ON IMPROVING AN INTEGRATED INVENTORY MODEL FOR A SINGLE VENDOR AND MULTIPLE BUYERS WITH A RELAXED MATERIAL ORDERING CYCLE POLICY

1. Introduction With the development of information technology and the increasing market competition, simultaneously reducing inventory costs of raw materials, work-in-process, and finished items in different stages has become a major focus for supply chain management. Industrial practice and academic research have shown that VMI (Vendor Managed Inventory) can improve supply chain performance by decreasing inventory levels and costs. VMI is a collaborative business initiative where the vend…     

Technical Note ：SINGLE-PERIOD TWO-PRODUCT INVENTORY MODEL WITH SUBSTITUTION： SOLUTION AND PROPERTIES

In this paper, we study a single-period two-product inventory model with stochastic demands and downward substitution. The optimal order quantities are presented and some properties are provided. Comparing with newsboy model, we prove that both the profit and the fill rate can be improved by using the substitution policy. This work was supported partly by NSFC/RGC Joint Research Program under grant 79910161987 and the National Science Foundation of China (79825102, 70231010, 70321001). Lianqiao CAI is lecturer of School of International Business, Beijing Foreign Studies University. He received his B.S. and M.S. from School of Economics and Management, Tsinghua University, in 1995 and 1998, and Ph.D of Management Science from Tsinghua University in 2002. His research areas include supply chain management, optimization techniques. Jian Chen is Professor and Chairman of the Management Science Department and co-director of the Research Center for Contemporary Management, Tsinghua University. He has over 80 papers published in some leading international journals and first class Chinese journals, and has been a principal investigator for over 20 grants or research contracts with National Science Foundation of China, governmental organizations, and companies. His main research interests include supply chain management, E-commerce, modeling and control for complex systems, decision support systems and information systems, and forecast and optimization techniques. He serves as associate editor of the “IEEE Transactions on Systems, Man and Cybernetics: Part A” and “IEEE Transactions on Systems, Man and Cybernetics: Part C”, and on the Editorial Board of “The International Journal of Electronic Business” and “System Research and Behavioral Science”. He is the recipient of the Outstanding Contribution Award of IEEE Systems, Man, and Cybernetics Society in 1996, and the Young Scientist Award of China in 1992. He was Secretary General of the 1996 IEEE International Conference on Systems, Man, and Cybernetics in Beijing, Co-chair of the IPC of 1998 International Conference on Systems Science and Systems Engineering, Chair of the First Asian eBiz Workshop in 2001, Co-chair of the Asian eBiz Workshop in 2002 and 2003, and Co-chair of the international conference on Global Supply Chain Management in 2002. Houmin Yan received his B.S. and M.S. from the Department of Automation, Tsinghua University, in 1982 and 1985, respectively, and his Ph.D. from the Faculty of Management, University of Toronto in 1993. From Jan. 1994, he is with the Department of Systems Engineering and Engineering Management, the Chinese University of Hong Kong as an Assistance, Associate and Full Professor. He was a tenured Associate Professor at School of Management, the Univ. of Texas at Dallas. His main research areas are operations management, stochastic models, simulations, and supply chain management. He has published in journals such as Operations Research, Manufacturing and Service Operations Management, Production and Operations Management, Journal of Optimization: Theory and Applications, IIE Transactions and IEEE Transactions. He consults several high-tech companies, such as Motorola, C&K Systems, and Oriental Power, on issues spanning from production planning and scheduling, supply chain management to business process re-engineering. He is a member of INFORMS.     

A NESTED PARTITIONS FRAMEWORK FOR SOLVING LARGE-SCALE MULTICOMMODITY FACILITY LOCATION PROBLEMS

Large-scale multicommodity facility location problems are generally intractable with respect to standard mixed-integer programming (MIP) tools such as the direct application of general-purpose Branch & Cut (BC) commercial solvers i.e. CPLEX. In this paper, the authors investigate a nested partitions (NP) framework that combines meta-heuristics with MIP tools (including branch-and-cut). We also consider a variety of alternative formulations and decomposition methods for this problem class. Our results show that our NP framework is capable of efficiently producing very high quality solutions to multicommodity facility location problems. For large-scale problems in this class, this approach is significantly faster and generates better feasible solutions than either CPLEX (applied directly to the given MIP) or the iterative Lagrangian-based methods that have generally been regarded as the most effective structure-based techniques for optimization of these problems. We also briefly discuss some other large-scale MIP problem classes for which this approach is expected to be very effective. This work is supported partly by the National Science Foundation under grant DMI-0100220, DMI-0217924, by the Air Force of Scientific Research under grant F49620-01-1-0222, by Rockwell Automation, and by John Deere Horicon Works. Leyuan Shi is a Professor of the Department of Industrial Engineering at University of Wisconsin-Madison. She received her Ph.D. in Applied Mathematics from Harvard University in 1992, her M.S. in Engineering from Harvard University in 1990, her M.S. in Applied Mathematics from Tsinghua University in 1985, and her B.S. in Mathematics from Nanjing Normal University in 1982. Dr. Shi has been involved in undergraduate and graduate teaching, as well as research and professional service. Dr. Shi’s research is devoted to the theory and applications of large-scale optimization algorithms, discrete event simulation and modeling and analysis of discrete dynamic systems. She has published many papers in these areas. Her work has appeared in Discrete Event Dynamic Systems, Operations Research, Management Science, IEEE Trans., and, IIE Trans. She is currently a member of the editorial board for Journal of Manufacturing & Service Operations Management, is an Associate Editor of Journal of Discrete Event Dynamic Systems, and an Associate Editor of INFORMS Journal on Computing. Dr. Shi is a member of IEEE and INFORMS. Robert R. Meyer received a B.S. in Mathematics from Caltech and an M.S. and a Ph.D. in Computer Sciences from the University of Wisconsin-Madison. He is currently Professor of Computer Sciences at the University of Wisconsin-Madison, where he has been on the faculty since 1973. He has co-edited six volumes of optimization conference proceedings and written more than 80 articles focusing on areas such as nonlinear network optimization, parallel decomposition algorithms for large-scale optimization, genetic algorithms, and theory and applications of discrete optimization. Mehmet Bozbay is a research assistant in the Department of Industrial Engineering at University of Wisconsin-Madison. He is currently working towards his Ph.D. degree in Industrial Engineering. He received M.S. degrees in Industrial Engineering and Computer Sciences from University of Wisconsin-Madison, and a B.S. degree in Mathematics from Beloit College. He has been dealing with real-life supply chain optimization problems for the last 4 years. He is a member of INFORMS. Andrew J. Miller is an Assistant Professor of the Department of Industrial Engineering at the University of Wisconsin—Madison. He received his Ph.D. in Industrial Engineering from the Georgia Institute of Technology in 1999, his M.S. in Operations Research from the Georgia Institute of Technology in 1996, and his B.S. in Mathematics from Furman University in 1994. Dr. Miller has been involved in research, undergraduate and graduate teaching, and professional service, as well as in some software prototyping and development. Dr. Miller’s research focuses on theoretical and computational aspects of mixed integer programming, and on its application to areas in production planning, supply chain design, and other fields. He has published several papers in these areas, including articles in Mathematical Programming, Operations Research, and European Journal of Operations Research. He has refereed numerous articles for the journals mentioned above, as well as for Management Science, Annals of Operations Research, and others. Dr. Miller is a member of INFORMS and of the Mathematical Programming Society.     

Simulating the supply disruption for the coordinated supply chain

There are many disruptive accidents in the supply chain operations system and achieving the coordination of supply chain is main objective for supply chain research. While disruptive accidents have increasingly influenced the coordinated operation of the supply chain, existing research literature on the supply chain coordination is setting in a stationary environment. The answer for how the disruptive accidents affect the coordinated supply chain is given in this paper. Based on the benchmark supply chain which is coordinated by the negative incentive mechanism, we study the impacts of supply disruption on the supply chain system by using simulation approach in which two different distribution function of random variable are used to express the supply disruption. Comparison between these two simulation results and possible coordination mechanism under the supply disruption are proposed. From the perspective of supply chain risk management, we provide the inspiration for the manager.     

AN AHP MODEL FOR BRINGING EXPERTS TO CONSENSUS ON MEDICAL PAYMENT STANDARDS

1. Introduction Many countries with health insurance systems conduct periodic payment standardsadjustments. The main reasons are to address continually rising health care costs and to ensure an equitable allocation of resources. From the248 JOURNAL OF SYSTEMS SCIENCE AND SYSTEMS ENGINEERING data of the Organization for Economic Co-operation and Development (OECD) countries, the proportion of health care costs as a percentage of Gross Domestic Product (GDP) continues to r…     

ADVANCES IN DECISION ANALYSIS AND SYSTEMS ENGINEERING FOR MANAGING LARGE-SCALE ENTERPRISES IN A VOLATILE WORLD： INTEGRATING BENEFITS, OPPORTUNITIES, COSTS AND RISKS （BOCR） WITH THE BUSINESS MOTIVATION MODEL （BMM）

1. Introduction Recent advances in Information and Communication Technologies (ICT) have occurred in a number of areas includinginformation quality (Chutimaskul and Wangpipatwong 2004), strategy (Sha, Hung and Lin 2004) organization (Crowne 2004), technological change (Mitchell 2004), and utility1. Introduction Recent advances in Information and Communication Technologies (ICT) have occurred in a number of areas includinginformation quality (Chutimaskul and Wangpipatwong 2004), strateg…     

A situation calculus based approach to dynamic management of e-business services

In the era of e-businesses, the traditional business services are greatly challenged by the ever-increasing demands from customers with various backgrounds and personalities. Large numbers of new e-businesses are driven by the needs of customers. The existing dynamics inherent in the customer needs require the corresponding dynamic management of services. Attempting to respond to customers in a rapid and intelligent way, this paper proposes a situation calculus based approach for dynamically managing e-Business services in the ubiquitous environment. By employing the formalism of the situation calculus to enable intelligence and automation, the approach can implement. the functions of service automatic composition and model verification. These functions will improve the degree of customer-orientation and enable fast responsiveness in the emerging e-service systems.     

A perishable inventory system with service facilities, <Emphasis Type="Italic">MAP</Emphasis> arrivals and <Emphasis Type="Italic">PH</Emphasis> — Service times

We consider a continuous review perishable （s,S） inventory system with a service facility consisting of a waiting hall of finite capacity and a single server. We assume two types of customers, ordinary and negative, arrive according to a Markovian Arrival Process （MAP）. An ordinary customer joins the queue and a negative customer instead of joining the queue removes one ordinary customer from the queue. The removal rule adopted in this paper is RCE （removal of a customer from the end）. The individual customer＇s unit demand is satisfied after a random time of service which is assumed to have a phase-type distribution. The life time of each item and the lead time of the reorders have been assumed to be independent exponential distributions. The joint probability distribution of the number of customers in the system and the inventory level is obtained for the steady state case. Various stationary system performance measures are computed and the total expected cost rate is calculated. The results are illustrated numerically.     

ELECTRONIC DISCLOSURE AND FINANCIAL KNOWLEDGE MANAGEMENT

1. Introduction Majority of financial information are disseminated through the Internet, every day, huge amount of financial information are generated but the utilization is not good enough: according to the 10th WWW User Survey by the Graphics, Visualization, and Usability Centre (GVU 1998), more than 45 percent of the respondents felt that they were not able to find the information they needed. A study of the Internet demography showed that searching, retrieving, and utilizing the avail…     

Expert discovery and knowledge mining in complex multi-agent systems

Complex problem solving requires diverse expertise and multiple techniques. In order to solve such problems, complex multi-agent systems that include both of human experts and autonomous agents are required in many application domains. Most complex multi-agent systems work in open domains and include various heterogeneous agents. Due to the heterogeneity of agents and dynamic features of working environments, expertise and capabilities of agents might not be well estimated and presented in these systems. Therefore, how to discover useful knowledge from human and autonomous experts, make more accurate estimation for experts＇ capabilities and find out suitable expert（s） to solve incoming problems （＂Expert Mining＂） are important research issues in the area of multi-agent system. In this paper, we introduce an ontology-based approach for knowledge and expert mining in hybrid multi-agent systems. In this research, ontologies are hired to describe knowledge of the system. Knowledge and expert mining processes are executed as the system handles incoming problems. In this approach, we embed more self-learning and self-adjusting abilities in multi-agent systems, so as to help in discovering knowledge of heterogeneous experts of multi-agent systems.     

PERSPECTIVES ON URBAN WATER INFRASTRUCTURE IN CHINA FOR THE 21^ST C0ENTURY： SDMUWEIC MODEL

Based on system dynamics approach, SDMUWEIC model is developed in order to evaluatefuture dynamics of urban water infrastructure development in China. Firstly, this paper presents thebasic structure and characteristics of the model, focusing on water infrastructure‘s dynamicrelationships with population increase, economic development, water resources shortage and waterconservation practices. Secondly, model veracity and robustness tests based on behavior reproductionand uncertainty analyses are illustrated. Thirdly, based on the model, future pattems of China‘s urbanwater infrastructure investment requirements are simulated, and effectiveness of two different policyscenarios are evaluated. Finally, conclusion,; and policy implications are drawn, allowing insights intoChina‘s sustainable water infrastructure policies and managements.     

A NOTE ON THE STOCHASTIC ROOTS OF STOCHASTIC MATRICES

In this paper, we study the stochastic root matrices of stochastic matrices. All stochastic roots of2×2 stochastic matrices are found explicitly.A method based on characteristic polynomial of matrix isdeveloped to find all real root matrices that are functions of the original 3×3 matrix, including allpossible(function)stochastic root matrices. In addition, we comment on some numerical methods forcomputing stochastic root matrices of stochastic matrices.     

FUZZY SATISFYING INTERACTIVE MULTIOBJECTIVE THERMAL POWER DISPATCH： SWT APPROACH

In multiobjective optimization, trade-off analysis plays an important role in determining most preferred solution. This paper presents an explicit interactive trade-off analysis based on the surrogate worth trade-off function to determine the best compromised solution. In the multiobjective framework thermal power dispatch problem is undertaken in which four objectives viz. cost, NOx emission, SOx emission and COx emission are minimized simultaneously. The interactive process is implemented using a weighting method by regulating the relative weights of objectives in systematic manner. Hence the weighting method facilitates to simulate the trade-offrelation between the conflicting objectives in non-inferior domain. Exploiting fuzzy decision making theory to access the indifference band, interaction with the decision maker is obtained via surrogate worth trade-off （SWT） functions of the objectives. The surrogate worth trade-off functions are constructed in the functional space and then transformed into the decision space, so the surrogate worth trade-off functions of objectives relate the decision maker＇s preferences to non-inferior solutions through optimal weight patterns. The optimal solution of thermal power dispatch problem is obtained by considering real and reactive power losses. Decoupled load flow analysis is performed to find the transmission losses. The validity of the proposed method is demonstrated on 11-bus, 17-lines IEEE system, comprising of three generators.     

FORECASTING AUTOMOBILE WARRANTY PERFORMANCE IN PRESENCE OF ‘MATURING DATA＇ PHENOMENA USING MULTILAYER PERCEPTRON NEURAL NETWORK

Automobile companies that spend billions of dollars annually towards warranty cost, give high priority to warranty reduction programs. Forecasting of automobile warranty performance plays an important role towards these efforts. The forecasting process involves prediction of not only the specific months-in-service (MIS) warranty performance at certain future time, but also at future MIS values. However, ‘maturing data‘ (also called warranty growth) phenomena that causes warranty performance at specific MIS values to change with time, makes such a forecasting task challenging. Although warranty forecasting methods such as log-log plots and dynamic linear models appear in literature, there is a need for applications addressing the well recognized issue of ‘maturing data‘. In this paper we use an artificial neural network for the forecasting of warranty performance in presence of ‘maturing data‘ phenomena. The network parameters are optimized by minimizing the training and testing errors using response surface methodology. This application shows the effectiveness of neural networks in the forecasting of automobile warranty performance in the presence of the ‘maturing data‘ phenomena.     

Data crystallization applied for designing new products

It is only the observable part of the real world that can be stored in data. For such incomplete and ill-structured data, data crystallizing aims at presenting the hidden structure among events including unobservable events. This is realized by data crystallization, where dummy items, corresponding to potential existence ofunobservable events, are inserted to the given data. These dummy items and their relations with observable events are visualized by applying KeyGraph to the data with dummy items, like the crystallization of snow where dusts are involved in the formation of crystallization of water molecules. For tuning the granularity level of structure to be visualized, the tool of data crystallization is integrated with human＇s process of understanding significant scenarios in the real world. This basic method is expected to be applicable for various real world domains where previous methods of chance-discovery lead human to successful decision making. In this paper, we apply the data crystallization with human-interactive annealing （DCHA） to the design of products in a real company. The results show its effect to industrial decision making.     

Conflict analysis approaches for investigating attitudes and misperceptions in the War of 1812

Formal systems engineering approaches to modeling misperceptions and attitudes are employed within the framework of the graph model for conflict resolution to systematically study the War of 1812 between the United States of America and Great Britain in order to provide enhanced insights into the causes of the war. More specifically, relational definitions for preferences, movements and stability concepts are defined for describing the attitudes and associated behavior of decision makers involved in a conflict. To capture misperceptions of decision makers in the War of 1812, attitudes are studied within the structure of a hypergame. Combining attitudes and misperceptions within the paradigm of the graph model furnishes the flexible analytical tool which demonstrates that misunderstanding of attitudes by Great Britain and the United States may have contributed to the outbreak of this nasty war.     

A MODIFIED ANT-BASED TEXT CLUSTERING ALGORITHM WITH SEMANTIC SIMILARITY MEASURE

1. Introduction As an important application field of the data clustering technologies (Jain and Murty et al. 1999), text clustering is unsupervised partitioning of a collection of textual documents into self-similar groups so that any item is more similar with another item in the same group thanwith an item outside the group. Such groups are called clusters, which are run-timely formed during the clustering process, instead of being pre-defined as in the case of text categorization, which comm…     

ROBUST DESIGN MODELS FOR CUSTOMER-SPECIFIED BOUNDS ON PROCESS PARAMETERS

1. Introduction Robust design (RD) techniques based on the concept of building quality into a design are increasingly popular in industry primarily because of their practicality. There have been many attempts to integrate Taguchi’s RD principles with well-established statistical techniques, such as response surface methodology (RSM), in order to model the response directly as a function of control factors (Vining and Myers 1990). RSM is a collection of mathematical and statistical techni…     

A COMPARATIVE STUDY OF DATA MINING METHODS IN CONSUMER LOANS CREDIT SCORING MANAGEMENT

1. Introduction Data mining (DM), sometimes referred to as knowledge discovery in database (KDD), is a systematic approach to find underlying patterns, trends, and relationships buried in data. Data mining has drawn much attention from bothresearchers and practitioners due to its wide applications in crucial business decisions. Basically, the research on DM can be classified into two categories: methodologies and technologies. According to Curt (1995), the technology part of DM consists of…     

STUDIES ON HORIZONTAL COMPETITION AMONG HOMOGENOUS RETAILERS THROUGH AGENT-BASED SIMULATION

1. Introduction A supply chain is a network composed osuppliers, manufacturers, retailers, etc., whichcooperate to offer a kind of goods or servicesThese supply chain members cooperate andcompete with each other to maximize theiown profits. For example,…