1 / 6

Elevator Group Control Problem – Stochastic and Dynamic Vehicle Routing Problem

Elevator Group Control Problem – Stochastic and Dynamic Vehicle Routing Problem. Janne Sorsa, janne.sorsa@kone.com Supervisors: Prof. Harri Ehtamo (HUT) Dr. Marja-Liisa Siikonen (KONE). Background. EGCP: Integer programming based methods not found in literature

kaiya
Download Presentation

Elevator Group Control Problem – Stochastic and Dynamic Vehicle Routing Problem

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Elevator Group Control Problem – Stochastic and Dynamic Vehicle Routing Problem Janne Sorsa, janne.sorsa@kone.com Supervisors: Prof. Harri Ehtamo (HUT) Dr. Marja-Liisa Siikonen (KONE)

  2. Background • EGCP: • Integer programming based methods not found in literature • Multi-deck control methods not available in literature • Contribution: Complete formulation and solution method ready for implementation for multi-deck elevators • SVRP: • Modeling framework and terminology • Typically optimal policies solved with heuristics • Contribution: practical way to solve SVRP to optimality for an on-line optimization

  3. Modeling and Analysis • Formulate VRP as • Generalized Assignment Problem as Integer Programming Problem • Traveling Salesman Problem for an elevator as stochastic optimal control problem • Solve GAP with Genetic Algorihm • Solve TSP as a Certainty Equivalent Controller (CEC) • Simulation of several case studies: • Measure computation time • Compare service level to other algorithms

  4. Results • Method realized for on-line optimization • Linear growth of computation time with respect to problem size • Optimization based method beats heuristics in passenger service level • Objective affects directly to the corresponding service level statistic • Objective of optimization not straightforward for double-deck elevators

  5. Conclusions and the way forward • Practical approach, which works in reality! • Future research: • Pickup and Delivery Problem formulation (partly done by Ruokokoski, 2007) • Integer Programming solution • Theoretical properties (lower bounds for WT etc à la Bertsimas and van Ryzin) • Effect and reliability of forecasting accuracy (HUT 2008?)

  6. References • Closs, 1970. The Computer Control of Passenger Traffic in Large Lift Systems. PhD Thesis • Google Scholar: 7 • Scopus: 4 • Bertsimas, van Ryzin, 1991. A stochastic and dynamic vehicle routing problem in the Euclidean plane • Google Scholar: 117 • Scopus: 49 (Transp. Sc., EJOR, OR)

More Related