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 optimisation of product design and manufacturing in a sustainable manufacturing system. This article 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 optimisation 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 sustainable manufacturing processes at the process planning and scheduling levels.

Divisions: College of Engineering & Physical Sciences
Additional Information: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 3.0 License ( which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (
Publication ISSN: 2041-2975
Last Modified: 29 Nov 2023 11:42
Date Deposited: 09 Nov 2018 14:59
Full Text Link: 10.1177/0954405414553069
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://journal ... 954405414553069 (Publisher URL)
PURE Output Type: Article
Published Date: 2015
Authors: Dai, Min
Tang, Dunbing
Xu, Yuchun
Li, Weidong


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