Optimization of bus allocation to depots by minimizing dead kilometers

Abstract

SUMMARYIn metropolitan cities, public transportation service plays a vital role in mobility of people, and it has to introduce new routes more frequently due to the fast development of the city in terms of population growth and city size. Whenever there is introduction of new route or increase in frequency of buses, the nonrevenue kilometers covered by the buses increases as depot and route starting/ending points are at different places. This non-revenue kilometers or dead kilometers depends on the distance between depot and route starting point/ending point. The dead kilometers not only results in revenue loss but also results in an increase in the operating cost because of the extra kilometers covered by buses. Reduction of dead kilometers is necessary for the economic growth of the public transportation system. Therefore, in this study, the attention is focused on minimizing dead kilometers by optimizing allocation of buses to depots depending upon the shortest distance between depot and route starting/ending points. We consider also depot capacity and time period of operation during allocation of buses to ensure parking safety and proper maintenance of buses. Mathematical model is developed considering the aforementioned parameters, which is a mixed integer program, and applied to Bangalore Metropolitan Transport Corporation (BMTC) routes operating presently in order to obtain optimal bus allocation to depots. Database for dead kilometers of depots in BMTC for all the schedules are generated using the Form-4 (trip sheet) of each schedule to analyze depot-wise and division-wise dead kilometers. This study also suggests alternative locations where depots can be located to reduce dead kilometers.

Deadheading minimization of the massive transportation system in the city of Cali Colombia

“…Note that this is not equivalent to minimizing dead kilometers because the fuel consumption efficiency in km/gallon is different for each type of bus. Constraints (2) ensure that the buses of each type coming from all WYs, allocated to each starting stop at each one-hour period, are equal to the required vehicles according to the specified hourly demand. Similarly, constraints (3) ensure that the buses of each type arriving at their ending stop at each onehour period leave the system to some WY selected by the model.…”

“…These problems are known to be NP-hard [1], and several approaches such as Mixed Integer Programming models (MIP), heuristics and metaheuristics, and exact algorithms have been applied to solve them. For example, [1] and [2] apply MIP models to minimize the total distance traveled by buses, considering depot capacities and time periods of operation; these models consider that the initial and final points in a route can be different. In [3], the authors minimize vehicle trips without passengers and the initial and final points in a route can also be different; another work minimizes the fleet size and operational costs by reducing deadheading [4].…”

“…In this work, the starting and ending points of a job must be the same. Some research addresses the depot location problem and the allocation of buses to depots to minimize dead kilometers and transportation system costs [1][2][3][4][5][6][7][8][9][10][11][12][13].…”

Efficient Heuristic Methods for Minimizing the Cost of City Bus Operations of Indian Urban Road Transportation Organizations

“…Based on a quick analysis of literature, it is observed that the ACBD problem considers two different broad objectives: one is minimizing the dead kilometers, and the other is minimizing the dead kilometer cost. The problem of minimizing the dead kilometers has been widely addressed in India and other countries (Prakash et al 1999;Mathirajan et al 2010;Djiba et al 2012;Eliiyi et al 2012;Nasibov et al 2013;Mahadikar et al 2015) with some assumptions. However, since every bus has a different mileage for every unit of fuel, minimizing the dead kilometer cost (DKC) is a more appropriate objective function.…”

The Distance Factor in Swedish Bus Contracts How far are operators willing to go?

“…Dead running has also been given more attention in the engineering literature when trying to optimize routes or implement new bus lines efficiently. A common finding is that dead running can often be reduced by optimized driving schemes (Prakash et al, 1999;Mahadikar et al, 2015), that can reduce the cost of dead running by some 10 percent (Kepaptsoglou et al, 2009).…”