Chan, Yupo (University of Arkansas at Little Rock)

Yupo Chan speaker

Yupo Chan
University of Arkansas at Little Rock
Little Rock, AR
USA 72204-1099

Phone: 501-569-8926


A Game-Theoretic Model for Secure International Communications

We developed a new defensive model for secure global voice communications. To insure the security, it uses an n-person, zero-sum, cooperative and non-cooperative game to optimize the revenue among service-provider coalitions. The cooperative game allows a coalition of network service providers (NSP) to be formed. It is based not only on their revenues from traffic, but also from incentive payments from a coalition leader. The non- cooperative game guards against adversarial tampering or attacks. In particular, we worry about these two revenues after an adversary attacks an NSP and renders it nonfunctional: coalition revenue and network provider revenue. We optimize these revenues by hardening NSPs and improving their respective revenues with federal incentive payments. A multi- criteria optimization problem was developed to establish the strategic competition between the coalition defender and attacker. Irrespective of the amount of incentive payments, an applicable hardening and tampering strategy can be obtained. It was shown that the option to harden NSPs has measurable value whether an incentive is provided to form the secure coalition. In addition, the adversary's tampering strategy is revealed in the shadow prices associated with the non-cooperative-game constraints. Intermediatet of Defense. Results are in part validated against Monte-Carlo simulation.


A Combined Inventory and Delivery Model for Repairable Items 
This paper considers a network composed of multiple depots that face uncertain demand for repairable items. It models the joint problem of determining how many units to repair and hold in inventory at each depot and how many to ship to other depots so as to minimize system-wide inventory, shortage and delivery costs in a single period. The fleet of vehicles are all stationed in the main depot and each depot has a certain holding and repair capacity. The formulation is a "multi-commodity" extension of Federgruen & Zipkin's combined vehicle-routing and inventory-allocation model (1984). The additional complexity is that each depot needs to decide how many units to repair and can send to any others-i.e., lateral re-supply. Being a nonlinear mixed integer program, the problem is solved using generalized Benders' decomposition.


A Multiple-depot, Multiple-vehicle, Location-routing Problem with Stochastically Processed Demands

We formulate a multiple-depot, multiple-vehicle, location-routing problem with stochastically processed demands, which are defined as demands that are generated upon completing site-specific service on their predecessors. When a factory is re-supplied with manufacturing materials, for example, demand for raw materials surfaces only after the existing inventory has been exhausted. A special separable case of the problem was solved, wherein probable demands are estimated by stochastic processes at the demand nodes (the factories) before the vehicle location-routing decisions. Posterior solutions to the complete 90-day instances of the problem help to gauge the performance of the a priori stochastic model. The 90 day-by-day instances also provide researchers with a benchmark data-set for future experimentation. It was shown that the a priori optimization solution provides a robust location-routing strategy for real-time decision-making in a medical-evacuation case study of the U.S. Air Force. Given this modest success, the same methodology can possibly be applied toward “pure” just-in-time deliveries in supply-chain management, where inventory storage is totally eliminated.


Single-commodity Multi-criteria Stochastic Networks: Improving Reliability vs. Throughput
There are three objectives in this research. First we measure the reliability of large single-commodity stochastic-networks. This is accomplished through an application of a factoring program developed by Page and Perry (1989). Second we develop a reliability-improvement model given that a practical reliability-expression does not exist. This is modelled by the Jain and Gopal (1990) heuristic through a linear improvement model. Finally, we examine the tradeoff between maximizing expected-flow and reliability. This is accomplished by generating bounds on the efficient frontier using an approximate multicriteria optimization approach. In this simplified approach, both expected flow and reliability can be measured practically and subsequent improvements made, providing insights into the operations of stochastic networks. Extensive computational experiences have been gained through experiments with three large-scale communication networks from the U S Department of Defense. Results are in part validated against Monte-Carlo simulation. Intermediate expected flow and reliability can be measured practically and subsequent improvements made, providing insights into the operations of stochastic networks. Extensive computational experiences have been gained through experiments with three large-scale communication networks from the U S Department of Defense. Results are in part validated against Monte-Carlo simulation.


A Three-dimensional Bin-packing Model: Exact Multi-criteria Solution and Computational Complexity

The three-dimensional or vector bin-packing problem is reexamined, in which the least number of bins is determined to accommodate p items to be packed. An exact analytical mixed-integer-programming formulation is offered to pack items by layers, while considering the layer height at the same time. Relaxation of the model is shown to yield the linear-programming bound advanced by Federgruen and Van Ryzin for heuristic solution to the multi-dimensional bin-packing problem. When the items to be packed are arranged in non-increasing order by height or by volume, this lower bound is shown to be of computational complexity O(p), both theoretically and computationally. Since ours is an exact solution rather than a heuristic solution, the bound is perfectly tight, thus mitigating the worries about the magnitude of error bounds. The linear computational complexity suggests that our analytical model can be solved efficiently irrespective of the number of items to be packed. At the same time, extensive computation using a layer-by-layer dynamic-programming heuristic by Hodgson, the Interactive Pallet Loading System (IPLS), corroborated the Federgruen and Van Ryzin lower bound as well. Limited computational results from the analytical model also validated the IPLS heuristic solution. A novel feature of the analytical model is that it yields Pareto optima, rather than a single optimum. This allows decision-makers to choose among the non-dominated solutions in consideration of other soft factors in bin packing. When the multicriteria model is executed in an interactive manner, it is able to consider considerations such as center-of-gravity and ease of unloading.


Location, Transport and Land-Use

This talk identifies the underlying principles that govern siting, community development, and product/service delivery. Included are procedures to perform: site location, land-use planning, location-routing, competitive allocation of products & services, and spatial forecasting. It suggests solution techniques for emergency-response to natural and manmade hazards, environmental planning, infrastructure management, intelligent transportation systems, real-estate development, satellite remote-sensing, supply-chain management, and urban land-use plans.


Incident Management on Highway Networks

In an Advanced Traveler Information System (ATIS), we study how to map a driver’s interest to real-time routing decisions. Accounting for en-route de-lays and alternate routing, ATIS networks exhibit non-FIFO behavior—drivers who depart earlier may not arrive ahead of those who depart later. Given a time-dependent network with full travel-time information, we model such dynamic routing decisions that include waiting en-route for an incident to clear. We employ a wait-time search algorithm to account for the best delays en-route. The algo-rithm elicits the bottlenecks in the network and obtains the optimal wait-times that would be favorable to a driver in achieving the fastest travel-time. We further model the driver’s risk-aversion behavior using a stochastic metric in the algo-rithm, by simultaneously considering risk and fastest travel time in path choice. Our defined routing policy takes decision at every node based on the current and future network states to determine the optimal wait-time and the next hop-node. Empirical results are obtained from a Central Arkansas Highway Network. The computational efficiency of the proposed algorithm has also been assessed. It is shown to be operationally acceptable for real-time applications.


Education & Background

  • Ph.D. Operations Research, MIT 
  • MS Transportation Systems/Economics, MIT 
  • BS, Civil Engineering, MIT 

Dr. Yupo Chan is Professor & Founding Chair of the Systems Engineering Department at the University of Arkansas at Little Rock. He received his BS degree in civil engineering, MS degree in transportation systems/economics and Ph.D. degree in operations research, all from the Massachusetts Institute of Technology. Dr. Chan has written numerous journal articles and books. The most recent ones include Location, Transport and Land-use: Modeling Spatial-Temporal Information, Springer-Verlag, Forthcoming, 750 pages (with Web-based software) and Location Theory and Decision Analysis, ITP/South-Western, 2000, 533 pages (with Computer Software disk).

His research interests include telecommunication systems, transportation systems, networks and combinatorial optimization, multi-criteria decision-making, spatial-temporal information, econometrics and technology assessment. He has previously held faculty positions at the Air Force Institute of Technology, Washington State University, State University of New York, Stony Brook and Pennsylvania State University. Dr. Chan is a Fellow of the American Society of Civil Engineers and has won numerous awards for his work, including the Harland Bartholomew Award of the American Society of Civil Engineers, the Koopman Prize of the Operations Research Society of America and a Congressional Fellowship with the Office of Technology Assessment.