Manufacturing cell formation in a fuzzy environment

The main objective of this study is to develop useful mathematical programming (FMP) models to solve cell formation (CF) problems in fuzzy environments. The dissertation was divided into three major parts. First, two mathematical programming models were developed to formulate the cell formation problems under consideration. The first model was a linear programming (LP) model for grouping parts and machines simultaneously into cells and solving the CF problem for dealing with exceptional elements (EEs). In second, a goal programming (GP) model to obtain a trade off between minimizing total cost of dealing with EEs and maximizing GE, a new similarity coefficient formula between parts also has been developed;In the second part, the fuzzy linear programming (FLP) methodology was applied to solve CF problems involving fuzzy situations. A new fuzzy operator, add-min, was proposed and its performances evaluated against the other six operators. Robustness and excellent performance in terms of clustering results and CPU executing time were verified for the FLP with the new operator. Fuzzy multiobjective linear programming (FMLP) then was used (1) to find the optimal trade-off between multiple goals in the proposed goal programming and (2) to compare the performance with the GP results. Numerical illustrations show that FMLP with the proposed operator performed much better than the GP did in terms of computational efficiency;Finally, an efficient heuristic genetic algorithm (HGA) was developed to solve all mathematical programming models, including the fuzzy models, presented in this dissertation. New heuristic crossover and mutation operators based on the special characteristics of CF were proposed to enhance computational performance. Our experiment showed that the proposed GA heuristic outperformed both the traditional GA approach and the mathematical programming models in terms of clustering results, computational time, and ease of use

Scheduling with subcontracting options

Department of Logistics2008-2009 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Meta-heuristics in cellular manufacturing: A state-of-the-art review

Meta-heuristic approaches are general algorithmic framework, often nature-inspired and designed to solve NP-complete optimization problems in cellular manufacturing systems and has been a growing research area for the past two decades. This paper discusses various meta-heuristic techniques such as evolutionary approach, Ant colony optimization, simulated annealing, Tabu search and other recent approaches, and their applications to the vicinity of group technology/cell formation (GT/CF) problem in cellular manufacturing. The nobility of this paper is to incorporate various prevailing issues, open problems of meta-heuristic approaches, its usage, comparison, hybridization and its scope of future research in the aforesaid area

Development of Manufacturing Cells Using an Artificial Ant-Based Algorithm with Different Similarity Coefficients

Although there exists several ways of solving the cellular manufacturing problem, including several ant-based algorithms, many of these algorithms focus on obtaining the best possible answer instead of efficiency. An existing artificial-ant based algorithm AntClass, was modified so that it is easier to manipulate. AntClass uses Euclidean vectors to measure the similarity between parts, because similarity is used to group parts together instead of distances, the modified version uses similarity coefficients. The concept of heaping clusters was also introduced to ant algorithms for cellular manufacturing. Instead of using Euclidean vectors to measure the distance to the center of a heap, as in the AntClass algorithm, an average similarity was introduced to measure the similarity between a part and a heap. The algorithm was tested on five common similarity coefficients to determine the similarity coefficient which gives the better quality solution and the most efficient process

Modeling reliability considerations in the design and analysis of cellular manufacturing systems.

Reliability plays a vital role in the overall performance of cellular manufacturing systems (CMSs). Machine failures significantly impact the fulfillment of due dates and other performance criteria, despite the option of part rerouting to alternative workstations. These facts suggest a need for the consideration of machine reliability during the operation allocation process. Attempting to improve a system\u27s reliability invariably results in higher costs. It follows that the ideal strategy for achieving optimum balance lies in an approach that integrates both cost and reliability information. A mixed integer multi-objective mathematical programming model that incorporates machine reliability and cost considerations is developed for the design of CMSs. The model selects processing route for each part type which maximizes the overall system reliability of machines along the route, while minimizing the overall costs. The proposed approach provides flexible routing, ensuring high CMS performance by minimizing the impact of machine failure through the provision of alternative process routes. To account for the constant and increasing failure pattern of manufacturing machines, the CMS design model considers both the exponential and Weibull distribution approaches. A performance evaluation criterion in terms of system availability for the part-process plan assignment based on the exponential distribution is also developed. Applicability of the model is demonstrated by solving example problems by following the ∈-constraint approach. Optimization techniques for solving such models for large practical-size problems require a substantial amount of time and memory space; therefore, a heuristic, based on the basic steps to simulated annealing and solution generation procedure of genetic algorithm is developed. The heuristic is evaluated by comparing the solutions generated by the heuristic with the LP relaxation solution for the large problems and optimal solution for the smaller-sized problems. The results reveal that the heuristic performs well in various problem instances for reliability and cost combinations. The sensitivity of the model outputs to key factors has also been investigated. A reliability-based, preventive maintenance (PM) planning model is also incorporated, allowing CMS to restrict deterioration of machines due to usage and age and improve system reliability. A procedure for the integration of PM planning into the CMS design model is included for overall reliability and cost improvement of the CMS. Example problems are solved to illustrate the model\u27s applicability.* *This dissertation is a compound document (contains both a paper copy and a CD as part of the dissertation).Dept. of Industrial and Manufacturing Systems Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2006 .D37. Source: Dissertation Abstracts International, Volume: 67-07, Section: B, page: 4032. Thesis (Ph.D.)--University of Windsor (Canada), 2006

Conception de systèmes manufacturiers cellulaires dans un environnement de chaînes d'approvisionnement

Classical cellular manufacturing system (CMS) design are performed on a single plant and are no more appropriate in an environment where material procurement and Customer delivery processes issues with the selection of multi-plant production system locations have to be considered simultaneously. Integration of these issues is a crucial challenge to supply chain managers. This thesis deals with the multi-plant CMS design in a supply chain environment. This context is established with considering the supply or the customer delivery processes, and with linked manufacturing plants. Three problems are examined. The first problem considers linked multi-plant CMS design integrated to material supply process. The solution approach is based on a proposed linearised model where the total system design cost to minimise combines the cost of the cellular configuration set on existing plants and the costs linked to supplier process selection. Experimentation demonstrates the potential benefits gained through increasing routing flexibility over plants on investment costs and the effect of integrating the supply process in a multi-plant cellular manufacturing configuration. The second problem combines multi-plant CMS design to manufacturing plant selection and customer demand allocation decisions. We propose a mathematical model and develop a simulated annealing based approach which aims to select the manufacturing plants to open and define the machine cells locations and structures and assign customer’s demand. We demonstrate the efficiency of the solution approach and show the cost savings due to integrated multi-stage multi-plant CMS with customer allocation decisions, compared to a sequential decision process. The third problem examines a context of multi-period customer demand where multi-plant production planning decisions, part transfer between plants, system reconfiguration and dynamic customer allocation decisions are coupled with dynamic multi-plant CMS configuration. It is shown that considering these issues in an integrated model reduces total design cost and enhances manufacturing and delivery flexibility