Mathematical model of sustainable multilevel supply chain with meta-heuristic algorithm approach (Case study: ATMOSPHERE GROUP : Industrial and Manufacturing Power Plant)

In the current complicated supply chains, sustainability and two social and environmental perspectives have significantly caught researchers’ attention due to their significant role in cost reduction. The present study aims to propose a sustainable multi-tier supply chain model for power plant products for industrial and manufacturing factories.

To this end, a mathematical model was proposed with three

maximizing the social responsibility, minimizing the emission of environmental pollutants, and reducing the costs of the supply chain. The whale and genetic metaheuristic algorithms were employed to propose and solve the model since sustainable supply chain planning was considered an NH-hard problem.

In order to solve the proposed model, the experimental sample was designed in three groups including small, medium, and large in terms of the data of Atmosphere Company. The results of whale optimization and genetic algorithms were compared according to the comparative indices of quality, dispersion, uniformity, and solving time.

Originality/Value:

 According to the results, the whale algorithm was able to provide higher quality and near-optimal solutions than genetic algorithm; in addition, by comparison, it could efficiently explore and extract possible areas of the solution in terms of quality and dispersion indices. However, a shorter amount of time was required for genetic algorithm to uniformly find solutions.