Explore the vast range of titles available.

We investigate firms' competitive behaviors in industries where customers are sensitive to both promised delivery time (PDT) and quality of service (QoS) measured by the on-time delivery rate. To study the competition in PDT at the marketing level, we construct an oligopoly game with an external QoS requirement. We show that there exists a unique Nash equilibrium, and the equilibrium QoS exhibits a switching surface structure with respect to capacities. To study the competition in capacity at the strategic level, we construct a two-stage game in which the firms compete in terms of their capacities in stage 1 and in terms of PDT in stage 2. We show the existence of two different types of pure strategy equilibria and characterize them. This study provides the following insights: an index of time-based competitive advantage (ITCA) and the first-mover advantage determine the positions of the firms in time-based competition; either the well-known prisoner's dilemma or off-equilibrium behaviors due to different preferences for equilibria (when multiple equilibria exist) may lead the firms to overinvest in capacity, but no one may gain a competitive advantage; a uniform improvement in internal efficiency (i.e., a uniform capacity cost reduction) may harm everyone; quality differentiation (i.e., going beyond the QoS benchmark) plays a dual role in time-based competition, either helping a firm with a larger ITCA to compete more effectively, or helping a firm possibly with a smaller ITCA to preempt competitors and protect its market advantage. This paper was accepted by Paul H. Zipkin, operations and supply chain management.

We study the problem of information sharing in a supply chain with two competing manufacturers selling substitutable products through a common retailer. Our analysis shows that the retailer's incentive to share information strongly depends on nonlinear production cost, competition intensity, and whether the retailer can offer a contract to charge a payment for the information. Without information contracting, the retailer has an incentive to share information for free when production economy is large but has no incentive to do so when there is production diseconomy. With information contracting, the retailer has an incentive to share information when either production diseconomy/economy is large or competition is intense. We characterize the conditions under which the retailer shares information with none, one, or both of the manufacturers. We also show that the retailer prefers to sell information sequentially rather than concurrently to the manufacturers, whereas the manufacturers' preferences are reversed.

When two firms compete for service-sensitive demands based on their product availability, their actions will affect the future market share reallocation. This problem was first studied by Hall and Porteus (2000) using a dynamic game model. We extend their work by incorporating a general demand model, which enables us to obtain properties that reveal the dynamics of the game and the behavior of the players. In particular, we provide conditions under which the market share of a firm has a positive value and give it an upper bound. We further extend the game competition model to an infinite-horizon setting. We prove that there exists a stationary equilibrium policy and that the dynamic equilibrium policy always converges to a stationary equilibrium policy. We demonstrate that demand patterns will dictate how firms compete rationally and show the likely outcomes of the competition.

In this paper, we study the tail behavior of the stationary queue length of an M/G/1 retrial queue. We show that the subexponential tail of the stationary queue length of an M/G/1 retrial queue is determined by that of the corresponding M/G/1 queue, and hence the stationary queue length in an M/G/1 retrial queue is subexponential if the stationary queue length in the corresponding M/G/1 queue is subexponential. Our results for subexponential tails also apply to regularly varying tails, and we provide the regularly varying tail asymptotics for the stationary queue length of the M/G/1 retrial queue. [PUBLICATION ABSTRACT]

We examine the service competition in a product replenishment system and in a service delivery system. To excel the competitors in the competition, a supplier needs to understand the impact of her decision on the market share, and make the best decision considering the possible actions and reactions of the competitors. In a product replenishment system with multiple suppliers, customer reaction to a stockout is either accepting the delayed delivery (backorder, usually with some incentive) or taking the order elsewhere (lost sales). When customers take the second action, the suppliers suffer not only a loss of sales but also a loss of goodwill. In this thesis, we present two availability competition models for this problem: (1) dynamic competition model, and (2) attraction model. In the dynamic model, we consider the problem with two suppliers in a multi-period setting, and assume that a proportion of unsatisfied customers will switch supplier in the next period. We show the existence and uniqueness of Nash equilibrium, and prove that the dynamic game will converge to a stationary game. In the attraction model, we consider the problem with multiple suppliers in the long run. We assume that the fill rate is a key dimension of the product. We show the existence, uniqueness of Nash equilibrium, and discuss how the firm behavior is affected by the competition. In a service delivery system, the supplier needs to quote a uniform delivery time for customers, while the quality of service, measured by the probability of on time delivery, must meet the market requirement. Both the lead time quotation and the service quality affect the attraction of the supplier, and hence the supplier needs to make suitable time decision to balance these two dimensions. We derive the optimal decision for the suppliers and provide some managerial insights. In particular, we show that whether a supplier should adopt an endogenous or exogenous service quality, or whether she should make the decision based on her own parameters or based on the market requirement, depends on the capacities of the competing suppliers.

An inventory-queue is an inventory system controlled by a processing station with queueing. They are natural building blocks for supply chain models. An important and largely open issue for inventory queues is the characterization of their departure processes. In an inventory-queue, departures are triggered either by a job arrival when the output buffer is not empty or otherwise by a service completion. Such departures are more difficult to analyze than departures from a standard queue. The main results in this study are expressions for the probability distributions and squared coefficient of variations of inter-departure times for base-stock inventory-queues with birth-death production processes. [PUBLICATION ABSTRACT]

Recently, many stochastic Alternating Direction Methods of Multipliers (ADMMs) have been proposed to solve large-scale machine learning problems. However, for large-scale saddle-point problems, the state-of-the-art (SOTA) stochastic ADMMs still have high per-iteration costs. On the other hand, the stochastic primal–dual hybrid gradient (SPDHG) has a low per-iteration cost but only a suboptimal convergence rate of (1/S). Thus, there still remains a gap in the convergence rates between SPDHG and SOTA ADMMs. Motivated by the two matters, we propose (accelerated) stochastic variance reduced primal–dual hybrid gradient ((A)SVR-PDHG) methods. We design a linear extrapolation step to improve the convergence rate and a new adaptive epoch length strategy to remove the extra boundedness assumption. Our algorithms have a simpler structure and lower per-iteration complexity than SOTA ADMMs. As a by-product, we present the asynchronous parallel variants of our algorithms. In theory, we rigorously prove that our methods converge linearly for strongly convex problems and improve the convergence rate to (1/S2) for non-strongly convex problems as opposed to the existing (1/S) convergence rate. Compared with SOTA algorithms, various experimental results demonstrate that ASVR-PDHG can achieve an average speedup of 2×∼5×.

The active site of a solid catalyst varies sensitively with the catalyzed reaction. Herein, using experimentally measured elementary surface reaction kinetics of CO 2 or CO hydrogenation reactions over a ZnO-ZrO 2 catalyst under working conditions in combinations with comprehensive structural characterizations and theoretical simulations, we unveil the distinctly different active sites in catalyzing the CO 2 or CO hydrogenation to methanol reaction. Zn 2+ cations with different local environments are present on the ZnO-ZrO 2 surface, including Zn 1 single atoms exclusively with a Zn-O-Zr local structure and Zn n clusters with both Zn-O-Zr and Zn-O-Zn local structures. The -Zr-O-Zr- structure bonded to the Zn n clusters is more easily to be reduced than that bonded to the Zn 1 single atoms. The Zn 1 -single atom (-Zr-O-Zn-O-Zr-) is the active site for catalyzing the CO 2 hydrogenation to methanol reaction, whereas the Zn n cluster bonded to an in situ formed -Zr-V o -Zr- structure (-Zn-O-Zn(-O-Zr-V o -Zr-)-O-Zr-) is the active site for catalyzing the CO hydrogenation to methanol reaction. These results provide a reliable and effective methodology of elementary surface reaction kinetics for identifications of active sites of working catalysts in complex reactions and unveil how sensitively the active site structure varies with the catalyzed reaction. The active site of a solid catalyst depends strongly on the specific reaction. Here, the authors reveal distinct active sites for CO 2 and CO hydrogenation to methanol over ZnO-ZrO 2 , using experimental surface kinetics, structural characterization, and theoretical modeling.

Poorly differentiated thyroid cancer (PDTC) and anaplastic thyroid cancer (ATC) present major challenges in treatment owing to extreme aggressiveness and high heterogeneity. In this study, deep-scale analyses spanning genomic, proteomic, and phosphoproteomic data are performed on 348 thyroid-cancer and 119 tumor-adjacent samples. TP53 (48%), TERT promoter (36.5%), and BRAF (23%) are most frequently mutated in PDTC and ATC. Ribosome biogenesis is identified as a common hallmark of ATC, and RRP9 silencing dramatically inhibits tumor growth. Proteomic clustering identified three ATC/PDTC subtypes. Pro-I subtype is characterized with aberrant insulin signaling and low immune cell infiltration, and Pro-II is featured with DNA repair signaling, while Pro-III harbors high frequency of TP53 and BRAF mutation and intensive C5AR1 + myeloid infiltration. Targeting C5AR1 synergistically improves antitumor effect of PD-1 blockade against ATC cell-derived tumors. These findings provide systematic insights into tumor biology and opportunities for drug discovery, accelerating precision therapy for virulent thyroid cancers. The treatment of poorly differentiated thyroid cancer and anaplastic thyroid cancer remains challenging. Here, the authors perform multi-omic analysis of thyroid samples and identify proteomic clusters associated with distinct tumour microenvironment features.

In flank milling process, machined surface is formed by the edge of the cutting tool while the position of tool is influenced by the movement of computer numerical control (CNC) machining axis and the status of tool is influenced by the cutting force; thus, the quality of the workpiece is affected by multiple error sources. In this paper, a new error prediction method is proposed to integrate errors of the process system, including tool rotation error, machine geometric error, and tool deformation error. All these errors are synthesized to generate the movement of tool edge in the workpiece coordinate system. Then, the machining error is obtained by projecting the point cloud of tool edge to the workpiece normal vector. Case studies are performed at the end, and the influence of tool rotation error on workpiece error is discussed. The model has been validated with an experiment.