Multiobjective optimization of machining center process route: Tradeoffs between energy and cost

Xiao, Yongmao; Zhang, Hua; Jiang, Zhigang; Gu, Quan; Yan, Wei

doi:10.1016/j.jclepro.2020.124171

Cited by 22 publications

(17 citation statements)

References 28 publications

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“…Figure 4 reflects the analysis in Table 3 and shows the percentage of studies using the sustainable pillars. For example, Satyro et al [48] considered the economic pillar in a holistic production planning approach, whereas Xiao et al [49] integrated the economic and environmental pillars while solving a routing problem. For more details, the 45 articles were classified as follows: 9 articles addressed economic sustainability indicators, 12 articles addressed environmental indicators, and only 1 article addressed social indicators [50], while the remaining 23 articles used integrated indicators of two or more sustainability pillars.…”

Section: Resultsmentioning

confidence: 99%

“…For more details, the 45 articles were classified as follows: 9 articles addressed economic sustainability indicators, 12 articles addressed environmental indicators, and only 1 article addressed social indicators [50], while the remaining 23 articles used integrated indicators of two or more sustainability pillars. Three of these articles used the integration of social, economic, and environmental indicators [2,30,49], while 20 articles used economic and environmental indicators together.…”

Section: Resultsmentioning

confidence: 99%

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An Analysis of Research Trends in the Sustainability of Production Planning

et al. 2022

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Sustainability has become of great interest in many fields, especially in production systems due to the continual increase in the scarcity of raw materials and environmental awareness. Recent literature has given significant attention to considering the three sustainability pillars (i.e., environmental, economic, and social sustainability) in solving production planning problems. Therefore, the present study conducts a review of the literature on sustainable production planning to analyze the relationships among different production planning problems (e.g., scheduling, lot sizing, aggregate planning, etc.) and the three sustainability pillars. In addition, we analyze the identified studies based on the indicators that define each pillar. The results show that the literature most frequently addresses production scheduling problems while it lacks studies on aggregate production planning problems that consider the sustainability pillars. In addition, there is a growing trend towards obtaining integrated solutions of different planning problems, e.g., combining production planning problems with maintenance planning or energy planning. Additionally, around 45% of the identified studies considered the integration of the economic and the environmental pillars in different production planning problems. In addition, energy consumption and greenhouse gas emissions are the most frequent sustainability indicators considered in the literature, while less attention has been given to social indicators. Another issue is the low number of studies that have considered all three sustainability pillars simultaneously. The finidings highlight the need for more future research towards holistic sustainable production planning approaches.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

An Analysis of Research Trends in the Sustainability of Production Planning

et al. 2022

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“…The method has proven to be a new intelligent algorithm, which can monitor carbon emission in real-time. Xiao et al [24] firstly proposed a combinatorial optimization algorithm that combines an improved genetic algorithm (GA) with an intelligent generation algorithm. This method can effectively perform initial screening and re-screening of the optimal solution set to ensure that the solution set has high-precision and effectiveness.…”

Section: The Solutions Of Carbon Emission Reductionmentioning

confidence: 99%

“…If we only consider environmental factors in the manufacturing process, this will inevitably increase the processing cost, so we should consider the environment, cost, and other factors in the process design. The machining cost of machine tools mainly includes the cutting tool replacement cost, processing cost, and other auxiliary costs [24,27,28].…”

Section: Cost Functionmentioning

confidence: 99%

“…To obtain the high-precision optimal solution of the multi-objective combinatorial optimization problem, the local optimization problem which often occurs in the previous algorithm solving process is abandoned. This research proposes a combinatorial optimization algorithm [24,38,39]. First of all, we designed an intelligent generation algorithm, which can perform preliminary intelligent screening among all feasible workstep sequence chains that meet the constraints and then combine the Adaptive Particle Swarm Optimization (APSO) [40,41] algorithm to further optimize the feasible workstep sequence chains, which aims to achieve the purpose of the optimal solution.…”

Section: Combinatorial Optimization Algorithmmentioning

confidence: 99%

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Research on Blank Optimization Design Based on Low-Carbon and Low-Cost Blank Process Route Optimization Model

et al. 2021

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The optimization of blank design is the key to the implementation of a green innovation strategy. The process of blank design determines more than 80% of resource consumption and environmental emissions during the blank processing. Unfortunately, the traditional blank design method based on function and quality is not suitable for today’s sustainable development concept. In order to solve this problem, a research method of blank design optimization based on a low-carbon and low-cost process route optimization is proposed. Aiming at the processing characteristics of complex box type blank parts, the concept of the workstep element is proposed to represent the characteristics of machining parts, a low-carbon and low-cost multi-objective optimization model is established, and relevant constraints are set up. In addition, an intelligent generation algorithm of a working step chain is proposed, and combined with a particle swarm optimization algorithm to solve the optimization model. Finally, the feasibility and practicability of the method are verified by taking the processing of the blank of an emulsion box as an example. The data comparison shows that the comprehensive performance of the low-carbon and low-cost multi-objective optimization is the best, which meets the requirements of low-carbon processing, low-cost, and sustainable production.

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A rapid modelling method for machine tool power consumption using transfer learning

Wang,

Chen,

Chen

et al. 2024

Int J Adv Manuf Technol

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for Q. Wang A rapid modelling method for machine tool power consumption using transfer learning Abstract：Accurate power consumption models are the basis for improving energy efficiency of machine tools. The acquisition of energy consumption characteristics of different machine tools requires a large number of calibration experiments, which leads to low modelling efficiency. This paper proposes a rapid modelling method using transfer-learning to obtain the power consumption model of the target machine tool. After obtaining the power consumption model of the source machine tool through detailed experiments, this method only needs a few experiments to obtain the power consumption model of the target machine tool, which greatly improves the modelling efficiency, and the method is experimentally verified on different machine tools.

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Multiobjective optimization of machining center process route: Tradeoffs between energy and cost

Cited by 22 publications

References 28 publications

An Analysis of Research Trends in the Sustainability of Production Planning

An Analysis of Research Trends in the Sustainability of Production Planning

Research on Blank Optimization Design Based on Low-Carbon and Low-Cost Blank Process Route Optimization Model

A rapid modelling method for machine tool power consumption using transfer learning

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