Non-dominated sorting genetic algorithm III with stochastic matrix-based population to solve multi-objective solid transportation problem

The transportation problems, which consist of multiple objectives with heterogeneous conveyances, are the pragmatic representation of the transportation occurring in the real world. However, situations do exist where the solutions obtained by the classical optimization techniques do not reflect the acumen of the decision-maker. Against this backdrop, a systematic algorithm is proposed in this paper to generate an initial population to tackle the multi-objective solid transportation problem efficiently. In addition, the non-dominated sorting genetic algorithm (NSGA) III is carried out to acquire solutions that can demonstrate the varying degrees of objectives. Furthermore, two problems of different sizes are framed, and their performance is compared via NSGA II, hybrid genetic algorithm, and fuzzy programming technique.