A probabilistic approach to pickup and delivery problems with time window uncertainty

In this paper we study a dynamic and stochastic pickup and delivery problem proposed recently by Srour, Agatz and Oppen. We demonstrate that the cost structure of the problem permits an effective solution method without generating multiple scenarios. Instead, our method is based on a careful analysis of the transfer probability from one customer to the other. Our computational results confirm the effectiveness of our approach on the data set of Srour et al

It's about time: Investing in transportation to keep Texas economically competitive - Appendices

APPENDIX A : PAVEMENT QUALITY (Zhanmin Zhang, Michael R. Murphy, Robert Harrison), 7 pages -- APPENDIX B : BRIDGE QUALITY (Jose Weissmann, Angela J. Weissmann), 6 pages -- APPENDIX C : URBAN TRAFFIC CONGESTION (Tim Lomax, David Schrank), 32 pages -- APPENDIX D: RURAL CORRIDORS (Tim Lomax, David Schrank), 6 pages -- APPENDIX E: ADDITIONAL REVENUE SOURCE OPTIONS FOR PAVEMENT AND BRIDGE MAINTENANCE (Mike Murphy, Seokho Chi, Randy Machemehl, Khali Persad, Robert Harrison, Zhanmin Zhang), 81 pages -- APPENDIX F: FUNDING TRANSPORTATION IMPROVEMENTS (David Ellis, Brianne Glover, Nick Norboge, Wally Crittenden), 19 pages -- APPENDIX G: ESTIMATING VEHICLE OPERATING COSTS AND PAVEMENT DETERIORATION (by Robert Harrison), 4 page

Review of state government motor vehicle resources

The LAC was requested to review state motor vehicle resources and make recommendations. It focused on three statewide objectives posed by the committees: (1) Determine if any wasteful duplication exists among state-owned vehicle maintenance facilities. (2) Identify any waste or inefficiency in the use of state owned vehicles. (3) Identify unnecessary or personal use of state-owned vehicles

Managing The Gig Economy Via Behavioral And Operational Lenses

This dissertation combines tools from operations management, econometrics, machine learning, and behavioral sciences to (i) study how on-demand workers learn and make decisions in complex environments, (ii) develop tools to help improve their decision-making, and (iii) inform the design of better policies to manage human-centered operations. Recent technologies create and accelerate new work arrangements that provide workers with flexibility in their schedule and choice of service. At the same time, the decisions a worker faces have become more complex. Platforms offer competing dynamic incentives, and the independent nature of gig work means that workers do not experience the benefits of learning from colleagues. The following three chapters investigate how behavioral operations management can be utilized to better manage the gig economy. (i) Behavioral and Economic Drivers of Decisions.We empirically investigate how on-demand workers decide on when to work and \emph{for how long} depending on varying financial incentives and personal goals. Using the comprehensive data from a ride-hailing industry partner, we develop an econometric framework that addresses empirical challenges such as sample selection bias and endogeneity. Our results demonstrate that, while workers exhibit positive income elasticity as predicted by standard income theory, their decisions are significantly influenced by their cumulative earnings (more likely to stop working when reaching their income goal) and recent work duration (tend to stay working after long hours of work or exhibit inertia ), more akin to the behavioral theory of labor supply. Inertia captures both the formation of work habits and the tendency to stay with the focal platform, suggesting that, amidst intensifying competition among platforms, platform loyalty could be induced through optimal incentive design. Thus, we propose a heuristic to optimize incentive allocation and demonstrate through counterfactual simulations the monetary and capacity benefits of accounting for our behavioral insights. (ii) Dynamic Decisions and Multihoming Behavior. We leverage proprietary data from our ride-hailing industry partner and the publicly available trip record data to develop and estimate a structural behavioral model of gig workers\u27 sequential dynamic decisions of when and where to work in the presence of alternative work opportunities. Our major contributions are in the modeling and estimation of dynamic decisions with temporal and spatial components and dynamic outside options, and the development of an efficient simulation-assisted machine learning-based estimation framework. Our results characterize gig workers\u27 forward-looking behavior and heterogeneous cost of working. We find that workers are strategic in their choice of initial service location to ensure high utilization and are prone to multihoming behavior when facing longer idle times. Then, we study how the firm can influence multihoming behavior among workers. Our counterfactual analyses demonstrate the effectiveness of strategies commonly used in practice and offer insights that can help retain workers during high demand or nudge them to quit during low demand. (iii) Improving Human Decision-Making with Machine Learning. We propose a novel machine-learning algorithm to automatically extract best practices from the trace data and infer simple tips that can help workers learn to make better decisions. We use an approach based on imitation learning and interpretable reinforcement learning and consider simple if-then-else rules that modify workers\u27 strategy in a way that most improve their performance, capture useful insights that are challenging for workers to learn by themselves, and are simple enough for workers to understand. To validate our approach and test the performance of our algorithm, we design a virtual kitchen-management game and conduct large-scale pre-registered behavioral studies on Amazon Mechanical Turk. Our experiments show that rules inferred from our algorithm are effective and significantly outperform rules from other sources at improving performance and speeding up learning among workers. In particular, we help workers identify optimal early actions that help them improve in the long term and discover additional optimal strategies beyond what is stated by our algorithm

Compilation of thesis abstracts, June 2007

NPS Class of June 2007This quarter’s Compilation of Abstracts summarizes cutting-edge, security-related research conducted by NPS students and presented as theses, dissertations, and capstone reports. Each expands knowledge in its field.http://archive.org/details/compilationofsis109452750

Pupil Transportation Manual: A Guide for Transportation Supervisors, 1975

The purpose of this manual is to serve as a guide for all those who may be involved in the administration or management of pupil transportation in the State of Iowa. It is especially designed for superintendents and transportation supervisors who are new in the field and have no prior experience in operating a pupil transportation system. It is hoped the information contained herein will be of assistance to those who have been assigned these responsibilities