Asia-Pacific Journal of Operational Research

Two results in a recent paper by Wang and Cheng [Wang, J-B and TCE Cheng (2007). Asia-Pacific Journal of Operational Research, 24, 245–261] are incorrect because job processing times are variable due to both deteriorating jobs and learning effects, which is not taken into account by the authors. In this note, we show by a counter-example that the published results are incorrect.

The return of used products (cores) is the beginning of remanufacturing. Although an appropriate pricing policy can effectively manage the returns, a static pricing policy cannot match the returns and demands because of the high uncertainties in both sides, which in turn results in high inventory cost or lost-sale cost. In this paper, we apply a dynamic pricing policy commonly used in retail setting to the core acquisition management in remanufacturing and study the pricing problem for used products with the objective of minimizing average cost over an infinite horizon. We formulate the pricing problem as a continuous-time Markov decision process and characterize the structural properties of the optimal policy. We also conduct a numerical study to investigate the benefit of dynamic pricing.

A search game on a finite and complete graph is studied between a (immobile) hider and a seeker. A strategy for the hider is to choose a node where he hides, except for a specified node. A strategy for the seeker is an ordering of nodes in which the seeker starts at the specified node, examines each node in that order until he finds the hider, traveling along edges. Associated with an examination of a node is the examination cost, and associated with a movement from a node to a node is a traveling cost. These costs are unknown for both players when they choose strategies. They know only probability laws of them. While the hider wishes to maximize the expectation of the sum of these costs which are required to find the hider, the seeker wishes to minimize it. The game is solved when the nodes are classified into two groups depending on the examination costs, and the edges are classified into three groups depending on both costs.

The selection methods of population-based metaheuristics provide the driving force to generate good solutions. These selection methods select the individuals with a higher fitness to be members of the population in the next iteration correspond to the natural rule of Darwin's principle survival-of-the-fittest. Harmony search algorithm is a population-based metaheuristic, which mimicking the musical improvisation process where a group of musicians play the pitches of their musical instruments seeking for a pleasing harmony. It improvises the new harmony based on three rules: memory consideration, random consideration, and pitch adjustment. In this paper, we investigate the replacement of the original random selection of memory consideration with a set of selection methods in order to speed-up the convergence. These selection methods include tournament, proportional, and liner rank of Genetic Algorithm, and Global-best of Particle Swarm Optimization. The proposed harmony search with the different memory consideration selection methods evaluated using standard dataset published in the first International Nurse Rostering Competition INRC2010. Nurse rostering problem is a combinatorial optimization problem tackled by assigning a set of nurses with different skills to a set of shifts over predefined scheduling period. Experimentally, the tournament memory consideration selection method achieved the best rate of convergence as well as the best results in comparison with the other memory consideration selection methods.

We consider the extended economic production quantity (EPQ) problem when demand follows a Poisson process in a production system. A fixed lot sizing policy is implemented to minimize fluctuation of workload, and to smooth production planning and inventory control. The considered costs include setup cost, inventory carrying cost, and shortage cost when demand cannot be satisfied from stock. The main contributions of this paper are two folds. We develop and analyze the extended EPQ model. Under some mild conditions, the expected cost per unit time can be shown to be convex. Via computational experiments, we demonstrate that, in comparison with classical EPQ model, the average reduction of expected cost is significant when demand is random and the proposed model is used to determine lot sizing policy. Our computational tests have also illustrated the impact of various parameters on the expected cost model and the lot sizing policy.

We study a M/M/1 queue in a multi-phase random environment, where the system occasionally suffers a disastrous failure, causing all present jobs to be lost. The system then moves to a repair phase. As soon as the system is repaired, it moves to phase i with probability q_i ≥ 0. We use two methods of analysis to study the probabilistic behavior of the system in steady state: (i) via probability generating functions, and (ii) via matrix geometric approach. Due to the special structure of the Markov process describing the disaster model, both methods lead to explicit results, which are related to each other. We derive various performance measures such as mean queue sizes, mean waiting times, and fraction of lost customers. Two special cases are further discussed.

We mainly study a taxpayer's optimal strategies of tax evasion and its relevant properties, in order to deduce some effective suggestions and theoretical bases for the tax authority to restrain tax evasion. Based on the Christiansen static model of tax evasion, we formulate a two-period model where the discovery of tax evasion in the second period induces a tax audit in the first period if it has not been done before. The taxpayer has to choose the amount of tax evasion in each period to maximize the total expected utility of the two periods. We show that the threat of having the first-period evasion discovered in the second period diminishes attractiveness of tax evasion in both periods. We also discuss the policy of audit power enhancement, where the audit probability in the second period will be increased if tax evasion is discovered in the first period. We find that this policy may play two roles. First, it can reduce the incentive of tax evasion in the second period if tax evasion is discovered in the first period. Second, a high potential increase of the audit probability may contain tax evasion in the first period but may cause more evasion in the second period.

In this paper, we consider a scheduling problem with position-based deteriorating jobs and past-sequence-dependent delivery times on a single machine or parallel machines. Each job's delivery time is dependent on its waiting time for processing. Because of the deteriorating effect, each machine will undergo multiple rate-modifying activities throughout the planning horizon to restore its processing rate. For the single-machine case, the objectives are to minimize the makespan and the total completion time. For the parallel-machine case, the objectives are to minimize the total completion time and the total machine load. We show that all these problems are solvable in polynomial time.

In this study, we investigate a single-item, periodic-review inventory problem where the production capacity is limited and unmet demand is backordered. We assume that customer demand in each period is a stationary, discrete random variable. Linear holding and backorder cost are charged per unit at the end of a period. In addition to the variable cost charged per unit ordered, a positive fixed ordering cost is incurred with each order given. The optimization criterion is the minimization of the expected cost per period over a planning horizon. We investigate the infinite horizon problem by modeling the problem as a discrete-time Markov chain. We propose a heuristic for the problem based on a particular solution of this stationary model, and conduct a computational study on a set of instances, providing insight on the performance of the heuristic.

As most of the global economic activity takes place in the form of projects/programs, their effective management and governance is becoming more and more critical to the competitive position of organizations and societies. Project auditing and risk management, elaborate on methodologies that could be used for analyzing project progress, by identifying potential risks and liabilities, and finally recommending corrective and preventive actions. In relation to these fields, this paper proposes a fuzzy set based approach for project risk ranking in large-scale programs. The proposed approach defines a generic list of risk factors which is used for the ranking and risk assessment of all projects of a program. Data describing projects' progress as well as expert's evaluation of risk factors and project's risk exposure are being used as input parameters to the fuzzy set system. The relative probability of risk appearance due to risk factors for each of the projects is being calculated by providing valuable means for efficient decision-making and success of the program. Finally, this paper describes a case study called Operational Program "Road Axes, Ports, Urban Development" of Community Support Framework III in Greece, where the proposed approach was successfully applied.