The iron and steel industry is a pillar industry of the national economy in many countries and is also a source of high energy consumption and pollution gas emissions. In addition to the economic aspect, there have been increasing concerns over how to minimise the negative environmental impact and enhance the awareness of social responsibility for iron and steel enterprises. Therefore, this study proposes an intelligent scheduling system for addressing the supplier selection problem by considering sustainable scheduling (SS) (ISS-AFLCSS) to achieve maximised benefits of logistics costs, carbon emission and fatigued driving for the Chengsteel Company. In the ISS-AFLCSS, first, a multiobjective mathematical optimisation model is formulated. Second, this study proposed a hybrid approach using an improved genetic algorithm (GA) to optimise multiple objectives of scenarios and adopting the technique for order preference by similarity to an ideal solution (TOPSIS) method with the analytic hierarchy process (AHP) to precisely optimise and select a best-ideal scenario. The results confirm that the proposed ISS-AFLCSS can provide accurate guidance in practicing SS for managers of enterprises.

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The coordinated development of companies and ecological protection are possible only with increasing environmental awareness. Therefore, this study aims to investigate how companies can achieve sustainable development. It is found that the scientific implementation of the vehicle scheduling problem (VSP) for just-in-time (JIT) delivery in the raw material procurement logistics of iron and steel companies can reduce the carbon emissions in the VSP process and, taking into account the negative correlation between weather conditions and PM10, can effectively reduce PM10. On this basis, a multiobjective optimization model is constructed with the objectives of minimizing carbon emissions and PM10 along with the traditional objective of cost optimization. A greedy algorithm with high computational efficiency and an embedded genetic algorithm (GA) is used to further improve the response time of the VSP. Verification shows that in practice, the model enables companies to effectively reduce not only logistics costs but also PM10 and carbon emissions; in theory, the model expands the applicability of JIT to all value-added activities, exploring all value-added activities in different spatial and temporal dimensions to achieve the optimal combination of company cost, environmental effects, and weather dimensions.