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Showing papers by "Chandrasekharan Rajendran published in 2006"


Journal ArticleDOI
TL;DR: In this article, a new ant-colony algorithm (NACO) has been developed in order to solve the flow shop scheduling problem, where the objective is to minimize the completion-time variance of jobs.

91 citations


Journal ArticleDOI
TL;DR: In this article, a Pareto-ranking based multi-objective GA with an archive of non-dominated solutions subjected to a local search (PGA-ALS) is proposed.
Abstract: In this paper the problem of permutation flow shop scheduling with the objectives of minimizing the makespan and total flow time of jobs is considered. A Pareto-ranking based multi-objective genetic algorithm, called a Pareto genetic algorithm (GA) with an archive of non-dominated solutions subjected to a local search (PGA-ALS) is proposed. The proposed algorithm makes use of the principle of non-dominated sorting, coupled with the use of a metric for crowding distance being used as a secondary criterion. This approach is intended to alleviate the problem of genetic drift in GA methodology. In addition, the proposed genetic algorithm maintains an archive of non-dominated solutions that are being updated and improved through the implementation of local search techniques at the end of every generation. A relative evaluation of the proposed genetic algorithm and the existing best multi-objective algorithms for flow shop scheduling is carried by considering the benchmark flow shop scheduling problems. The non-dominated sets obtained from each of the existing algorithms and the proposed PGA-ALS algorithm are compared, and subsequently combined to obtain a net non-dominated front. It is found that most of the solutions in the net non-dominated front are yielded by the proposed PGA-ALS.

85 citations


Journal ArticleDOI
TL;DR: Six critical factors are identified and an instrument is developed and validated so as to measure the customer’s perception of quality management in the software industry.
Abstract: Most of the available literature on quality management is based on management's perception; few studies examine critical issues of quality management from the customer's perspective, especially in the software industry. In order to gain an insight into what customers expect from a product/service, an analysis of quality management from customer's point of view is essential. Such an understanding would help the managers to adopt strategies that can enhance the satisfaction level of their customers. The present study highlights the critical factors of quality management in the software industry from the customer's perspective. Six critical factors are identified: and an instrument, comprising these factors, is developed and validated so as to measure the customer's perception of quality management in the software industry.

52 citations


Journal ArticleDOI
TL;DR: It was found after extensive computational investigation that the proposed ant colony algorithm gives promising and better results, as compared to those solutions given by the existing ant colony algorithms and the existing heuristics, for the flowshop scheduling problem under study.
Abstract: The problem of scheduling in flowshops with sequence-dependent setup times of jobs is considered and solved by making use of ant colony optimization (ACO) algorithms. ACO is an algorithmic approach, inspired by the foraging behavior of real ants, that can be applied to the solution of combinatorial optimization problems. A new ant colony algorithm has been developed in this paper to solve the flowshop scheduling problem with the consideration of sequence-dependent setup times of jobs. The objective is to minimize the makespan. Artificial ants are used to construct solutions for flowshop scheduling problems, and the solutions are subsequently improved by a local search procedure. An existing ant colony algorithm and the proposed ant colony algorithm were compared with two existing heuristics. It was found after extensive computational investigation that the proposed ant colony algorithm gives promising and better results, as compared to those solutions given by the existing ant colony algorithm and the existing heuristics, for the flowshop scheduling problem under study.

50 citations


Journal ArticleDOI
TL;DR: Heuristics to obtain base-stock levels are proposed, and heuristic solutions are introduced in the initial population of the RKGGA to expedite the convergence of the genetic search process.

46 citations


Journal ArticleDOI
TL;DR: In this paper, the problem of scheduling in dynamic assembly job shops with jobs having weights for holding and tardiness of jobs has been investigated and new priority dispatching rules have been proposed to minimize the performance measures related to weighted flowtime and weighted tardness of jobs.
Abstract: The problem of scheduling in dynamic shops is an important operational problem in view of its complexity and significance in terms of associated costs of scheduling. While a number of research studies have investigated the problem of scheduling in flow shops and job shops, only some attempts have been done to study the problem of scheduling in assembly job shops that manufacture multi-level jobs. The problem of scheduling in dynamic assembly job shops with jobs having weights for holding and tardiness of jobs deserves due attention. In this study an attempt has been made to propose new priority dispatching rules that minimize the performance measures related to weighted flowtime and weighted tardiness of jobs. The existing unweighted dispatching rules have been modified in view of the consideration of weights for flowtime and tardiness of jobs. The performances of the (modified) existing dispatching rules and the proposed dispatching rules are compared through exhaustive simulation experiments with the consideration of a number of different experimental settings involving due-date setting, utilization levels and types of job structures. The proposed dispatching rules are found to perform better than the existing ones in most experimental settings and with respect to a number of measures of performance.

42 citations


Journal ArticleDOI
TL;DR: In this article, a non-dominated and normalized distance-ranked sorting multi-objective genetic algorithm (NDSMGA) was proposed to solve the problem of extended permutation flowshop scheduling with the intermediate buffers.
Abstract: In this paper, we consider the problem of extended permutation flowshop scheduling with the intermediate buffers. The Kanban flowshop problem considered involves dual-blocking by both part type and queue size acting on machines, as well as on material handling. The objectives considered in this study include the minimization of mean completion time of containers, mean completion time of part types, and the standard deviation of mean completion time of part types. An attempt is made to solve the multi-objective problem by using a proposed genetic algorithm, called the “non-dominated and normalized distance-ranked sorting multi-objective genetic algorithm” (NDSMGA). In order to evaluate the NDSMGA, we have made use of randomly generated flowshop scheduling problems with input and output buffer constraints in the flowshop. The non-dominated solutions for these problems are obtained from each of the existing methods, namely multi-objective genetic local search (MOGLS), elitist non-dominated sorting genetic algorithm (ENGA), gradual priority weighting genetic algorithm (GPWGA), modified MOGLS, and the NDSMGA. These non-dominated solutions are combined to obtain a net non-dominated solution set for a given problem. Contribution in terms of number of solutions to the net non-dominated solution set from each of these algorithms is tabulated, and the results reveal that a substantial number of non-dominated solutions are contributed by the NDSMGA.

22 citations