Optimal asset management strategies for mixed transit fleet. (November 2018)

• Record Type: Journal Article
• Authors: Ngo, Huan Hoang; Shah, Rohan; Mishra, Sabyasachee
• Is Part Of: Transportation research. Volume 117(2018)
• Page Start: 103
• Page End: 116
• Publication Date: 2018-11
• Subjects: Optimal resource allocation -- Transit asset management -- Rehabilitation and replacement -- Integer programming -- Branch and bound algorithm; Transportation -- Research -- Periodicals; 388.011
• DOI: 10.1016/j.tra.2018.08.013
• Languages: English
• ISSNs: 0965-8564

Abstract

Highlights: Framework allows for optimal transit asset management without cutting existing capacity. Strategy is financially and operationally more efficient than manual or no allocation. Nearly 30% of the fleet receives some form of rehabilitation treatment every year. Case-study application shows a 40% higher passenger mileage generated over ten years. Abstract: Transit agencies require equitable and optimal allocation of funds among transit agencies for not just regular operations and maintenance, but also for asset management including purchase of new buses and rehabilitation of aging fleet. The paper proposes a hierarchical structure of resource allocation where federal funding is routed through the state, and ultimately to local transit agencies. The framework encompasses multiple dimensions such as selection of different improvement program options (rehabilitation, remanufacturing, and replacement) of a mixed transit fleet spread over a temporally continuous planning period. It leverages optimization models for capital allocation among transit agencies in the state. Four sub-models are developed—two maximizing passenger miles traveled, and the other two maximizing the total fleetwide remaining life, all under agency-specific budget, capacity and policy constraints, and planning objectives. They are applied on real-world data from set of transit agencies spread across the state of Tennessee, containing a heterogenous fleet of 254 total buses at various levels of aging. Results indicate that by application of the framework, an average 40 percent additional mileage is generated through the planning period with the same levels of fleet size, with nearly 30 percent of the fleet receiving some form of improvement treatment per year.