Asia-Pacific Journal of Operational Research

The fixed-charge capacitated multicommodity network design problem is a fundamental optimization problem arising in many network configurations. The solution of the problem provides an appropriate network design as well as routes of multicommodity flows aimed at minimizing the total cost, which is the sum of the flow costs and fixed-charge costs over a network with limited arc capacities. In the present paper, we introduce a combined approach with a capacity scaling procedure for finding an initial feasible solution and an MIP neighborhood search for improving the solutions. Besides, we modify the procedure for application to large-scale problems. Computational experiments demonstrate the effectiveness of the proposed approach, and high-quality solutions are obtained for two problem sets from the literature.

This paper investigates the optimal procurement strategy for an original equipment manufacturer (OEM) under different information and channel structures. We consider two possible sourcing strategies, Consignment and Turnkey, in which Consignment represents the situation when an OEM procures components itself and only assigns the production activity to a contract manufacturer (CM) and Turnkey is defined as when an OEM delegates both the procurement and production activities to the CM. We specifically consider two channel structures, a single channel with one OEM and one CM and two competing channels in which each consists of an OEM and a CM. Under each channel structure, the CM’s purchasing cost may be public or private information. We find that under both channel structures, in the absence of information asymmetry, the OEM will always choose Turnkey if its cost is higher than the CM’s average cost. However, in the presence of information asymmetry, the OEM is more likely to adopt Consignment with the increase in market size. We also find that when the two CMs’ cost correlation is small (large), the structure of the competing channels increases the possibility that the OEM adopts Turnkey (Consignment) in equilibrium.

We propose a variance-based modified backward-forward algorithm with a stochastic approximation version of Armijo’s line search, which is robust with respect to an unknown Lipschitz constant, for solving a class of stochastic variational inequality problems. A salient feature of the proposed algorithm is to compute only one projection and two independent queries of a stochastic oracle at each iteration. We analyze the proposed algorithm for its asymptotic convergence, sublinear convergence rate in terms of the mean natural residual function, and optimal oracle complexity under moderate conditions. We also discuss the linear convergence rate with finite computational budget for the proposed algorithm without strong monotonicity. Preliminary numerical experiments indicate that the proposed algorithm is competitive with some existing algorithms. Furthermore, we consider an application in dealing with an equilibrium problem in stochastic natural gas trading market.

Buy-price auction has been successfully used as a new channel of online sales. This paper studies an online sequential buy-price auction problem, where a seller has an inventory of identical products and needs to clear them through a sequence of online buy-price auctions such that the total profit is maximized by optimizing the buy price in each auction. We propose a methodology by dynamic programming approach to solve this optimization problem. Since the consumers’ behavior affects the seller’s revenue, the consumers’ strategy used in this auction is first investigated. Then, two different dynamic programming models are developed to optimize the seller’s decision-making: one is the clairvoyant model corresponding to a situation where the seller has complete information about consumer valuations, and the other is the Bayesian learning model where the seller makes optimal decisions by continuously recording and utilizing auction data during the sales process. Numerical experiments are employed to demonstrate the impacts of several key factors on the optimal solutions, including the size of inventory, the number of potential consumers, and the rate at which the seller discounts early incomes. It is shown that when the consumers’ valuations are uniformly distributed, the Bayesian learning model is of great efficiency if the demand is adequate.

Some works in modified compressive sensing (CS) show that reconstruction of sparse signals can obtain better results than traditional CS using the partially known support. In this paper, we extend the idea of these works to the null space tuning algorithm with hard thresholding, feedbacks ($\mathrm{\text{NST}}+\mathrm{\text{HT}}+\mathrm{\text{FB}}$) and derive sufficient conditions for robust sparse signal recovery. The theoretical analysis shows that including prior information of partially known support relaxes the preconditioned restricted isometry property condition comparing with the $\mathrm{\text{NST}}+\mathrm{\text{HT}}+\mathrm{\text{FB}}$. Numerical experiments demonstrate that the modification improves the performance of the NST+HT+FB, thereby requiring fewer samples to obtain an approximate reconstruction. Meanwhile, a systemic comparison with different methods based on partially known support is shown.

In this paper, the flow shop resource allocation scheduling with learning effect and position-dependent weights on two-machine no-wait setting is considered. Under common due date assignment and slack due date assignment rules, a bi-criteria analysis is provided. The optimality properties and polynomial time algorithms are developed to solve four versions of the problem. For a special case of the problem, it is proved that the problem can be optimally solved by a lower order algorithm.

In some fully sequential ranking and selection procedures, such as the KN procedure and Rinott’s procedure, some initial samples must be taken to estimate the variance. We analyze the impact of the initial sample size (ISS) on the total sample size and propose an algorithm to calculate the ISS in this type of procedure. To better illustrate our approach, we implement this algorithm on the KN procedure and propose the KN-ISS procedure. Comprehensive numerical experiments reveal that this procedure can significantly improve the efficiency compared with the KN procedure and still deliver the desired probability of correct selection.

We investigate the pricing strategies of unbundling and mixed-bundling for a firm that produces both a product and a compatible integrated content, respectively. The firm can be viewed as a two-sided transaction platform between sellers and customers, and decides whether to sell the product and the integrated content separately or jointly. Sellers develop independent content and are charged a per-unit royalty rate for each transaction on the platform, and customers are required to pay the prices for the product and the contents. With the consideration of stochastic demand, we study the impacts of cross-side network effect on the pricing strategies of unbundling and mixed-bundling, respectively. Moreover, we compare the impact of unbundling and mixed-bundling on the pricing strategy of the platform. Compared with no cross-side network effect, we show that the firm (with unbundling and mixed-bundling) need not subsidize customers and sellers in the presence of the effect under certain conditions, indicating that it can extract the most surplus from both sides to maximize the profit. This stands in sharp contrast to the finding of the literature on two-sided markets that the platform should subsidize one side of the market in order to make profit from the other side. Moreover, our result suggests that mixed-bundling can help the firm make more profit with fewer products by subsidizing one side of the market compared with the unbundling.

Since the onset of the COVID-19 outbreak in Wuhan, China, numerous forecasting models have been proposed to project the trajectory of coronavirus infection cases. Most of these forecasts are based on epidemiology models that utilize deterministic differential equations and have resulted in widely varying predictions. We propose a new discrete-time Markov chain model that directly incorporates stochastic behavior and for which parameter estimation is straightforward from available data. Using such data from China’s Hubei province (for which Wuhan is the provincial capital city and which accounted for approximately 82% of the total reported COVID-19 cases in the entire country), the model is shown to be flexible, robust, and accurate. As a result, it has been adopted by the first Shanghai assistance medical team in Wuhan’s Jinyintan Hospital, which was the first designated hospital to take COVID-19 patients in the world. The forecast has been used for preparing medical staff, intensive care unit (ICU) beds, ventilators, and other critical care medical resources and for supporting real-time medical management decisions.