Dynamic Facility Layout Problem Research Articles

A large-scale hybrid simulated annealing algorithm for cyclic facility layout problems

The cyclic facility layout problem (CFLP) is a special case of the dynamic facility layout problem (DFLP) in which there are several production periods and the production cycle repeats itself by going to the first period after the last one because of the seasonal nature of products. In this article, a mixed integer programming formulation is developed for the CFLP. In the DFLP literature, department shapes are assumed to be given or fixed. However, this assumption does not hold in the case of the CFLP because the facility size is limited and the area requirements of the departments change significantly throughout the planning horizon. Therefore, department dimensions and sizes are considered as decision variables in the CFLP. A large-scale hybrid simulated annealing algorithm (LS-HSA) is proposed to solve the formulated problem and shown to be effective and versatile as it can be applied to various facility layout problems.

A hybrid particle swarm optimisation for dynamic facility layout problem

The dynamic facility layout problem (DFLP) aims to minimise the sum of handling and re-layout costs by devising an individual layout for each distinctive production period. In this paper, a hybrid particle swarm optimisation (HPSO) algorithm is proposed to find near-optimal solutions of DFLP. We use a coding and decoding technique that permits a one to one mapping of a solution in discrete space of DFLP to a PSO particle position in continuous space. The proposed PSO will further use this coding technique to explore the continuous solution space. For further enhancement, the proposed PSO is hybridised with a simple and fast simulated annealing. The developed algorithm is capable of being extended to more general cases although equal area machines and standardised handling equipments with identical unit costs are assumed for the time being. Computational results show the efficiency of the proposed algorithm compared to other heuristics.

Managing a new multi-objective model for the dynamic facility layout problem

In today’s manufacturing environment, the facility layout needs to be adaptable to changes. This situation requires the solution of the dynamic layout problem. But in previous studies of dynamic facility layout optimization, the main objective is to minimize the sum of the re-arrangement and material handling costs. To be more realistic, each of these cost terms in objective function might be of different importance to decision makers. In this research, the objective function has been considered as two distinct functions. This formulation enables decision makers to apply their own views. On the other hand, in the proposed model the adjacency-based objective aims at maximizing adjacency scores between the facilities in a facility layout has also been used. The proposed multi-objective model was defined as a complex combinatorial optimization problem. It has also been the objective of the present study to evaluate some of the known methods that have been proposed to solve the multi-objective problem. The results for test problems showed that the population based metaheuristic methods are useful tools in solving proposed model.

A new intelligent algorithm for dynamic facility layout problem in state of fuzzy constraints

In the present paper, the dynamic facilities layout problem is studied in presence of ambiguity of information flow. Product demand (and consequently material flow) is defined as fuzzy numbers with different membership functions. The problem is modeled in fuzzy programming. Three models of expected value, chance-constrained programming and dependent-chance programming and two hybrid intelligent algorithms are then presented. At the end, efficiency of algorithms for solving fuzzy models of dynamic facilities layout is shown through some numerical examples.

Metaheuristics for fuzzy dynamic facility layout problem with unequal area constraints and closeness ratings

Dynamic facility layout problem deals with the problem of arranging and rearranging the layout plan of a system throughout several periods. In each period, the material handling costs are different from the past period due to the change in the market demand and product mix. In this paper, the uncertainty that exists in transportation values’ forecast is modeled by fuzzy theory. In this paper, departments have unequal areas. This means that in each period, departments can be placed only in certain places due to different spacing requirements. In addition, closeness rating matrix is considered in order to model the goodness of different layout plans with regard to qualitative factors according to decision maker. Accordingly, fuzzy dynamic facility layout problem with unequal areas and closeness rating matrix is considered that aims to minimize the uncertain material handling costs as well as the shifting costs, and maximize closeness rating with regard to space requirements of unequal area departments. A number of fuzzy algorithms are developed in order to deal with the problem. A number of ranking criteria from the literature are implemented in order to compare the performance of the developed algorithms.

A hybrid heuristic for the unequal-area dynamic facility layout problem

The dynamic facility layout problem (DFLP) is the problem of finding positions of departments on the plant floor for a multi-period planning horizon such that departments do not overlap, and the sum of the material handling and rearrangement costs is minimised. In this paper, the departments may have unequal-areas and can be placed anywhere within the boundary of the continuous plant floor. Because of the complexity of the problem, only small-size problems can be solved in reasonable time using exact methods. As a result, a hybrid heuristic, which consists of a dual simplex method and a tabu search (TS) heuristic, is developed for the DFLP, and the heuristic was tested on some instances from the literature. The results obtained demonstrate the effectiveness of the heuristic.

Multi-floor dynamic facility layout: a simulated annealing-based solution

Today’s consumer market demands that manufacturing companies be competitive. Every manufacturing company wants to prevail over the uncertainties of the demand. Being able to meet the demand determines the success of a manufacturing company. A company can look for improvements in planning, manufacturing, distributing, marketing as well as other areas. Within a manufacturing system, the facility layout is one of the most appropriate criteria to reduce costs. Dynamic facility layout, as a problem that considers the flow changes between departments with time, can be extended and used for this problem. In this paper, with consideration of changes in material flow data over time, a mathematical model to formulate the multi-floor dynamic facility layout problem is developed and to solve this model, a simulated annealing-based solution method is proposed. Consideration of dynamic concept and capacity constraint for elevators leads to more compatible model with real world.

A new data structure of solution representation in hybrid ant colony optimization for large dynamic facility layout problems

Abstract A dynamic facility layout problem (DFLP) is concerned with finding a set of facility layouts across multiple time periods that minimizes the total cost of material flows and rearrangement costs. Unlike other heuristic approaches that focus mainly on the searching aspect, this research takes another approach by streamlining the data structure of solution representation to improve the solution swapping and storing activities within a meta-heuristic framework. The experimental results from testing the data encoding and decoding schemes on a DFLP data set have been quite promising in terms of solution quality and computational time.

A multi-objective particle swarm optimisation algorithm for unequal sized dynamic facility layout problem with pickup/drop-off locations

This paper deals with a multi-objective unequal sized dynamic facility layout problem (DFLP) with pickup/drop-off locations. First, a mathematical model to obtain optimal solutions for small size instances of the problem is developed. Then, a multi-objective particle swarm optimisation (MOPSO) algorithm is implemented to find near optimal solutions. Two new heuristics to prevent overlapping of the departments and to reduce ‘unused gaps’ between the departments are introduced. The performance of the MOPSO is examined using some sets of available test problems in the literature and various random test problems in small, medium, and large sizes. The percentage of improvements on the initial solutions is calculated for small, medium and large size instances. Also, the generation metric and the space metric for non-dominated solutions are examined. These experiments show the good performance of the developed MOPSO and sensitivity analysis show the robustness of the obtained solutions.

Dynamic facility layout problem based on flexible bay structure and solving by genetic algorithm

Finding positions of departments on the plant floor for multiple periods while minimizing the sum of material handling and rearrangement costs is the base consideration of dynamic facility layout problems (DFLP). In this paper, a new kind of DFLP using flexible bay structure is studied. In a plant layout, based on flexible bay structure, departments are assigned to parallel bays in a plant floor. Departments could be free oriented and may have unequal areas as well as a mixed integer programming formulated to find optimal solutions. Due to complexity, only small-size problems could be solved in logical time while using exact methods. Therefore, a genetic algorithm (GA) was proposed to solve this optimization problem. This method was tested on some test problems of the DFLP literature. The results show the effectiveness of the proposed algorithm.

Parallel variable neighborhood search for solving fuzzy multi-objective dynamic facility layout problem

In spite of the classic approaches of solution of dynamic facility layout problem, which only material handling and rearrangement costs are considered as objective function, these problems are the multi-objective problems. In this paper, a mixed integer linear programming formulation is presented for multi-objective dynamic facility layout problem concerning flexible bay structure. In addition, three current objectives in dynamic facility layout problems including minimizing material handling and rearrangement costs, maximizing adjacency rate, and minimizing shape ratio difference have been considered. Also, for solving this problem, two methods including the GAMS software and proposed parallel variable neighborhood search (PVNS) algorithm are used. So, it is worth mentioning that four test problems are solved by them, and the results show that the proposed PVNS algorithm is more efficient than the GAMS software.

New Tabu search heuristics for the dynamic facility layout problem

A manufacturing facility is a dynamic system that constantly evolves due to changes such as changes in product demands, product designs, or replacement of production equipment. As a result, the dynamic facility layout problem (DFLP) considers these changes and is defined as the problem of assigning departments to locations during a multi-period planning horizon such that the sum of the material handling and re-arrangement costs is minimised. In this paper, three tabu search (TS) heuristics are presented for this problem. The first heuristic is a simple TS heuristic. The second heuristic adds diversification and intensification strategies to the first, and the third heuristic is a probabilistic TS heuristic. To test the performances of the heuristics, two sets of test problems from the literature are used in the analysis. The results show that the second heuristic out-performs the other proposed heuristics and the heuristics available in the literature.

A New Optimization via Simulation Approach for Dynamic Facility Layout Problem with Budget Constraints

A new efficient heuristic algorithm has been developed for the dynamic facility layout problem with budget constraint (DFLPB) using optimization via simulation technique. The heuristic integrates integer programming and discrete event simulation to address DFLPB. In the proposed algorithm, the nonlinear model of the DFLP has been changed to a pure integer programming (PIP) model. Then, the optimal solution of the PIP model has been used in a simulation model that has been designed in a similar manner as the DFLP for determining the probability of assigning a facility to a location. After a sufficient number of runs, the simulation model obtains near optimum solutions. Finally, to test the performance of the algorithm, several test problems have been taken from the literature and solved. The results show that the proposed algorithm is more efficient in terms of speed and accuracy than other heuristic algorithms presented in previous works.

Dynamic facility layout problem under uncertainty: a Pareto-optimality based multi-objective evolutionary approach

Abstract In this paper, we investigate an evolutionary approach to solve the multi-objective dynamic facility layout problem (FLP) under uncertainty that presents the layout as a set of Pareto-optimal solutions. Research examining the dynamic FLP usually assumes that data for each time period are deterministic and known with certainty. However, production uncertainty is one of the most challenging aspects in today’s manufacturing environments. Researchers have only recently modeled FLPs with uncertainty. Unfortunately, most solution methodologies developed to date for both static and dynamic FLPs under uncertainty focus on optimizing just a single objective. To the best of our knowledge, the use of Pareto-optimality in multi-objective dynamic FLPs under uncertainty has not yet been studied. In addition, the approach proposed in this paper is tested using a backward pass heuristic to determine its effectiveness in optimizing multiple objectives. Results show that our approach is an efficient evolutionary dynamic FLP approach to optimize multiple objectives simultaneously under uncertainty.

Genetic algorithm using a modified backward pass heuristic for the dynamic facility layout problem *

AbstractLayout planning in a manufacturing company is an important economical consideration. In the past, research examining the facility layout problem (FLP) generally concerned static cases, where the material flows between facilities in the layout have been assumed to be invariant over time. However, in today’s real-world scenario, manufacturing system must operate in a dynamic and market-driven environment in which production rates and product mixes are continuously adapting. The dynamic facility layout problem (DFLP) addresses situations in which the flow among various facilities changes over time. Recently, there is an increasing trend towards implementation of industrial robot as a material handling device among the facilities. Reducing the robot energy usage for transporting materials among the facilities of an optimal layout for completing a product will result in an increased life for the robots and thus enhance the productivity of the manufacturing system. In this paper, we present a hybrid genetic algorithm incorporating jumping genes operations and a modified backward pass pair-wise exchange heuristic to determine its effectiveness in optimizing material handling cost while solving the DFLP. A computational study is performed with several existing heuristic algorithms. The experimental results show that the proposed algorithm is effective in dealing with the DFLP.

A particle swarm optimisation for fuzzy dynamic facility layout problem

Dynamic facility layout problem (DFLP) deals with arranging and rearranging the layout plan of a manufacturing system or a service provider throughout several periods. In each period, the material handling costs are different from the previous periods due to the change in the market demand and product mix. Since the problem is NP-hard, numerous approaches have been proposed in the literature in order to find near-optimum solutions of the DFLP. In this paper, we model the natural uncertainty in material handling costs with fuzzy theory. A fuzzy particle swarm optimisation (FPSO) algorithm is proposed to solve the problem. We implement a number of ranking criteria from the literature in order to test the performance of the developed algorithm. Computational results confirm the efficiency and effectiveness of the proposed mechanisms.

A simulated annealing heuristic for the dynamic layout problem with budget constraint

Facility layout problem has been extensively studied in the literature because the total material handling cost can be a significant portion in the operational costs for a company and in the manufacturing cost of a product. Today’s severe global competition, rapid changes in technology and shortening life cycle of products force companies to evaluate and modify their facility layout in a periodic fashion. This type of layout problems is categorized as the dynamic facility layout problem (DFLP). As a realistic dimension of the problem, one has to consider also the limited budget to cover the cost of changing the layout. In this study, we propose a simulated annealing heuristic for the DFLP with budget constraint, and show the effectiveness of this heuristic on a set of numerical experiments.

Combining Ant Colony Optimization and Dynamic Programming for Solving a Dynamic Facility Layout Problem

Combining Ant Colony Optimization and Dynamic Programming for Solving a Dynamic Facility Layout Problem

Heuristics for the dynamic facility layout problem with unequal-area departments

The dynamic facility layout problem (DFLP) is the problem of finding positions of departments on the plant floor for multiple periods (material flows between departments change during the planning horizon) such that departments do not overlap, and the sum of the material handling and rearrangement costs is minimized. In this paper, the departments may have unequal-areas and free orientations, and the layout for each period is generated on the continuous plant floor. Because of the complexity of the problem, only small-size problems can be solved in reasonable time using exact techniques. As a result, a boundary search (construction) technique, which places departments along the boundaries of already placed departments, is developed for the DFLP. The solution is improved using a tabu search heuristic. The heuristics were tested on some instances from the DFLP and static facility layout problem (SFLP) literature. The results obtained demonstrate the effectiveness of the heuristics.

A clonal selection algorithm for dynamic facility layout problems

The dynamic facility layout problem (DFLP) aims to minimize the sum of handling and re-layout costs by devising an individual layout for each distinctive production period. It is a complex combinatorial optimization problem. This paper proposes a clonal selection algorithm (CSA) for DFLP. The algorithm is capable of being extended to more general cases although equal area machines and standardized handling equipments with identical unit costs are assumed for the time being. The performance of the proposed algorithm is examined by using three test problems from the literature. CSA reached the best known solutions–even found better solutions for large sized problems–in 88% of the instances, while each of the former methods was successful only in a small fraction of those 50 problems. CSA also outperformed all the present methods as regards solution times.