How managerial perspectives affect the optimal fleet size and mix model: a multi-objective approach

We examine the interplay between the business and logistical aspects of a heterogeneous vehicle mix and fleet size problem using inputs from a dairy cooperative in India. Five objective functions have been modelled and simultaneously solved using a mixed-integer linear fuzzy goal programming method. These include net profit as a maximization function, and transportation cost, transportation time, lost sales due to non-service, and in-transit damage or loss as minimization functions. Our paper contributes to the literature by evaluating critical business objectives such as net profits, lost sales due to non-service, and in-transit loss in conjunction with the typical heterogeneous fleet size and mix optimization decisions. The paper proposes two different solution methods: the Competing and the Compensatory method, which may be viewed as two extreme ends of the solution spectrum. Under the Competing method, all five objectives are assumed to be equally important, while the Compensating method allows the optimal solution to endogenously attach priorities to the different objectives. The two provide very different solutions since the Compensatory method considers the synergies between the objectives while the Competing method ignores them. The sensitivity analysis in the paper will also aid to managerial decision-making by evaluating different priorities for the multiple objectives during different periods. Given the impreciseness in the goal definitions and incomplete information available to a decision-maker, our paper the strengthens vehicle fleet size and mix decision modelling problems by adding other managerial perspectives.