Divakar Rajamani

Cellular Manufacturing Systems Design, Planning and Control

No abstract

Cell formation considering alternate routeings

Cell formation (CF) consists of identifying machine groups (MGs) and part families (PFs). Many CF procedures use a part machine matrix as an input and attempt to obtain a block diagonal form. A perfect diagonalization of the part machine matrix to form exclusive PFs and MGs is not possible in many instances. Considering alternate routeings (i.e. alternate plans for the parts and additional copies of machines) improves this diagnonalization. This aspect has not been adequately dealt with in literature. Moreover, existing CF procedures consider indirect measures such as similarity indices, rank order, bond energy etc., that may not obtain a good block diagonalization of the part machine matrix. Also these procedures decouple cell formation and cell evaluation procedures. In this paper a non-linear integer programming model is developed for CF to identify PFs and MGs simultaneously considering alternative routeings. The model combines the evaluation procedure by considering the minimization of a weighted sum...

Design of cellular manufacturing systems

In this paper, we develop a mixed integer program for the design of cellular manufacturing systems. We assume that there are alternate process plans for each part and that each operation in these plans can be performed on alternate machines. The objective of the model is to minimize the sum of investment, processing and material handling costs. Processing times, capacities of machines and cell size restrictions are considered in the process. Part families, machine groups and part plans are identified concurrently. For the efficient solution of the relaxed linear program an efficient column generation scheme is provided. In the problem under consideration columns are generated by solving simple semi-assignment problems. Three different strategies are tested for generating the columns and the best scheme is selected for subsequent use in the branch and bound procedure developed. Finally, computational experience is provided for randomly generated test problems.

Assignment allocation and simulated annealing algorithms for cell formation

In this paper a nonlinear mathematical programming model is developed for cell formation that identifies part families and machine groups simultaneously with no manual intervention or subjective judgement. The objective of the model is minimization of the weighted sum of the voids and the exceptional elements. Changing weights for void and exceptional elements aids the designer with a systematic generation of different solutions, i.e., forming large loose cells or small tight cells. An assignment allocation algorithm (AAA) and a simulated annealing algorithm (SAA) are developed to solve the model. AAA and SAA compare favorably with many well-known procedures for the problems tested. AAA is less computer-intensive and hence large problems with 400 parts and 240 machines were solved with AAA in less than a minute on Sun Sparc station. However, AAA is sensitive to the initial machine grouping solution input to the algorithm. SAA gives consistent results but requires more computational time.

A mathematical model for cell formation considering investment and operational costs

Abstract The majority of the cell formation models consider grouping of parts and machines, based on clustering techniques. The performance of cells thus formed indicates that the cellular systems perform more poorly in terms of work-in-process inventory, average job waiting time and job flow time than the improved job shops. However, they have superior performance in terms of average move times and setup times. The main reason for such a poor performance is that the current cell design procedures do not consider the operational aspects during the cell formation. Therefore, the objective of this paper is to consider the investment and operational costs simultaneously during the design of a cellular manufacturing system. For this purpose we develop a mixed integer programming model and illustrate the trade-off relationships between the investment and operational variables, such as sequence dependence setup, machine idle time, part inventory, part early and late finish (compared with due date), by considering examples. Computational experience is provided for randomly generated test problems.

Selection of parts and machines for cellularization : a mathematical programming approach

Abstract The extent to which a company accounts for the manufacturing activities (in terms of annual hours) in cells is referred to as ‘degree of cellularization’ of the company. The objective of this paper is to provide a mathematical framework to consider the issues related to selection of parts and machines for cellularization. The models developed aid in deciding the optimal variety of parts, portion of demand to be produced in cells, machines to be selected and the plans to produce the parts. We provide efficient column generation schemes for the linear and relaxed mixed integer programs. Examples are solved to illustrate the solution methodology. Computational experience on test problems is reported.

A model for cell formation in manufacturing systems with sequence dependence

SUMMARY In this paper we consider the cell formation in a manufacturing environment where there are signficant sequence dependent setup times and costs. The trade-off between saving on sequence dependent setup costs and additional investment on new machines is considered for determining the economic number of cells. Accordingly, we develop a mixed integer program and mention a variety of manufacturing situations where this model can be useful. We also include an illustrative example

Cell formation considering fuzzy demand and machine capacity

The concept of cellular manufacturing requires that machines and parts be grouped together to form cells. Many researchers have addressed this cell formation problem for crisp (or certain) input data. However, if the input data is not exact or is imprecise (fuzzy), how is the decision made to form cells and assign parts determined? In this paper, crisp and fuzzy mathematical models are developed to optimally determine machine grouping and parts assignment under fuzzy demand and machine capacity. The object of these models is to minimise the processing and the material handling costs. Comparisons between the crisp and fuzzy results are made to show how outcomes differ when uncertainty is introduced. The example problems are solved using the Hyperlindo software package to illustrate the ability of the model to react under different input parameters. To reduce the computation time, nonlinear representations of the above crisp and fuzzy models are developed. These nonlinear formulations allow each model to be elegantly decomposed into two submodels. An iterative solution procedure is proposed which utilises these submodels to reduce computation time substantially. Example problems are solved using both the crisp and the fuzzy optimal models and the iterative procedure. The solutions and computational experience for the two approaches are compared.

Fuzzy logic concepts applied to machine—component matrix formation in cellular manufacturing

Abstract Conventional cell formation procedures assume that each part feature is developed on a single machine. In many realistic situations, however, alternate machines are suitable for developing the same part feature. This is dependent on the uncertainties in the dimensional machine tolerance and processing times. Also, each machine can develop more than one part feature during a single operation. In addition, the development of a certain part feature is dependent on the preceding part features. In this paper, fuzzy logic methodology is employed to deal with these uncertainties and handle the dependency or interaction between part features during the determination of the overall suitability of a machine to process a part. The concept of linguistic hedges or modifiers is used to indicate the relative importance of a feature during this exercise. The resulting machine suitabilities are depicted in the form of a non-binary machine-component matrix. An example is also solved to illustrate our approach.

Quality Assurance in Engineering Education: Comparison of Accreditation Schemes and ISO 9001

SUMMARY In recent years, the world of engineering education has been changing rapidly. Emerging communication technologies offer new means of distance education, the job market is getting narrower and increasingly competitive, and numerous stakeholders are concerned about the quality of education the students receive. To offer quality assurance to interested parties and the general public, engineering faculties in the US and Canada undergo accreditation processes, fostered by the Accreditation Board for Engineering and Technology and the Canadian Engineering Accreditation Board, which are professional boards. However, some university-level institutions in Europe have turned to the ISO 9000 family of international standards as a framework for quality assurance. This paper provides an outline of these three quality assurance schemes, followed by a comparison of their common elements. It is argued that engineering faculties can establish meaningful ISO 9000 quality systems on the basis of, and integrated wit...