arXiv subjects

Shahin Rahimifard

Publications and source records attributed to Shahin Rahimifard.

4 recordsLinked to original sources

arXiv2026

A Hybrid Discrete-Event and Agent-Based Simulation Approach to Model Circular Supply Chains in Healthcare: A Case Study of Laparoscopic Scissors

Circular healthcare supply chains are inherently complex, characterised by interdependencies among their actors and high uncertainty in product flows and performance. Current methods used to predict the outcomes of transitioning to circular economy (CE) are limited and mostly static. This paper demonstrates the use of simulation to assess the effect of introducing circular products and the implications across the healthcare supply chain accounting for variability. The laparoscopic scissors supply chain is chosen as a case study example. To the best of our knowledge, this is the first study that assesses the implications of introducing circular product (medical devices) designs at both the individual supply chain member and overall system level. The model can be also used to inform optimal inventory strategies for hospitals, to ensure that patient safety and hospital operations are maintained. Our findings suggest that adopting circular products can reduce the environmental impact, but to achieve significant reductions in both cost and emissions, it requires significant upfront investment. We discuss the theoretical and practical implications of our study in developing tools to support the transition to CE.

eess.SY↗

arXiv2025

Synchronized Object Detection for Autonomous Sorting, Mapping, and Quantification of Materials in Circular Healthcare

The circular economy paradigm is gaining interest as a solution to reducing both material supply uncertainties and waste generation. One of the main challenges in realizing this paradigm is monitoring materials, since in general, something that is not measured cannot be effectively managed. In this paper, we propose a real-time synchronized object detection framework that enables, at the same time, autonomous sorting, mapping, and quantification of solid materials. We begin by introducing the general framework for real-time wide-area material monitoring, and then, we illustrate it using a numerical example. Finally, we develop a first prototype whose working principle is underpinned by the proposed framework. The prototype detects 4 materials from 5 different models of inhalers and, through a synchronization mechanism, it combines the detection outputs of 2 vision units running at 12-22 frames per second (Fig. 1). This led us to introduce the notion of synchromaterial and to conceive a robotic waste sorter as a node compartment of a material network. Dataset, code, and demo videos are publicly available.

cs.CV↗

arXiv2024

Towards a Thermodynamical Deep-Learning-Vision-Based Flexible Robotic Cell for Circular Healthcare

The dependence on finite reserves of raw materials and the production of waste are two unsolved problems of the traditional linear economy. Healthcare, as a major sector of any nation, is currently facing them. Hence, in this paper, we report theoretical and practical advances of robotic reprocessing of small medical devices. Specifically, on the theory, we combine compartmental dynamical thermodynamics with the mechanics of robots to integrate robotics into a system-level perspective, and then, propose graph-based circularity indicators by leveraging our thermodynamic framework. Our thermodynamic framework is also a step forward in defining the theoretical foundations of circular material flow designs as it improves material flow analysis (MFA) by adding dynamical energy balances to the usual mass balances. On the practice, we report on the on-going design of a flexible robotic cell enabled by deep-learning vision for resources mapping and quantification, disassembly, and waste sorting of small medical devices.

cs.RO↗

arXiv2023

Visual Material Characteristics Learning for Circular Healthcare

The linear take-make-dispose paradigm at the foundations of our traditional economy is proving to be unsustainable due to waste pollution and material supply uncertainties. Hence, increasing the circularity of material flows is necessary. In this paper, we make a step towards circular healthcare by developing several vision systems targeting three main circular economy tasks: resources mapping and quantification, waste sorting, and disassembly. The performance of our systems demonstrates that representation-learning vision can improve the recovery chain, where autonomous systems are key enablers due to the contamination risks. We also published two fully-annotated datasets for image segmentation and for key-point tracking in disassembly operations of inhalers and glucose meters. The datasets and source code are publicly available.

cs.CV↗