Optimization of employee transportation network

A transportation network for employees at a local company faced problems with unbalanced utilization and passenger complains caused by occasional overcrowding in vehicles. The objective of this work was to develop a method for planning transportation routes and establish its reliability in consisten...

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Bibliographic Details
Main Author: Ng, Gar Yan
Other Authors: Appa Iyer Sivakumar
Format: Theses and Dissertations
Language:English
Published: 2014
Subjects:
Online Access:http://hdl.handle.net/10356/60528
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Institution: Nanyang Technological University
Language: English
Description
Summary:A transportation network for employees at a local company faced problems with unbalanced utilization and passenger complains caused by occasional overcrowding in vehicles. The objective of this work was to develop a method for planning transportation routes and establish its reliability in consistently delivering a near or optimal solution. Two models were built each with a different Integer programming formulation- Formulation I and II. Formulation I consists of vehicle cost and penalty cost components in its objective function so that the optimal solution would balance actual cost and passenger service level. The model was implemented in the EXCELbased Risk Solver Platform but could not find a feasible solution for even a small set of 4-node problem. Formulation II, built upon experience from the first model, had an improved cost function that excluded the penalty cost component. An implementation in XPRESS-MP and subsequent verification with enumeration solutions proved its ability to solve both the 3-node and 4-node problems to optimality. A profile of objective value over various vehicle capacity values served as a guide to setting passenger service level. The XPRESS-MP model was implemented in Stage II of this work on a 13-node problem based on actual data. The solution was compared with manually generated routes and was found to outperform the latter by at least 9%. Even with passenger service level imposed on the model, a 5% cost saving was attained. Proactive efforts in data collection and maintenance are encouraged to ensure successful full scale implementation of this model in the future.