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Carbon and Water Conservation Value of Independent, Place-Based Repair in Lima, Peru
(John Wiley and Sons Inc, 2023-06-01)
To what extent do repair and maintenance of consumer electronics conserve the materials and energy they embody? In this paper we examine the conservation value of a cluster of independent third-party electronics repair businesses in Lima, Peru. Drawing on a combination of methods that include fieldwork, digital methods for online sociology, and life cycle assessment (LCA) of phones and tablets we quantify the conservation value of typical repairs performed at businesses in this cluster in terms of CO2 equivalent (CO2e) and water consumption relative to new manufactures of the same categories of electronics. We model typical repair scenarios and find that repair can offer substantial conservation benefits. However, these benefits vary by device sub-unit repaired (e.g., replacing a camera vs. replacing a display). For example, while two screen repairs through replacement is nearly equivalent to replacement with a whole new device, repairing with components that are already in the market could save around 10% of total emissions in global warming potential (GWP) for both devices. Further, we discuss the politics of attributing the conservation value achieved by the third-party repair cluster in Lima to either domestic (that is, Peruvian) or foreign CO2e and water consumption. Whose conservation of CO2e and water is this? How do the answers to that question shape understandings of the relevance of location for industrial ecology? Our work contributes to the emerging subfield of political industrial ecology and its incorporation of spatially explicit LCAs.
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Evolutionary multi-objective multi-agent deep reinforcement learning for sustainable maintenance scheduling
(Elsevier BV, 2025-05-26)
In recent years, sustainability has emerged as a major priority for businesses across various industries, and the manufacturing sector is no exception. Production and maintenance processes now need to be economically profitable while also adopting practices that adhere to the principles of environmental integrity and social responsibility. This article explores an innovative approach aimed at optimizing maintenance scheduling from an economic perspective (considering maintenance, breakdown, downtime costs), an environmental perspective (considering the carbon footprint produced during production) and a social perspective (considering the fatigue experienced by technicians during maintenance activities). To the best of our knowledge, this is the first study to propose a manufacturing scheduling approach that considers all three pillars of sustainability. Another significant contribution of this research is the innovative way in which the optimization problem is addressed. We propose an evolutionary multi-objective multi-agent Deep Q-network-based approach, where multiple agents explore the preference space to maximize the hypervolume of these sustainable objectives. Our methodology uses industrially representative data that incorporate realistic machine degradation signals, carbon intensity indicators, and technician constraints. The results demonstrate the trade-offs between these objectives when compared to traditional maintenance policies such as corrective and condition-based maintenance, as well as different Deep Q-network policies trained with various preferences. Our approach demonstrates superior performance compared to both baselines. Specifically, we observe an 11.6% improvement in hypervolume over Deep Q-network and an 18.9% improvement over Proximal Policy Optimization, resulting in significantly increased profitability within the system.
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A sustainable hub location-allocation model considering the inspection of defective wagons in the rail freight network
(Inderscience Publishers, 2025-01-01)
The increasing demand for rail transport necessitates an effective transportation network design for the shipment of goods with minimum cost and time. Since the breakdown of wagons is a main delay factor in rail transportation, the maintenance and repairs of defective wagons becomes prominent. In this study, main stations are considered as hubs, and hubs are places where defective wagons are collected. For this purpose, a robust multi-objective mathematical model is proposed to minimise transportation costs considering customer demand. Also, the model seeks to minimise the total transportation time and emissions. The AEC method is exploited to solve and validate the proposed model. Moreover, the sensitivity analysis is performed to demonstrate the effect of changing the main parameters on the outcomes. The results show that repairs and maintenance can affect the capacity. Also, the findings demonstrate the applicability and validity of the proposed model in the railway sector. [Submitted: 29 May 2023; Accepted: 16 January 2024]
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