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 Hybrid Coral Reefs Optimization – Variable Neighborhood Search Approach for the Unequal Area Facility Layout Problem

The Unequal Area Facility Layout Problem (UA-FLP) is a relevant optimization problem related to industrial design, that deals with obtaining the most effective allocation of facilities, that make up the rectangular manufacturing plant layout. The UA-FLP is known to be a hard optimization problem, where meta-heuristic approaches are a good option to obtain competitive solutions. Many of these computational approaches, however, usually fall into local optima, and suffer from lack of diversity in their population, mainly due to the huge search spaces and hard fitness landscapes produced by the traditional representation of UA-FLP. To solve these issues, in this paper we propose a novel hybrid meta-heuristic approach, which combines a Coral Reefs Optimization algorithm (CRO) with a Variable Neighborhood Search (VNS) and a new representation for the problem, called Relaxed Flexible Bay Structure (RFBS), which simplifies the encoding and makes its fitness landscape more affordable. Thus, the use of VNS allows more intensive exploitation of the searching space with an affordable computational cost, as well as the RFBS allows better management of the free space into the plant layout. This combined strategy has been tested over a set of UA-FLP instances of different sizes, which have been previously tackled in the literature with alternative meta-heuristics. The tests results show very good performance in all cases

Heuristics and Metaheuristics Approaches for Facility Layout Problems: A Survey

Facility Layout Problem (FLP) is a NP-hard problem concerned with the arrangement of facilities as to minimize the distance travelled between all pairs of facilities. Many exact and approximate approaches have been proposed with an extensive applicability to deal with this problem. This paper studies the fundamentals of some well-known heuristics and metaheuristics used in solving the FLPs. It is hoped that this paper will trigger researchers for in-depth studies in FLPs looking into more specific interest such as equal or unequal FLPs

New Mixed Integer Programming for Facility Layout Design without Loss of Department Area

This paper proposes a New Mixed Integer Programming (NMIP) for solving facility layout problem (FLP). The formulation is extensively tested on problems from literature to minimize material handling cost when addresses on the shop floor. Every department and shop floor in tested problem were fixed dimension of length and width. Classic Mixed Integer Programming (MIP) solves FLP with fixed layout area and preset each department’s lower-upper limit of length and width. As a result, there might be some departments with 5-10% area less than initial layout design requirements which leads to problem in design and construction process. It is infeasible to address the department on the actual shop floor thus adjustment of result from MIP is required. The main purpose of NMIP is to eliminate such infeasibility and show the efficiency of this model. Keywords: mixed integer programing, facility layout design, facility layout problem, heuristic

Dynamic Facility Layout for Cellular and Reconfigurable Manufacturing using Dynamic Programming and Multi-Objective Metaheuristics

The facility layout problem is one of the most classical yet influential problems in the planning of production systems. A well-designed layout minimizes the material handling costs (MHC), personnel flow distances, work in process, and improves the performance of these systems in terms of operating costs and time. Because of this importance, facility layout has a rich literature in industrial engineering and operations research. Facility layout problems (FLPs) are generally concerned with positioning a set of facilities to satisfy some criteria or objectives under certain constraints. Traditional FLPs try to put facilities with the high material flow as close as possible to minimize the MHC. In static facility layout problems (SFLP), the product demands and mixes are considered deterministic parameters with constant values. The material flow between facilities is fixed over the planning horizon. However, in today’s market, manufacturing systems are constantly facing changes in product demands and mixes. These changes make it necessary to change the layout from one period to the other to be adapted to the changes. Consequently, there is a need for dynamic approaches of FLP that aim to generate layouts with high adaptation concerning changes in product demand and mix. This thesis focuses on studying the layout problems, with an emphasis on the changing environment of manufacturing systems. Despite the fact that designing layouts within the dynamic environment context is more realistic, the SFLP is observed to have been remained worthy to be analyzed. Hence, a math-heuristic approach is developed to solve an SFLP. To this aim, first, the facilities are grouped into many possible vertical clusters, second, the best combination of the generated clusters to be in the final layout are selected by solving a linear programming model, and finally, the selected clusters are sequenced within the shop floor. Although the presented math-heuristic approach is effective in solving SFLP, applying approaches to cope with the changing manufacturing environment is required. One of the most well-known approaches to deal with the changing manufacturing environment is the dynamic facility layout problem (DFLP). DFLP suits reconfigurable manufacturing systems since their machinery and material handling devices are reconfigurable to encounter the new necessities for the variations of product mix and demand. In DFLP, the planning horizon is divided into some periods. The goal is to find a layout for each period to minimize the total MHC for all periods and the total rearrangement costs between the periods. Dynamic programming (DP) has been known as one of the effective methods to optimize DFLP. In the DP method, all the possible layouts for every single period are generated and given to DP as its state-space. However, by increasing the number of facilities, it is impossible to give all the possible layouts to DP and only a restricted number of layouts should be fed to DP. This leads to ignoring some layouts and losing the optimality; to deal with this difficulty, an improved DP approach is proposed. It uses a hybrid metaheuristic algorithm to select the initial layouts for DP that lead to the best solution of DP for DFLP. The proposed approach includes two phases. In the first phase, a large set of layouts are generated through a heuristic method. In the second phase, a genetic algorithm (GA) is applied to search for the best subset of layouts to be given to DP. DP, improved by starting with the most promising initial layouts, is applied to find the multi-period layout. Finally, a tabu search algorithm is utilized for further improvement of the solution obtained by improved DP. Computational experiments show that improved DP provides more efficient solutions than DP approaches in the literature. The improved DP can efficiently solve DFLP and find the best layout for each period considering both material handling and layout rearrangement costs. However, rearrangement costs may include some unpredictable costs concerning interruption in production or moving of facilities. Therefore, in some cases, managerial decisions tend to avoid any rearrangements. To this aim, a semi-robust approach is developed to optimize an FLP in a cellular manufacturing system (CMS). In this approach, the pick-up/drop-off (P/D) points of the cells are changed to adapt the layout with changes in product demand and mix. This approach suits more a cellular flexible manufacturing system or a conventional system. A multi-objective nonlinear mixed-integer programming model is proposed to simultaneously search for the optimum number of cells, optimum allocation of facilities to cells, optimum intra- and inter-cellular layout design, and the optimum locations of the P/D points of the cells in each period. A modified non-dominated sorting genetic algorithm (MNSGA-II) enhanced by an improved non-dominated sorting strategy and a modified dynamic crowding distance procedure is used to find Pareto-optimal solutions. The computational experiments are carried out to show the effectiveness of the proposed MNSGA-II against other popular metaheuristic algorithms

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

Facility layout planning. An extended literature review

[EN] Facility layout planning (FLP) involves a set of design problems related to the arrangement of the elements that shape industrial production systems in a physical space. The fact that they are considered one of the most important design decisions as part of business operation strategies, and their proven repercussion on production systems' operation costs, efficiency and productivity, mean that this theme has been widely addressed in science. In this context, the present article offers a scientific literature review about FLP from the operations management perspective. The 232 reviewed articles were classified as a large taxonomy based on type of problem, approach and planning stage and characteristics of production facilities by configuring the material handling system and methods to generate and assess layout alternatives. We stress that the generation of layout alternatives was done mainly using mathematical optimisation models, specifically discrete quadratic programming models for similar sized departments, or continuous linear and non-linear mixed integer programming models for different sized departments. Other approaches followed to generate layout alternatives were expert's knowledge and specialised software packages. Generally speaking, the most frequent solution algorithms were metaheuristics.The research leading to these results received funding from the European Union H2020 Program under grant agreement No 958205 `Industrial Data Services for Quality Control in Smart Manufacturing (i4Q)'and from the Spanish Ministry of Science, Innovation and Universities under grant agreement RTI2018-101344-B-I00 `Optimisation of zerodefectsproduction technologies enabling supply chains 4.0 (CADS4.0)'Pérez-Gosende, P.; Mula, J.; Díaz-Madroñero Boluda, FM. (2021). Facility layout planning. An extended literature review. International Journal of Production Research. 59(12):3777-3816. https://doi.org/10.1080/00207543.2021.189717637773816591

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