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Du N.,Shanghai University of Finance and Economics | Du N.,Key Laboratory of Mathematical Economics SUFE | Huang H.,Shanghai Key Laboratory of Financial Information Technology | Li L.,Shanghai University of Finance and Economics | Li L.,Key Laboratory of Mathematical Economics SUFE
Decision Support Systems | Year: 2013

Consumer ratings are crucial in creating and sustaining trust and trustworthiness in e-commerce markets. Thus, it is important to know whether online trading can survive bad mouthing among participants. We use controlled lab experiments to test whether market efficiency (measured by the percentage of successful trades) is affected by unfair negative ratings, and whether announcing the percentage of unfair ratings in the market makes any difference. We find that market efficiency is higher when rating information is provided than when no rating information is provided, even when unfair and ambiguous ratings are present. We also find that buyers behave differently when unfair rating information exists; however, no matter whether the percentage of unfair ratings is known, market efficiency is not significantly different from that in the market without unfair ratings. © 2012 Elsevier B.V.


Zhang T.,Shanghai University of Finance and Economics | Zhang T.,Shanghai Key Laboratory of Financial Information Technology | Zheng Q.P.,University of Central Florida | Fang Y.,West Virginia University | Zhang Y.,Fudan University
Computers and Industrial Engineering | Year: 2015

This paper proposes a nonlinear integer programming model which co-optimizes the multi-level inventory matching and order planning for steel plants while combining Make-To-Order and Make-To-Stock policies. The model considers order planning and inventory matching of both finished and unfinished products. It combines multiple objectives, i.e., cost of earliness/tardiness penalty, tardiness penalty within delivery time window, production cost, inventory matching cost, and order cancelation penalty. This paper also proposes an improved Particle Swarm Optimization (PSO) method, where strategies to repair infeasible solutions and inventory-rematching scheme are introduced. Parameters of PSO and the rematching scheme are also analyzed. Three sets of real data from a steel manufacturing company are used to perform computational experiments for PSO, local search, and improved PSO. Numerical results show the validity of the model and efficacy of the improved PSO method. © 2015 Elsevier Ltd. All rights reserved.


Huang Y.,Tongji University | Wang L.,Tongji University | Wang L.,Shanghai Key Laboratory of Financial Information Technology | Wu Q.,Tongji University
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

Home energy management system is an important part in smart home, smart home is assumed to be equipped with smart meter which smart control of generators, storages, and demand response programs. In this paper, we study a versatile convex optimization framework for the automatic energy management of various household loads in a smart home. The scheduling algorithm determines how energy resources available to the end-users considering a number of constraints. Hence, a hybrid PSO-DE algorithm approach is proposed. We devise a model accounting for a typical household user, and present computational results showing that it can be efficiently solved in real-life instances. © Springer International Publishing Switzerland 2014.


Guo Y.,Tongji University | Kang Q.,Tongji University | Wang L.,Tongji University | Wang L.,Shanghai Key Laboratory of Financial Information Technology | Wu Q.,Tongji University
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

This paper presents a multivariable grey neural network (MGM-NN) model for predicting the state of industrial equipments. It combines the merit of MGM model and RBF-NN model on time series forecast. This mode takes the dynamic correlations among multi variables and environment’s impact on state of equipment into consideration. The proposed approach is applied to the melt channel state forecast. The results are contrasted to MGM model executed on the same test set. The results show the accuracy and promising application of the proposed model. © Springer International Publishing Switzerland 2014.


Cai X.,Tongji University | Cai X.,Taiyuan University of Science and Technology | Li W.,Tongji University | Li W.,Jiaxing Vocational Technical College | And 4 more authors.
Journal of Bionanoscience | Year: 2014

Bat algorithm is a novel swarm intelligent algorithm inspired by the echolocation behavior of bats with varying pulse rates of emission and loudness. In this paper, a new variant which is called adaptive bat algorithm is designed and applied to solve the toy model prediction problem. In this algorithm, a linearly dynamic pulse rate selection strategy is designed. To test the performance, short sequences, Fibonacci sequence and real protein sequences are selected to compare, simulation results show adaptive bat algorithm is validity. Copyright © 2014 American Scientific Publishers.


Cai X.,Tongji University | Cai X.,Taiyuan University of Science and Technology | Wang L.,Tongji University | Wang L.,Shanghai Key Laboratory of Financial Information Technology | And 2 more authors.
International Journal of Wireless and Mobile Computing | Year: 2015

Bat algorithm is a novel swarm intelligent algorithm inspired by the echolocation behaviour of bats. Pulse rate of emission is one important parameter, which is adjusted in an exponential manner in the standard version, however, this manner provides a fast convergent behaviour in the first several generations. With this manner, the exploitation capability is limited. In this paper, a new linear adjusted manner is employed to enhance the local search capability. To test the performance, it is applied to solve the optimal coverage problem of Wireless Sensor Network (WSN). Simulation results show it is effective when compared with the standard version of bat algorithm. Copyright © 2015 Inderscience Enterprises Ltd.


Wang J.,Tongji University | Kang Q.,Tongji University | Tian H.,Tongji University | Wang L.,Tongji University | And 2 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

This work proposes an efficient charging regulation strategy based on optimal charging priority and location of plug-in electric vehicles (PEVs). It employs a hybrid particle swarm optimization for optimal charging priority and location of PEVs in distribution networks, with the objectives of minimization of charging cost, power loss reduction and voltage profile improvement. The algorithm is executed on IEEE 30-bus test system. The results are compared with those that are gained by executing sample genetic algorithm (SGA) with diverse parameters on the same system. The results indicate the effectiveness and promising application of the proposed methodology. © Springer International Publishing Switzerland 2014.


Huang Y.,Tongji University | Huang Y.,De Zhou Vocational and Technical College | Wang L.,Tongji University | Wang L.,Shanghai Key Laboratory of Financial Information Technology | And 2 more authors.
Electric Power Components and Systems | Year: 2016

This article describes a method for establishing an appliance scheduling scheme that can optimally coordinate a group of appliances in a consumer's premises. Finite-horizon scheduling optimizations are formulated to schedule the operation of appliances using a modeled predictive control method that incorporates both forecasts and newly updated information. A complex mixed discrete-continuous non-linear model is here in established, and a novel algorithm that hybridizes particle swarm optimization and constraint handing methods is proposed to derive optimum solutions within a limited computational time. Simulation results showed that the proposed algorithm can efficiently resolve real-life instances and can be embedded in resource limited devices. © Taylor & Francis Group, LLC.


Gong J.,Shanghai Key Laboratory of Financial Information Technology | Lu C.,Shanghai Key Laboratory of Financial Information Technology | Liu X.,Shanghai Key Laboratory of Financial Information Technology
Lecture Notes in Electrical Engineering | Year: 2014

Demand forecasting of auto parts is important for the management of the auto supply chain. After comprehensively exploring the characteristic of the auto parts and considering the strengths of some individual forecasting methods, this article chose ARIMA, SVR and RBF neutral network based regression to develop a nonnegative variable weight combination model to forecast demand of auto parts for the auto aftermarket. This model improved accuracy and stability and had wider applicability. © 2014 Springer-Verlag Berlin Heidelberg.


Shi L.,China University of Petroleum - East China | Shi L.,Shanghai Key Laboratory of Financial Information Technology | Jia C.,China University of Petroleum - East China | Gong J.,Shanghai Key Laboratory of Financial Information Technology | And 2 more authors.
Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | Year: 2016

Concerning the resource-limited RFID tags, this paper presents a lightweight mutual authentication scheme based on Hash function, combining with the pseudo-random number and shared secret mechanisms, and implements the mutual authentication among the end database, reader and the tags. The anti-attack performance and the overhead of the scheme are analyzed in detail. Afterwards, the protocol security model is formalized using BAN logical analysis method. Theoretical analysis shows that the proposed authentication scheme could achieve the desired security goals, has good anti-attack performance and high efficiency. It can be applied to big population RFID since its low overhead for RFID tags. © 2016, Science Press. All right reserved.

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