Energy Efficient Process Planning System – The ENEPLAN Project

Part 1: SustainabilityInternational audienceThe key factor to success, towards a competitive energy consumption reduction, is the effective involvement of SMEs both in the use of more efficient machines and in the Design of an Environment for new products. A specific product can be manufactured in different ways, based on cost optimization rather than on production flexibility and energy efficiency with the involvement of different suppliers. The ENEPLAN project aims at the development of a digital and real, energy-efficient, multi-process, networked, manufacturing system, adapted to the functional specifications of metal formed or machined parts for automotive, aeronautic and domestic appliances. Seventeen partners, coming from seven European countries and the participation of OEMs, SMEs, RTD and technology providers, who work jointly to deliver through ENEPLAN a manufacturing planning decision support tool (meta-CAM tool), for the optimization of the plant operation. This tool will be used from the conceptual phase of the product (final blueprints) to its final dispatch to the customer. This paper will provide a short overview of the ENEPLAN project and its ongoing developments, while the existing results will be presented and discussed and the next steps will be described

Energy-aware integrated process planning and scheduling for job shops

Process planning that is based on environmental consciousness and energy-efficient scheduling currently plays a critical role in sustainable manufacturing processes. Despite their interrelationship, these two topics have often been considered to be independent of each other. It, therefore, would be beneficial to integrate process planning and scheduling for an integrated energy-efficient optimization of product design and manufacturing in a sustainable manufacturing system. This chapter proposes an energy-aware mathematical model for job shops that integrates process planning and scheduling. First, a mixed integrated programming model with performance indicators such as energy consumption and scheduling makespan is established to describe a multi-objective optimization problem. Because the problem is strongly non-deterministic polynomial-time hard (NP-hard), a modified genetic algorithm is adopted to explore the optimal solution (Pareto solution) between energy consumption and makespan. Finally, case studies of energy-aware integrated process planning and scheduling are performed, and the proposed algorithm is compared with other methods. The approach is shown to generate interesting results and can be used to improve the energy efficiency of manufacturing processes at the process planning and scheduling levels