Heidari Ali, Sheikh-Azadi Amir-Hosein, Hasan-Zadeh Atefeh, Kazemzadeh Yousef
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Fouman Faculty of Engineering, College of Engineering, University of Tehran, P.O. Box 4358139115, Fouman, Iran.
Sci Rep. 2024 Oct 29;14(1):25966. doi: 10.1038/s41598-024-77217-9.
The growth of the global economy is accompanied by significant energy consumption, and greenhouse gas emissions create various problems such as global warming and environmental degradation. To protect the environment, governments are seeking to reduce carbon emissions. Production systems that operate solely based on economic factors in the workshop only consider problems such as production speed, cost, and processing time. Two aspects can be effective in saving energy and reducing emissions at the production planning level: using routing to find the shortest path for collecting workpieces to the workshop, and turning off machines with long idle times and restarting them at the appropriate time. If the workshop production problem is combined with vehicle routing, a new problem arises. According to the research conducted so far, an integrated mathematical model for production routing has not been designed in a situation where the routing is before the production workshop. In this research, this bi-objective model is introduced, and it is solved using the augmented epsilon-constraint (AEC) method. The proposed mixed-integer linear programming model of this research includes three dimensions: environmental, social (customer satisfaction), and economic simultaneously. Given the high complexity of the mathematical model, MATLAB software and MOPSO and NSGA-II algorithms were used to solve it at higher dimensions. Seven evaluation criteria were used to compare the two proposed algorithms, and the results show that the MOPSO algorithm performs better. The findings suggest that minimizing pollution may involve sacrificing on-time delivery to customers. Consequently, decision-makers must carefully weigh the trade-off between reducing environmental impact and maintaining satisfactory delivery performance, ultimately deciding on an acceptable pollution level.
全球经济的增长伴随着大量的能源消耗，温室气体排放引发了诸如全球变暖和环境退化等各种问题。为了保护环境，各国政府都在寻求减少碳排放。仅基于车间经济因素运行的生产系统只考虑生产速度、成本和加工时间等问题。在生产计划层面，有两个方面对于节能和减排是有效的：利用路径规划找到将工件收集到车间的最短路径，以及关闭长时间闲置的机器并在适当时间重新启动它们。如果将车间生产问题与车辆路径规划相结合，就会出现一个新问题。根据迄今为止的研究，在路径规划先于生产车间的情况下，尚未设计出生产路径规划的综合数学模型。在本研究中，引入了这种双目标模型，并使用增强型ε-约束（AEC）方法对其进行求解。本研究提出的混合整数线性规划模型同时包括环境、社会（客户满意度）和经济三个维度。鉴于数学模型的高度复杂性，使用MATLAB软件以及MOPSO和NSGA-II算法在更高维度上对其进行求解。使用七个评估标准来比较所提出的两种算法，结果表明MOPSO算法表现更好。研究结果表明，将污染降至最低可能意味着牺牲准时向客户交货。因此，决策者必须仔细权衡减少环境影响与保持令人满意的交货绩效之间的权衡，最终确定可接受的污染水平。
