Asia-Pacific Journal of Operational Research

This paper considers software systems consisting of fault-tolerant components. These components are built from functionally equivalent but independently developed versions characterized by different reliability and execution times. Because of resource constraints, the number of versions that can run simultaneously is limited. The expected system execution time and its reliability (defined as probability of obtaining the correct output within a specified time) strictly depends on the sequence of versions execution. Different sequences can provide either maximal system reliability or minimal expected execution time. An optimization procedure is suggested for finding such sequences. The procedure is based on an algorithm for determining system execution time distribution that uses the moment generating function approach and on the genetic algorithm. Both N-version programming and the recovery block scheme are considered within a universal model. An illustrated example is presented.

A pair of Mond–Weir type nondifferentiable second-order symmetric primal and dual problems in mathematical programming is formulated. Weak duality, strong duality, and converse duality theorems are established under η-pseudobonvexity assumptions. Symmetric minimax mixed integer primal and dual problems are also investigated. Moreover, the self duality theorem is also discussed.

Software reliability plays an important role in assuring the quality of a software. To ensure software reliability, the software is tested thoroughly during the testing phase. The time invested in the testing phase or the optimal software release time depends on the level of reliability to be achieved. There are two different concepts related to software reliability, viz., testing reliability and operational reliability. In this paper, we compare both types of software reliabilities to determine the optimal testing time of the software so as to minimize the total expected software maintenance cost. We consider a software has a number of clusters of modules, each having a different number of errors and a different failure rate. A hyperexponential model is employed for analyzing software reliability growth. Parameter estimation using the maximum likelihood estimation technique is also discussed. Numerical illustrations are taken to explore the effect of various parameters on reliability and maintenance cost. It is noticed that the operational reliability concept should be adopted for the software testing time problem.

This study suggests time-based clustering models integrating change-point detection and neural networks, and applies them to financial time series forecasting. The basic concept of the proposed models is to obtain intervals divided by change points, to identify them as change-point groups, and to involve them in the forecasting model. The proposed models consist of two stages. The first stage, the clustering neural network modeling stage, is to detect successive change points in the dataset, and to forecast change-point groups with backpropagation neural networks (BPNs). In this stage, three change-point detection methods are applied and compared. They are: (1) the parametric approach, (2) the nonparametric approach, and (3) the model-based approach. The next stage is to forecast the final output with BPNs. Through the application to financial time series forecasting, we compare the proposed models with a neural network model alone and, in addition, determine which of three change-point detection methods performs better. Furthermore, we evaluate whether the proposed models play a role in clustering to reflect the time. Finally, this study examines the predictability of the integrated neural network models based on change-point detection.

This study applies the real options approach to examine the maximum net present value of the market entry/exit thresholds given uncertain cash flows. The discount and growth factors are determined in the proposed entry/exit models, facilitating the complex calculation of the discount and growth rates to determine the present value of cash flow streams. Accordingly, this work successfully combines the maximum net present value method and the real options approach for decision-making by simply considering the discount and growth factors.

In this paper, we propose an assignment-based local search method for solving vehicle routing problems. This method is a multi-route improvement algorithm that can operate on several routes at a time. To evaluate the performance of the proposed method, extensive computational experiments on the proposed method applied to a set of benchmark problems are carried out. The results show that the proposed method, when coupled with metaheuristics such as simulated annealing, is comparable with other efficient heuristic methods proposed in the literature.

The steady-state availability of a two-component system in series and parallel subject to individual failures (I-failures) and common-cause shock (CCS) failures is studied from a Bayesian viewpoint with different types of priors assumed for the unknown parameters in the system. Monte Carlo simulation is used to derive the posterior distribution for the steady-state availability and subsequently the highest posterior density intervals. A numerical example illustrates the results.

For a finite planning horizon, there has been a considerable body of research papers in the area of operations management that dealt with four different inventory shortage models in the last two decades. In this paper, we establish the models to reflect the fact that the longer the waiting time, the smaller the backlogging rate. We then use maximizing profit as the objective to make an appropriate comparison among those four alternatives. The theoretical results reveal that Model 4 provides the highest profit only if the profit margin is sufficiently low. Otherwise, in general, Model 3 has the highest profit among them.