Facility layout problem: Bibliometric and benchmarking analysis

Facility layout problem is related to the location of departments in a facility area, with the aim of determining the most effective configuration. Researches based on different approaches have been published in the last six decades and, to prove the effectiveness of the results obtained, several instances have been developed. This paper presents a general overview on the extant literature on facility layout problems in order to identify the main research trends and propose future research questions. Firstly, in order to give the reader an overview of the literature, a bibliometric analysis is presented. Then, a clusterization of the papers referred to the main instances reported in literature was carried out in order to create a database that can be a useful tool in the benchmarking procedure for researchers that would approach this kind of problems

A novel Island Model based on Coral Reefs Optimization algorithm for solving the unequal area facility layout problem

This paper proposes a novel approach to address the Unequal Area Facility Layout Problem (UA-FLP), based on the combination of both an Island Model and a Coral Reefs Optimization (CRO) algorithm. Two different versions of this Island Model based on Coral Reefs Optimization Algorithm (IMCRO) are proposed and applied to the UA-FLP. The structure of flexible bays has been selected as effective encoding to represent the facility layouts within the algorithm. The two versions of the proposed approach have been tested in 22 UA-FLP cases, considering small, medium and large size categories. The empirical results obtained are compared with previous state of the art algorithms, in order to show the performance of the IMCRO. From this comparison, it can be extracted that both versions of the proposed IMCRO algorithm show an excellent performance, accurately solving the UA-FLP instances in all the size categories

A novel multi-objective Interactive Coral Reefs Optimization algorithm for the Unequal Area Facility Layout Problem

The Unequal Area Facility Layout Problem (UA-FLP) has been widely analyzed in the literature using several heuristics and meta-heuristics to optimize some qualitative criteria, taking into account different restrictions and constraints. Nevertheless, the subjective opinion of the designer (Decision Maker, DM) has never been considered along with the quantitative criteria and restrictions. This work proposes a novel approach for the UA-FLP based on an Interactive Coral Reefs Optimization (ICRO) algorithm, which combines the simultaneous consideration of both quantitative and qualitative (DM opinion) features. The algorithm implementation is explained in detail, including the way of jointly considering quantitative and qualitative aspects in the fitness function of the problem. The experimental part of the paper illustrates the effect of including qualitative aspects in UA-FLP problems, considering three different hard UA-FLP instances. Empirical results show that the proposed approach is able to incorporate the DM preferences in the obtained layouts, without affecting much to the quantitative part of the solutions

A Hybrid Coral Reefs Optimization – Variable Neighborhood Search Approach for the Unequal Area Facility Layout Problem

The Unequal Area Facility Layout Problem (UA-FLP) is a relevant optimization problem related to industrial design, that deals with obtaining the most effective allocation of facilities, that make up the rectangular manufacturing plant layout. The UA-FLP is known to be a hard optimization problem, where meta-heuristic approaches are a good option to obtain competitive solutions. Many of these computational approaches, however, usually fall into local optima, and suffer from lack of diversity in their population, mainly due to the huge search spaces and hard fitness landscapes produced by the traditional representation of UA-FLP. To solve these issues, in this paper we propose a novel hybrid meta-heuristic approach, which combines a Coral Reefs Optimization algorithm (CRO) with a Variable Neighborhood Search (VNS) and a new representation for the problem, called Relaxed Flexible Bay Structure (RFBS), which simplifies the encoding and makes its fitness landscape more affordable. Thus, the use of VNS allows more intensive exploitation of the searching space with an affordable computational cost, as well as the RFBS allows better management of the free space into the plant layout. This combined strategy has been tested over a set of UA-FLP instances of different sizes, which have been previously tackled in the literature with alternative meta-heuristics. The tests results show very good performance in all cases

Using eye-tracking into decision makers evaluation in evolutionary interactive UA-FLP algorithms

Unequal area facility layout problem is an important issue in the design of industrial plants, as well as other fields such as hospitals or schools, among others. While participating in an interactive designing process, the human user is required to evaluate a high number of proposed solutions, which produces them fatigue both mental and physical. In this paper, the use of eye-tracking to estimate user’s evaluations from gaze behavior is investigated. The results show that, after a process of training and data taking, it is possible to obtain a good enough estimation of the user’s evaluations which is independent of the problem and of the users as well. These promising results advice to use eye-tracking as a substitute for the mouse during users’ evaluations

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

Dynamic Facility Layout for Cellular and Reconfigurable Manufacturing using Dynamic Programming and Multi-Objective Metaheuristics

The facility layout problem is one of the most classical yet influential problems in the planning of production systems. A well-designed layout minimizes the material handling costs (MHC), personnel flow distances, work in process, and improves the performance of these systems in terms of operating costs and time. Because of this importance, facility layout has a rich literature in industrial engineering and operations research. Facility layout problems (FLPs) are generally concerned with positioning a set of facilities to satisfy some criteria or objectives under certain constraints. Traditional FLPs try to put facilities with the high material flow as close as possible to minimize the MHC. In static facility layout problems (SFLP), the product demands and mixes are considered deterministic parameters with constant values. The material flow between facilities is fixed over the planning horizon. However, in today’s market, manufacturing systems are constantly facing changes in product demands and mixes. These changes make it necessary to change the layout from one period to the other to be adapted to the changes. Consequently, there is a need for dynamic approaches of FLP that aim to generate layouts with high adaptation concerning changes in product demand and mix. This thesis focuses on studying the layout problems, with an emphasis on the changing environment of manufacturing systems. Despite the fact that designing layouts within the dynamic environment context is more realistic, the SFLP is observed to have been remained worthy to be analyzed. Hence, a math-heuristic approach is developed to solve an SFLP. To this aim, first, the facilities are grouped into many possible vertical clusters, second, the best combination of the generated clusters to be in the final layout are selected by solving a linear programming model, and finally, the selected clusters are sequenced within the shop floor. Although the presented math-heuristic approach is effective in solving SFLP, applying approaches to cope with the changing manufacturing environment is required. One of the most well-known approaches to deal with the changing manufacturing environment is the dynamic facility layout problem (DFLP). DFLP suits reconfigurable manufacturing systems since their machinery and material handling devices are reconfigurable to encounter the new necessities for the variations of product mix and demand. In DFLP, the planning horizon is divided into some periods. The goal is to find a layout for each period to minimize the total MHC for all periods and the total rearrangement costs between the periods. Dynamic programming (DP) has been known as one of the effective methods to optimize DFLP. In the DP method, all the possible layouts for every single period are generated and given to DP as its state-space. However, by increasing the number of facilities, it is impossible to give all the possible layouts to DP and only a restricted number of layouts should be fed to DP. This leads to ignoring some layouts and losing the optimality; to deal with this difficulty, an improved DP approach is proposed. It uses a hybrid metaheuristic algorithm to select the initial layouts for DP that lead to the best solution of DP for DFLP. The proposed approach includes two phases. In the first phase, a large set of layouts are generated through a heuristic method. In the second phase, a genetic algorithm (GA) is applied to search for the best subset of layouts to be given to DP. DP, improved by starting with the most promising initial layouts, is applied to find the multi-period layout. Finally, a tabu search algorithm is utilized for further improvement of the solution obtained by improved DP. Computational experiments show that improved DP provides more efficient solutions than DP approaches in the literature. The improved DP can efficiently solve DFLP and find the best layout for each period considering both material handling and layout rearrangement costs. However, rearrangement costs may include some unpredictable costs concerning interruption in production or moving of facilities. Therefore, in some cases, managerial decisions tend to avoid any rearrangements. To this aim, a semi-robust approach is developed to optimize an FLP in a cellular manufacturing system (CMS). In this approach, the pick-up/drop-off (P/D) points of the cells are changed to adapt the layout with changes in product demand and mix. This approach suits more a cellular flexible manufacturing system or a conventional system. A multi-objective nonlinear mixed-integer programming model is proposed to simultaneously search for the optimum number of cells, optimum allocation of facilities to cells, optimum intra- and inter-cellular layout design, and the optimum locations of the P/D points of the cells in each period. A modified non-dominated sorting genetic algorithm (MNSGA-II) enhanced by an improved non-dominated sorting strategy and a modified dynamic crowding distance procedure is used to find Pareto-optimal solutions. The computational experiments are carried out to show the effectiveness of the proposed MNSGA-II against other popular metaheuristic algorithms

Genetic Programming + Unfolding Embryology in Automated Layout Planning

Automated layout planning aims to the implementation of computational methods for the generation and the optimization of floor plans, considering the spatial configuration and the assignment of activities. Sophisticated strategies such as Genetic Algorithms have been implemented as heuristics of good solutions. However, the generative forces that derive from the social structures have been often neglected. This research aims to illustrate that the data that encode the layout’s social and cultural generative forces, can be implemented within an evolutionary system for the design of residential layouts. For that purpose a co-operative system was created, which is composed of a Genetic Programming algorithm and an agent-based unfolding embryology procedure that assigns activities to the spaces generated by the GP algorithm. The assignment of activities is a recursive process which follows instructions encoded as permeability graphs. Furthermore, the Ranking Sum Fitness evaluation method is proposed and applied for the achievement of multi-objective optimization. Its efficiency is tested against the Weighted-Sum Fitness function. The system’s results, both numerical and spatial, are compared to the results of a conventional evolutionary approach. This comparison showed that, in general, the proposed system can yield better solutions

New Mixed Integer Programming for Facility Layout Design without Loss of Department Area

This paper proposes a New Mixed Integer Programming (NMIP) for solving facility layout problem (FLP). The formulation is extensively tested on problems from literature to minimize material handling cost when addresses on the shop floor. Every department and shop floor in tested problem were fixed dimension of length and width. Classic Mixed Integer Programming (MIP) solves FLP with fixed layout area and preset each department’s lower-upper limit of length and width. As a result, there might be some departments with 5-10% area less than initial layout design requirements which leads to problem in design and construction process. It is infeasible to address the department on the actual shop floor thus adjustment of result from MIP is required. The main purpose of NMIP is to eliminate such infeasibility and show the efficiency of this model. Keywords: mixed integer programing, facility layout design, facility layout problem, heuristic

Design of plant layout having passages and inner structural wall using particle swarm optimization

The FLP has applications in both manufacturing and the service industry. The FLP is a common industrial problem of allocating facilities to either maximize adjacency requirement or minimize the cost of transporting materials between them. The “maximizing adjacency” objective uses a relationship chart that qualitatively specifies a closeness rating for each facility pair. This is then used to determine an overall adjacency measure for a given layout. The “minimizing of transportation cost” objective uses a value that is calculated by multiplying together the flow, distance, and unit transportation cost per distance for each facility pair. The resulting values for all facility pairs are then added. Most of the published research work for facilities layout design deals with equal-area facilities. By disregarding the actual shapes and sizes of the facilities, the problem is generally formulated as a quadratic assignment problem (QAP) of assigning equal area facilities to discrete locations on a grid with the objective of minimizing a given cost function. Heuristic techniques such as simulated annealing, simulated evolution, and various genetic algorithms developed for this purpose have also been applied for layout optimization of unequal area facilities by first subdividing the area of each facility in a number of “unit cells”. The particle swarm optimization(PSO) technique has developed by Eberhart and Kennedy in 1995 and it is a simple evolutionary algorithm, which differs from other evolutionary computation techniques in that it is motivated from the simulation of social behavior. PSO exhibits good performance in finding solutions to static optimization problems. Particle swarm optimization is a swarm intelligence method that roughly models the social behavior of swarms. PSO is characterized by its simplicity and straightforward applicability, and it has proved to be efficient on a plethora of problems in science and engineering. Several studies have been recently performed with PSO on multi objective optimization problems, and new variants of the method, which are more suitable for such problems, have been developed. PSO has been recognized as an evolutionary computation technique and has features of both genetic algorithms (GA) and Evolution strategies (ES). It is similar to a GA in that the System is initialized with a population of random solutions. However, unlike a GA each population individual is also assigned a randomized velocity, in effect, flying them through the solution hyperspace. As is obvious, it is possible to simultaneously search for an optimum solution in multiple dimensions. In this project we have utilized the advantages of the PSO algorithm and the results are compared with the existing GA. Need Statement of Thesis: To Find the best facility Layout or to determine the best sequence and area of facilities to be allocated and location of passages for minimum material handling cost using particle swarm optimization and taking a case study. The criteria for the optimization are minimum material cost and adjacency ratios. Minimize F = ∑∑ . ……………………………………………... (1) = = M i M j ij f ij d 1 1 * g1= αi min – αi ≤ 0,………………………………………………………… (2) g2= αi - αi max ≤ 0, ……………………………………………………… (3) g3= ai min – ai ≤ 0,…………………………………………………………. (4) g4= ∑ - A = M i ai 1 available ≤ 0,…………………………………………………... (5) g5= αi min – αi ≤ 0,………………………………………………………… (6) g6= αi min – αi ≤ 0,………………………………………………………… (7) g7 = (xi r - xi i.s.w ) (xi i . s.w - xi l ) ≤ 0,…………………………………………... (8) Where i, j= 1, 2, 3…….M, S= 1, 2, 3…P fij : Material flow between the facility i and j, dij : Distance between centroids of the facility i and j, M: Number of the facilities, αi : Aspect ratio of the facility i, αi min and αi max : Lower and upper bounds of the aspect ratio αi ai : Assigned area of the facility i, ai min and ai max : Lower and upper bounds of the assigned area ai Aavailable : Available area, P: Number of the inner structure walls, Since large number of different combination are possible, so we can’t interpret each to find the best one. For this we have used particle swarm optimization Techniques. The way we have used is different way of PSO. The most interesting facts that the program in C that we has been made is its “Generalized form”. In this generalized form we can find out the optimum layout configuration by varying: Different area of layout Total number of facilitates to be allocated. Number of rows Number of facilities in each row Area of each Facility Dimension of each passage Now we have compared it with some other heuristic method like Genetic algorithm, simulated annealing and tried to include Maximum adjacency criteria and taking a case study