Asia-Pacific Journal of Operational Research

Fluid contamination is one of the main reasons for the wear failure and the related downtime in a hydraulic power system. Filters play an important role in controlling the contamination effectively, increasing the reliability of the system, and maintaining the system economically. Due to the uncertainties of system parameters, the complicated relationship among components, as well as the lack of effective approach, managing filters is becoming one of the biggest challenges for engineers and decision makers. In this study, a robust interval-based minimax-regret analysis (RIMA) method is developed for the filter management in a fluid power system (FPS) under uncertainty. The RIMA method can handle the uncertainties existed in contaminant ingressions of the system and contaminant holding capacity of filters without making assumption on probabilistic distributions for random variables. Through analyzing the system cost of all possible filter management alternatives, an interval element regret matrix can be obtained, which enables decision makers to identify the optimal filter management strategy under uncertainty. The results of a case study indicate that the reasonable solutions generated can help decision makers understand the consequence of short-term and long-term decisions, identify optimal strategies for filter allocation and selection with minimized system-maintenance cost and system-failure risk.

In this study, we consider a permutation flow shop scheduling problem on a three-machine with deteriorating jobs (a deteriorating job means that the job's processing time is an increasing function of its starting time) so as to minimize the makespan. We model job deterioration as a function that is proportional to a linear function of time. For some special cases, we prove that the problem can be solved in polynomial time. We develop branch-and-bound and heuristic procedures for the general case. Computational experiments for the branch-and-bound algorithm and heuristic algorithm are presented.

This study develops an analytic framework for joint modeling of age-based preventive maintenance and (s, Q) spare component provisioning policy for k-out-of-n:G systems with i.i.d. nonrepairable components. We assume that during system maintenance, instead of discarding the whole system, only failed components are replaced by spares if the on-handspares are sufficient. The objective is to determine a proper preventive maintenance interval and the ordering parameters to minimize the undiscounted long run average cost under a system availability constraint. Our study sheds light on the lumpy demand scenario in the problems of joint modeling of preventive maintenance and spare provisioning.

Based on some new interpolation conditions, a quadratic interpolation model is constructed to approximate the objective function, and then a class of modified BFGS methods with function value information is presented. The new methods satisfy some new weak secant equations and there is a parameter γ in the update formulae which ranges from zero to one. The global and local superlinear convergence properties of the new modified BFGS methods are proved. Numerical results for standard test problems from CUTE are reported, which indicate that all the methods in the proposed class perform well. Ensuring the sufficient positive definiteness of the updating matrices, an adaptive BFGS quasi-Newton method by dynamically choosing the parameter γ is proposed, which may be competitive with other BFGS modifications.

Many companies are facing the challenges of dynamic market and capital shortage. To overcome these challenges, we examine the case in which the company develops early sales strategy that entices customers to commit to their orders at a discount price prior to the selling season. The time between payment and fulfillment of these pre-committed orders provides an opportunity for the company to "lock" part of market demand and relieve the company's procurement budgetary constraint. This paper evaluates the benefits of the early sales strategy. Firstly, we provide a novel method to compute the optimal discount rate of the early sales strategy; comparative static analysis of parameters (e.g., market size, sensitivity level of the market responding to price change, and the probability of a customer purchasing the company's product) toward the optimal discount rate is also investigated. Secondly, we investigate the model of the early sales where the company is with procurement budgetary constraint. It is theoretically proved that the optimal discount rate in the model is larger than that without the constraint. Computational study demonstrates that early sales can significantly relieve the budgetary shortage, and substantially increase the expected profit.

Using additive models for aggregation of criteria is an important procedure in many multicriteria decision methods. This compensatory approach, which scores the alternatives straightforwardly, may have significant drawbacks. For instance, the Decision Maker (DM) may prefer not to select alternatives which have a very low performance in whatever criterion. In contrast, such an alternative may have the best overall evaluation, since the additive model may compensate this low performance in one of the criteria as a result of high performance in other criteria. Thus, additive-veto models are proposed with a view to considering the possibility of vetoing alternatives in such situations, particularly for choice and ranking problems. A numerical application illustrates the use of such models, with a detailed discussion related to real practical problems. Moreover, the results obtained from a numerical simulation show that it is not so rare for a veto of the best alternative to occur in the additive model. This is of considerable relevance depending on the DM's preference structure.

We present a full-Newton step infeasible interior-point algorithm based on a new search direction. The algorithm decreases the duality gap and the feasibility residuals at the same rate. During this algorithm we construct strictly feasible iterates for a sequence of perturbations of the given problem and its dual problem. Each main iteration of the algorithm consists of a feasibility step and some centering steps. We show that the algorithm converges and finds an approximate solution in a polynomial time complexity. A numerical study is done for its numerical performance.

Precedent research based on data envelopment analysis (DEA) has been conducted to rank and benchmark the achievements of participating nations/regions in the Olympics, while this paper contributes to the Asian Games, with further concerns on two main issues, namely the common comparison basis for ranking decision making units (DMUs) and the reference feasibility between the inefficient DMUs and their benchmark targets. This paper extends previous DEA research by introducing an improved context-dependent DEA model, in which empirical results establish a unique and fair ranking system for all participating nations/regions, implying two corresponding suggestions for rank improvement. A series of stepwise learning targets are further identified, alternatively providing a gradual performance improvement path for the inefficient participants. The above results will be helpful for strategic decision making in sport management of the Asian nations/regions.

Geological-mining projects are usually associated with relatively high risk and uncertainty in many aspects, including geological, mining, ecologic, economic, market, legal and social conditions. A mineral deposit is an underground natural resource and hence it is difficult to unequivocally predict the actual results of its discovery. Depending on the extent of the resource, the operation of the mine can extend to a few decades. It is necessary to conduct investment actions in successive stages and to evaluate the results of the work stage by stage. This reduces the investment risk and facilitates the decision-making process. In this paper, the use of a specific kind of game, the so-called "game against Nature," is suggested before a final decision on deposit development is made. This methodology was tested on the example of one of the zinc-lead ore deposits in the Silesia-Cracow region. Apart from supporting the decision-making process, this methodology offers the means to evaluate further research and costs which may be incurred for obtaining supplementary information related to the ore deposit parameters, specifically its reserves.

We consider a retailer, facing uncertain supply and price-sensitive stochastic demand, who has to make stocking and pricing decisions for a given selling period. We also consider the case when the demand is price-sensitive deterministic and provide a unified framework for the model with additive errors. For both scenarios, we look at the case when the price is set before receiving the supply, called simultaneous pricing and the case when the price is set after receiving it, which is called postponed pricing. We develop a procedure for finding the optimal policy for the retailer with general distributions for the supply and the demand. To study the effect of supply uncertainty on expected profit, we conduct sensitivity analysis and develop results for both pricing scenarios and give insights. The results have important implications for a retailer in the supply chain, where a portion of the inventory may be lost due to variety of factors including mishandling and failure to meet quality standards. The findings shed light on the nature and role of prices and their relationship to supply and demand.