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To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2006.07.010 Byline: Joseph Geunes (a), Zuo-Jun Max Shen (b), Akin Emir (c) Keywords: Revenue management; Pricing; Vehicle routing problem Abstract: Classical vehicle routing problems typically do not consider the impact of delivery price on the demand for delivery services. Existing models seek the minimum sum of tour lengths in order to serve the demands of a given set of customers. This paper proposes approximation models to estimate the impacts of price on delivery services when demand for delivery service is price dependent. Such models can serve as useful tools in the planning phase for delivery service providers and can assist in understanding the economics of delivery services. These models seek to maximize profit from delivery service, where price determines demand for deliveries as well as the total revenue generated by satisfying demand. We consider a variant of the model in which each customer's delivery volume is price sensitive, as well as the case in which customer delivery volumes are fixed, but the total number of customers who select the delivery service provider is price sensitive. A third model variant allows the delivery service provider to select a subset of delivery requests at the offered price in order to maximize profit. Author Affiliation: (a) Department of Industrial and Systems Engineering, University of Florida, United States (b) Department of Industrial Engineering and Operations Research, University of California, 4141 Etcheverry Hall, Berkeley, CA 94720-1777, United States (c) Merck and Co., Inc., West Point, PA, United States Article History: Received 5 January 2006; Accepted 17 July 2006

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2006.04.015 Byline: Leon Yang Chu (a)(b), Zuo-Jun Max Shen (b) Keywords: Mechanism design; Double auction; Strategy-proof mechanism Abstract: With the growth of electronic markets, designing double auction mechanisms that are applicable to emerging market structures has become an important research topic. In this paper, we investigate two truthful double auction design approaches, the Trade Reduction Approach and the Multi-Stage Design Approach, and compare their resulting mechanisms in various exchange environments. We find that comparing with the Trade Reduction Approach, the Multi-Stage Design Approach offers mechanisms applicable to more complicated exchange environments. Furthermore, for the known trade reduction mechanisms, we prove that the corresponding mechanisms under the multi-stage design approach are superior in terms of both social efficiency and individual payoffs, in each exchange environment of interest. Our computational tests show that the mechanisms under the multi-stage design approach achieve very high efficiency in various scenarios. Author Affiliation: (a) Marshall School of Business, University of South California, Los Angeles, CA 90089, United States (b) Department of Industrial Engineering and Operations Research, University of California, Berkeley, CA 94720, United States Article History: Received 23 May 2005; Accepted 3 April 2006

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.ejor.2006.03.032 Byline: Zuo-Jun Max Shen (a), Lian Qi (b) Keywords: Location models; Vehicle routing; Inventory; Integrated supply chain design models Abstract: We consider a supply chain design problem where the decision maker needs to decide the number and locations of the distribution centers (DCs). Customers face random demand, and each DC maintains a certain amount of safety stock in order to achieve a certain service level for the customers it serves. The objective is to minimize the total cost that includes location costs and inventory costs at the DCs, and distribution costs in the supply chain. We show that this problem can be formulated as a nonlinear integer programming model, for which we propose a Lagrangian relaxation based solution algorithm. By exploring the structure of the problem, we find a low-order polynomial algorithm for the nonlinear integer programming problem that must be solved in solving the Lagrangian relaxation sub-problems. We present computational results for several instances of the problem with sizes ranging from 40 to 320 customers. Our results show the benefits of having an integrated supply chain design framework that includes location, inventory, and routing decisions in the same optimization model. Author Affiliation: (a) Department of Industrial Engineering and Operations Research, University of California, 4141 Etcheverry Hall, Berkeley, CA 94720-1777, USA (b) Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA Article History: Received 9 March 2005; Accepted 25 March 2006

• A data-driven framework is developed to quantify EV performance degradation. • An iterative learning process is proposed for impact decoupling. • EV big data are used for range and energy consumption assessments. • Multiple data sources are coupled by using spatiotemporal data. Electric vehicle (EV) performance in terms of the available driving range per charge and the energy consumption rate continuously degrades during its service life. Quantitative assessments of EV performance degradation play an important role in EV residual value analysis, battery management, and battery recycling. However, EV performance degradation is highly sensitive to both ambient temperature and battery aging states; coupled factors make its quantification challenging. Here, a novel big data-driven decoupling framework is proposed to investigate the partial relationships between EV performance degradation and each individual variable (e.g., temperature and total driving distances). The core innovation involves the decoupling process that can enable real-world and large-scale degradation assessments. The basic functionality of the decoupling is achieved by an iterative learning framework where different machine learning-based models can communicate with each other. It achieves the advantages of unsupervised training and high performance; the mean absolute error can be controlled less than 0.1 in the model validation of EV ranges. Its effectiveness is verified using different real-world EV datasets. By utilizing the framework, the changes in the range and energy consumption of EVs across 10 urban areas in China are assessed. The results show that the range and energy consumption rate of EVs are more greatly influenced by ambient temperature than by battery aging. Less consideration of variable decoupling may yield misleading results in EV performance analysis. Our proposed framework opens avenues for quantifying EV performance degradation via real-world EV data, which is critical to onboard and cloud-based EV research. [ABSTRACT FROM AUTHOR]

• A novel framework for large-scale EV charging energy predictions is introduced. • The MAPE retains at 2.5–3.8% with a testing/training ratio varying from 0.1 to 1000. • MICs and PCCs are combined for feature analyses of charging energy predictions. • Multiple data sources are coupled by linking the timestamps and location data. Large-scale and high-precision predictions of the charging energy required for electric vehicles (EVs) are essential to ensure the safety of EVs and provide reliable inputs for grid-load calculations. However, the complex and dynamic operating conditions of EVs make it challenging to accurately predict the charging energy under real-world conditions, especially for large-scale EV utilization. In this study, a novel data-driven framework for large-scale charging energy predictions is developed by individually controlling the strongly linear and weakly nonlinear contributions. The proposed framework concurrently addresses the overfitting of nonlinear networks using a low proportion of training data as well as the poorly descriptive ability of linear networks under complex environments. For each charging session, the charging energy predictions appropriately account for important factors such as the variations in the state of charge (SOC) of the battery, ambient temperatures, charging rates, and total driving distances. The results suggest that, compared with existing prediction models (such as the random forest, xgboost, and neural network), the proposed framework persists with evidently higher accuracy and stability over a wide range of the ratio between the number of EVs used for testing and training; its mean absolute percentage error (MAPE) is maintained at 2.5–3.8% when the ratio ranges from 0.1 to 1000. The proposed models can be further utilized for cloud-based battery diagnoses and large-scale forecasting of the energy demands of EVs. [ABSTRACT FROM AUTHOR]

Increasing wildfire activity globally has become an urgent issue with enormous ecological and social impacts. In this work, we focus on analyzing and quantifying the influence of landscape topology, understood as the spatial structure and interaction of multiple land-covers in an area, on fire ignition. We propose a deep learning framework, Deep Fire Topology, to estimate and predict wildfire ignition risk. We focus on understanding the impact of these topological attributes and the rationale behind the results to provide interpretable knowledge for territorial planning considering wildfire ignition uncertainty. We demonstrate the high performance and interpretability of the framework in a case study, accurately detecting risky areas by exploiting spatial patterns. This work reveals the strong potential of landscape topology in wildfire occurrence prediction and its implications to develop robust landscape management plans. We discuss potential extensions and applications of the proposed method, available as an open-source software. • We show the impact of different landscape topologies on wildfire ignitions. • We develop a novel and interpretable deep learning framework. • The model detects and highlights low/high-risk land-cover topological patterns. • We obtain accurate fire occurrence predictions only using land-cover data. • Our framework can be applied in any field exploiting computer vision. [ABSTRACT FROM AUTHOR]