A carbon efficiency upgrading method for mechanical machining based on scheduling optimization strategy

Abstract

Low-carbon manufacturing (LCM) is increasingly being regarded as a new sustainable manufacturing model of carbon emission reduction in the manufacturing industry. In this paper, a two-stage low-carbon scheduling optimization method of job shop is presented as part of the efforts to implement LCM, which also aims to reduce the processing cost and improve the efficiency of a mechanical machining process. In the first stage, a task assignment optimization model is proposed to optimize carbon emissions without jeopardizing the processing efficiency and the profit of a machining process. Non-dominated sorting genetic algorithm II and technique for order preference by similarity to an ideal solution are then adopted to assign the most suitable batch task of different parts to each machine. In the second stage, a processing route optimization model is established to plan the processing sequence of different parts for each machine. Finally, niche genetic algorithm is utilized to minimize the makespan. A case study on the fabrication of four typical parts of a machine tool is demonstrated to validate the proposed method.

Keywords

Low-carbon manufacturing / carbon efficiency / multi-objective optimization / two-stage scheduling / job shop

References

[1] Department of Energy Statistics National Bureau of Statistics People’s Republic of China. China Energy Statistical Yearbook 2016. Beijing: China Statistics Press, 2016 (in Chinese)

[2] Ma F, Zhang H, Hon K K B, An optimization approach of selective laser sintering considering energy consumption and material cost. Journal of Cleaner Production, 2018, 199: 529–537

[3] Liu Y, Liu X, Zhao J. Research on job-shop scheduling optimization method with limited resources. International Journal of Advanced Manufacturing Technology, 2008, 38(3–4): 386–392

[4] Tang D, Dai M, Salido M A, Energy-efficient dynamic scheduling for a flexible flow shop using an improved particle swarm optimization. Computers in Industry, 2016, 81: 82–95

[5] Mouzon G C, Yildirim M B. A framework to minimise total energy consumption and total tardiness on a single machine. International Journal of Sustainable Engineering, 2008, 1(2): 105–116

[6] Mouzon G C. Operational methods and models for minimization of energy consumption in a manufacturing environment. Dissertation for the Doctoral Degree. Wichita: Wichita State University, 2008

[7] Giglio D, Paolucci M, Roshani A. Integrated lot sizing and energy-efficient job shop scheduling problem in manufacturing/remanufacturing systems. Journal of Cleaner Production, 2017, 148: 624–641

[8] Salido M A, Escamilla J, Barber F, Energy efficiency, robustness, and makespan optimality in job-shop scheduling problems. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 2016, 30(3): 300–312

[9] Li L L, Li C B, Li L, An integrated approach for remanufacturing job shop scheduling with routing alternatives. Mathematical Biosciences and Engineering: MBE, 2019, 16(4): 2063–2085

[10] Piroozfard H, Wong K Y, Wong W P. Minimizing total carbon footprint and total late work criterion in flexible job shop scheduling by using an improved multi-objective genetic algorithm. Resources, Conservation and Recycling, 2018, 128: 267–283

[11] Zhang C, Gu P, Jiang P. Low-carbon scheduling and estimating for a flexible job shop based on carbon footprint and carbon efficiency of multi-job processing. Proceedings of the Institution of Mechanical Engineers. Part B, Journal of Engineering Manufacture, 2015, 229(2): 328–342

[12] Zhang Y, Liu Q, Zhou Y, Integrated optimization of cutting parameters and scheduling for reducing carbon emissions. Journal of Cleaner Production, 2017, 149: 886–895

[13] Lei D. Novel teaching-learning-based optimization algorithm for low carbon scheduling of flexible job shop. Control and Decision, 2017, 32(9): 1621–1627 (in Chinese)

[14] Zhou G, Tian C, Zhang J, Multi-objective process route optimization considering carbon emissions. International Journal of Advanced Manufacturing Technology, 2018, 96(1–4): 1195–1213

[15] Verfaillie H A, Bidwell R. Measuring eco-efficiency: A guide to reporting company performance. World Business Council for Sustainable Development, 2000

[16] Cao H, Li H, Cheng H, A carbon efficiency approach for life-cycle carbon emission characteristics of machine tools. Journal of Cleaner Production, 2012, 37: 19–28

[17] Jiang Z, Ding Z, Zhang H, Data-driven ecological performance evaluation for remanufacturing process. Energy Conversion and Management, 2019, 198: 111844

[18] Zhu S, Jiang Z, Zhang H, A carbon efficiency evaluation method for manufacturing process chain decision-making. Journal of Cleaner Production, 2017, 148: 665–680

[19] Li C, Tang Y, Cui L, A quantitative approach to analyze carbon emissions of CNC-based machining systems. Journal of Intelligent Manufacturing, 2015, 26(5): 911–922

[20] Deb K, Agrawal S, Pratap A, A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: Schoenauer M, Deb K, Rudolph G, et al., eds. Parallel Problem Solving from Nature PPSN VI. PPSN 2000. Lecture Notes in Computer Science, vol 1917. Berlin: Springer, 2000, 849–858

[21] Mirjalili S. Genetic algorithm. In: Mirjalili S, ed. Evolutionary Algorithms and Neural Networks. Cham: Springer, 2019, 43–55

[22] Ma F, Zhang H, Gong Q, A novel energy evaluation approach of machining processes based on data analysis. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2019, 1–15

[23] Ding Z, Jiang Z, Zhang H, An integrated decision-making method for selecting machine tool guideways considering remanufacturability. International Journal of Computer Integrated Manufacturing, 2018, 1–12

RIGHTS & PERMISSIONS

Higher Education Press