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

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

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

Hybridization of modified sine cosine algorithm with tabu search for solving quadratic assignment problem

Sine Cosine Algorithm (SCA) is a population-based metaheuristic method that widely used to solve various optimization problem due to its ability in stabilizing between exploration and exploitation. However, SCA is rarely used to solve discrete optimization problem such as Quadratic Assignment Problem (QAP) due to the nature of its solution which produce continuous values and makes it challenging in solving discrete optimization problem. The SCA is also found to be trapped in local optima since its lacking in memorizing the moves. Besides, local search strategy is required in attaining superior results and it is usually designed based on the problem under study. Hence, this study aims to develop a hybrid modified SCA with Tabu Search (MSCA-TS) model to solve QAP. In QAP, a set of facilities is assigned to a set of locations to form a one-to-one assignment with minimum assignment cost. Firstly, the modified SCA (MSCA) model with cost-based local search strategy is developed. Then, the MSCA is hybridized with TS to prohibit revisiting the previous solutions. Finally, both designated models (MSCA and MSCA-TS) were tested on 60 QAP instances from QAPLIB. A sensitivity analysis is also performed to identify suitable parameter settings for both models. Comparison of results shows that MSCA-TS performs better than MSCA. The percentage of error and standard deviation for MSCA-TS are lower than the MSCA which are 2.4574 and 0.2968 respectively. The computational results also shows that the MSCA-TS is an effective and superior method in solving QAP when compared to the best-known solutions presented in the literature. The developed models may assist decision makers in searching the most suitable assignment for facilities and locations while minimizing cost

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

The single row layout problem with clearances

The single row layout problem (SRLP) is a specially structured instance of the classical facility layout problem, especially used in flexible manufacturing systems. The SRLP consists of finding the most efficient arrangement of a given number of machines along one side of the material handling path with the purpose of minimising the total weighted sum of distances among all machine pairs. To reflect real manufacturing situations, a minimum space (so-called clearances) between machines may be required by observing technological constraints, safety considerations and regulations. This thesis intends to outline the different concepts of clearances used in literature and analyse their effects on modelling and solution approaches for the SRLP. In particular the special characteristics of sequence-dependent, asymmetric clearances are discussed and finally extended to large size clearances (machine-spanning clearances). For this, adjusted and novel model formulations and solution approaches are presented. Furthermore, a comprehensive survey of articles published in this research area since 2000 is provided which identify recent developments and emerging trends in SRLP

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

AWALBP-L2 : the Accessibility Windows Assembly Line Balancing Problem Level 2 : formalization and solution methods

This doctoral thesis tackles an assembly line balancing problem with restricted access to the workpieces that has been entitled AWALBP: the Accessibility Windows Assembly Line Balancing Problem. The problem is described and a general classification for its main optimization levels is proposed. The thesis focuses on a specific case of the optimization level AWALBP-L2. The AWALBP-L2 consists of two subproblems that need to be solved simultaneously: (i) the computation of a feasible movement scheme and (ii) the assignment of each task to one workstation and one stationary stage of the cycle. In the particular case of AWALBP-L2 addressed in this thesis, for each task a single workstation is compatible. The review of the state of the art reveals that relatively few studies have been published concerning the AWALBP. Regarding the solution of the AWALBP-L2, the only available previous work is a mathematical programming model, but the model is not tested or validated. In order to fill this research gap, the aim of this thesis is three-fold: i) to describe the AWALBP and characterize its main optimization levels, ii) to propose exact methods for the case of AWALBP-L2 considered, and iii) to develop solution procedures for the challenging instances that are out of reach of the former methods. Consequently, in this doctoral thesis the AWALBP is characterized and the AWALBP-L2 case is addressed through four main approaches. First, the problem is formalized and solved via two mixed integer linear programming (MILP) models. Second, an approach combining a matheuristic and a MILP model is proposed. The third approach considers hybridizing metaheuristics with mathematical programming models. Finally, the fourth approach proposes sequential combinations of the aforementioned hybrid metaheuristics and a MILP model. The performance of all approaches is evaluated via an extensive computational experiment based on realistic instances, and an optimal solution could be found for a large number of them. Future research work may include additional assumptions on the problem, such as precedence relationships among tasks or several workstations compatible for each task. The methods proposed in this thesis are open in nature and extend perspectives for combining (meta)heuristics and mathematical programming models, either for improving the solution of the AWALBP-L2 or for tackling other combinatorial optimization problems.Esta tesis doctoral aborda un problema de equilibrado de líneas con acceso limitado a las piezas que ha sido titulado AWALBP: Accessibility Windows Assembly Line Balancing Problem. Se describe el problema y se propone una clasificación general de sus principales niveles de optimización. La tesis se centra en un caso específico del nivel AWALBP-L2. El AWALBP-L2 consta de dos subproblemas que deben ser resueltos simultáneamente: (i) cálculo de un esquema de movimiento factible y (ii) asignación de cada tarea a una estación y a una de las etapas estacionarias del ciclo. En el caso particular de AWALBP-L2 tratado en esta tesis, para cada tarea existe una única estación compatible. La revisión del estado del arte revela que relativamente pocos estudios han sido publicados sobre el AWALBP. Respecto a la resolución del AWALBP-L2, el único trabajo anterior disponible es un modelo de programación matemática, el cual no está probado o validado. Con tal de cubrir este hueco de investigación, el objetivo de la presente tesis es triple: i) describir el AWALBP y caracterizar sus principales niveles de optimización, ii) proponer métodos exactos para el caso considerado de AWALBP-L2, y iii) desarrollar métodos de resolución para los ejemplares más difíciles que quedaron fuera del alcance de los métodos anteriores. Por consiguiente, en esta tesis doctoral se caracteriza el AWALBP y se aborda el caso de AWALBP-L2 mediante cuatro enfoques principales. En primer lugar, el problema se formaliza y se resuelve mediante dos modelos de programación lineal entera mixta (PLEM). En segundo lugar se propone una mateheurística combinada con un modelo de PLEM. El tercer enfoque consiste en hibridizar metaheurísticas con modelos de programación matemática. Finalmente, el cuarto enfoque propone combinaciones secuenciales de las mencionadas metaheurísticas híbridas con un modelo de PLEM. Los enfoques propuestos se evalúan mediante una extensa experiencia computacional con ejemplares realistas, y se obtuvo una solución óptima para un gran número de ellos. Las líneas propuestas de investigación futura incluyen supuestos adicionales tales como relaciones de precedencia entre tareas o varias estaciones compatibles para una misma tarea. Los métodos propuestos en esta tesis son de naturaleza abierta y ofrecen perspectivas para la combinación de (meta)heurísticas con modelos de programación matemática, tanto para mejorar la solución del AWALBP-L2 como para abordar otros problemas de optimización combinatoria

A New Multicommodity Flow Model for the Job Sequencing and Tool Switching Problem

Artigo científico.In this paper a new multicommodity flow mathematical model for the Job Sequencing and Tool Switching Problem (SSP) is presented. The proposed model has a LP relaxation lower bound equal to the number of tools minus the tool machine’s capacity. Computational tests were performed comparing the new model with the models of the literature. The proposed model performed better, both in execution time and in the number of instances solved to optimality.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES

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