Integrated Logistics R. Ravi, CMU Co-organizers: Adam Meyerson, CMU Moses Charikar, Princeton Ted Gifford, Schneider Logistics

Motivations Integrate models across many application areas: Databases, Genetic clustering, Supply Chain Management Bridge across various disciplines working on same models: CS, OR, Industry Postdoc-propelled! Adam Meyerson from Stanford, going to the faculty at UCLA

Sample Applications Trucking logistics (Gifford) Clustering expression data (Munagala) Databases (Guha, Mettu) Survivable Telecomm Networks (Balakrishnan, Mirchandani) Supply Chain Models (Goetschalckx, Schaefer) Disk Placement (Khuller) Network Games (Tardos. Wexler) Overlay Multicast Networks (Maggs)

Affiliations of Participants CS Departments: CMU, Princeton, MIT, Berkeley, Penn, Cornell, UMCP, Dartmouth, Stanford CS Research Labs: IBM, Bell-Labs, Microsoft Business Schools: CMU, Pitt, UT Austin, Georgia Tech Industry:

“Integrated” Logistics Integrate formulations and approaches in Logistics across applications and disciplines Integrating theory and practice Integrate models of facility location and transportation into one comprehensive model (as opposed to handling them in two distinct tactical stages)

Research Goals and Plan Bring researchers from CS, OR and practitioners together for dialogue Hope to stimulate new work motivated by exchange Adapt and integrate algorithmic approaches across areas Disseminate algorithms in course, web depot, teaching modules

Viewpoints Algorithms viewpoints – Surveys by Meyerson and Shmoys First WorkshopFirst Workshop Industry perspective Ted’s PresentationTed’s Presentation Business School/OR perspective of Logistics Marc's PresentationMarc's Presentation

Online Facility Location Given facilities with opening costs f in a metric, locate them to minimize total facility opening costs plus distances from all clients to their resp. closest facility We start with some graph and its solution, but we will have to add more vertices in the future, without disturbing our current setup The demands of incoming clients are based on some known function, generally of distance Goal: what do we do with each incoming point as it arrives to stay close to optimal?

Online Facility Location With each new client, we do one of two things: (1)Connect our new client to an existing facility, or (2)Make a new facility at the new point location What do we do with incoming vertices?

Theoretical Result (Meyerson) The probability that a Facility is created out of a given incoming point is d/f –Where d = the distance to the nearest facility –And f = the cost of opening a facility Worst case cost is expected 8 times the optimal cost

Goals of REU Investigation (Bleimes, Garrod, Meyerson) Motivation: Rather than a new approach, examine the realistic behavior of existing techniques for facility location Task: Run simulations over both real and random data sets, to get average data on the performance of known algorithms for this problem Expected Results: –Both speed and accuracy are important, but for different reasons and applications –Realistic data will help determine how best to use these algorithms

Research Accomplishments 11 papers on topics ranging from networking, routing, orienteering, designing mechanisms to scheduling New ideas on online cost-distance, truth-telling mechanisms for network pricing, discount-reward TSP

Education & Outreach Guest Lectures in “Algorithms in the Real World” Class and College Teachers Workshop Graduate Student Training (Garrod, Dhamdhere, Konemann, Sinha) and REU (Bleimes,Kitchin) Graduate Class on “Planarity” (Spring 2003)

Future Research New results on applying approximation algorithms for two-stage stochastic optimization problems in Facility Location and Network Design (Ravi & Sinha, submitted) Web depot on Logistics implementations, benchmarks and test sets (Meyerson)