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June 24, 2007
Organized by IBM Research Lab in Haifa, Israel
Abstracts PDF version for printing (280 KB)
Service Engineering: Data-Based Science & Teaching in Support of Service Management
Avishai Mandelbaum, Technion
I shall describe examples of complex service operations for which data-based simple models have been found useful. The examples cover hospitals, banks, courts and more, but most attention will be given to telephone call centers.
Through the examples, I shall overview an emerging scientific discipline which is now being referred to as "Service Engineering". I focus on empirical findings which motivate or are motivated by (or both) interesting research questions. These findings give rise to model-features that are prerequisite for useful service models, for example customers' (im)patience, time-varying service demand (predictable variability), heterogeneity of customers and servers (skills-based routing), over-dispersion in Poisson arrivals, generally-distributed (as opposed to exponential) service- and patience-durations, and more. Empirical analysis also enables validation of existing models and protocols, either supporting or refuting their relevance and robustness.
Simple Approximation for the Time-Varying M(t)/M/s
Zohar Feldman, IBM Haifa Research Lab
This paper proposes a simple approximation for the transient behavior of queueing systems with time-varying arrival rates. In our approach, the system is analyzed as two separate but closely-integrating sub-systems: the service system and the queue system. We use the Chapman-Kolmogorov equations and generating functions to derive two differential equations corresponding to the expected sub-systems sizes. These equations are dependent on the instantaneous delay probability and the probability to encounter a non-empty queue, which we approximate in terms of the instantaneous expected size of both sub-systems. The immediate results of this approximation are the expected number of busy servers, the expected queue length and the delay probability - at all times. Other performance measures can then be derived or approximated using these values. The main contribution is providing a simple and quick approximation that accommodates the analysis of performance measures in the most general Mt/M/N that may be over-loaded for some part of or even the entire time-horizon.
Stable Matching - Assignment of Students to the Dormitories
Uriel Rothblum, Technion
In this paper we report a case study of the assignment of students to dormitories at the Technion - Israel Institute of Technology. Two criteria are applied: first, the privilege of getting on-campus housing is determined by personal socio-economic parameters, and second, the priority in the assignment of the students who gained on-campus housing privilege to specific dorms is determined by academic seniority and academic excellence. A modification of the classic stable matching model that allows for an "entrance criterion" is developed and analyzed, including the description of an algorithm that produces a stable outcome and a characterization of the output of this algorithm. The algorithm was implemented successfully for the assignment of students to dormitories at the Technion toward the 2004/5 academic year.
Using Stochastic Local Search for Optimizing Vehicle Configuration
Bella Dubrov, IBM Haifa Research Lab
Any given truck is characterized by hundreds of attributes, or variables, such as the type of the engine, wheels, cabin, etc. In addition there are complex rules that determine the valid assignments to these variables. These rules originate from manufacturing, inventory, pricing and so on. The rules are re-evaluated and changed periodically.
In the truck configuration problem, we have to find a truck configuration that assigns values to the variables while respecting the needs of a specific customer. We will present our ongoing work on a technology for solving this problem based on a general-purpose stochastic constraint satisfaction solver that we developed.
Cost Allocation in Supply Chains and Service Systems
Shoshana Annily, Tel-Aviv University
In this talk we discuss the issue of cost allocation in supply chains (SC) and in service management (SM) systems. Both consist of a number of facilities/service providers that may combine forces in order to improve the efficiency of the whole system. A vast amount of literature deals with such optimization models, and good policies have been developed. Once a policy is proposed, a natural question to be posed is how to allocate the cost among the various entities in the system in a fair way. One possible objective is finding a core allocation, meaning an allocation of the total costs such that no sub-coalition of entities has an incentive to deviate and act on its own. Core allocation schemes guarantee the stability of the system for the long-run, as they ensure that the formation of the grand coalition pays off to each of the individual entities.
In this talk we present the cost allocation problem for two specific models: In SC we consider the power-of-two policy for the deterministic, stationary Joint Replenishment Problem (JRP) in the infinite continuous time horizon. In this model each retailer is associated with its demand rate, holding cost rate and (minor) inventory reorder set-up cost. In addition, there exists a major set-up cost, such that each time a subset of retailers reorder simultaneously, the total set-up ordering cost charged is the sum of the major set-up and of the individual minor set-up costs of the retailers that reorder. The cost of any coalition is the average time total ordering and holding cost of the retailers in the coalition where shortages or backlogging are not allowed. In SM we consider a system that consists of a collection of servers, each characterized by its own capacity and its own stream of customers. A coalition of servers generates a joint entity, which is a single server system with arrival and service rates being the sum of the respective individual rates. The cost of any coalition is the steady-state mean number of customers in the respective system.
Keynote: Robust Solutions of Uncertain Optimization Problems
Aharon Ben-Tal, Technion
We survey the main developments in Robust Optimization (RO), a methodology aimed at solving optimization problems (static and dynamic) affected by uncertainty.
We focus primarily on issues of computational tractability of the robust counterparts emerging from conic optimization problems (linear, conic quadratic and semidefinite programming). We then show how results pertaining to the latter issues can be used to solve difficult chance-constrained programs under partial stochastic information. Finally, we discuss the synthesis of uncertainty affected discrete-time linear control systems by the RO methodology and illustrate the results by treating a supply chain problem.
Multi-Agent Systems: The Agent Perspective
Moshe Tennenholtz, Technion
In this talk we consider the challenging and fundamental problem of providing a prescriptive theory of agent-centric decision making in multi-agent systems. We aim at offering several complementary approaches to tackling the task of providing the agent with advice about the actions it should choose in multi-agent encounters.
Exact and Approximate Solutions of Source Localization Problem
Amir Beck, Technion
We consider the problems of locating a radiating source from range measurements or from range-difference measurements collected using an array of passive sensors. Both least squares (LS) and maximum likelihood (ML) approaches are studied. Despite the fact that the resulting optimization problems are nonconvex, we provide exact solution procedures for efficiently computing the LS estimates of both problems. Numerical simulations suggest that the exact LS estimate outperforms existing approximations of the LS solution as well as approximations of the ML solution, which are based on a semi-definite relaxation.
Online Ascending Auctions for Gradually Expiring Items
Ron Lavi, Technion
We consider auction mechanisms for the allocation of multiple items in dynamic and unpredictable environments, where buyers frequently enter and leave the markets, and where items may expire. An example for such an environment is the new Internet market places, that became increasingly popular in recent years. Our goal is to design mechanisms that maximize the social welfare, without assuming any knowledge about an underlying distribution. The lack of distributional knowledge, and especially the lack of common priors, seems to be a fundamental property of such dynamic environments, and an essential requirement from any realistic mechanism design in such settings. Under this framework we obtain three results.
Calculating the Business Importance of Entities in a Service-Oriented Enterprise
Segev Wasserkrug, IBM Haifa Research Lab
Component Business Modeling (CBM) serves as a powerful analytical framework for reasoning about the business as a set of business components that collaborate through the provision and consumption of business services. This talk describes a method for calculating the relative importance of the entities that make up a componentized enterprise architecture. The proposed method includes a formal definition of the importance of each entity in the business architecture calculated from the high level business values.
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