javad rezaeian zaidi

This research discusses the multi-objective modeling of vehicle routing by considering time windows, traffic conditions and customer clustering in mbazar online stores. Considering the traffic situation of Tehran city and the necessity of timely delivery of goods to customers, especially customers, it is necessary to consider the amount of traffic in the route of store vehicles. This research presents a two-objective model for the vehicle routing problem by considering priority time windows, traffic conditions, and customer clustering. The first objective is minimizing the transportation fleet costs and the second is maximizing customer satisfaction. The relevant indices have been calculated, and acceptable results have been obtained to compare the weed algorithm with the exact solution method of the mathematical model. Finally, a sensitivity analysis shows changes in the objective functions. According to the obtained results, with the increase in average dissatisfaction, the value of the first objective function is constant, but the value of the second objective function has increased. There has really been a lot of dissatisfaction.

This research proposes and solves a mathematical problem of parallel machine scheduling to minimize the total completion time and energy cost. This research aims to design and optimize a multi-objective mathematical model by minimizing energy consumption and total completion time for the parallel machine scheduling problem in Semnan Polyethylene Factory. First, the mathematical model of the problem is provided, and then the solution method is investigated using the epsilon constraint method in the GAMS optimization software and the meta-heuristic imperialist competitive algorithm (ICA). The mathematical model is validated using GAMS software and the constraint epsilon method and a real problem is implemented in large dimensions regarding the case study of the polyethylene factory in Semnan province using the meta-heuristic ICA. Finally, the performance of the ICA is measured in terms of the RPI index for small dimensions and the MID index for examples with large dimensions. Numerical results show that the value of the index for distance from the ideal point in the ICA is lower than that of the index obtained from solving the problem in GAMS. With these interpretations, it can be concluded that the ICA has a better performance than GAMS for optimizing the parallel machine scheduling problem in this research. According to the obtained answers, it can be concluded that with the increase in the time to do a task, the time to complete all tasks also increases and the cost of energy remains constant. While the cost of doing the task and the price of the electricity signal increase, energy costs increase and the time to complete tasks remains constant.