T. T. Narendran

An assignment model for the part-families problem in group technology

SUMMARY The problem of grouping of parts has been addressed in the past using clustering methods and integer programming. This paper presents an assignment model to solve the grouping problem. A similarity coefficient matrix is used as the input to the assignment problem. Closed loops in the form of subtours are identified after solving the problem and are used as the basis for grouping. The method has been applied to a number of examples. Compared with the earlier mathematical programming model, viz., the p-median model, the assignment method emerges as a distinctly superior technique both in terms of quality of solution and computational time.

GRAFICS—a nonhierarchical clustering algorithm for group technology

An efficient nonhierarchical clustering algorithm, based on initial seeds obtained from the assignment method, for finding part-families and machine cells for group technology (GT) is presented. By a process of alternate clustering and generating seeds from rows and columns, the zero-one machine-component incidence matrix was block-diagonalized with the aim of minimizing exceptional elements (intercell movements) and blanks (machine idling). The algorithm is compared with the existing nonhierarchical clustering method and is found to yield favourable results.

CASE: A clustering algorithm for cell formation with sequence data

Cellular manufacturing is a well-known strategy for reducing lead times in batch production systems. Most of the methods of cell formation are based on machinecomponent incidence alone. However, other factors such as production sequence and product volumes, if incorporated, can enhance the quality of the solutions. This study uses sequence data for cell-formation. A new similarity measure is defined for this purpose and appropriate measures of performance for evaluating solutions are introduced. A clustering approach of the non-hierarchical type is proposed. With new seeding techniques, the proposed algorithm clusters machines and components on the basis of sequence data. The algorithm gives encouraging results when applied to sample problems.

Design of an automated guided vehicle-based material handling system for a flexible manufacturing system

SUMMARY Automated guided vehicle (AGV)-based material handling systems (MHSs), which are widely used in several flexible manufacturing system (FMS) installations, require a number of decisions to be made. These include the number of vehicles required, the track layout, traffic pattern along the AGV tracks, and solving traffic control problems. This paper addresses the key issues involved in the design and operation of AGV-based material handling systems for an FMS. The problems arising from multi-vehicle systems are analysed, and strategies for resolving them are examined using analytical and simulation models.

Cell formation in manufacturing systems through simulated annealing: an experimental evaluation

Abstract Simulated annealing is a general random search method for finding near-global optimal solutions for optimization problems and, in particular, for certain NP-complete problems in combinatorial optimization. This paper presents an algorithm based on simulated annealing to solve the machine-component grouping problem for the design of cells in a manufacturing system. The proposed algorithm has been tested on sample problems and its sensitivity to some of its parameters, investigated. When compared with the K -means algorithm, the algorithm fares better for large problems. It is also easy to implement.

CLOVES: A cluster-and-search heuristic to solve the vehicle routing problem with delivery and pick-up

This paper addresses the vehicle routing problem with sequence-constrained delivery and pick-up (VRPDP). We propose a multi-phase constructive heuristic that clusters nodes based on proximity, orients them along a route using shrink-wrap algorithm and allots vehicles using generalized assignment procedure. We employ genetic algorithm for an intensive final search. Trials on a large number of test-problems have yielded encouraging results.

Machine-cell formation through neural network models

Abstract Identification of machine-cells is one of the most important problems in the design of cellular manufacturing systems (CMSs). It involves decomposing a manufacturing system into machine-cells by grouping machines and parts. Several algorithms with varying degrees of success have been proposed and utilised to solve this problem. Among the modern tools, neural network models have the potential to solve the machine-cell formation problem. Choosing the competitive learning model, adaptive resonance theory (ART) model and self-organizing feature map (SOFM) model from neural network theory for this purpose, we demonstrate their suitability for solving the machine-cell formation problem. Applications on trial problems show the viability for solving the machine-cell formations problem and stand testimony to the practical utility of neural network models in designing CMSs.

A framework for assessing the flexibility of manufacturing systems

This paper develops an analytical approach to quantifying manufacturing system flexibility (MSF). Measures of flexibility, namely, producibility, processivity, transferability and introducibility are developed based on a state-transition formalism, the reachability graph. The uncertainty in system operating conditions is specified in terms of certain disturbing factors, external and internal to the system, which impinge on system performance. The contribution of each factor to the value of a given type of flexibility is estimated using the analytic hierarchy process (AHP). The individual flexibility measures are thus combined to give a measure of MSF under a given operating environment. A Petri net model is used to construct the reachability graphs used in flexibility measurement. Uses of the proposed measures at the design and operational analysis stages of FMS are presented

Heuristics and sequence-dependent set-up jobs in flow line cells

A key operational issue in cellular manufacturing systems is the scheduling of jobs within a family at each cell. Sequence-dependent set-up times during changeovers from one job to another afford scope for the exploitation of similarities at this stage to minimize the time spent on set-ups. This paper proposes heuristics for scheduling jobs within a part family by identifying subfamilies and sequencing them to improve the use of machines within a cell and to reduce the tardiness as well as the number of tardy jobs. The proposed heuristic, when evaluated by comparison with existing benchmark heuristics, yielded encouraging results.

PRISM: PRIority based SiMulated annealing for a closed loop supply chain network design problem

Concerns over environmental degradation, legislative requirements and growing needs of business have fueled the growth of Closed Loop Supply Chains (CLSC). We consider a CLSC and address the issues of designing the network and of optimizing the distribution. Four variants of the problem are considered. The problem is modeled as an Integer Linear Program (ILP). We develop a constructive heuristic based on Vogels approximation method-total opportunity cost method to provide good initial solutions to a priority-based simulated annealing heuristic, to accelerate its convergence. Trials on a set of hypothetical datasets have yielded encouraging results. The methodology is also tested using a case study data of a company producing electronic products. Implications for sustainability are discussed.