We consider a variation of an Erlang loss system in which jobs are routed to servers according to the Shortest Idle Server First service discipline. Specifically, we consider a system in which idle servers are arranged in a stack; servers are returned to the top of the stack upon service completion; and arriving jobs are assigned to the server currently at the top of the stack. When busy, servers accumulate age and incur an age-dependent operating cost. For such systems, we (i) formulate a continuous-time Markov chain model to characterize the system’s transient behavior, and (ii) develop maintenance policies consisting of two possible actions: server group replacement and stack inversion. The stack inversion may be performed at any time prior to group replacement to achieve a more evenly distributed utilization among servers. We develop an optimization model to determine the optimal inversion and replacement times so as to minimize the long-run expected cost rate. Because the model is nonlinear and non-convex, we develop a set of algorithms to solve for the optimal replacement and inversion time. Lastly, we establish a lower bound for the inversion cost threshold below which it is optimal to invert the stack of servers before their replacement.

Multivariate networked time series data are ubiquitous in many applications, where multiple variables of interest are sequentially collected over time for each vertex as multivariate time series. Data of different vertices may have heterogeneous influence on each other through the network topology. These time series may usually exhibit multimodal marginal distributions, due to complex system variations. In this article, we propose a novel approach for such data modeling. In particular, we assume that each vertex has multiple latent states and exhibits state-switching behaviors according to a Markov process. The multivariate time series of each vertex depend on a defined latent effect variable influenced by both its own latent state and the latent effects of its neighbors through a heterogeneous network autoregressive model according to the network topology. Furthermore, the influence of some exogenous covariates on the time series can also be incorporated in the model. Some model properties are discussed, and a variational EM algorithm is proposed for model parameter estimation and state inference. Extensive synthetic experiments and a real-world case study demonstrate the effectiveness and applicability of the proposed model.

Corporate income tax constitutes a significant portion of financial costs in business operations. A typical tax system is characterized by the tax structure, accounting method, and loss carryover rule. In this article, we study the optimal inventory policies under taxation and accrual accounting. We consider a lost-sales periodic-review inventory system under a proportional tax with loss carryforward. We formulate the problem as a cyclic stochastic dynamic program with multiple accounting periods, each of which consists of multiple review periods. We show that an income-dependent basestock policy is optimal. We find that tax function convexity and loss carryforward may introduce conflicting incentives into inventory decisions: the former drives risk-averse decisions whereas the latter induces risk-seeking behavior. We identify two intertemporal effects of taxation, the intra-accounting-period effect and the inter-accounting-period effect, both inducing smaller basestock levels. We further extend the analysis to the progressive tax, backordering system, and cash accounting method. Our numerical study examines the effects of various characteristics of taxation on the optimal policies. Our results demonstrate that ignoring taxation in inventory decisions may lead to significant losses especially when the tax rate and demand uncertainty are sufficiently high, which reveals the value of tax considerations in inventory management.

Despite more than 40 years of counterdrug interdiction efforts in the Western Hemisphere, cocaine trafficking, or ‘‘narco-trafficking’’, networks continue to evolve and increase their global reach. Counterdrug interdiction continues to fall short of performance targets, due to the adaptability of narco-trafficking networks and spatially complex constraints on interdiction operations (e.g., resources, jurisdictional). Due to these dynamics, current modeling approaches offer limited strategic insights into time-varying, spatially optimal allocation of counterdrug interdiction assets. This study presents coupled agent-based and spatial optimization models to investigate the co-evolution of counterdrug interdiction deployment and narco-trafficking networks’ adaptive responses. Increased spatially optimized interdiction assets were found to increase seizure volumes. However, the value per seized shipment concurrently decreased and the number of active nodes increased or was unchanged. Narco-trafficking networks adaptively responded to increased interdiction pressure by spatially diversifying routes and dispersing shipment volumes. Thus, increased interdiction pressure had the unintended effect of expanding the spatial footprint of narco-trafficking networks. This coupled modeling approach enabled the study of narco-trafficking network evolution while being subjected to varying interdiction pressure as a spatially complex adaptive system. Capturing such co-evolution dynamics is essential for simulating traffickers’ realistic adaptive responses to a wide range of interdiction scenarios.

Three-dimensional (3-D) point cloud data are increasingly being used to describe a wide range of physical objects in detail, corresponding to customized and flexible shape designs. The advent of a new generation of optical sensors has simplified and reduced the costs of acquiring 3-D data in near-real-time. However, the variation of the acquired point clouds, and methods to describe them, create bottlenecks in manufacturing practices such as Reverse Engineering (RE) and metrology in additive manufacturing. We address this issue by developing an automated variance modeling algorithm that utilizes a physical object’s local geometric descriptors and Bayesian Extreme Learning Machines (BELMs). Our proposed ensemble and residual BELM-variants are trained by a scanning history that is composed of multiple scans of other, distinct objects. The specific scanning history is selected by a new empirical Kullback–Leibler divergence we developed to identify objects that are geometrically similar to an object of interest. A case study of our algorithm on additively manufactured products demonstrates its capability to model the variance of point cloud data for arbitrary freeform shapes based on a scanning history involving simpler, and distinct, shapes. Our algorithm has utility for measuring the process capability of 3-D scanning for RE processes.

This article proposes a general fixture layout design framework that directly integrates the system equation with the convex relaxation method. Note that the optimal fixture design problem is a large-scale combinatorial optimization problem; we relax it to a convex Semi-Definite Programming (SDP) problem by adopting sparse learning and SDP relaxation techniques. It can be solved efficiently by existing convex optimization algorithms and thus generates a near-optimal fixture layout. A real case study in the half-to-half fuselage assembly process indicates the superiority of our proposed algorithm compared to the current industry practice and state-of-art methods.

When an ultrasound wave propagates through a volume of biomass medium, the majority of the energy in the acoustic field is absorbed locally by the biomass, resulting in the generation of heat. This torrefaction effect results in a temperature increase of the biomass, converting biomass into a coal-like intermediate with upgraded fuel properties over the original biomass. However, few analyses can be found in the literature explaining the mechanism of ultrasound-assisted biomass torrefaction. This research aims to model an ultrasound-assisted biomass torrefaction system. The developed multiphysics model depicts the piezoelectric effect of a transducer, the vibration amplitude at the output end of the ultrasonic horn, and the acoustic intensity and temperature distributions in the biomass medium. The vibration amplitude and frequency of the ultrasonic horn were measured by a non-contact capacitive sensor, and it is verified the model can accurately simulate the ultrasonic vibration of the experimental system. The temperature at the center of the biomass was measured to validate the model’s temperature prediction. Both simulation and experiment showed that ultrasound-assisted biomass torrefaction can create torrefied fuel pellet within 60 seconds.

In this article we provide a novel perspective on risk pooling approaches by characterizing and comparing their asymptotic performance, highlighting the conditions under which one approach dominates the other. More specifically, we determine the inventory policy and the expected total costs of systems under physical and information pooling as the number of locations grows. We show that physical pooling dominates information pooling in settings with no additional per-location costs for operating the centralized system. In the presence of such costs, however, information pooling becomes a viable alternative to physical pooling. Through asymptotic analysis, we also address the grouping problem, the division of a given set of non-identical locations into an ordered collection of mutually exclusive and collectively exhaustive subsets of predetermined sizes and demonstrate that homogeneous groups, comprising locations with similar demand volatility, achieve a lower expected total cost. Finally, the convergence of the expected total costs and the base stock levels under the two pooling approaches is demonstrated through a simple numerical illustration. Our analysis supports the assertion that it is important to consider not only the individual characteristics of each location in isolation, but also the interactions among them, when designing pooling systems.

We consider the interdependent decisions on assortment, inventory and pricing of substitutable products that are differentiated by some primary and secondary attributes captured by a nested logit consumer choice. We examine a newsvendor-type setting with several products competing for demand over a single selling season. We assume that all products have equal profit margins. The demand has a multiplicative-additive structure where both variance and coefficient of variation depend on the common profit margin, which adds to the model applicability at the expense of tractability. Under a Taylor series-type approximation, we show that the expected profit is unimodal in the common margin of products in a given assortment. Then, we compare the optimal profit margin to the case under ample inventory, which allows understanding the effect of inventory on pricing. We also study the optimal assortment problem under exogenous pricing. We show that the classic result on the optimality of popular sets holds under tight approximations of the profit function. We finally propose a heuristic for jointly deciding on assortment, pricing, and inventory decisions, which assumes equal profit margins of products, and exploit popular sets only. Our detailed numerical study shows that this equal-margin heuristic produces high quality solutions.