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A framework proposal for scheduling environmental impact evaluation in manufacturing systems.

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Title: A framework proposal for scheduling environmental impact evaluation in manufacturing systems.
Authors: Resende Veneroso, Ciele1 (AUTHOR) cresendeveneroso@gmail.com, Franciosi, Chiara2 (AUTHOR), Iannone, Raffaele1 (AUTHOR), Fera, Marcello3 (AUTHOR), Riemma, Stefano1 (AUTHOR)
Source: International Journal of Production Research. Oct2025, Vol. 63 Issue 20, p7386-7404. 19p.
Subject Terms: *ENVIRONMENTAL impact analysis, *RESOURCE allocation, *SIMULATION methods & models, *MANUFACTURING processes, *SCHEDULING, GREENHOUSE gases, SUSTAINABILITY, MACHINE performance
Abstract: The environmental performance of scheduling decisions in complex manufacturing systems is influenced by machine deterioration, resource consumption, and dynamic operation states. However, existing approaches often lack comprehensive environmental impact assessments while ensuring adaptability across diverse production environments. This study presents the Scheduling Environmental Impact Evaluation (SEIE) framework, which integrates an object-oriented hierarchical data representation with a simulation-based methodology to assess the Expected Environmental Impact (EEI) scheduling solutions under machine deterioration. Unlike traditional scheduling models, SEIE treats scheduling solutions as inputs rather than generating new ones. The framework is designed for scalability and adaptability, supporting various production systems (e.g. flow shop, job shop) and accommodating different data availability conditions, including real-time monitoring, historical data, and stochastic or deterministic simulations. To demonstrate its applicability, a hypothetical case study evaluates scheduling solutions based on greenhouse gas (GHG) emissions from energy consumption while incorporating machine deterioration effects on operation time and energy efficiency. Results confirm SEIE's effectiveness in modelling complex production systems and structuring environmental impact evaluations. The framework supports structured decision-making for modern scheduling systems' design and operation by quantifying sustainability impacts and comparing scheduling alternatives. It enables manufacturers to assess environmental implications, refine scheduling strategies, and integrate sustainability considerations into production planning. [ABSTRACT FROM AUTHOR]
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Database: Business Source Index
Description
Abstract:The environmental performance of scheduling decisions in complex manufacturing systems is influenced by machine deterioration, resource consumption, and dynamic operation states. However, existing approaches often lack comprehensive environmental impact assessments while ensuring adaptability across diverse production environments. This study presents the Scheduling Environmental Impact Evaluation (SEIE) framework, which integrates an object-oriented hierarchical data representation with a simulation-based methodology to assess the Expected Environmental Impact (EEI) scheduling solutions under machine deterioration. Unlike traditional scheduling models, SEIE treats scheduling solutions as inputs rather than generating new ones. The framework is designed for scalability and adaptability, supporting various production systems (e.g. flow shop, job shop) and accommodating different data availability conditions, including real-time monitoring, historical data, and stochastic or deterministic simulations. To demonstrate its applicability, a hypothetical case study evaluates scheduling solutions based on greenhouse gas (GHG) emissions from energy consumption while incorporating machine deterioration effects on operation time and energy efficiency. Results confirm SEIE's effectiveness in modelling complex production systems and structuring environmental impact evaluations. The framework supports structured decision-making for modern scheduling systems' design and operation by quantifying sustainability impacts and comparing scheduling alternatives. It enables manufacturers to assess environmental implications, refine scheduling strategies, and integrate sustainability considerations into production planning. [ABSTRACT FROM AUTHOR]
ISSN:00207543
DOI:10.1080/00207543.2025.2497953