In this paper, a new hybrid algorithm based on multi-objective genetic algorithm (MOGA) using simulated annealing (SA) is proposed for scheduling unrelated parallel machines with sequence-dependent setup times, varying due dates, ready times and precedence relations among jobs. Our objective is to minimize makespan (Maximum completion time of all machines), number of tardy jobs, total tardiness and total earliness at the same time which can be more advantageous in real environment than considering each of objectives separately. For obtaining an optimal solution, hybrid algorithm based on MOGA and SA has been proposed in order to gain both good global and local search abilities. Simulation results and four well-known multi-objective performance metrics, indicate that the proposed hybrid algorithm outperforms the genetic algorithm (GA) and SA in terms of each objective and significantly in minimizing the total cost of the weighted function.

Green supply chain management (GSCM) has become an emerging concept among the environmental management topics during the last few years. There are some pressures that push industries to adopt GSCM like: governmental regulations, tough market competition for green image, pressure from Non-governmental organizations, media pressures and other pressures for environmental actions. These pressures can be more considerable for special industries like mining and mineral industries, because of their activities which can cause more damages to environment. As it is not possible to respond to all of the pressures at the same time, identifying and ranking the most important pressures can be very useful for managers & apos; decisions. This study aims to identify and evaluate the pressures for GSCM adopting according to Iranian mining experts & apos; opinions by using grey methodology.