on simulated annealing and the other two based on tabu search, relying on different neighborhood definitions. The two former approaches make use of a classical neighborhood, obtained by applying local changes to a current solution. The latter approach, in contrast, draws ideas from the corridor method to define a corridor around the current solution, via the imposition of exogenous constraints on the solution space of the problem. An extensive computational experiment is carried out to test the performance of the proposed approaches, improving the best results published to date.Postprint (author's final draft

Optimización de problemas de distribución en planta mediante algoritmos evolutivos

Este trabajo de investigación acomete el problema de distribución en planta. De forma resumida, este problema comprende la distribución de los diferentes departamentos que integran una planta industrial de la forma más satisfactoria posible teniendo en cuenta ciertos criterios y restricciones. Dependiendo de las características del problema, pueden originarse multitud de taxonomías o subproblemas de distribución en planta. En esta tesis doctoral, se abordará el problema de distribución en planta de áreas desiguales que ha sido uno de los más estudiados. Para resolver este problema de distribución en planta de áreas desiguales (UAFLP en inglés), han sido utilizadas multitud de propuestas con el objetivo de obtener el diseño más satisfactorio de la planta industrial. En este sentido, los algoritmos evolutivos han sido ampliamente utilizados en la bibliografía. Por otro lado, dentro de los posibles criterios a considerar cuando se resuelve el problema de distribución en planta, el coste de flujo de material ha sido el más empleado, ya que está directamente relacionado con el coste total de una planta industrial. Es por esta razón que esta tesis doctoral pretende resolver el problema de distribución en planta teniendo en cuenta el criterio del coste de flujo de material, con el objetivo de obtener mejores soluciones que las existentes hasta el momento en la bibliografía de referencia. Para ello, se ha empleado una novedosa y reciente metaheurística que se basa en el comportamiento existente en los arrecifes de corales marinos. Esta nueva metaheurística ha sido empleada con mucho éxito en diferentes problemas complejos de optimización, logrando obtener unos resultados muy satisfactorios en diferentes ámbitos y áreas. Este algoritmo de optimización basado en algoritmos de arrecifes de coral ha sido aplicado al problema de distribución en planta de áreas desiguales considerando el coste de flujo de material como criterio de optimización. La aplicación de esta propuesta es una contribución totalmente original al problema de distribución en planta, ya que, hasta el momento no había sido probado en este campo. La propuesta de optimización basada en los arrecifes de coral ha sido probada de forma empírica con multitud de problemas de referencia de la bibliografía de diferente complejidad. Como resultado se ha mejorado las soluciones existentes hasta el momento en la mayoría de los casos probados. Por otro lado, con el objetivo de dar más diversidad a la población y para evitar que el algoritmo caiga en óptimos locales, se ha propuesto una mejora sobre esta metaheurística que se basa en un modelo de islas de arrecifes de coral, lo que permite realizar una paralelización del algoritmo inicial y así, evolucionar diferentes poblaciones de arrecifes de coral al mismo tiempo. Se ha realizado una experimentación empírica con multitud de problemas de referencia de la bibliografía que ha permitido validar este nuevo enfoque bioinspirado, ofreciendo como resultado mejoras sobre las soluciones existentes hasta el momento en referencia a la mayoría de los casos probados (incluso mejores soluciones que las obtenidas por la propuesta inicial de algoritmo de arrecifes de coral). Mediante este nuevo modelo de islas de arrecifes de coral, se consigue también aumentar la diversidad de las soluciones del problema, lo que permite encontrar nuevas soluciones con mejores aptitudes en términos de coste de flujo de material y en menor tiempo de cómputo. Este nuevo modelo de islas de arrecifes de coral, es una nueva metaheurística que ha sido creada en esta investigación y es totalmente original. Ya que hasta ese momento, no existía ninguna propuesta paralelizada del algoritmo de optimización basado en arrecifes de coral. Por lo que, este nuevo modelo ha contribuido de una manera muy considerable en el estado del arte del problema de distribución en planta de áreas desiguales y también en el ámbito de la computación evolutiva y las metaheurísticas.This research work tackles the facility layout problem, in summary, this problem includes the distribution of the different departments that make up an industrial plant in the most satisfactory way possible, taking into account certain criteria y restrictions. Depending on the characteristics of the problem, a multitude of facility layout taxonomies or subproblems can arise. In this doctoral thesis, the unequal area facility layout problem is addressed, which has been one of the most studied in the related references. To solve the unequal area facility layout problem (UAFLP), many proposals have been used to obtain the most satisfactory design of the industrial plant. In this sense, evolutionary algorithms have been the most used in the literature. On the other hand, among the possible criteria to consider when solving the unequal area facility layout problem, the cost of material flow has been the most employed, since it is directly related to the total cost of an industrial plant. This is the reason why this doctoral thesis aims to solve the unequal area facility layout problem taking into account the criterion of the cost of material flow, intending to obtain better solutions than the consequences so far in the reference bibliography. For this, a new y recent metaheuristic has been used that is based on the behaviour existing in the marine coral reefs. This new metaheuristic has been used with great success in different complex optimization problems, achieving very satisfactory results in different fields y areas. This optimization algorithm based on coral algorithms has been applied to the unequal area facility layout problem by considering the cost of material flow as an optimization criterion. The application of this proposal is a totally original contribution to the facility layout problem, since, until now, it had not been tested in this field. The optimization proposal based on coral reefs has been empirically tested with a multitude of bibliographic reference problems of different complexity. As a result, the solutions improved so far have been improved in the references in most of the cases tested. Finally, to give more diversity to the population y to avoid the algorithm falling into local optimums, an improvement has been proposed on this metaheuristic that is based on a model of coral reef islands, which allows parallelization of the initial algorithm y thus, evolve different coral reef populations at the same time. Empirical experimentation with a multitude of bibliographic benchmark problems was carried out to validate this new bioinspired approach, y it has resulted in improvements over the solutions that have existed so far in the references in the majority of cases tested (even better solutions than ones obtained by the initial proposal of the coral reefs optimization algorithm). Through this new model of coral reef islands, it is also possible to increase the diversity of the solutions to the problem, allowing to find new designs with better skills in terms of material flow cost y in less computing time. This new island model of coral reef is a new metaheuristic that has been created in this research y is totally original. Since until then, there was no parallelized proposal for the coral reef-based optimization algorithm. Therefore, this new island model has contributed in a very considerable way in the state of the art of the unequal area facility layout problem, and also, in the evolutionary computation and metaheuristics