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

Heuristics and Metaheuristics Approaches for Facility Layout Problems: A Survey

Facility Layout Problem (FLP) is a NP-hard problem concerned with the arrangement of facilities as to minimize the distance travelled between all pairs of facilities. Many exact and approximate approaches have been proposed with an extensive applicability to deal with this problem. This paper studies the fundamentals of some well-known heuristics and metaheuristics used in solving the FLPs. It is hoped that this paper will trigger researchers for in-depth studies in FLPs looking into more specific interest such as equal or unequal FLPs

Hybrid Optimisation Method for the Facility Layout Problem

International audienc

The aperiodic facility layout problem with time-varying demands and an optimal master-slave solution approach

In many seasonal industries, customer demands are constantly changing over time, and accordingly the facility layout should be re-optimized in a timely manner to adapt to changing material handling patterns among manufacturing departments. This paper investigates the aperiodic facility layout problem (AFLP) that involves arranging facilities layout and re-layout aperiodically in a dynamic manufacturing environment during a given planning horizon. The AFLP is decomposed into a master problem and a combination set of static facility layout problems (FLPs, the slave problems) without loss of optimality, and all problems are formulated as mixed-integer linear programming (MILP) models that can be solved by MIP solvers for small-sized problems. An exact backward dynamic programming (BDP) algorithm with a computational complexity of O(n 2) is developed for the master problem, and an improved linear programming based problem evolution algorithm (PEA-LP) is developed for the traditional static FLP. Computational experiments are conducted on two new problems and twelve well-known benchmark problems from the literature, and the experimental results show that the proposed solution approach is promising for solving the AFLP with practical sizes of problem instances. In addition, the improved PEA-LP found new best solutions for five benchmark problems

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

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 approaches for the unequal area facility layout problem

Esta tesis doctoral aborda el problema de distribución en planta, el cuál en líneas generales, pretende asignar o distribuir instalaciones en una planta industrial. Existen muchos problemas diferentes dependiendo de las características que sean consideradas de la planta industrial, como por ejemplo, la forma de las instalaciones, el número de plantas, la flexibilidad requerida en los sistemas de producción, el tipo de producto que se fabrica, etcétera. Uno de los problemas más abordados, ha sido el problema de distribución en planta con instalaciones de área desigual. Para solucionar este tipo problemas existen muchas técnicas que pretenden alcanzar un diseño eficiente de la planta industrial. Entre ellas, una de las estrategias más usadas por los investigadores ha sido la de los Algoritmos Genéticos (AGs). Los AGs requieren definir un esquema de codificación para representar el diseño de la planta industrial como una estructura de datos. Esta estructura determina el tipo de soluciones que pueden ser obtenidas, e influencia la capacidad del AG para encontrar buenas soluciones. Aunque existen varios trabajos que revisan el estado del arte de los problemas de distribución en planta, no hay ninguno que centre su revisión en los esquemas de codificación y los operadores evolutivos usados por los AGs. Así, una de las contribuciones de la tesis que se presenta, es el estudio de los esquemas de codificación y los operadores evolutivos empleados por los AGs en problemas de distribución en planta. Además, este estudio se completa con una clasificación de las diferentes estructuras de codificación utilizadas por los autores, un estudio de sus características y objetivos, y finalmente, la identificación de los operadores de cruce y mutación que pueden ser aplicados dependiendo de la estructura de codificación. Por otro lado, en esta tesis se propone un AG para el problema de distribución en planta de instalaciones de área desigual, teniendo en cuenta aspectos que pueden ser cuantificados, tales como: el de flujo de material, las relaciones lógicas entre las actividades que se realizan en los centros de producción (comúnmente, instalaciones) y la forma de cada uno. Para ello, se sugiere una nueva forma de representar las plantas industriales. Este algoritmo se ha integrado en una aplicación informática que permite a los usuarios introducir los datos y configurar los parámetros del algoritmo, así como mostrar las soluciones propuestas de una manera sencilla y amigable. Finalmente, el algoritmo ha sido probado con varios problemas y sus resultados comparados con los obtenidos en otros trabajos citados en la bibliografía. Aunque el problema de distribución en planta de instalaciones de área desigual ha sido resuelto con muchas estrategias, siempre ha sido abordado teniendo en cuenta criterios cuantificables. Sin embargo, existen características subjetivas que resultan muy interesantes para este problema. Dicha características son muy difíciles de tener en cuenta mediante los métodos clásicos de optimización. Por esta razón, se propone un Algoritmo Genético Interactivo (AGI) para el problema de distribución en planta de instalaciones de área desigual, el cuál permite la interacción entre el algoritmo y el diseñador. Con la implicación del conocimiento del diseñador en la propuesta, el proceso de búsqueda es guiado y ajustado a las preferencias de aquél en cada iteración del algoritmo. Para evitar sobrecargar al diseñador, la población de soluciones es clasificada en grupos mediante un método de clustering. Así, sólo un elemento de cada grupo es evaluado. Durante todo este proceso, aquellas soluciones que resulten interesantes para el diseñador son almacenadas en memoria. Las pruebas realizadas muestran que el AGI propuesto es capaz de captar las preferencias del diseñador, y que además, progresa hacia una buena solución en un número de iteraciones razonable

Scheduling With Alternatives Machine Using Fuzzy Inference System And Genetic Algorithm.

As the manufacturing activities in today's industries are getting more and more complex, it is required for the manufacturing company to have a good shop floor production scheduling to plan and schedule their production orders. Industri pengeluarcim kini telah berkembang pesat dan aktiviti pengeluarannya semakin kompleks, dengan itu syarikat pengeluar memerlukan jadual lantai pengeluaran (shop floor) yang terbaik untuk merancang permintaan pengeluaran (product)

Mathematical optimization approaches for facility layout problems: The state-of-the-art and future research directions

Sem PDF conforme despacho. Fundacao para a Ciencia e a Tecnologia - PEstOE/MAT/UI0297/2014.Facility layout problems are an important class of operations research problems that has been studied for several decades. Most variants of facility layout are NP-hard, therefore global optimal solutions are difficult or impossible to compute in reasonable time. Mathematical optimization approaches that guarantee global optimality of solutions or tight bounds on the global optimal value have nevertheless been successfully applied to several variants of facility layout. This review covers three classes of layout problems, namely row layout, unequal-areas layout, and multifloor layout. We summarize the main contributions to the area made using mathematical optimization, mostly mixed integer linear optimization and conic optimization. For each class of problems, we also briefly discuss directions that remain open for future research.publishe

Models and algorithms for complex system optimization problems : applications to hospital layout and LED traffic signal maintenance

"December 2010.""A Thesis presented to the Faculty of the Graduate School at the University of Missouri In Partial Fulfillment of the Requirements for the Degree Master of Science, Industrial Engineering."Thesis supervisor: Dr. Mustafa Sir.Due to rising healthcare costs, it is increasingly important to design health care buildings to be efficient and effective. One aspect of a healthcare facility's design is the size and layout of the building and departments. In this paper we review hospital design and the various layout methods that can be applied to hospitals. We formulate a mixed-integer linear programming model to determine the optimal size (i.e. width and length of each floor and number of floors) and department layout of a hospital. The model has multiple objectives; we consider department size requirements to determine a cost-efficient facility size and then place departments to minimize inter-departmental flows. Finally, we use the model to design a multi-floor hospital with seven departments and test the computation time for a variety of scenarios. The Energy Policy Act of 2005 specifies that all traffic signals manufactured after January 1, 2006 must realize the energy efficiency achieved by LED technology [10]. These new LED traffic signals use less energy and last longer than their predecessors, but they deteriorate gradually and require customized maintenance schedules to optimize their useful life and maximize public safety. In the second half of this paper we review the advantages of LED traffic signals and the current literature on their maintenance. We present three models and algorithms to compute optimal maintenance schedules. The first model is designed to model routine maintenance and includes routing costs. The second model is an approximation of the first model that can be solved for scenarios which include very large quantities of traffic signals. The final model allows for two actions, inspection and replacement, and introduces stochastic deterioration. We test the computation time of each algorithm and assess the resulting schedules.Includes bibliographical references (pages 163-165)