A novel sustainable multi-objective optimization model for forward and reverse logistics system under demand uncertainty

Document Type

Journal Article

Publication Title

Annals of Operations Research






School of Business and Law




Zarbakhshnia, N., Kannan, D., Mavi, R. K., & Soleimani, H. (2020). A novel sustainable multi-objective optimization model for forward and reverse logistics system under demand uncertainty. Annals of Operations Research, 295, 843-880. https://doi.org/10.1007/s10479-020-03744-z


© 2020, Springer Science+Business Media, LLC, part of Springer Nature. The paper aims to present a multi-product, multi-stage, multi-period, and multi-objective, probabilistic mixed-integer linear programming model for a sustainable forward and reverse logistics network problem. It looks at original and return products to determine both flows in the supply chain—forward and reverse—simultaneously. Besides, to establish centres of forward and reverse logistics activities and make a decision for transportation strategy in a more close-to-real manner, the demand is considered uncertain. We attempt to represent all major dimensions in the objective functions: First objective function is minimizing the processing, transportation, fixed establishing cost and costs of CO2 emission as environmental impacts. Furthermore, the processing time of reverse logistics activities is developed as the second objective function. Finally, in the third objective function, it is tried to maximize social responsibility. Indeed, a complete sustainable approach is developed in this paper. In addition, this model provides novel environmental constraint and social matters in the objective functions as its innovation and contribution. Another contribution of this paper is using probabilistic programming to manage uncertain parameters. Moreover, a non-dominated sorting genetic algorithm (NSGA-II) is configured to achieve Pareto front solutions. The performance of the NSGA-II is compared with a multi-objective particle swarm optimization (MOPSO) by proposing 10 appropriate test problems according to five comparison metrics using analysis of variance (ANOVA) to validate the modeling approach. Overall, according to the results of ANOVA and the comparison metrics, the performance of NSGA-II algorithm is more satisfying compared with that of MOPSO algorithm.