A Systematic Approach of Process Planning and Scheduling Optimization for Sustainable Machining

  • S. Wang
  • X. Lu
  • X. X. Li
  • W. D. LiEmail author


The implementation of sustainability in manufacturing companies, whose activities are usually characterized by high variety and low volume, has been crippled by the lack of effective process planning and scheduling solutions for sustainable management of manufacturing shop floors. To address the challenge, an innovative and systematic approach for machining process planning and scheduling optimization has been developed. This approach consists of a process stage and a system stage, augmented with intelligent mechanisms for enhancing the adaptability and responsiveness to job dynamics in machining shop floors. In the process stage, key operational parameters for machining a part are optimized adaptively to meet multiple objectives and constraints, i.e., energy efficiency of the machining process and productivity as objectives and surface quality as a constraint. In the consecutive system stage, to achieve higher energy efficiency and shorter makespan in the entire shop floor, sequencing/set-up planning of machining features, operations and scheduling for producing multiple parts on different machines are optimized. Artificial neural networks are used for establishing the complex nonlinear relationships between the key process parameters and measured data sets of energy consumption and surface quality. Intelligent algorithms, including pattern search, genetic algorithm, and simulated annealing, are applied and benchmarked to identify optimal solutions. Experimental tests indicate that the approach is effective and configurable to meet multiple objectives and technical constraints for sustainable process planning and scheduling. The approach, validated through industrial case studies provided by a European machining company, demonstrates significant potentials of research applicability in practice.


Sustainable manufacturing Computer numerical control machining Process planning Process scheduling Intelligent algorithm Machining feature 


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© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Engineering, Environment and ComputingCoventry UniversityCoventryUK

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