@inbook{35a1933e29904a249833648518ed502b,
title = "Fleet size and mix vehicle routing: A multi-criterion grouping genetic algorithm approach",
abstract = "The FSMVRP is concerned with the determination the fleet size and the composition or mix of heterogeneous vehicles. It is assumed that the number of vehicles of each type is unlimited. This chapter presents a multi-criterion GGA for solving the multi-criterion FSMVRP with fixed and variable costs. Computational experiments were conducted based on benchmark problems in the literature. Comparative analysis showed that the GGA approach obtained best known solutions. Moreover, the GGA approach performed competitively in terms of computation time. In terms of the average cost, GGA also demonstrated competitive performance. The work presented offers useful research contributions for the logistics and transportation industry. The proposed GGA approach uses unique grouping genetic operators. When compared to related approaches in the literature, the algorithm demonstrates its competitive performance. Further research directions should include the design of more efficient and flexible algorithms for solving the FSMVRP problems in which the customer demand and/or time window is uncertain or fuzzy. It may also be fruitful to research further on FSMVRP with fuzzy time windows. The approach can also be extended to similar problems such as home healthcare staff schedule and heterogeneous fixed fleet vehicle routing problem.",
author = "Michael Mutingi and Charles Mbohwa",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2017.",
year = "2017",
doi = "10.1007/978-3-319-44394-2_8",
language = "English",
series = "Studies in Computational Intelligence",
publisher = "Springer Verlag",
pages = "141--159",
booktitle = "Studies in Computational Intelligence",
address = "Germany",
}