MULTI-OBJECTIVE OPTIMIZATION FOR SUSTAINABLE VEHICLE ROUTING PROBLEM UNDER UNCERTAINTY USING THE L AGRANGIAN RELAXATION ALGORITHM-CASE : FOOD INDUSTRY COMPANY

The current study indicates a multi-objective optimization model for vehicle routing problems in the sustainable supply chain under uncertainty conditions. The proposed optimization model seeks to take into consideration the economic, environmental, and social aspects. The focus research is on social indicators among the dimensions of sustainability, for which the weights of the significance of the adverse social effects, including the risk of accidents, working leaves, and the positive social effects, including impartiality between employees, more job opportunities, and heightened levels of welfare for employees, are calculated using the Group Best-Worst method (GBWM) and simultaneously included in the model. Also, the possibilisticrobust programming (PRP) approach was employed to adjust the robustness level of the outputting decisions against the uncertainty of the parameters. A single-objective model can be created by utilizing an extended epsilon-constraint method from a multi-objective one. and Lagrangian relaxation heuristic is used to solve the proposed model given its medium to large scale, and a case study of a food industry company is examined to verify the applicability of the proposed model for real-life data. The numerical results and the obtained optimal routes indicate that the model can greatly enhance the decision-making capacities of supply chain executives.