Increased demand for transportation is inevitable along with the growth of social welfare and economic development. Furthermore, providing cheap transportation services has brought about a bulk of benefits in the ever-expanding development of countries. Therefore, the optimization of fuel consumption in the transportation sector is essential for the continued low cost of transportation and reduced amount of pollution caused by the traffic. A major strategy for optimizing fuel consumption and transportation costs in the transportation of goods and passengers is the proper design of transportation networks. Accordingly, in this study, hub covering location problem is modeled taking into account the queuing system in order to minimize the cost of the whole network as well as to minimize emissions of greenhouse gases. The model presented in this study includes uncertain parameters of demand and transportation costs. To control the parameters, a robust-box optimization method is used. As the hub covering network model is multi-objective and NP-hard, three meta-heuristic algorithms; namely MOPSO, NSGA II and MOALO are presented. Computational results show the high efficiency of the MOALO algorithm for obtaining efficient problem-solving in large sizes.

In today’s world, due to rapid changes in the market, scheduling as one of the most fundamental issues of competitive production, plays a very important role in maintaining the competitive position and survival of manufacturing organizations, therefore, development of scheduling models in order to improve the timing criteria is of great importance. In this research, we put the development of a no-wait flow-shop scheduling model alongside with the effect of learning into consideration to minimize the cost of consumption of resources. Finding the correct sequence of two machines’ performance and optimized allocation of the resources for any performance on each machine, were considered as the main goal in this study. To solve the problem, metaheuristic genetic algorithms, particle swarm optimization, imperialist competitive algorithms, optimization of the whale and the League Champions algorithms, have been used. The statistical comparisons and also using of TOPSIS Multi-Criteria Decision Making method, indicate high level of efficiency of the League Champions algorithm with the utility weight of 0.9516.