{"id":{"repo_id":"exeter","oai_identifier":"oai:figshare.com:article/32532003"},"canonical_url":"https://search.dev.ndltd.org/etd/exeter/oai:figshare.com:article/32532003","repository":{"repo_id":"exeter","name":"University of Exeter","base_url":"https://api.figshare.com/v2/oai"},"display":{"title":"Integrating Simulation–Optimisation for Multi-Objective Blood Supply Chain Network Design","abstract":"Natural disasters such as earthquakes and floods can severely disrupt blood supply chains (BSCs), making the timely distribution of blood products a critical challenge. These systems are highly vulnerable to risks, shortages, wastage, and operational disruptions that may prevent life-saving blood from reaching patients. Therefore, it is essential to design efficient collection and distribution systems that minimise cost while ensuring rapid and reliable delivery from donors to patients. This research develops a novel multi-objective framework for the BSC location–allocation problem. The model incorporates demand uncertainty and environmental considerations, specifically CO₂ emissions, to determine the optimal number, location, and allocation of both permanent and mobile blood collection facilities while minimising total cost, reducing CO₂ emissions, and maximising donor coverage. Unlike traditional static approaches, the study integrates Agent-Based Simulation (ABS) with optimisation to capture stochastic donor behaviour and dynamic system interactions. To solve this complex problem, two metaheuristic approaches are employed: a Tabu Search algorithm applied to the p-median problem, and an Ant Colony Optimisation (ACO) algorithm designed to simultaneously optimise facility number and location. The framework is validated using a benchmark earthquake dataset and a real-world case study in Devon, UK. The results demonstrate that the optimised configurations achieve up to 14.6% reduction in total cost while improving donor coverage and maintaining similar CO₂ emission levels. The ACO algorithm outperforms benchmark methods by obtaining the lowest total cost ($166,823.868) with competitive transportation time (406.024) seconds. Additionally, the findings reveal a trade-off between cost and environmental impact, where reducing mobile facilities increases emissions (up to 45.41 kg), highlighting the importance of balanced network design. Overall, the proposed framework provides a robust decision-support tool for designing cost-efficient, sustainable, and resilient blood supply networks.<p></p>","abstract_html":"Natural disasters such as earthquakes and floods can severely disrupt blood supply chains (BSCs), making the timely distribution of blood products a critical challenge. These systems are highly vulnerable to risks, shortages, wastage, and operational disruptions that may prevent life-saving blood from reaching patients. Therefore, it is essential to design efficient collection and distribution systems that minimise cost while ensuring rapid and reliable delivery from donors to patients. This research develops a novel multi-objective framework for the BSC location–allocation problem. The model incorporates demand uncertainty and environmental considerations, specifically CO₂ emissions, to determine the optimal number, location, and allocation of both permanent and mobile blood collection facilities while minimising total cost, reducing CO₂ emissions, and maximising donor coverage. Unlike traditional static approaches, the study integrates Agent-Based Simulation (ABS) with optimisation to capture stochastic donor behaviour and dynamic system interactions. To solve this complex problem, two metaheuristic approaches are employed: a Tabu Search algorithm applied to the p-median problem, and an Ant Colony Optimisation (ACO) algorithm designed to simultaneously optimise facility number and location. The framework is validated using a benchmark earthquake dataset and a real-world case study in Devon, UK. The results demonstrate that the optimised configurations achieve up to 14.6% reduction in total cost while improving donor coverage and maintaining similar CO₂ emission levels. The ACO algorithm outperforms benchmark methods by obtaining the lowest total cost ($166,823.868) with competitive transportation time (406.024) seconds. Additionally, the findings reveal a trade-off between cost and environmental impact, where reducing mobile facilities increases emissions (up to 45.41 kg), highlighting the importance of balanced network design. Overall, the proposed framework provides a robust decision-support tool for designing cost-efficient, sustainable, and resilient blood supply networks.&lt;p&gt;&lt;/p&gt;","abstract_has_math":false,"creators":["Lama Soliman Khaled (21040352)"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026-04-20T00:00:00Z","date_published":"2026-04-20T00:00:00Z","updated_at":"2026-07-27T19:32:53Z","subjects":["Blood supply chain","facility location-allocation","blood collection"],"languages":[],"rights":["All rights reserved","Open Access after 2027-12-01"],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["10779/exe.32532003.v1"],"render_values":[{"text":"10779/exe.32532003.v1","href":null,"code":true}]}]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Lama Soliman Khaled (21040352)"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2026-04-20T00:00:00Z"]},{"key":"dc:relation","label":"Dc Relation","values":["https://figshare.com/articles/thesis/Integrating_Simulation_Optimisation_for_Multi-Objective_Blood_Supply_Chain_Network_Design/32532003"]},{"key":"dc:type","label":"Dc Type","values":["Text","Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Blood supply chain","facility location-allocation","blood collection"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["All rights reserved","Open Access after 2027-12-01"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["10779/exe.32532003.v1"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Natural disasters such as earthquakes and floods can severely disrupt blood supply chains (BSCs), making the timely distribution of blood products a critical challenge. These systems are highly vulnerable to risks, shortages, wastage, and operational disruptions that may prevent life-saving blood from reaching patients. Therefore, it is essential to design efficient collection and distribution systems that minimise cost while ensuring rapid and reliable delivery from donors to patients. This research develops a novel multi-objective framework for the BSC location–allocation problem. The model incorporates demand uncertainty and environmental considerations, specifically CO₂ emissions, to determine the optimal number, location, and allocation of both permanent and mobile blood collection facilities while minimising total cost, reducing CO₂ emissions, and maximising donor coverage. Unlike traditional static approaches, the study integrates Agent-Based Simulation (ABS) with optimisation to capture stochastic donor behaviour and dynamic system interactions. To solve this complex problem, two metaheuristic approaches are employed: a Tabu Search algorithm applied to the p-median problem, and an Ant Colony Optimisation (ACO) algorithm designed to simultaneously optimise facility number and location. The framework is validated using a benchmark earthquake dataset and a real-world case study in Devon, UK. The results demonstrate that the optimised configurations achieve up to 14.6% reduction in total cost while improving donor coverage and maintaining similar CO₂ emission levels. The ACO algorithm outperforms benchmark methods by obtaining the lowest total cost ($166,823.868) with competitive transportation time (406.024) seconds. Additionally, the findings reveal a trade-off between cost and environmental impact, where reducing mobile facilities increases emissions (up to 45.41 kg), highlighting the importance of balanced network design. Overall, the proposed framework provides a robust decision-support tool for designing cost-efficient, sustainable, and resilient blood supply networks.<p></p>"]},{"key":"dc:title","label":"Title","values":["Integrating Simulation–Optimisation for Multi-Objective Blood Supply Chain Network Design"]}]}],"canonical_facts":{"dc:creator":["Lama Soliman Khaled (21040352)"],"dc:date":["2026-04-20T00:00:00Z"],"dc:description":["Natural disasters such as earthquakes and floods can severely disrupt blood supply chains (BSCs), making the timely distribution of blood products a critical challenge. These systems are highly vulnerable to risks, shortages, wastage, and operational disruptions that may prevent life-saving blood from reaching patients. Therefore, it is essential to design efficient collection and distribution systems that minimise cost while ensuring rapid and reliable delivery from donors to patients. This research develops a novel multi-objective framework for the BSC location–allocation problem. The model incorporates demand uncertainty and environmental considerations, specifically CO₂ emissions, to determine the optimal number, location, and allocation of both permanent and mobile blood collection facilities while minimising total cost, reducing CO₂ emissions, and maximising donor coverage. Unlike traditional static approaches, the study integrates Agent-Based Simulation (ABS) with optimisation to capture stochastic donor behaviour and dynamic system interactions. To solve this complex problem, two metaheuristic approaches are employed: a Tabu Search algorithm applied to the p-median problem, and an Ant Colony Optimisation (ACO) algorithm designed to simultaneously optimise facility number and location. The framework is validated using a benchmark earthquake dataset and a real-world case study in Devon, UK. The results demonstrate that the optimised configurations achieve up to 14.6% reduction in total cost while improving donor coverage and maintaining similar CO₂ emission levels. The ACO algorithm outperforms benchmark methods by obtaining the lowest total cost ($166,823.868) with competitive transportation time (406.024) seconds. Additionally, the findings reveal a trade-off between cost and environmental impact, where reducing mobile facilities increases emissions (up to 45.41 kg), highlighting the importance of balanced network design. Overall, the proposed framework provides a robust decision-support tool for designing cost-efficient, sustainable, and resilient blood supply networks.<p></p>"],"dc:identifier":["10779/exe.32532003.v1"],"dc:relation":["https://figshare.com/articles/thesis/Integrating_Simulation_Optimisation_for_Multi-Objective_Blood_Supply_Chain_Network_Design/32532003"],"dc:rights":["All rights reserved","Open Access after 2027-12-01"],"dc:subject":["Blood supply chain","facility location-allocation","blood collection"],"dc:title":["Integrating Simulation–Optimisation for Multi-Objective Blood Supply Chain Network Design"],"dc:type":["Text","Thesis"]},"updated_at":"2026-07-27T19:32:53Z"}