Showing papers in "Computers & Operations Research in 1995"

TL;DR: A method for the determination of objective weights which is based on the quantification of two fundamental notions of MCDM: the contrast intensity and the conflicting character of the evaluation criteria is proposed.

TL;DR: A Genetic Algorithm is developed for finding (approximately) the minimum makespan of the n-job, m-machine permutation flowshop sequencing problem and the performance of the algorithm is compared with that of a naive Neighbourhood Search technique and with a proven Simulated Annealing algorithm.

TL;DR: Improvements obtained in dealing with job shop scheduling using a heuristic technique based on Genetic Algorithms are presented.

TL;DR: A class of approximation algorithms is described for solving the minimum makespan problem of job shop scheduling and can find shorter makespans than the shifting bottleneck heuristic or a simulated annealing approach with the same running time.

TL;DR: This paper presents computational results which show that this genetic algorithm approach to QAP finds solutions competitive with those of the best previously-known heuristics, and argues that genetic algorithms provide a particularly robust method for QAP and its more complex extensions.

TL;DR: The nature of the connections between the methods is explored, and it is shown that a variety of opportunities exist for creating hybrid approaches to take advantage of their complementary features.

TL;DR: It is indicated that when shortage is allowed, the model leads to lower average total cost, and a comparison of policies is made.

TL;DR: This paper compares the performance of several crossover operators, including two new operators and a new faster formulation of a previously published operator and describes a method for designing problem specific crossover incorporating a novel tie-breaking algorithm.

TL;DR: In this article, a nonlinear 0-1 goal programming model is proposed to take advantage of hardware and software sharing among IS applications, and the model is validated by applying it to real-world IS project selection data.

TL;DR: This work examines the efficacy of using genetic search to develop near optimal schedules in a single-stage process where setup times are sequence dependent.

TL;DR: A procedure to find the optimal stationary operating policy under a linear cost structure is developed and it is shown that the system size distribution decomposes into two random variables one of which is the system sizes of ordinary M x / G /1 queue.

TL;DR: A construction heuristic is developed which uses a look-ahead approach to solve the Split Delivery Vehicle Scheduling Problem with Time Windows, and two improvement heuristics are also described.

TL;DR: It is the intention in this paper to introduce measures of interdependences between the objectives, in order to provide for a better understanding of the decision problem, and to find effective and more correct solutions to MCDM problems.

TL;DR: An optimal timing algorithm is presented which decides the optimal starting time of each job in a given job sequence which is proved to outperform existing heuristic methods.

TL;DR: This chapter extends the EPL model to cases where production rate is a decision variable, and the proposed model for special unit production cost functions becomes a function of production rate.

TL;DR: In this article, a feasibility study is described in which a simple genetic algorithm has been developed in order to examine the suitability of such an approach, and the results of the present experiments are presented, and further complexities are discussed in the context of the genetic approach.

TL;DR: This paper reviews the existing heuristic solution procedures, then presents two new algorithms to solve the rural postman problem near-optimally, and shows that the proposed new algorithms significantly outperformed the existing solution procedures.

TL;DR: Some lower bounding schemes for the HMST are presented which are based on lagrangean relaxation combined with subgradient optimization and some computational results taken from a set of complete graphs with up to 40 nodes are presented.

TL;DR: It is suggested that the most appropriate measure for a given situation is often highly dependent on the type of improvement for a system that is being envisaged, and a framework for possible new measures is suggested.

TL;DR: The steady state probability distribution and expressions for the measures of effectiveness for these two systems are obtained, and the complementarity relationships between these two queueing systems are established.

TL;DR: Given a limited investment, this paper provides a general solution procedure that examines the trade-offs and allocates that investment optimally for quality improvement and setup reduction and finds that in the face of a budget constraint, it is usually necessary to begin with either quality improvement or setup reduction.

TL;DR: Efficient solution procedures for finding the optimal permutation schedules for various performance measures, including maximum flowtime, mean flow time, mean completion time of machines, the number of tardy jobs, maximum lateness, and maximum tardiness, are developed.

TL;DR: A dynamic approach to discrete reliability theory based on discrete hazard rate functions is studied and an application of these functions, to characterizations of aging properties of discrete lifetimes distributions, is described.

TL;DR: This paper begins with a situation where the seller, as the leader, has the power to enforce his strategies on the buyer, but vice versa is not true, and extends to a situation wherein the buyer can also influence the seller's decisions and address the issue of system cooperation.

TL;DR: It is shown how to superimpose the tabu search framework conveniently on basis exchange (or “pivoting”) operations specialized to the transportation network context, and thereby to guide these operations to overcome the limitations of local optimality.

TL;DR: This paper develops simple, empirically based estimators of the optimal tour length using regression and neural network models and shows that these models can produce reasonably good estimates easily.

TL;DR: A novel O(N2) algorithm is presented to minimize the weighted number of tardy jobs with unit processing times, integer ready times and deadlines, and M homogeneous parallel machines, where N is the number of jobs to be scheduled.

TL;DR: A novel stage binary matrix format is applied to describe each stage in a flow network, which allows proper representation of possible routes as binary strings to be used by a genetic algorithm.

TL;DR: The expected annual cost of the model is obtained and a procedure to compute the optimal parameters, r, b, and Q∗, is given and the percentage of cost savings depends on the fill rate.

TL;DR: Optimal algorithms for the Euclidean case in the plane for the 1-median with several existing facilities are proposed and a general heuristic algorithm based on solving several p -median problems is presented.