Single Stage Integer Programming Model for Long Term Transit Fleet Resource Allocation
Authors: Sabyasachee Mishra, Tom V. Mathew, and Snehamay Khasnabis (2010)
Report
Synopsis: The authors present a procedure for resource allocation among transit agencies for transit fleet management, specifically focusing on the purchase of new buses and rebuilding of existing buses. The model is formulated as a non-linear optimization problem of maximizing the total weighted average remaining life of the fleet subject to budgetary, policy and other constraints. The problem is solved using Integer Programming (IP) and its application is demonstrated through a case study utilizing actual transit fleet data from the Michigan Department of Transportation.
Authors: Tom V. Mathewa, Snehamay Khasnabisb, and Sabyasachee Mishra (2010)
Report
Synopsis: Most transit agencies require government support for the replacement of their aging fleet. A procedure for equitable resource allocation among competing transit agencies for the purpose of transit fleet management is presented in this study. The proposed procedure is a 3-dimensional model that includes the choice of a fleet improvement program, agencies that may receive them, and the timing of investments. Earlier efforts to solve this problem involved the application of one or 2-dimensional models for each year of the planning period. These may have resulted in suboptimal solution as the models are blind to the impact of the fleet management program of the subsequent years. Therefore, a new model to address a long-term planning horizon is proposed. The model is formulated as a non-linear optimization problem of maximizing the total weighted average remaining life of the fleet subjected to improvement program and budgetary constraints. Two variants of the problem, one with an annual budget constraint and the other with a single budget constraint for the entire planning period, are formulated. Two independent approaches, namely, branch and bound algorithm and genetic algorithm are used to obtain the solution. An example problem is solved and results are discussed in details. Finally, the model is applied to a large scale real-world problem and a detailed analysis of the results is presented.
Authors: Sabyasachee Mishra, Tom V. Mathew, and Snehamay Khasnabis (2010)
Report
Synopsis: The authors present a procedure for resource allocation among transit agencies for transit fleet management, specifically focusing on the purchase of new buses and rebuilding of existing buses. The model is formulated as a non-linear optimization problem of maximizing the total weighted average remaining life of the fleet subject to budgetary, policy and other constraints. The problem is solved using Integer Programming (IP) and its application is demonstrated through a case study utilizing actual transit fleet data from the Michigan Department of Transportation.
Optimal Resource Allocation Among Transit Agencies for Fleet Management
Authors: Tom V. Mathewa, Snehamay Khasnabisb, and Sabyasachee Mishra (2010)
Report
Synopsis: Most transit agencies require government support for the replacement of their aging fleet. A procedure for equitable resource allocation among competing transit agencies for the purpose of transit fleet management is presented in this study. The proposed procedure is a 3-dimensional model that includes the choice of a fleet improvement program, agencies that may receive them, and the timing of investments. Earlier efforts to solve this problem involved the application of one or 2-dimensional models for each year of the planning period. These may have resulted in suboptimal solution as the models are blind to the impact of the fleet management program of the subsequent years. Therefore, a new model to address a long-term planning horizon is proposed. The model is formulated as a non-linear optimization problem of maximizing the total weighted average remaining life of the fleet subjected to improvement program and budgetary constraints. Two variants of the problem, one with an annual budget constraint and the other with a single budget constraint for the entire planning period, are formulated. Two independent approaches, namely, branch and bound algorithm and genetic algorithm are used to obtain the solution. An example problem is solved and results are discussed in details. Finally, the model is applied to a large scale real-world problem and a detailed analysis of the results is presented.