Facility Layout Problem Research Articles

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%.

A Queuing Network Model for Solving Facility Layout Problem in Multifloor Flow Shop

A Queuing Network Model for Solving Facility Layout Problem in Multifloor Flow Shop

A Thin-Provisioned and Functionalized Memetic Algorithm for the Single Row Facility Layout Problem

A Thin-Provisioned and Functionalized Memetic Algorithm for the Single Row Facility Layout Problem

Solving Multi-Objective Paired Single Row Facility Layout Problem Using Hybrid Variable Neighborhood Search

The footwear industry is distinguished by its manual assembly line and a high proportion of shared workstation configuration. This study focuses on a subset of the single row facility layout problem known as the paired single row facility layout problem. As one of type of single-row facility layout, the paired single row facility layout problem cannot be solved quickly. Further, different objectives also need to be considered in the decision-making process. Therefore, multi-objective approaches are proposed to minimize the penalty of material handler usage while maximizing the adjacency function based on each workstation's closeness rating. A Single Row Facility Layout is an NP-hard problem; this problem also belongs to the NP-hard problem class. As a result, we propose a hybrid method combining variable neighborhood search (VNS) and genetic algorithm (GA) to solve the problem of obtaining the optimal configuration of a multi-objective paired single-row assembly line. A heuristic approach was used to create the schematic representation solution. To obtain the neighborhood solutions, a hybrid VNSGA was used. The schematic representation solution employs crossover and variable neighborhood descent. Using the concept of VNS, the neighborhood was changed in each generation.

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.

Integrating Hoisting Efficiency into Construction Site Layout Plan Model for Prefabricated Construction

Construction site layout plans (CSLP) are vitally significant to prefabricated construction project management; in particular, abundant hoisting operations reinforce the importance of the location of tower cranes and precast component supply points. However, previous studies on CSLP have been conducted on traditional construction sites, and there is a gap in the research regarding the prefabricated construction site layout plan. This paper attempts to establish a comprehensive CSLP model to optimize the facility layout problem on prefabricated construction sites, integrating the hoisting efficiency of precast components into previous studies on CSLP considering safety risks and transportation cost. To solve the proposed multiobjective model, the Genetic algorithms (GAs) is applied to obtain Pareto optimal solutions due to its strong global search capability. To verify the feasibility and effectiveness of the proposed model, a prefabricated residential building project is studied as a case study. This paper attempts to improve the CSLP model for the scene of prefabricated construction in the preconstruction stage to optimize safety risk, transportation cost, and hoisting efficiency, which may facilitate future research attempting to improve the prefabricated construction site layout considering more complex actual situations.

Firefly algorithm for facility layout problem optimization

This paper shows the result of a research about the applications of bio-inspired algorithms in the field of production engineering in the Distrital University Francisco José de Caldas, covering the topics of industrial layout distribution in manufacturing plant layout. It is intended to seek the optimization of some problems of those fields, using artificial intelligence from the implementation of a firefly algorithm as metaheuristic planning tool and optimization of layout problem. With the goal of finding the best spatial allocation of work stations or cells. Theoretical concepts explored and results are presented.
 First, a state-of-the-art review on the subject was made, and then the possible solution algorithms were evaluated to identify the objective function to be optimized, to finally apply the firefly algorithm, and evaluate the results of performance against the Initial layout as the plant.

Proposed Office Facilities Layout in Low Voltage Electric Panel Manufacturer

<p>The electrical equipment industry in Indonesia is one of the factors that triggers economic growth. Labor productivity determines the success of project implementation. The non-optimal facility layout is one of the factors that triggers low labor productivity. The non-optimal facility layout affects the flow of information in the company. This study examines the problem of non-optimal office facility layout with TSP, ARC and ARD methods at PT. Duta Wijaya Elektrindo Engineering (PT. DWP). The method stated above can minimize the total distance traveled between office facilities so that work efficiency and workforce productivity may increase. The results of data processing indicate that the recommended office facility layout recommendations that are in accordance with the level of closeness between departments based on PST, ARC and ARD is the first recommendation. With reduced total distance traveled in the first layout of the proposed office facilities, the level of employee productivity and work efficiency will increase. The smallest total distance traveled on the proposed layout of the office facilities is 244.22 meters with a reduction of 36% from the initial conditions.</p>

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

Optimizing 2-opt-based heuristics on GPU for solving the single-row facility layout problem

The optimization of most combinatorial problems is NP-hard, which can be solved by heuristic algorithms to obtain approximately optimal solutions, especially for large-scale problems. Many heuristic algorithms can apply the 2-opt local search to find better solutions. In complete 2-opt local search, every valid neighboring solutions of swapping mechanism will be compared, which is very time consuming especially when a sequential algorithm is adopted. Nowadays, graphic processing units (GPUs) have evolved into powerful and flexible parallel computation platforms that have been widely used to accelerate the solving of NP-hard problems. Therefore, in this study, we focus on how to use GPUs to solve the single-row facility layout problem (SRFLP) with the 2-opt-based simulated annealing (SA) heuristic algorithm. As far as we know, this study is the first to solve SRFLP by using a GPU. After analyzing the fitness function and the move gains calculation between parent and child solutions, we propose a prefix-sum formula table to eliminate a large amount of replicated computation. To calculate move gains for the 2-opt local search during each iteration, many GPU threads are created and they construct and lookup the table in parallel. According to experimental results, if the prefix-sum formula table is not adopted, the GPU version outperforms the sequential CPU counterpart with the best speedup of 123. However, if the table is used in the GPU version, the best speedup can reach up to 3208. Because the proposed parallelization approach with the prefix-sum formula table is based on the features of the 2-opt local search and the SRFLP fitness function, it can be applied to any 2-opt-based heuristic algorithm for solving SRFLP even though the SA algorithm is used in this study.

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.

Using projected cutting planes in the extended cutting plane method

In this paper we show that simple projections can improve the algorithmic performance of cutting plane-based optimization methods. Projected cutting planes can, for example, be used as alternatives to standard cutting planes or supporting hyperplanes in the extended cutting plane (ECP) method. In the paper we analyse the properties of such an algorithm and prove that it will converge to a global optimum for smooth and nonsmooth convex mixed integer nonlinear programming problems. Additionally, we show that we are able to solve two old but very difficult facility layout problems (FLP), with previously unknown optimal solutions, to verified global optimum by using projected cutting planes in the algorithm. These solution results are also given in the paper.

Enhancement of bi-objective function model to master straight-line facilities sequences using frequency from-to chart

PurposeThe purpose of this study is to understand the functional power of frequency from-to chart (FFTC) as an independent solution-key for generation optimal (exact) facilities sequences with an equal distance of straight-line flow patterns. The paper will propose a bi-objective function model based on the Torque Method then will turn it into a computer-based technique with a permutative manner using the full enumeration method. This model aims to figure out if there is a difference between the moment minimization and backtracking treatment. Furthermore, the proposed technique will measure the performance of related works from literature to numerically highlight their limitations.Design/methodology/approachThe literature of related works provided two-principles assumed mastering material flow sequences. The researchers gathered and analyzed the three methods – used FFTC as an independent technique – mentioned in the literature then measured their performance with the proposed technique. The proposed technique is based on the computation of torque value using an enhancement of bi-objective function model then application a permutative approach with full enumeration methodology. The bi-objective function model used once to mimic the grand moment value of FFTC and again to study the reflection of minimizing the congestion of backtracking movements on the minimization of total transportation cost.FindingsBased on the analysis of literature and comparative results of its three case studies using the proposed technique, it is found that: there are optimum facilities sequences with rich opportunities of exact pathway selection. Reduction methodology is an inefficient way to generate exact results. There is a gap between combining the minimization of the grand moment and the treatment of the backtracking problem.Research limitations/implicationsThis study is one of the first contributions that discusses the assumption of integration between optimization moment value and its relation to treatment backtracking problem. Also, the illness of reduction methodology to reach optimal solutions. The further direction of this research will highlight the conjecture of searching the exact results for small size problems, analyzing the given data and its logical dimensions, developing logical rules for solving and verifying large size problems based on the exact results (The conjecture of P = NP).Originality/valueThis paper provides a detailed numerical analysis of the most common problems generally faced facility layout problems through understanding the lack of integration between moment minimization and backtracking minimization. Also, the inefficiency of reliance on reduction methodology either in scores of frequencies between facilities with weak relation or the number of permutations. Based on those findings, further study will search the logical philosophy exactly optimizing FFTC manually or without having to deal with a permutative approach for large size problems – which considered non-deterministic polynomial-time problem.

An improved GRASP method for the multiple row equal facility layout problem

As it is well documented in the literature, an effective facility layout design of a company significantly increases throughput, overall productivity, and efficiency. Symmetrically, a poor facility layout results in increased work-in process and manufacturing lead time. In this paper we focus on the Multiple Row Equal Facility Layout Problem (MREFLP) which consists in locating a given set of facilities in a layout where a maximum number of rows is fixed. We propose a Greedy Randomized Adaptive Search Procedure (GRASP), with an improved local search that relies on an efficient calculation of the objective function, and a probabilistic strategy to select those solutions that will be improved. We conduct a through preliminary experimentation to investigate the influence of the proposed strategies and to tune the corresponding search parameters. Finally, we compare our best variant with current state-of-the-art algorithms over a set of 552 diverse instances. Experimental results show that the proposed GRASP finds better results spending much less execution time.

Parallel hybrid BBO-TS algorithm for QAP-formulation of FLP

This paper deals with the facility layout problem (FLP) in manufacturing systems, the main purpose of the paper is to solve the static facility layout problem as a quadratic assignment problem QAP using approximated approaches. We present Bio-geography Based Optimization meta-heuristic BBO and a parallel hybrid BBO with tabu search PBBO-TS algorithm to solve the facility layout problem. Parallel computing is implemented to diversify the search, to increase the performance of BBO algorithm, and to accelerate the speed of the running time. The proposed approaches were tested on QAPLIB benchmark instances and compared with some of the best performing sequential and parallel algorithms in the literature. The comparisons prove that the proposed algorithm PBBO-TS produce satisfactory results. In addition, the performance of the parallel implementation of the program is studied in terms of speedup and efficiency; the obtained results show that the parallel implemented system is scalable.

A comparative study of hybrid estimation distribution algorithms in solving the facility layout problem

The Estimation Distribution Algorithm (EDA) is an evolutionary algorithm that uses probabilistic models to create candidate solutions. Previous researchers have suggested various hybrid methods to avoid the premature convergence of EDA. This research conducts a comparative study between several variations of hybridization in EDA with regards to the descriptive statistics in the objective values.This study also proposes a new hybrid approach, named Adapted EDA (AEDA), by adapting the structure of EDA by adding a lottery procedure, an elitism strategy, and a neighborhood search. The proposed AEDA, several hybridizations of EDA, and Genetic Algorithm (GA) plus Tabu Search (TS) are applied to the facility layout design in manufacture – Enhanced Facility Layout Problem (EFLP) – to analyze their solutions. The hybrid EDAs that are being compared are EDA plus GA (EDAGA), EDA plus Particle Swarm Optimization (EDAPSO), the combination of EDAPSO plus TS (EDAhybrid), and AEDA. The experimental results show that the AEDA can significantly improves the solution quality in solving all the EFLP instances compared to other algorithms.

An improved construction approach using ant colony optimization for solving the dynamic facility layout problem

This paper presents an improved Ant Colony Optimization (ACOII) algorithm to solve the dynamic facility layout problem for construction sites. The algorithm uses a construction approach in building the layout solutions over time and uses a discrete dynamic search with heuristic info based on both relocation and flow costs to influence facilities’ placement in different time periods. The performance of ACOII is investigated using randomly generated data sets where the number of facilities and the number of time periods in the planning horizon vary to mimic what happens on a construction site over time. The experimental results show that ACOII is effective in solving the problem. A benchmarking study using instances from the literature showed promising results with improved solutions for all instances with very large number of facilities and periods.