On Strategic Planning of a Dynamic Allocation of Vehicles with Stochastic Breakdown to Destinations with Multiple Alternative Routes for Returns Maximization

Abstract

This paper proposes a dynamic programming (DP) approach for a stochastic multi-period allocation problem, whereby fleet of vehicles are assigned from stations to destinations with multiple alternative routes in order to maximize returns, while the vehicles are subject to random failure. In the process of managing the business, the company is assumed to incur proportional management costs and pay tax to government. The expected returns is modelled as a function of random failure of vehicles due to bad roads and depreciation. The depreciation rate is assumed to follow a straight-line approach. The breakdown rate is modelled as function of the rate of bad roads on the fleets, depreciation rate and expiration time of the fleets. The sum of the probability rate of bad roads on the fleets and depreciation rate, is referred to in this paper, as "decay rate" of the fleets. This paper aim at: (i) modelling the breakdown rates of the vehicles over time; (ii) modelling a stochastic multi-period allocation of the vehicles from stations to destinations with multiple alternative routes and random breakdown of the vehicles; (iii) maximizing the expected net returns of the operations over a period of time; and (iv) determining the optimal management costs and tax payable to government over finite time horizon. Stochastic models and optimal policies of the fleet of vehicles allocation are considered, and problem is solved using DP approach. As a result, the optimal expected net returns from all the destinations and the sum total for all the stations, both for the absence and presence of stochastic break down are obtained. Also obtained are the optimal management costs and tax accrued to government from the investment process over time. Some sensitivity analysis are also carried out in this paper. It was found in this paper that as the breakdown rate of vehicles increases, the expected net returns decreases, and vice versa. Finally, the proposed models were validated using data from some of the transport companies in Nigeria.