A Comprehensive Strategic-Tactical Multi-Objective Sustainable Supply Chain Model with Human Resources Considerations

This study proposes a multi-objective Sustainable Supply Chain Network (SSCN) model considering human resources limitations with different levels of expertise. The proposed model includes multiple suppliers, factories, and customers, where the construction of factories is a strategic decision, and determining the amount of production and allocating human resources with different levels of expertise is taken as a tactical decision. Also, the capital recovery factor has been used in the mathematical model to prevent the influence of strategic decisions on tactical decisions. The results from the mathematical models of epsilon limit, Non-dominated Sorting Genetic Algorithm II (NSGA II), and Multi-Objective Particle Swarm Optimization (MOPSO) show that by reducing the amount of shortage, the amount of production has increased, and as a result, the costs of production, supply and distribution and transportation have increased. Also, with the increase in the production and transportation of products, greenhouse gas emissions have also increased. Examining the impact of the uncertainty rate on the Robust Fuzzy Optimization (RFO) model also shows that with the increase of this coefficient, due to the increase in the demand in the network, the total costs of production, distribution, purchase of raw materials, and transportation have increased. Examining different comparison indices between solution methods also shows that heuristic methods have higher efficiency than exact methods. MOPSO is more efficient than NSGA II for the designed mathematical model in these investigations.