Scholarlink

<p>The transition toward sustainable industrial production requires operational models that balance environmental performance with economic viability. This study develops a mixed-integer linear programming (MILP) framework to simultaneously minimize total energy consumption, CO₂ emissions, and operational cost in manufacturing plants. An exact ε-constraint method is applied to generate Pareto-optimal trade-offs under constraints on demand, capacity, and environmental policy. The model is implemented in Python using Pyomo and solved with the Gurobi optimizer. A representative case study involving five processes and three products over a ten-period planning horizon demonstrates the applicability of the framework. Results indicate that renewable energy integration reduces emissions by approximately 30% with only a 0.4% increase in operating cost, while efficiency-oriented scheduling achieves balanced energy–cost trade-offs. Sensitivity analysis confirms the robustness of the framework under varying emission caps and energy quotas. The framework provides a scalable, regulation-compliant decision-support architecture.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Multi-Objective Optimization of Sustainable Production Systems: Minimizing Energy use and Emissions in Industrial Operations

  • 期刊论文

  • 发表日期：

    2026年4月28日

查看全文
  • Tejinder Singh Lakhwani

摘要

The transition toward sustainable industrial production requires operational models that balance environmental performance with economic viability. This study develops a mixed-integer linear programming (MILP) framework to simultaneously minimize total energy consumption, CO₂ emissions, and operational cost in manufacturing plants. An exact ε-constraint method is applied to generate Pareto-optimal trade-offs under constraints on demand, capacity, and environmental policy. The model is implemented in Python using Pyomo and solved with the Gurobi optimizer. A representative case study involving five processes and three products over a ten-period planning horizon demonstrates the applicability of the framework. Results indicate that renewable energy integration reduces emissions by approximately 30% with only a 0.4% increase in operating cost, while efficiency-oriented scheduling achieves balanced energy–cost trade-offs. Sensitivity analysis confirms the robustness of the framework under varying emission caps and energy quotas. The framework provides a scalable, regulation-compliant decision-support architecture.