G Srinivasan

Group Technology (GT) aims at improving productivity in batch manufacturing. Here components are divided into families and machines into cells such that every component in a part family visits maximum number of machines in the assigned cell with an objective of minimizing inter-cell movement. In situations where too many inter-cell moves exist, fractional cell formation using remainder cells can be used. Here, machines are grouped into GT cells and a remainder cell, which functions like a job shop. Component families are formed such that the components visit the assigned cell and the remainder cell and do not visit other cells. The fractional cell formation problem to minimise inter-cell moves is formulated as a linear integer programming problem. Here, movement between machine cells and remainder cells is not counted as inter-cell moves but movement of components among GT cells is considered as inter-cell movement. The fractional cell formation problem is solved using Simulated Annealing. A heuristic algorithm is developed to solve large sized GT matrices. These have applied to a variety of matrices from GT literature and tested on randomly generated matrices. Computational experiences with the algorithms are presented. © 1995 Taylor & Francis Group, LLC.

This paper discusses the application of a Decision Support System (DSS) for making operational decisions in a food processing industry. A model is developed for determining the optimum production scenario for every week based on the tradeoffs between service levels, costs, inventories, changeovers and capacity. The experiences of the authors in designing, developing, and implementing the Decision Support System are shared in this paper. © 1998 Elsevier Science Ltd. All rights reserved.

In this paper we discuss a single machine scheduling problem with the objective of minimizing the variance of job completion times. The CTV problem has been proved to be NP hard (Oper. Res. Lett. 14 (1993) 49) and no polynomial time algorithm exists to find an optimal solution for CTV minimization on single machine. Hence enumerative techniques and heuristics are used to get optimal and near optimal solutions, respectively. We present a branch and bound algorithm and extend the same algorithm to generate epsilon optimal solutions for large sized problems (i.e., number of jobs > 30). The algorithm has been computationally tested, with randomly generated problems involving up to 100 jobs, using a personal computer (PC) with a 64 MB RAM capacity. The computational time required for generating optimal solutions are in few seconds for problems with jobs between 25 and 30. The performance of the branch and bound algorithm is compared with the pseudo-polynomial algorithm (Oper. Res. 40 (1992) 1148) for small sized problems. For problems with greater number of jobs, the epsilon optimal solutions obtained using branch and bound algorithm are compared with results of simulated annealing (Single machine scheduling with some non-regular objectives, M.S. Thesis, IIT Madras, 1997), tabu search (Proceedings of Operations Management Conference, IIT Madras, 2000) and heuristic proposed by Manna and Prasad (Eur. J. Oper. Res. 114 (1999) 411). Scheduling jobs in manufacturing systems considering non-regular measures of performance have gained importance in recent years. The objectives of minimizing completion time variance (CTV problem) and minimizing squares of deviations of job completion times from a given common due-date (MSD problem) are the two problems that use non-regular measures, and have attracted attention in single machine scheduling and flowshop scheduling when jobs require uniform treatment i.e., each entity spends approximately sametime in the system or waits for service as every other entity. This problem is very much relevant when a manufacturing/service system places emphasis on Just-in-Time philosophy. The purpose of this paper is to present an algorithm for minimizing completion time variance for a set of jobs to be processed on a single machine. The proposed algorithm is computationally evaluated with randomly generated problems involving up to 100 jobs and results are reported. © 2002 Elsevier Science Ltd. All rights reserved.

Many algorithms for cell formation have been developed for past three decades in cellular manufacturing. Some use binary data for cell formation and others use production data such as operation sequence, processing times, production volumes, etc. for cell formation. All these algorithms assume that the conversion of job shop to cellular manufacturing is performed comprehensively. (In other words, they assume that all the cells are formed at a time.) However, this is far from reality. In practice, cell formation is done incrementally, one after the other, rather than comprehensively. None of the algorithms developed so far addresses the issue of incremental cell formation. In this paper, the incremental cell formation problem is defined and various categories of problems are mentioned. One type of those categories is selected for solving. Two methods, namely the branch and bound technique and a heuristic based on a multistage programming approach, have been applied to solve the chosen problem. Data sets have been generated to compare these two methods in terms of quality of solution and demand on computational time. It has been found that the branch and bound technique gives a superior quality solution, but is computationally more demanding, where as heuristic based on a multistage programming approach is computationally far superior.

We consider a deterministic n-job, single machine scheduling problem with the objective of minimizing the Mean Squared Deviation (MSD) of job completion times about a common due date (d). The MSD measure is non-regular and its value can decrease when one or more completion times increases. MSD problem is connected with the Completion Time Variance (CTV) problem and has been proved to be NP-hard. This problem finds application in situations where uniformity of service is important. We present an exact algorithm of pseudo-polynomial complexity, using ideas from branch and bound and dynamic programming. We propose a dominance rule and also develop a lower bound on MSD. The dominance rule and lower bound are effectively combined and used in the development of the proposed algorithm. The search space is explored using the breadth first branching strategy. The asymptotic space complexity of the algorithm is O(nd). Irrespective of the version of the problem - tightly constrained, constrained or unconstrained - the proposed algorithm provides optimal solutions for problem instances up to 1000 jobs size under different due date settings. © 2013 Elsevier B.V. All rights reserved.

In this paper we address the operator assignment/reassignment problem in incremental cell formation. In incremental cell formation, cells are created based on parts and products that have high contributions. When these are created, it is necessary to assign operators, both from existing cells and new operators to work in the new cells. We consider three scenarios, where in the first we consider the assignment of operators to the new cells only. In the other two scenarios, we consider reallocation of operators to the existing cells if some operators from these are allotted to new cells. We show integer programming and network formulation for the three scenarios. We also test the network problems using randomly generated large sized problem instances. © 2010 Inderscience Enterprises Ltd.

The problem of forming machine cells and part families is the most important among the strategic issues in cellular manufacturing. This problem has been addressed by several researchers and every practitioner in this field. While researchers have developed several mathematical models that provide optimal and heuristic solutions to this "hard" and difficult problem, practitioners believe that simple rules and experience can create effective cells. This chapter traces the progress of algorithms for cell formation that uses mathematical programming and network models over the last four decades.

Group technology tries to exploit the similarity between parts and machines and forms machine groups and part families. Just-in-time production tries to manufacture the parts whenever required there by reducing the inventory and eliminating waste. In order to apply JIT in a GT cell, the cell is divided into modules and parts move from one module to another in small transfer batches. This paper addresses the problem of operator allocation for the modules and sequencing the variety of parts with the objective of minimizing the makespan. Six different methodologies have been presented and the results compared in terms of makespan and computational time. © 1995.

This paper addresses a distribution problem in a multi-product, multi-echelon supply chain. We develop a mixed integer linear programming formulation that considers allocation of vehicles with varying capacities for the transportation of products between the stages of the supply chain. The cost of transportation includes the fixed and variable costs of using the vehicles. The proposed formulations are solved optimally up to certain sizes, and we propose a heuristic based on total opportunity penalty cost method to solve the large sized problems. These proposed solution procedure is tested over a set of hypothetical problem sets. The results indicate that the proposed heuristic algorithm yields solutions within 5% from the optimal solutions.

This paper addresses the problem of operator allocation and sequencing in a product line that manufactures different varieties of products. The line is divided into cells such that there is single piece movement within the cell and batch production between cells. Operators are assumed to be multiskilled and process parts on all the machines in the cell, carrying them from one machine to another. The number of operators allotted to a cell can be less than the number of machines, and the time to produce a batch depends on the operator allocation. Algorithms for operator allocation and sequencing of jobs to minimize makespan are developed. © 1997 Elsevier Science Ltd. All rights reserved.