Traditional system optimization models for traffic network focus on the treatment of congestion, which usually have an objective of minimizing the total travel time. However, the negative externality of congestion, such as environment pollution, is neglected in most cases. Such models fall short in taking Greenhouse Gas (GHG) emissions and its impact on climate change into consideration. In this paper, a social-cost based system optimization (SO) model is proposed for the multimodal traffic network considering both traffic congestion and corresponding vehicle emission. Firstly, a variation inequality model is developed to formulate the equilibrium problem for such network based on the analysis of travelers’ combined choices. Secondly, the computational models of traffic congestion and vehicle emission of whole multimodal network are proposed based on the equilibrium link-flows and the corresponding travel times. A bi-level programming model, in which the social-cost based SO model is treated as the upper-level problem and the combined equilibrium model is processed as the lower-level problem, is then presented with its solution algorithm. Finally, the proposed models are illustrated through a simple numerical example. The study results confirm and support the idea of giving the priority to the development of urban public transport, which is an effective way to achieve a sustainable urban transportation.

This research introduces a holistic framework called Design for Availability that uses the principles of Lean Sigma and Design for X to cost-effectively optimize the availability of capital goods (i.e., technical systems used in the production of end-products or -services such as medical systems, airplanes, and manufacturing equipment) throughout their entire lifetime. Manufacturers require such a framework because users of capital goods increasingly insist on high system availability levels against reduced lifetime costs. The Design for Availability framework allows manufacturers to determine the current status of system availability and associated lifetime costs, and to identify opportunities to create additional value for themselves and their customers. A case study at a global manufacturer of capital goods in the food processing industry illustrates how the framework can be used in practice and to what extent the manufacturer and customers may profit from applying Design for Availability.

Recent years have witnessed an increasing interest in interval-valued data analysis. As one of the core topics, linear regression attracts particular attention. It attempts to model the relationship between one or more explanatory variables and a response variable by fitting a linear equation to the interval-valued observations. Despite of the well-known methods such as CM, CRM and CCRM proposed in the literature, further study is still needed to build a regression model that can capture the complete information in interval-valued observations. To this end, in this paper, we propose the novel Complete Information Method (CIM) for linear regression modeling. By dividing hypercubes into informative grid data, CIM defines the inner product of interval-valued variables, and transforms the regression modeling into the computation of some inner products. Experiments on both the synthetic and real-world data sets demonstrate the merits of CIM in modeling interval-valued data, and avoiding the mathematical incoherence introduced by CM and CRM.

Capacity acquisition and pricing decisions are a company’s long-term strategic decisions. However, demand uncertainty and substitutability of multiple products cause the difficulty to solve capacity and pricing decision problems. In this paper, we address a multiple product pricing and multiple resource capacity acquisition problem with demand uncertainties and competition. The company needs to determine capacity commitment for each resource and product prices before demands are realized so that the total profit is maximized. If the demand exceeds the committed capacity, extra amounts can be purchased from the spot market. Variable unit production costs, capacity acquisition and maintenance costs are considered. We first analyze a single company basic problem and find the optimal solutions on prices and capacity. Based on the single company model, we address the two-product, two-firm capacity commitment and pricing problem considering across product and across company price competition factors. The existence and uniqueness of equilibrium on price and capacity commitment are proved, and then we extended the results to the multiple product, multiple company case.

Owing to the changing fashion trends and a volatile market situation, demand in fashion and textile (FT) industry is unpredictable and could vary and change completely in a short time, which makes it more difficult to coordinate a FT supply chain. A change in product preference due to fashion trends is the main reason why the demand of FT industry shows more variations than other industries. In this paper, we use a well known contract, the all-unit quantity discount policy (AQDP), to coordinate a FT supply chain with certain demand, and we further consider it under the demand variations scenario to investigate whether it can still coordinate the supply chain. In detail, before the selling season, an AQDP is provided by the manufacturer to the retailer, and under which the FT supply chain coordination achieved with a certain demand. During the selling season, demand variation is realized after an abrupt changing of fashion trends, therefore, the manufacturer may need to revise the original AQDP to insure the supply chain is still coordinated. Utilizing the mechanism design theory, we prove that: (i) while the traditional AQDP can coordinate the supply chain when no demand variations, it cannot always coordinate the supply chain after the demand variations; (ii) when the AQDP fails, we can use the proposed capacitated linear pricing policy (CLPP) to achieve a new coordination; (iii) a more dominant decision maker, who can set a higher profit goal, is favorable to stabilization of the supply chain system under demand variations. Numerical examples are proposed also to show our results.

In this paper, we develop models to determine operational and financial decisions of a supply chain under the condition that the retailer faces a financial constraint and the manufacturer can offer trade credit to assist the retailer. We first study the case where the retailer is risk-neutral, and derive the optimal ordering and financial decisions. Then, the case where the retailer is risk-averse (downside risk) is studied and the effects of the risk on the retailer and manufacturer’s operational and financial decisions are discussed. Finally, numerical examples are provided to conduct managerial analysis.