رها محمد زاده

The main problem of this research, which is introduced by the abbreviation RMO_DRVRMTW, is a multi-objective optimization problem with robustness under uncertainty. In the RMO_DRVRMTW problem, three different objective functions are considered: 1-economic (minimization of cost), 2-environmental (minimization of environmental effects) and 3-customer-oriented (minimization of dissatisfaction from order delivery time). Also, in this issue, the uncertainty related to the time of moving vehicles between network nodes (depots and customers) is also considered as random scenarios, and the stability property of the response under uncertainty is also considered. To deliver customers' orders, a heterogeneous fleet of vehicles is considered, which differ from 4 factors: capacity, fixed/variable cost, environmental impact, and finally, transit time. For each customer, a set of order delivery time windows are considered, which are prioritized for different customers. Based on the customer-oriented objective function, customers' dissatisfaction with order delivery time is minimized so that the time window with higher priority is selected. In the economic objective, the total costs of removal and establishment of depots and fixed and variable costs of vehicles should be minimized. . Due to the multi-objective nature of the problem (considering economic, environmental and social goals) and uncertainty control, we have used the stable scenario-oriented stochastic optimization (RSSO) approach to model the problem, in which, in addition to the average of the objective functions under different scenarios, the optimal It is possible, the variance of the output or the cost is minimized in the pessimistic mode. For the final stable multi-objective optimization model of this research, first the exact solution method of AEC and then the NSGA-II meta-heuristic approximation method have been developed. The result of the numerical performance eva‎luation of AEC and NSGA-II methods shows that AEC performs better for small and even medium-sized problems and works optimally and accurately, while NSGA-II is an option in large dimensions where AEC cannot be used. Suitable for use. Also, the analysis of the uncertainty and the stability value of the model in controlling the uncertainty of the displacement time shows that by using the proposed RSSO approach, we have been able to create both the stability and the optimal stability under uncertainty, which has caused the deviation from the optimality to be be significantly reduced compared to the average/nominal value approach.

مسيريابي وسايل نقليه , پنجره زماني تحويل , مكانيابي و بازطراحي , عدم قطعيت , بهينه سازي چندهدفه , بهينه سازي استوار

Vehicle routing , Delivery time window , Positioning and redesign , uncertainty , Multi-objective optimization , Robust Optimization

Raha Mohammadzade

Dr. Javad Vahidi