Facility Planning and Associated Problems: A Survey

In this study, we have classified and reviewed different types of problems which are related to facility planning and layout design for different types of manufacturing processes. The main problems which are related to location of  facilities which also affects the system performance  such as distribution of man, material and machine in a plant or a factory and their optimization technique while using of mathematical models, their solutions and application related to whole problems is presented. For solving this type of problems, intelligent techniques such as expert systems, fuzzy logic and neutral networks have been used. In this paper the recent analysis on facility layout is incorporated and facility layout problem is surveyed. Many intelligent techniques and conventional algorithms for solving FLP are presented. In our discussion different research direction, general remarks and tendencies have been mentioned Keywords—Facility Planning, Material handling Optimization metho

SUSTAINABLE WASTE MANAGEMENT: A CASE FROM INDIAN CEMENT INDUSTRY

Sustainability means meeting the needs of the present without compromising the ability of future generations to meet their own needs, and Sustainable Waste Management is using waste produced efficiently so that use of amount of material resources get reduced. India, is the second amongst cement producers in the world with a total capacity of 245.40 Million Tonnes (MT), has a huge cement industry and produces about 7% of world’s total production. The Indian cement industry has on one hand, enormous pressure to increase profit and margins, while on other; there is considerable public interest on a sustainable and environment friendly usage of natural resources. The objective of this paper is to pursue sustainable waste management for a cement industry through replacement of coal with some alternative fuel, which actually belongs to the group of hazardous wastes and which could benefit the plant economically and environmentally, and improve sustainability of plant. The use of alternative fuels will help in reducing energy costs and providing a competitive edge for a cement plant. Furthermore, this will reduce the burden of waste disposal considerably. So, it also supports to fulfilling Sustainable Waste Management issue

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

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

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

Combining evolutionary computation and dynamic programming for solving a dynamic facility layout problem

This paper presents an algorithm combining dynamic programming and genetic search for solving a dynamic facility layout problem. While the quadratic assignment formulation of this problem has been deeply investigated there are very few papers solving it for departments of unequal size. We describe a model which can cope with an unequal and changing size. The genetic algorithm evolves a population of layouts for each time period while th dynamic programming provides the evaluation of the fitness ot the layouts

Heuristics for the dynamic facility layout problem with unequal area departments

The facility layout problem (FLP) is a well researched problem of finding positions of departments on a plant floor such that departments do not overlap and some objective(s) is (are) optimized. In this dissertation, the FLP with unequal area rectangular shaped departments is considered, when material flows between departments change during the planning horizon. This problem is known as the dynamic FLP. The change in material flows between pairs of departments in consecutive periods may require rearrangements of departments during the planning horizon in order to keep material handling costs low. The objective of our problem is to minimize the sum of the material handling and rearrangement costs. Because of the combinatorial structure of the problem, only small sized problems can be solved in reasonable time using exact techniques. As a result, construction and improvement heuristics are developed for the proposed problem. The construction algorithms are boundary search heuristics as well as a dual simplex method, and the improvement heuristics are tabu search and memetic heuristics with boundary search and dual simplex (linear programming model) techniques. The heuristics were tested on a generated data set as well as some instances from the literature. In summary, the memetic heuristic with the boundary search technique out-performed the other techniques with respect to solution quality

Programação da produção: Otimização de Layouts Industriais

O problema conhecido na literatura como “Facility layout problem (FLP)”, em que se pretende determinar a disposição de recursos de produção e a sua interação num determinado espaço, é um problema estratégico para a implementação do chão de fábrica de uma empresa pelo impacto que tem na performance da produção. O problema consiste em encontrar um posicionamento único entre instalações (departamentos, máquinas, células de produção, armazéns, etc.) e localizações no chão de fábrica, de forma a otimizar um ou mais objetivos de produção. O objetivo da criação de layout consiste na otimização do espaço existente, minimização do tempo de produção, redução do custo de manuseamento de matérias, aumento do grau de flexibilidade, entre outros. A solução do problema deverá especificar a localização relativa de cada departamento (layout em bloco) e numa fase posterior poderá especificar o layout detalhado dentro de cada departamento. Na presente tese serão apresentados alguns modelos matemáticos para criação de um layout, neste caso vamos usar uma formulação matemática Quadratic Assignment Problem (QAP), uma formulação matemática Mixed Integer Programming (MIP) e uma heurística de Particle Swarm Optimization (PSO) para resolver problemas de layout. Todas estas formulações e modelos serão postos em prática para a resolução de problemas fictícios. Numa primeira abordagem iremos resolver problemas fictícios onde abordaremos a formulação QAP para problemas de atribuição de espaço de duas dimensões (x,y) e MIP e em seguida iremos usar a heurística PSO para a resolução de problemas em escala maior e real.The problem known in the literature as "Facility layout problem (FLP)", which is intended to determine the physical layout of industrial facilities, is a strategic problem for the implementation of a company by the impact it has on the production performance. The problem is to find an unambiguous allocation between facilities (departments, machines, production cells, warehouses, etc.) and locations on the shop floor in order to optimize one or more production goals. The objectives often considered are the optimization of the space, minimizing production time, reduce the handling costs of materials, increased flexibility, among others. The solution of the problem should specify the relative location of each department (block layout) and at a later stage it can specify the detailed layout within each department. In this thesis will be presented some methods of resolution in this case we use a discrete Quadratic Assignment formulation (QAP), a Mixed Integer Linear Programming formulation (MIP) and a Particle Swarm Optimization heuristic (PSO) to solve layout problems. All these heuristics will be implemented for solving fictitious problems. In a first approach we will solve simpler problems where we use the QAP and MIP formulation and following we will use the PSO heuristic to solve problems on a larger scale

Programação da produção: Otimização de Layouts Industriais

O problema conhecido na literatura como “Facility layout problem (FLP)”, em que se pretende determinar a disposição de recursos de produção e a sua interação num determinado espaço, é um problema estratégico para a implementação do chão de fábrica de uma empresa pelo impacto que tem na performance da produção. O problema consiste em encontrar um posicionamento único entre instalações (departamentos, máquinas, células de produção, armazéns, etc.) e localizações no chão de fábrica, de forma a otimizar um ou mais objetivos de produção. O objetivo da criação de layout consiste na otimização do espaço existente, minimização do tempo de produção, redução do custo de manuseamento de matérias, aumento do grau de flexibilidade, entre outros. A solução do problema deverá especificar a localização relativa de cada departamento (layout em bloco) e numa fase posterior poderá especificar o layout detalhado dentro de cada departamento. Na presente tese serão apresentados alguns modelos matemáticos para criação de um layout, neste caso vamos usar uma formulação matemática Quadratic Assignment Problem (QAP), uma formulação matemática Mixed Integer Programming (MIP) e uma heurística de Particle Swarm Optimization (PSO) para resolver problemas de layout. Todas estas formulações e modelos serão postos em prática para a resolução de problemas fictícios. Numa primeira abordagem iremos resolver problemas fictícios onde abordaremos a formulação QAP para problemas de atribuição de espaço de duas dimensões (x,y) e MIP e em seguida iremos usar a heurística PSO para a resolução de problemas em escala maior e real.The problem known in the literature as "Facility layout problem (FLP)", which is intended to determine the physical layout of industrial facilities, is a strategic problem for the implementation of a company by the impact it has on the production performance. The problem is to find an unambiguous allocation between facilities (departments, machines, production cells, warehouses, etc.) and locations on the shop floor in order to optimize one or more production goals. The objectives often considered are the optimization of the space, minimizing production time, reduce the handling costs of materials, increased flexibility, among others. The solution of the problem should specify the relative location of each department (block layout) and at a later stage it can specify the detailed layout within each department. In this thesis will be presented some methods of resolution in this case we use a discrete Quadratic Assignment formulation (QAP), a Mixed Integer Linear Programming formulation (MIP) and a Particle Swarm Optimization heuristic (PSO) to solve layout problems. All these heuristics will be implemented for solving fictitious problems. In a first approach we will solve simpler problems where we use the QAP and MIP formulation and following we will use the PSO heuristic to solve problems on a larger scale