Jatinder N. D. Gupta

A review of scheduling research involving setup considerations

The majority of scheduling research assumes setup as negligible or part of the processing time. While this assumption simplifies the analysis and/or reflects certain applications, it adversely affects the solution quality for many applications which require explicit treatment of setup. Such applications, coupled with the emergence of production concepts like time-based competition and group technology, have motivated increasing interest to include setup considerations in scheduling problems. This paper provides a comprehensive review of the literature on scheduling problems involving setup times (costs). It classifies scheduling problems into batch and non-batch, sequence-independent and sequence-dependent setup, and categorizes the literature according to the shop environments of single machine, parallel machines, flowshops, and job shops. The suggested classification scheme organizes the scheduling literature involving setup considerations, summarizes the current research results for different problem types, and finally provides guidelines for future research.

Semi-Supervised and Unsupervised Extreme Learning Machines

Extreme learning machines (ELMs) have proven to be efficient and effective learning mechanisms for pattern classification and regression. However, ELMs are primarily applied to supervised learning problems. Only a few existing research papers have used ELMs to explore unlabeled data. In this paper, we extend ELMs for both semi-supervised and unsupervised tasks based on the manifold regularization, thus greatly expanding the applicability of ELMs. The key advantages of the proposed algorithms are as follows: 1) both the semi-supervised ELM (SS-ELM) and the unsupervised ELM (US-ELM) exhibit learning capability and computational efficiency of ELMs; 2) both algorithms naturally handle multiclass classification or multicluster clustering; and 3) both algorithms are inductive and can handle unseen data at test time directly. Moreover, it is shown in this paper that all the supervised, semi-supervised, and unsupervised ELMs can actually be put into a unified framework. This provides new perspectives for understanding the mechanism of random feature mapping, which is the key concept in ELM theory. Empirical study on a wide range of data sets demonstrates that the proposed algorithms are competitive with the state-of-the-art semi-supervised or unsupervised learning algorithms in terms of accuracy and efficiency.

Single facility scheduling with nonlinear processing times

Abstract This paper considers the static single facility scheduling problem where the processing times of jobs are a monotonically increasing function of their starting (waiting) times and the objective is to minimize the total elapsed time (called the makespan) in which all jobs complete their processing. Based on the combinatorial analysis of the problem, an exact optimization algorithm is developed for the general processing time function which is then specialized for the linear case. In view of the excessive computational burden of the exact optimization algorithm for the nonlinear processing time functions, heuristic algorithms are proposed. The effectiveness of these proposed alogrithms is empirically evaluated and found to indicate that these heuristic algorithms yield optimal or near optimal schedules in many cases.

Flowshop scheduling research after five decades

Abstract Since Johnson’s seminal paper in 1954, flowshop scheduling problems have received considerable research attention over the last fifty years. As a result, several optimization and heuristic solution procedures are available to solve a variety of flowshop scheduling problems. This paper provides a brief glimpse into the evolution of flowshop scheduling problems and possible approaches for their solution over the last fifty years. It briefly introduces the current flowshop problems being solved and the approaches being taken to solve (optimally or approximately) them. The paper concludes with some fruitful directions for future research.

Neural networks in business: techniques and applications for the operations researcher

Abstract This paper presents an overview of the different types of neural network models which are applicable when solving business problems. The history of neural networks in business is outlined, leading to a discussion of the current applications in business including data mining, as well as the current research directions. The role of neural networks as a modern operations research tool is discussed. Scope and purpose Neural networks are becoming increasingly popular in business. Many organisations are investing in neural network and data mining solutions to problems which have traditionally fallen under the responsibility of operations research. This article provides an overview for the operations research reader of the basic neural network techniques, as well as their historical and current use in business. The paper is intended as an introductory article for the remainder of this special issue on neural networks in business.

Comparing backpropagation with a genetic algorithm for neural network training

This article shows that the use of a genetic algorithm can provide better results for training a feedforward neural network than the traditional techniques of backpropagation. Using a chaotic time series as an illustration, we directly compare the genetic algorithm and backpropagation for effectiveness, ease-of-use, and efficiency for training neural networks.

Schedules for a two-stage hybrid flowshop with parallel machines at the second stage

Approximate solution algorithms are developed to find a minimum makespan schedule in a two-stage hybrid flowshop when the second stage consists of multiple identical machines. Computational experience comparing the ‘approximate’ makespans with their respective global lower bounds for large problems indicates that proposed polynomially bounded approximate algorithms are quite effective. It is shown that the proposed heuristic algorithms can be used to improve the efficiency of an existing branch and bound algorithm.

Creating Knowledge Based Organizations

Creating Knowledge Based Organizations brings together high quality concepts closely related to organizational learning, knowledge workers, intellectual capital, virtual teams and will include the methodologies, systems and approaches needed to create and manager knowledge-based organizations of the 21st Century.

SCHEDULING A FLOWLINE MANUFACTURING CELL WITH SEQUENCE DEPENDENT FAMILY SETUP TIMES

This paper considers the problem of scheduling part families and jobs within each part family in a flowline manufacturing cell where the setup times for each family are sequence dependent and it is desired to minimize the makespan while processing parts (jobs) in each family together. Lower bounds on the optimal makespan value and eAcient heuristic algorithms for finding permutation schedules are proposed and empirically evaluated as to their eAectiveness in finding optimal permutation schedules. ” 2000 Elsevier Science B.V. All rights reserved.

Evolutionary algorithms for scheduling a flowshop manufacturing cell with sequence dependent family setups

This paper considers the problem of scheduling part families and jobs within each part family in a flowshop manufacturing cell with sequence dependent family setups times where it is desired to minimize the makespan while processing parts (jobs) in each family together. Two evolutionary algorithms-a Genetic Algorithm and a Memetic Algorithm with local search-are proposed and empirically evaluated as to their effectiveness in finding optimal permutation schedules. The proposed algorithms use a compact representation for the solution and a hierarchically structured population where the number of possible neighborhoods is limited by dividing the population into clusters. In comparison to a Multi-Start procedure, solutions obtained by the proposed evolutionary algorithms were very close to the lower bounds for all problem instances. Moreover, the comparison against the previous best algorithm, a heuristic named CMD, indicated a considerable performance improvement.