Showing papers in "Computers & Operations Research in 1993"

TL;DR: The proposed TOPSIS for MODM algorithm is developed for solving multiple objective decision-making problems by considering two reference points of the positive ideal solution and the negative ideal solution simultaneously, and a numerical nutrition problem is solved.

TL;DR: Heuristics for the problem of rescheduling a machine on occurrence of an unforeseen disruption are developed and are shown to be effective in that the schedule stability can be increased significantly with little or no sacrifice in makespan.

TL;DR: The features of Bilevel Linear Programming are reviewed by presenting prior results as well as providing new results, including the capability of the problem to formulate any piecewise linear function and its connection to other optimization problems.

TL;DR: A very general discrete-time queueing model with one single server and an infinite waiting room is studied, finding that the number of arrivals during any discrete time-unit as well as the service time of each customer have general probability distributions.

TL;DR: Problems derived from a real situation (a Belgian bank network) are solved under different scenarios: the deterministic case, and the stochastic case in which travel times are random.

TL;DR: A network synthesis problem is considered to find a network G n,e ∗ that maximizes system reliability over the class of all networks having n nodes and e edges and an upper bound of maximum reliability is derived in terms of node degrees.

TL;DR: Several properties that are used to calculate lower bounds on total tardiness of jobs for a given partial sequence and to identify sequences dominated by others are presented and a branch and bound algorithm using these bounds and dominance rule is developed.

TL;DR: The problem considered in this paper is to find the first k quickest looping paths from the source to the sink, and an algorithm with time complexity of O(m 2 + (m + k)n log n + k 3 2 log k) is developed.

TL;DR: Artificial neural networks are new methods for classification that can solve some difficult classification problems where classical models cannot and are more robust in that they are less sensitive to changes in sample size, number of groups,Number of variables, proportions of group memberships, and degrees of overlap among groups.

TL;DR: A variation of the hypercube model that accommodates preference ties is developed and applied to the emergency medical system of Greenville County, S.C.

TL;DR: This paper will compare the performance of implementations of simulated annealing, genetic algorithms, tabu search, the Great Deluge algorithm and the Record-to-Record Travel algorithm for the problem of balancing hydraulic turbine runners.

TL;DR: A microcomputer-based decision support system for nurse scheduling in hospitals that incorporates a wide range of hospital and nurse objectives and is flexible, allowing it to be implemented in different hospital environments.

TL;DR: A binary search procedure that determines the optimal integer starting times for the operations in a cyclic transportation schedule with a given route and dependent time-windows to minimize the cycle time is introduced.

TL;DR: The solution algorithm developed in this paper unifies Benders decomposition and Lagrangean relaxation into a single framework that involves successive solutions to a Benders (primal) subproblem and a Lagrangeans (dual) sub problem.

TL;DR: A heuristic procedure is developed to construct a primal feasible solution from the dual solution obtained by the dual procedure developed, which is well demonstrated by the computational experiments conducted with a variety of test problems.

TL;DR: The algorithm presented is seen to be effective in solving TBGAP problems to optimality and the algorithm is illustrated by an example and computational experience is reported.

TL;DR: The algorithm is a hybrid of a greedy approach, followed by a simulated annealing search of the V-shaped sequence solution space, which gives better solutions than the heuristics previously presented in the literature.

TL;DR: The forward algorithm is shown to be asymptotically linear in computational requirements, in contrast to the case for the classical lot size model which has exponential computing requirements.

TL;DR: Four major approaches of Nonlinear Goal Programming are reviewed and discussed; 1. simplex based; 2. direct search; 3. gradient search and 4. interactive approaches.

TL;DR: Fast iterative methods which generate a restricted number of paths in a particular neighbourhood represented by a special shift graph which is very efficient and out performs different variants of simulated annealing and tabu search.

TL;DR: The Generalized Reduced Gradient method (GRG) implemented as GINO is found to be the most suitable for solving machining optimization models.

TL;DR: A solution methodology, based on dynamic programming, that is pseudo-polynomial in its complexity is proposed, and its extensibility to a broader class of early/tardy problems is discussed.

TL;DR: Five stochastic programming models are developed for identifying cost-effective acid rain control strategies that include both traditional forms in which individual constraint reliability levels are user-prescribed constants as well as a joint chance constrained model in which reliability Levels are cast as decision variables.

TL;DR: The Lagrangean algorithm proves to be quite effective and stable in producing good primal and dual solutions to the CMLSP.