Facility Layout Problem Research Articles

A Modified Simulated Annealing and Enhanced Harmony Search Algorithms for the Continuous Facility Layout Problem (CFLP)

This paper focuses on solving the Continuous Facility Layout Problem (CFLP), which aims to minimize material handling costs by strategically placing facilities in a known or unknown area. The objective is to find optimal or near-optimal facility arrangements while adhering to non-overlapping and spatial constraints, thus enhancing the efficiency of production systems. The study addresses various layout scenarios, including single-row facility layout (with and without clearance), continuous layout, and unequal-area facility layout problems. To achieve this, the authors propose a mixed-integer nonlinear programming (MINLP) model tailored for continuous layouts. Two meta-heuristic algorithms are developed to optimize this model: a hybrid Simulated Annealing-Genetic Algorithm (SA-GA), which leverages genetic crossover operations, and an Enhanced Harmony Search Algorithm (EHSA), featuring dynamic parameters and a novel improvisation technique. The proposed methods provide flexible and efficient solutions for both small and large-scale layout problems, offering decision-makers practical tools for real-world applications.

A New Approach to the Solution of Facility Layout Problems with Filled Function Method

Facility layout problems are generally solved by stochastic methods in the literature. The large number of iterations used in these methods is quite costly in terms of solution time. In this study, in order to get rid of this disadvantage, the facility layout problem was solved using the filled function method, which is known to be very successful in solving global optimization problems. In order to demonstrate the effectiveness of the filled function method, the classical linear facility layout problem was handled in a non-linear manner and the problem was deliberately made more difficult. In order to use the filled function method, the facility layout problem was transformed into an unconstrained and multimodal (including more than one local minima) global optimization problem by using the hyperbolic smoothing technique and the penalty function method. Thus, in this first study in the literature where a deterministic method is combined with the solution of the facility layout problem, it is shown that the non-convex facility layout problem can be solved with the filled function method with very few iterations and short solution times.

Solving dynamic facility layout problem using a hybridized heuristic dynamic programming approach

Today’s market volatility shortens product lifecycles and drives constant changes in product mix and demand. Changes in product mix and demand necessitate, in turn, changes to the shopfloor layout. The dynamic facility layout problem (DFLP) addresses layout development over multiple periods, with rearrangements from one period to another. To optimize the DFLP using dynamic programming (DP), the DP state space should be restricted as the problem is NP-hard. Therefore, a two-phased hybridized solution algorithm is being proposed and developed in this article. In the first phase, a heuristic approach is used to determine the set of layouts to be considered in each period. In the second phase, a metaheuristic approach is used to solve the recursive formulation of DP. A genetic algorithm (GA) searches for the best subsets of layouts, each represented by one chromosome. Notably, the GA incorporates a heuristic selection operator guided by a deep neural network algorithm. The best subset of layouts that results in the best multi-period layout plan is found throughout the different GA generations. The proposed method’s efficiency is statistically validated through rigorous statistical tests, affirming its superior performance, particularly for large-sized instances of the problem, and showcasing more efficient solutions.

Unequal area facility layout problem considering transporters interaction– a queuing theory and machine learning approach

ABSTRACT This study presents a novel analytical framework that merges queueing theory with deep neural networks to optimize facility layout and transporter selection in manufacturing systems. It addresses critical factors such as stochastic service times of facilities, random demand, transporter capacity, speed, and transportation batch size. Three objectives are considered: minimizing material handling costs (MHC), work-in-process (WIP), and the interaction probability of transporters (IP). The latter objective focuses on reducing instances where transporters cross paths to prevent accidents or disruptions. WIP is computed using a multi-class open queueing network model, while IP is determined using a deep neural network. The model facilitates the identification of facilities and the assignment of suitable transporters, considering empty transporter travels to minimize MHC and WIP. Results from the model are compared to a simulation model for validation across various scenarios, demonstrating acceptable accuracy. Additionally, a multi-objective meta-heuristic optimization algorithm is employed to solve the model. The effectiveness of the optimization method is evaluated against other approaches, highlighting its applicability in enhancing manufacturing system performance.

A Novel Approach for Material Handling-Driven Facility Layout

Material handling is a widely used process in manufacturing and is generally considered a non-value-added process. The Dynamic Facility Layout Problem (DFLP) considered in this paper minimizes the total material handling and re-arrangement cost. In this study, an integrated DFLP model with unequal facility areas, assignment of material handling devices (MHD), and flexible bay structure (FBS) is considered, and it is aimed to propose fast solution approaches. Two different solution methods are proposed for the problem, which are the genetic algorithm and the simulated annealing algorithm, respectively. In both methods, a non-linear mathematical model solution was used to calculate the fitness values. Thus, the solutions in the feasible solution space are utilized. The proposed solution approaches were applied to solve four problems published in the literature. The computational experiments have validated the effectiveness of the algorithms and the quality of solutions produced.

An efficient computational model for unequal-area dynamic facility layout problems considering input/output locations under algorithms case

An efficient computational model for unequal-area dynamic facility layout problems considering input/output locations under algorithms case

Optimizing dynamic facility layout problems: genetic algorithm with local search integration

Optimizing dynamic facility layout problems: genetic algorithm with local search integration

A GRASP method for the Bi-Objective Multiple Row Equal Facility Layout Problem

The Bi-Objective Multiple Row Equal Facility Layout Problem considers both quantitative and qualitative objectives that are very useful in many scenarios like the factory design. In this work, a new multi-objective GRASP approach is proposed which applies an ensemble of four different constructive methods followed by the combination of two local search procedures, improving the results from the state of the art. Due to the superiority of this proposal, a new dataset of larger problem instances is generated, providing detailed metrics of the obtained solutions.

On the automatic generation of metaheuristic algorithms for combinatorial optimization problems

Metaheuristic algorithms have become one of the preferred approaches for solving optimization problems. Finding the best metaheuristic for a given problem is often difficult due to the large number of available approaches and possible algorithmic designs. Moreover, high-performing metaheuristics often combine general-purpose and problem-specific algorithmic components. We propose here an approach for automatically designing metaheuristics using a flexible framework of algorithmic components, from which algorithms are instantiated and evaluated by an automatic configuration method. The rules for composing algorithmic components are defined implicitly by the properties of each algorithmic component, in contrast to previous proposals, which require a handwritten algorithmic template or grammar. As a result, extending our framework with additional components, even problem-specific or user-defined ones, automatically updates the design space. Furthermore, since the generated algorithms are made up of components, they can be easily interpreted. We provide an implementation of our proposal and demonstrate its benefits by outperforming previous research in three distinct problems from completely different families: a facility layout problem, a vehicle routing problem and a clustering problem.

Simulation Model for AGVs in Production Environments under Consideration of the Facility Layout Problem

Simulation Model for AGVs in Production Environments under Consideration of the Facility Layout Problem

A new filled function for global optimization and its deterministic efficiency in solving the facility layout/location problem

Although many real-world problems can be modelled as optimization problems, the multimodal nature of such problems and the fact that traditional algorithms are often trapped within local minima make it very difficult to solve these problems. The filled function method is one of the highly effective deterministic global optimization techniques that can overcome such difficulties. However, it is known that the use of non-differentiable and parameter-sensitive filled functions in the solution of global optimization problems could cause high time and computational costs. In this study, a continuously differentiable filled function is proposed to overcome such disadvantages. In addition, for the first time in the literature, the solution of the facility layout problem has been introduced using the filled function method. Moreover, it has been shown in the numerical experiment that, even with more than twenty facilities, this problem can be solved within a very satisfactory time and computational cost.

Usulan Perancangan Tata Letak Fasilitas Produksi Dengan Metode BLOCPLAN Untuk Meningkatkan Produktivitas CV. Madu Apiari Mutiara

CV. Madu Apiari Mutiara is a company that operates in the field of derivative honey products such as pure honey, candy, soap, and health products such as propolis and other derivative products which have various shapes and compositions. This condition makes it necessary to have the most efficient spatial layout of work areas and production facilities in order to increase work morale and work productivity in terms of the layout of production facilities. The method used to solve this problem is the BLOCPLAN method. This method can solve facility layout problems in CV Madu Apiari Mutiara production houses with the level of close relationship, area requirements, taking into account the distance of material movement and final layout. The area on CV. Madu Apiari Mutiara is 100 m2, the total area requirement is 70.48 m2. There are 3 alternative layouts in the BLOCPLAN method, the proposed layout chosen is based on the R-Score value which is close to 1, namely the 2nd layout with a material movement distance efficiency of 8.1 meters or 20.9%.

Dynamic content layout optimization for news website front pages

Purpose The home page design of a digital news website is a key factor in determining its attractiveness to readers. This study aims to propose an approach to manage the frequent adjustment of the dynamic layout of the news content on the website home page in a real-time environment to increase its attractiveness to readers. Design/methodology/approach This paper shows that this news website layout design problem can be modeled as an optimization problem based on the information of news contents that change within a multiple-period planning horizon similar to the dynamic facility layout problem. A hybrid genetic algorithm-based approach integrated with local search heuristic methods is also proposed to improve the solution. Findings This paper finds that the DPLP model is effective in modeling the changing layout of a digital news website. The problem can solved in a timely manner using the proposed hybrid genetic algorithm. Research limitations/implications This paper was based on hypothetical data and on the assumption of equal section size. Actual data would help fine-tune the application of the dynamic facility layout model. As well the algorithm could be enhanced for unequal size sections. Practical implications The model should help online newspapers apply sophisticated algorithms to optimize the layout of news websites dynamically in a timely manner. Social implications News websites are increasingly the desired medium to consume news. So it has an important role in educating society. Thus optimizing and improving the process will help in this regard. Originality/value To the best of the authors’ knowledge, this paper is the first one to apply the DPLP model to the digital newspaper website dynamic design problem.

Automated placement of analog integrated circuits using priority-based constructive heuristic

This paper presents a heuristic approach for solving the placement of Analog and Mixed-Signal Integrated Circuits. Placement is a crucial step in the physical design of integrated circuits. During this step, designers choose the position and variant of each circuit device. We focus on the specific class of analog placement, which requires so-called pockets, their possible merging, and parametrizable minimum distances between devices, which are features mostly omitted in recent research and literature. We formulate the problem using Integer Linear Programming and propose a priority-based constructive heuristic inspired by algorithms for the Facility Layout Problem. Our solution minimizes the perimeter of the circuit’s bounding box and the approximated wire length. Multiple variants of the devices with different dimensions are considered. Furthermore, we model constraints crucial for the placement problem, such as symmetry groups and blockage areas. Our outlined improvements make the heuristic suitable to handle complex rules of placement. With a search guided either by a Genetic Algorithm or a Covariance Matrix Adaptation Evolution Strategy, we show the quality of the proposed method on both synthetically generated and real-life industrial instances accompanied by manually created designs. Furthermore, we apply reinforcement learning to control the hyper-parameters of the genetic algorithm. Synthetic instances with more than 200 devices demonstrate that our method can tackle problems more complex than typical industry examples. We also compare our method with results achieved by contemporary state-of-the-art methods on the MCNC and GSRC datasets.

Iterated local search with ejection chains for the space-free multi-row facility layout problem

This paper presents an iterated local search algorithm based on ejection chains to solve the space-free multi-row facility layout problem. The aim of this problem is to find a non-overlapping layout of facilities on a given number of rows such that there is no space between two adjacent facilities. In addition, the left-most facility of the arrangement must have zero abscissa. Our algorithm looks for a local optimal solution by continuously alternating ejection moves and trial moves to form an ejection chain. Since the ejection chain can compound simple moves to create more complex and powerful moves, it has a greater chance to discover better solutions. Additionally, we propose a directional perturbation strategy to produce a solution of both high quality and good diversity. The idea is to calculate the score of each facility based on the location concentration of facilities in the elite solution set and the historical perturbation times of facilities in the search process, and to select the facility with the highest score for perturbation. Experimental results on four benchmark sets containing a total of 290 instances reveal that the proposed algorithm performs better for 131 (45.2%) instances in terms of best solution values and for 196 (80.0%) instances in terms of average solution values than a recently reported state-of-the-art algorithm.

Gym-flp: A Python Package for Training Reinforcement Learning Algorithms on Facility Layout Problems

Reinforcement learning (RL) algorithms have proven to be useful tools for combinatorial optimisation. However, they are still underutilised in facility layout problems (FLPs). At the same time, RL research relies on standardised benchmarks such as the Arcade Learning Environment. To address these issues, we present an open-source Python package (gym-flp) that utilises the OpenAI Gym toolkit, specifically designed for developing and comparing RL algorithms. The package offers one discrete and three continuous problem representation environments with customisable state and action spaces. In addition, the package provides 138 discrete and 61 continuous problems commonly used in FLP literature and supports submitting custom problem sets. The user can choose between numerical and visual output of observations, depending on the RL approach being used. The package aims to facilitate experimentation with different algorithms in a reproducible manner and advance RL use in factory planning.

A Practical Methodology for Reproducible Experimentation: An Application to the Double-Row Facility Layout Problem.

Reproducibility of experiments is a complex task in stochastic methods such as evolutionary algorithms or metaheuristics in general. Many works from the literature give general guidelines to favor reproducibility. However, none of them provide both a practical set of steps or software tools to help in this process. In this article, we propose a practical methodology to favor reproducibility in optimization problems tackled with stochastic methods. This methodology is divided into three main steps, where the researcher is assisted by software tools which implement state-of-the-art techniques related to this process. The methodology has been applied to study the double-row facility layout problem (DRFLP) where we propose a new algorithm able to obtain better results than the state-of-the-art methods. To this aim, we have also replicated the previous methods in order to complete the study with a new set of larger instances. All the produced artifacts related to the methodology and the study of the target problem are available in Zenodo.

The influence of distance metrics on the facility layout problem

The optimization of an assembly line layout can be modeled as a facility layout problem. Since an accurate representation of the real-world distances is essential for the validity of the optimization results, this paper compares the accuracy of the approximation of the real-world distances by the commonly used rectilinear and Euclidean distance metrics. Further, the influence of the size and aspect ratio of the machines on the approximation accuracy and the influence of the distance metrics on the solver runtime is studied. The results show the Euclidean distance metric being more accurate but extending computation time.

Solving Unequal Area Facility Layout Problems with Differential Evolution and Particle Swarm Optimization

The unequal area facility layout problem (UAFLP) is a problem in industrial engineering that aims to minimize material handling costs between a group of unequal-shaped facilities. In this study, Differential Evolution (DE) with continuous variables and Particle Swarm Optimization (PSO) are employed to solve the UAFLP. This study also compares the performance of the DE method (never used to solve UAFLP) and the PSO method (frequently used to solve facility layout problems). The experimental results demonstrate that PSO is effective in solving small-sized datasets, while DE works well in solving datasets with larger areas.

Comparative Study of Reinforcement Learning Algorithms to solve Reconfigurable Facilities Layout Problem

Facilities Layout Problems (FLPs) aim to efficiently allocate facilities within a given space, considering various constraints such as minimizing transportation distances. These problems are commonly encountered in various types of advanced manufacturing systems, including Reconfigurable Manufacturing Systems (RMSs). RMSs enable easier layout changes to accommodate shifts in product mix, production volume, or process requirements thanks to their modularity and changeability. Reinforcement Learning (RL) has proven its efficiency in addressing decision-making problems. Therefore, this paper introduces a comparative study between two RL algorithms to solve FLPs: Advantage Actor-Critic (A2C) and Q-learning algorithms.