Determining optimal resource recycling boundary at regional level: A case study on Tokyo Metropolitan Area in Japan

Conventionally, material inputs to industries come from either natural resources or intermediate products. With the increasing attention on greening the industrial process, municipal solid waste (MSW) can be separated and utilized as inputs to large-scale industries. As such, waste flows need to be managed to meet the requirements of industries by considering both quality and scale. Small scales of municipal recycling centers (MRCs) for pre-treating recyclable wastes often lead to higher pre-treatment cost and lower recycling efficiencies. Theoretically, appropriate facility scale and recycling boundary should be based upon a balanced consideration between economies of scale and transportation costs. However, only a few empirical studies on recycling boundaries have been conducted. Existing modeling studies are mostly case studies for planning purposes and do not theoretically examine the mechanisms and factors that determine recycling boundary. Under such a circumstance, this paper fills such a gap by quantitatively seeking the determinants for recycling boundaries and the related mechanism. An optimization model applying on the recycling of waste plastics is developed and tested in the Tokyo Metropolitan Region (TMR) in Japan. The results indicate two determinants for recycling boundary at the regional level: spatial density of separated wastes and the ratio of unit transportation cost to unit treatment cost. Separated wastes that are collected in low spatial density and have a low ratio of unit transportation cost to unit treatment cost should be better recycled in a large region. The finding implies that regional recycling networks should have multiple layers designed for different types of wastes due to the differences in their spatial density, unit transportation cost and unit treatment cost.     

Production planning and pricing policy in a make-to-stock system with uncertain demand subject to machine breakdowns

We consider a make-to-stock system served by an unreliable machine that produces one type of product, which is sold to customers at one of two possible prices depending on the inventory level at the time when a customer arrives (i.e., the decision point). The system manager must determine the production level and selling price at each decision point. We first show that the optimal production and pricing policy is a threshold control, which is characterized by three threshold parameters under both the long-run discounted profit and long-run average profit criteria. We then establish the structural relationships among the three threshold parameters that production is off when inventory is above the threshold, and that the optimal selling price should be low when inventory is above the threshold under the scenario where the machine is down or up. Finally we provide some numerical examples to illustrate the analytical results and gain additional insights.     

Pricing to accelerate demand learning in dynamic assortment planning for perishable products

Retailers, from fashion stores to grocery stores, have to decide what range of products to offer, i.e., their product assortment. Frequent introduction of new products, a recent business trend, makes predicting demand more difficult, which in turn complicates assortment planning. We propose and study a stochastic dynamic programming model for simultaneously making assortment and pricing decisions which incorporates demand learning using Bayesian updates. We show analytically that it is profitable for the retailer to use price reductions early in the sales season to accelerate demand learning. A computational study demonstrates the benefits of such a policy and provides managerial insights that may help improve a retailer’s profitability.     

Action semantics: A unifying conceptual framework for the selective use of multimodal and modality-specific object knowledge

Our capacity to use tools and objects is often considered one of the hallmarks of the human species. Many objects greatly extend our bodily capabilities to act in the physical world, such as when using a hammer or a saw. In addition, humans have the remarkable capability to use objects in a flexible fashion and to combine multiple objects in complex actions. We prepare coffee, cook dinner and drive our car. In this review we propose that humans have developed declarative and procedural knowledge, i.e. action semantics that enables us to use objects in a meaningful way. A state-of-the-art review of research on object use is provided, involving behavioral, developmental, neuropsychological and neuroimaging studies. We show that research in each of these domains is characterized by similar discussions regarding (1) the role of object affordances, (2) the relation between goals and means in object use and (3) the functional and neural organization of action semantics. We propose a novel conceptual framework of action semantics to address these issues and to integrate the previous findings. We argue that action semantics entails both multimodal object representations and modality-specific sub-systems, involving manipulation knowledge, functional knowledge and representations of the sensory and proprioceptive consequences of object use. Furthermore, we argue that action semantics are hierarchically organized and selectively activated and used depending on the action intention of the actor and the current task context. Our framework presents an integrative account of multiple findings and perspectives on object use that may guide future studies in this interdisciplinary domain.     

Inner and outer loop optimization in semiconductor manufacturing supply chain management

Supply chain management (SCM) in semiconductor manufacturing differs from many other SCM applications in that it has to simultaneously consider both long and short time scale stochasticity and nonlinearity. We present a two-level hierarchical structure for SCM motivated by these considerations. A linear programming (LP)-based strategic planning module forms the outer loop which makes long timescale decisions on the starts of factories. A model predictive control (MPC) based tactical execution module forms the inner loop which generates short timescale decisions on the starts of factories by considering the stochasticity and nonlinearity on both supply and demand sides. Two representative case studies are examined under diverse realistic conditions with this integrated framework. It is demonstrated that given conditions of stochasticity, nonlinearity, and forecast error this hierarchical decision structure can be tuned to manage representative semiconductor manufacturing supply chains in a manner appealing to operations. This work was supported by grants from the Intel Research Council and the National Science Foundation (CMMI-0432429).     

Existence of equilibria in a decentralized two-level supply chain

In this paper, we analyze equilibria in competitive environments under constraints across players’ strategies. This means that the action taken by one player limits the possible choices of the other players. In this context, the usual approach to show existence of equilibrium, Kakutani’s fixed point theorem, cannot be applied directly. In particular, best replies against a given strategy profile may not be feasible. We devise a new fixed point correspondence to deal with the feasibility issue.