William T. Scherer

Intelligent Scheduling Systems

Preface. Issues in Scheduling: A Survey of Intelligent Scheduling Systems D. Brown, J. Marin, W. Scherer. Schedulers & Planners: What and How Can we Learn from Them? K. McKay, F. Safayeni, J. Buzacott. Decision-Theoretic Control of Constraint Satisfaction and Scheduling O. Hansson, A. Mayer. Guided Forward Search in Tardiness Scheduling of Large One Machine Problems T. Morton, P. Ramnath. Production Scheduling: An Overview of Tabu Search Approaches to Production Scheduling Problems J. Barnes, M. Laguna, F. Glover. Measuring the Quality of Manufacturing Schedules K. Gary, R. Uzsoy, S.P. Smith, K. Kempf. A Methodology and Architecture for Reactive Scheduling S.F. Smith. Intelligent Scheduling with Machine Learning S. Park, S. Piramuthu, N. Raman, M. Shaw. Transportation Scheduling: Solving Large Integer Programs Arising from Air Traffic Flow Problems R. Burlingame, A. Boyd, K. Lindsay. Intelligent Scheduling Support for the U.S. Coastguard K. Darby-Dowman, C. Lucas, G. Mitra, R. Fink, L. Kingsley, J. Smith. Index.

Exploring Imputation Techniques for Missing Data in Transportation Management Systems

Many states have implemented large-scale transportation management systems to improve mobility in urban areas. These systems are highly prone to missing and erroneous data, which results in drastically reduced data sets for analysis and real-time operations. Imputation is the practice of filling in missing data with estimated values. Currently, the transportation industry generally does not use imputation as a means for handling missing data. Other disciplines have recognized the importance of addressing missing data and, as a result, methods and software for imputing missing data are becoming widely available. The feasibility and applicability of imputing missing traffic data are addressed, and a preliminary analysis of several heuristic and statistical imputation techniques is performed. Preliminary results produced excellent performance in the case study and indicate that the statistical techniques are more accurate while maintaining the natural characteristics of the data.

Solution Procedures for Partially Observed Markov Decision Processes

We present three algorithms to solve the infinite horizon, expected discounted total reward partially observed Markov decision process POMDP. Each algorithm integrates a successive approximations algorithm for the POMDP due to A. Smallwood and E. Sondik with an appropriately generalized numerical technique that has been shown to reduce CPU time until convergence for the completely observed case. The first technique is reward revision. The second technique is reward revision integrated with modified policy iteration. The third is a standard extrapolation. A numerical study indicates the potentially significant computational value of these algorithms.

Data-driven methodology for signal timing plan development: A computational approach

Traffic signal systems serve as one of the most powerful control tools available to improve the efficiency of surface transportation travel. A large number of signal systems currently operate using the time-of-day (TOD) approach. In TOD systems, a day is segmented into a number of intervals in which a different timing plan is used. Thus, the challenge in operating a TOD system effectively is to (1) identify appropriate TOD intervals, and (2) develop optimal timing plans for each interval. The existing procedures used by traffic engineers to address these challenges are time consuming and use relatively small sets of data. This research effort developed a new timing plan development methodology that takes advantage of the large sets of archived traffic data (volume and occupancy) that modern systems are equipped to compile. Based upon statistical cluster analysis, this methodology (1) automates the identification of TOD intervals using a high-resolution definition of system state, and (2) provides representative volumes for plan optimization based on the set of archived data. The results of a case study reported in this paper demonstrate that the methodology supports the development of a TOD system that provides benefits when considering performance measures such as delay, when compared to currently used techniques.

Finite-Memory Suboptimal Design for Partially Observed Markov Decision Processes

We develop bounds on the value function and a suboptimal design for the partially observed Markov decision process. These bounds and suboptimal design are based on the M most recent observations and actions. An a priori measure of the quality of these bounds is given. We show that larger M implies tighter bounds. An operations count analysis indicates that ( # A # Z ) M +1 ( # S ) multiplications and additions are required per successive approximations iteration of the suboptimal design algorithm, where A , Z , and S are the action, observation, and state spaces, respectively, suggesting the algorithm is of potential use for problems with large state spaces. A preliminary numerical study indicates that the quality of the suboptimal design can be excellent.

Research Collaboration and ITS Topic Evolution: 10 Years at T-ITS

This paper investigates the collaboration patterns and research topic trends in the publications of the IEEE Transactions on Intelligent Transportation Systems (T-ITS) over the past decade. We find that coauthorship is prevalent and that the coauthorship networks possess the scale-free property on high degree nodes. Collaborations usually occur within the same research institutions and countries. Interorganization/region collaboration structures are usually connected through a few productive/high-impact authors. Typical international collaborations are between the U.S. and other countries such as China, Germany, U.K., and Italy. Active topics studied in IEEE T-ITS publications in the past ten years include traffic management and machine vision, among others. Authors can be partitioned into common interest groups, of which machine vision and automatic vehicle control attract more researchers.

A comparison of systems engineering programs in the United States

Given the length of time systems engineering has been taught in the US, it is now appropriate to examine the types of programs being offered and to compare and contrast these programs. The paper provides this comparison with a view toward the future of systems engineering education in the US. In particular, we first examine the US undergraduate and graduate programs in systems engineering in order to understand what is taught and how it is taught. Using cluster analysis, we identify four distinct types of systems engineering undergraduate programs, and an informal analysis examines the directions in the systems engineering graduate programs. Next we look at issues in systems engineering education, which have shaped the development of the curricula over the last thirty years. These include the definition of systems engineering, associated professional societies, similar degree types, the role of an undergraduate systems engineering degree, and the role of information technology in systems engineering. We conclude with opportunities for systems engineering education within the US with regard to curricula directions and job opportunities.

Combinatorial optimization for spacecraft scheduling

The application of combinatorial optimization techniques to the automatic spacecraft scheduling problem is described. The problem is to search over the candidate sequences of experiments for a sequence that maximizes the value of the science conducted while minimizing constraint conflicts. Exploratory computational results indicate that pseudorandom research techniques, such as simulated annealing, generate viable sequences in reasonable times.<<ETX>>

Data mining tools for real-time traffic signal decision support & maintenance

This paper describes research investigating the application of data mining tools to aid in the development of traffic signal timing plans. A case study was conducted to illustrate that the use of hierarchical cluster analysis can be used to automatically identify time-of-day (TOD) intervals,, based on the data, that support the design of a TOD signal control system. The cluster analysis approach is able to utilize a high-resolution system state, definition that takes full advantaged of the extensive set of sensors deployed in a traffic signal system and cluster validation supports the hypotheses presented. The results of this research indicate that advanced data mining techniques hold high potential to provide automated signal control techniques.

A Survey of Expert Systems for Equipment Maintenance and Diagnostics

Considerable research is being conducted in the area of expert systems for diagnosis. Early work was concentrated in medical diagnostic systems (Clancey and Shortliffe, {21}). MYCIN (Buchanan and Shortliffe, {15}) appears to represent the first medical diagnostic expert system. Current efforts are expanding to the area of equipment maintenance and diagnostics, with numerous systems having been built during the past several years. We concentrate our effort in this survey on expert systems for diagnosis and refer the reader to (Hayes-Roth, Waterman, and Lenat, {42} Hayes-Roth, {43}), (Waterman, {101}), and (Charniak and McDermott, {19}) for introductions to expert systems. We remark that it is common for diagnostic systems to integrate concepts from artificial intelligence (expert systems), decision theory, and operations research.