Facility Layout Problem Research Articles

A Multistage Methodology Approach for Constraints Prioritisation of Facility Layout Problem (FLP) in Enhancing the Performance of Building Energy Efficiency

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.

A sequential solution heuristic for continuous facility layout problems

A sequential solution heuristic for continuous facility layout problems

Solving fuzzy robust facility layout problem equipped with cranes using MPS algorithm and modified NSGA-II

Facility layout problems are defined as the placement of facilities in a plant region. In this paper a facility layout problem was clarified by the facility dimensions and the materials flow between facilities are uncertain as fuzzy random variables and the plant region was equipped with the wall mounted jib cranes and the small gate cranes and there were some forbidden areas in the plant region where the placement of facilities were forbidden. The objective functions of the problems have been contemplated as minimizing materials handling cost of the operators and the cranes and for the first time maximizing cranes usability of wall mounted jib cranes and small gate cranes by calculating their covered area on the facilities. To solve the problem a multi-objective population-based on simulated annealing algorithm (MPS) and a Modified Non-dominated Sorting Genetic Algorithm II (MNSGA-II) was proposed then six case studies were solved by them. The results reveal that run time of the MNSGA-II for large size problems are better than the MPS algorithm and the MPS algorithm finds better solutions with materials handling cost of the operators and the cranes and the MNSGA-II finds better solutions with cranes usability.

A fast two-stage hybrid meta-heuristic algorithm for robust corridor allocation problem

The optimisation of the corridor allocation problem (CAP) belongs to the optimisation of the efficiency of the automated production line. The goal is to reduce the material handling cost (MHC) in the production process through a reasonable layout of the facilities, so as to save expenses for the enterprise. In recent years, with the acceleration of market changes, product design and production process adjustments have become more frequent, and more attention has been paid to the research on the layout of facilities under the condition of changes in the flow of materials between production facilities over time. On the basis of the CAP model, this paper considers the optimisation problem of row layout when the flow of materials between facilities fluctuates in a certain range. The new model can be utilised to obtain the overall optimisation solution under the condition of the floating material flow matrix, so as to achieve the goal of optimising the total MHC in the entire production process. As the new model introduces more variables and intermediate parameters, a two-stage solution method is previously required, which greatly increases the time to solve the problem. This paper proposes a targeted meta-heuristic algorithm optimisation method combining the advantages of tabu search algorithm and harmony search algorithm, which simplifies the solution phase of calling the precise solver in the two-stage algorithm of row facility layout problem, improves the problem solving efficiency, and makes the solution of large-scale problems become possible. The proposed model is verified through Lingo software, and then the model and the hybrid algorithm in the MATLAB environment are verified with each other. Finally, the proposed simplified algorithm is utilised to solve the large-scale problems that could not be solved by the two-stage algorithm before.

A Variable Neighborhood Search Approach for the Dynamic Single Row Facility Layout Problem

The dynamic single row facility layout problem (DSRFLP) is defined as the problem of arranging facilities along a straight line during a multi-period planning horizon with the objective of minimizing the sum of the material handling and rearrangement costs. The material handling cost is the sum of the products of the flow costs and center-to-center distances between facilities. In this paper, we focus on metaheuristic algorithms for this problem. The main contributions of the paper are three-fold. First, a variable neighborhood search (VNS) algorithm for the DSRFLP is proposed. The main version of VNS uses an innovative strategy to start the search from a solution obtained by constructing an instance of the single row facility layout problem (SRFLP) from a given instance of the DSRFLP and applying a heuristic algorithm for the former problem. Second, a fast local search (LS) procedure is developed. The innovations of this procedure are two-fold: (i) the fast insertion and swap neighborhood exploration techniques are adapted for the case of the dynamic version of the SRFLP; and (ii) to reduce the computational time, the swap operation is restricted on pairs of facilities of equal lengths. Provided the number of planning periods is a constant, the neighborhood exploration procedures for n facilities have only O(n2) time complexity. The superiority of these procedures over traditional LS techniques is also shown by performing numerical tests. Third, computational experiments on DSRFLP instances with up to 200 facilities and three or five planning periods are carried out to validate the effectiveness of the VNS approach. The proposed VNS heuristic is compared with the simulated annealing (SA) method which is the state of the art algorithm for the DSRFLP. Experiments show that VNS outperforms SA by a significant margin.

A hybrid Dantzig-Wolfe decomposition algorithm for the multi-floor facility layout problem

The multi-floor facility layout problem (MFLP) is one of the most important and complex facility layout problems that has many applications in designing the facilities of manufacturing and service sectors. In this study, a hybrid version of the Dantzig-Wolfe decomposition algorithm is proposed to solve the MFLP for the first time. The proposed solution approach is performed in two steps. In the first step, a mathematical formulation is applied to assign the departments to the floors in a way that the departments with higher material flow between them be located on the same or closer floors. In the second step, the output of the first step is considered and the MFLP is decomposed into a master problem and some sub-problems to form the Dantzig-Wolfe decomposition algorithm and find the optimal layout of each floor separately. Then the integrated layout of multiple floors is formed easily. The proposed algorithm is evaluated using some sample problems from the literature and some newly generated test problems. The obtained results show the superiority of the proposed algorithm compared to the approaches of the literature.

Exact approaches for the combined cell layout problem

We consider the Combined Cell Layout Problem (CCLP) as well as the Multi-Bay Facility Layout Problem (MBFLP) and Layouts with Pier-Type Patterns, which are both special cases of the CCLP. Given a number of cells of type single-row or directed circular, pairwise distances between the cells and a set of one-dimensional departments with pairwise transport weights between them, the CCLP asks for an assignment of the departments to the cells such that departments in the same cell do not overlap and such that the sum of the weighted center-to-center distances is minimized. Distances between departments in the same cell are measured according to the layout type of the cell and otherwise their distance equals the sum of the distances to the associated (un)loading positions of the cells plus the distance between the cells. The CCLP and its variations have wide applications. Nevertheless approaches for solving the CCLP exactly have not been presented in the literature before. We solve the CCLP exactly by enumerating over all assignments of the departments to the cells and solving several CCLP with fixed-cell assignment. We show how to reduce the number of distinguishable cell assignments significantly by merging two cells of type single-row. This leads to a new well-performing exact approach for the CCLP where arising subproblems are solved via (new) mixed-integer linear programming models. In a computational study we compare the computation times and the optimal values of various facility layout problems in order to support the decision maker to choose a layout.

Performance Comparison of Particle Swarm Optimization and Genetic Algorithm Combined with A* Search for Solving Facility Layout Problem

Optimization metaheuristics have become necessary due to the growing demand for better and more realistic designs. This paper proposes a metaheuristic-based approach for solving design problems in a reasonable time while browsing large spaces of solutions. The objective of this article is to compare the performance of two methods Genetic Algorithm GA and Particle swarm optimization PSO, combined with A* algorithm, in solving a constrained facility layout problem. The two chosen metaheuristics have been successfully applied in many search problems. We consider their speed and performance. The performance of the obtained solutions is measured in terms of the total distance traveled by products in the workshop. In order to determine the shortest path in a realistic way between workstations in a given irregular area (with aisle structure, or material storage areas, lunchrooms and offices), the A* algorithm was integrated with them. The comparison therefore concerns <GA, A*> and <PSO, A*>. GA and PSO algorithms generate configurations for which the shortest path for any couple of machines is identified through the A* search algorithm taking into account of obstacles. The mathematical model used and the parameters of the genetic algorithm are those developed in (Besbes et al. 2019). The numerical results show the feasibility and effectiveness of both approaches. Our results demonstrate that GA yields a better solution than Particle Swarm Optimization in total distance travelled while PSO is faster.

Indicators for the facility layout design in MSMEs in the textile sector with a resilient approach

Introduction. The capacity to respond and adapt to the risks and problems in an organization is critical for business success. Any type of weakness causes inefficient use of resources. On the contrary, flexible facilities can ensure the continuity of operations in the face of disruptive events, which significantly harm the company if they are not controlled. However, flexibility is not achieved only with the optimization of facilities, as resilient approaches can enhance it. Objective. To synthesize the variables and indicators with greater use in three different areas, business resilience, the textile industry, and the facility layout problem (FLP) in the textile industry. Methodology. The research is of a bibliographic-documentary nature. A systematic literature review was conducted, using the Fink methodology, considering 99 studies published between 2010 and 2021. The documents were analyzed using the Atlas.ti software; subsequently, a 4W (when, who, what, and where) analysis was used; finally, answers were given to three research questions posed through the PICO strategy. Results. The findings indicate that, there is a scarcity of studies about resilient FLPs, however, it is notable that the scientific interest regarding resilience has increased in the last six years, specifically in assessment methods and approaches to identify resilience factors and indicators in the industry through fuzzy mathematical models. Conclusions. Studies about resilience applied to FLP are not developed to a great extent worldwide, in the Ecuadorian context resilience has not been explored in depth. Eventually, the importance of indicators for an accurate model is highlighted and a series of indicators for the analysis of the behavior of facility layout with a resilient approach based on FLP factors is proposed.

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.

A modified squirrel search algorithm for solving facility layout problems

With the huge advance in artificial intelligence and the rapid development of intelligent swarm algorithms, the exploration of facility layout problem (FLP) with its non-deterministic polynomial-time (NP-Hard) nature has gained much more attention. The squirrel search algorithm is one of the swarm algorithms that is known for its effective gliding feature that provides cheap exploration of lengthy distances. In this work, Msqrl algorithm is presented as a modification of squirrel search algorithm to be capable of handling permutation-specific FLP. The modification is done by introducing two new operators: Msqrl-Exchange and Msqrl-Winter. It is used to investigate the effectiveness in finding acceptable solutions to variable-size, single-row FLPs in a fast and efficient manner. Tests included small and large benchmark instances for comparisons. Outcomes show that Msqrl was able to improved quite a few previously found solutions by acting efficiently and converging rapidly to solutions. It outperformed both semidefinite programming and cuckoo optimization in finding optimal solutions in an acceptable number of iterations and relatively small population sizes.

A differential evolution algorithm combined with linear programming for solving a closed loop facility layout problem

Closed loop layout problem (CLLP) is an important class of design problems encountered in flexible manufacturing system. It is to determine the locations of manufacturing cells along a closed material handling loop. Existing studies on CLLPs typically ignore clearances between adjacent cells and do not optimize the dimension of the closed loop. However, separating adjacent cells with suitable clearances may achieve a better solution with lower material flow cost, and optimizing cell sequence and the dimension of closed loop simultaneously can also improve the solution quality. In this paper, a mixed integer programming formulation (MIP) is established for the CLLP. Then, based on the formulation, a hybrid approach combining an improved differential evolution algorithm and an exact approach (iDE-EA) is proposed to solve the CLLP. The iDE with a hybrid coding is utilized to optimize the dimension of closed loop and the placement sequence of cells simultaneously. The EA is to determine the exact location of each cell (i.e., the clearance between each pair of adjacent cells). iDE-EA is verified on 10 problem instances, and experiments show that iDE-EA improves upon the best-known material handling cost by 0–9.97% compared against the traditional differential evolution algorithm and three state-of-the-art approaches. In addition, using EA to further optimize clearances among cells in the solution obtained by iDE can achieve a more competitive layout in most cases.

Solving the constrained Single-Row Facility Layout Problem with Integer Linear Programming

The Single-Row Facility Layout Problem (SRFLP) is one of the most studied facility layout problems in the literature. It asks for an optimal arrangement of departments with given lengths on a row such that the weighted sum of all centre-to-centre distances between department pairs is minimised. Real-world facility layouts may require taking different restrictions on the placement of departments into account, such as arrangement on a fixed position, pairwise placement, or precedence considerations. Therefore, we consider the constrained Single-Row Facility Layout Problem (cSRFLP) that additionally considers positioning, ordering, and relation constraints on single-row facility layouts. In this work, we suggest a new Integer Linear Programming (ILP) formulation for the cSRFLP, which outperforms the best available exact approach in literature. In an extensive computational study, we apply our ILP approach as well as an LP-based cutting plane algorithm on SRFLP and cSRFLP instances from the literature. We provide optimal cSRFLP layouts as well as strong lower bounds for instances with up to 42 departments. Further, we present new results for SRFLP instances from the literature. Additionally, we demonstrate the individual impact of the constraint sets on the run times of cSRFLP instances to emphasise further research on this rarely studied practice-oriented Facility Layout Problem.

Energy-Saving Oriented Manufacturing Workshop Facility Layout: A Solution Approach Using Multi-Objective Particle Swarm Optimization

Low-carbon sustainable development has become the consensus of manufacturing enterprises to fulfill their social responsibilities. Facility layout is an essential part of manufacturing system planning. Current research has demonstrated the advantages of energy saving on the manufacturing system level where operational methods (e.g., energy-efficient production scheduling and path planning) can be utilized and do not require massive investment in the existing legacy system. However, these efforts are mostly based on the existing fixed facility layout. Meanwhile, although facility layout problems have been extensively studied so far, the related work seldom involves the optimization of energy consumption (EC) or other EC-related environmental impact indicators, and does not clearly reveal if EC can be an independent optimization objective in facility layout. Accordingly, whether the energy-saving potential of a manufacturing system can be further tapped through rational facility layout is the gap of the current study. To address this, an investigation into energy-saving oriented manufacturing workshop facility layout is conducted. Correspondingly, an energy-efficient facility layout (EFL) model for the multi-objective optimization problem that minimizes total load transport distance and EC is formulated, and a multi-objective particle swarm optimization-based method is proposed as the solution. Furthermore, experimental studies verify the effectiveness of the presented model and its solution, indicating that EC can be regarded as an independent optimization objective during facility layout, and EFL is a feasible energy-saving approach for a manufacturing system.

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.

A mathematical model and a simulated annealing algorithm for unequal multi-floor dynamic facility layout problem based on flexible Bay structure with elevator consideration

PurposeAs far as the authors know, no research has already been carried out on the multi-floor dynamic facility layout problem (MF-DFLP) in the continuous form regarding the flexible bay structure, the number and the variable location of the elevator. Therefore, the present paper models the given problem and attempts to find a sub-optimal solution for it using a meta-heuristic simulated annealing (SA) algorithm.Design/methodology/approachThe efficient use of resources has always been a prominent matter for decision-makers. Many reasons including land use, construction considerations and proximity of departments have led to the design of multi-floor facilities. On the other hand, their fast-evolving environment calls for dynamic planning. Therefore, in this paper, a model and the SA algorithm for MF-DFLP are presented.FindingsAfter presenting a mathematical model, the problem was solved precisely in a small size using the GAMS software. Also, a near-optimal solution method using a SA meta-heuristic algorithm is suggested and the proposed algorithm was run in the MATLAB software. To evaluate the presented model and the proposed solution, some test cases were considered in two aspects. The first aspect was the test cases that are newly generated in small, medium and large sizes to compare the exact optimal solution with the results of the meta-heuristic algorithm. Eight test cases with small sizes were solved using the GAMS software, the optimum solutions were obtained in a reasonable time, and the cost of their solutions was equal to that of the SA algorithm. Eight test cases with medium sizes were run in the GAMS software with the time limit of 80,000 s, and the SA algorithm had performed better for these test cases. Two test cases were also considered in large size that GAMS could not solve them, whereas the SA algorithm successfully found a proper solution for each. The second aspect included the test cases from the literature. The result showed that suggested algorithm is more capable of finding best solutions than compared algorithms.Originality/valueIn this paper, an unequal area MF-DFLP was studied in a continuous layout form in which the location and number of the elevators were considered to be variable, and the layouts were considered with flexible bay structure. These conditions were investigated for the first time.

재구성제조시스템에서 설비배치와 Job Shop 스케줄링의 통합 최적화에 관한 연구

In manufacturing systems, the facility layout problem(FLP) and the job shop scheduling problem(JSSP) are two main factors influencing on productivity and efficiency of the systems, and a number of previous researches have dealt with these independently. However, FLP and JSSP need to be considered concurrently because the two factors affect each other’s performance, and the modularity of production lines in reconfigurable manufacturing system(RMS) makes it easy and cost-effective to change the facility layout. In this paper, we proposed an integrated algorithm considering FLP and JSSP simultaneously, and it was compared with a method that considers JSSP only. As a result, it was found that the modularity of RMS was worthwhile and the proposed integrated algorithm was effective in deriving optimal solution combination of FLP and JSSP.

A MATHEMATICAL OPTIMIZATION APPROACH BASED ON LINEARIZED MIP MODELS FOR SOLVING FACILITY LAYOUT PROBLEMS

One of the strategies used to optimize production processes is to define the best layout. For this, the relative positioning of the various equipment, areas, or functional activities inside the company is studied. Proper arrangement of facilities will result in shorter process times and higher productivity. In general, the objective function of the facility layout problem (FLP) is to reduce the total material handling cost. Although over six decades have been passed since the first work on FLP modeling was published, research on many aspects of this problem is still in an early stage and needs to be further explored, which motivated this study. In this paper, the unequal area of rectangular blocks with fixed dimensions and input/output points are considered for FLPs. Four new mixed-integer programming (MIP) models based on previous research formulations are developed. Then, a mathematical optimization approach based on the linearization of the models is applied. An algorithm that solves the linearized MIP model by CPLEX setting a time limit for the solution obtained excellent results for different test problems when compared to those reported in the literature.

A simulated annealing approach for optimizing layout design of reconfigurable manufacturing system based on the workstation properties

The problem studied focuses on facility layout problems (FLP) that arise in reconfigurable manufacturing systems. The goal is to reduce the distance traveled by an automatic guided vehicle (AGV) to move products between several workstations. The proposed solving approach takes into account the empty and loaded travels made by this vehicle as well as the machine's properties (i.e. layout, orientations, shapes of workstations). We use simulated annealing to optimize this problem. The findings demonstrate the impact of the modification of the layout, the orientation, the shape, and the benefits of using the three together. In addition, we study two different ways to optimize the solution: each property independently (i.e. sequentially) or all properties together. Preliminary results show that the sequential method outperforms the latter in terms of distance gain.

Comparative solution approach with quadratic and genetic programming to multi-objective healthcare facility location problem

Facility layout planning is an indispensable part of the manufacturing and service operations. In the literature, there are two main different approaches for the solution of facility layout problem: While the first one aims to minimize both total transportation and assignment costs in a quantitative way, the second one aims to maximize the closeness relationship in a more qualitative way. In this study, we use both of these approaches to model a multi-objective facility layout optimization problem depending on a Quadratic Assignment Problem (QAP) formulation.We consider three objective which are minimizing the total walking distance, and the maximization of area satisfaction level and closeness relationships. Since these objectives have different scales, we normalize them to prevent a potential domination effect. Then, we solve the problem using Quadratic Assignment Problem (QAP) formulation and a genetic algorithm approach and compare the results. These approaches provide non-dominated solutions to multi-objective problem being considered. In this way, we assign the departments of a healthcare facility to predetermined areas. In addition, our formulation enables the planners to prevent patient losses and increase the patient satisfaction.