Robust Platelet Logistics Planning in Disaster Relief Operations Under Uncertainty: a Coordinated Approach

© 2017, Springer Science+Business Media, LLC. Resource sharing, as a coordination mechanism, can mitigate disruptions in supply and changes in demand. It is particularly crucial for platelets because they have a short lifespan and need to be transferred and allocated within a limited time to prevent waste or shortages. Thus, a coordinated model comprised of a mixed vertical-horizontal structure, for the logistics of platelets, is proposed for disaster relief operations in the response phase. The aim of this research is to reduce the wastage and shortage of platelets due to their critical role in wound healing. We present a bi-objective location-allocation robust possibilistic programming model for designing a two-layer coordinated organization strategy for multi-type blood-derived platelets under demand uncertainty. Computational results, derived using a heuristic ε-constraint algorithm, are reported and discussed to show the applicability of the proposed model. The experimental results indicate that surpluses and shortages in platelets remarkably declined following instigation of a coordinated disaster relief operation

Balancing quality, cost, and uncertainty in pharmaceutical supply chain: A robust possibilistic flexible programming approach

The pharmaceutical supply chain (PSC) plays a crucial role in ensuring the timely and reliable availability of essential drugs while maintaining high-quality standards. Balancing the triad of cost, time, and quality is paramount in optimizing the complexities of this supply chain. In this research, a multi-objective PSC optimization model is developed to maximize the key business factors. The dynamic nature of the PSC can significantly compromise the effectiveness of the decision making process. To deal with this challenge, a robust possibilistic flexible programming approach (RPFPA) solution methodology is proposed. This methodology provides a robust and flexible framework to tackle the uncertainties within the supply chain. To validate the proposed model and methodology, a computational analysis of a case study is conducted. The results of the analysis demonstrate the effectiveness of the model and methodology in addressing the uncertainties and complexities of the PSC. Specifically, the findings reveal that by accepting a 23.8% increase in costs, decision-makers can achieve a desirable level of robustness in their decisions. Moreover, the study identifies that the assignment of higher priority to cost objectives leads to more centralized decisions within the supply chain, while a greater emphasis on quality objectives results in a more decentralized approach. By employing the proposed approach, decision-makers can efficiently deal with the complexities and uncertainties inherent in the PSC, making well-informed choices that balance cost, time, and quality

A new bi-objective model of the urban public transportation hub network design under uncertainty

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Fairness-Based Transportation Resilience for Communities under Tsunami Hazard

Abstract: Natural disasters such as tsunamis have catastrophic impacts on the functionality and resilience of transportation networks in impacted areas, and they can damage coastal regions hundreds of kilometers away from the earthquake that caused them, resulting in a significant number of casualties. As a result, the ultimate goal of this study was to develop a fair-based evacuation model under tsunami hazards. The proposed fairness-based evacuation model used in this study aimed to give evacuees equal access to emergency facility centers and assembly areas, reducing the number of casualties and assessing the capability of providing the evacuees' needs

Enhancing Evacuation Planning in Public Buildings: Optimising Egress Location and Protection

Effective evacuation strategies are crucial for ensuring the safety of individuals during emergencies and disasters. Despite significant progress in evacuation planning, the intricate dynamics of disaster scenarios and uncertainties inherent in such situations need to be better incorporated in planning egress locations to enhance safety in buildings. This work focuses on strategically locating egress points within public buildings, acknowledging their pivotal role in facilitating secure evacuations. Optimising egress points improves evacuation efficiency and minimises associated risks, significantly improving evacuation. This research introduces an innovative approach that integrates optimisation models, addresses decision-making complexities, explores practical applications, and considers potential attack scenarios. The study explores evacuation dynamics across diverse scenarios, elevating preparedness, and safety protocols to protect public assets and lives. Developing mixedinteger programming models establishes a foundation for optimising egress locations. MCDM is then employed, leveraging the F-AHP to address uncertainties in egress selection. Practicality is realised through integrating Revit and AnyLogic software, facilitating assessment through BIM and ABM. A stochastic BP model is formulated, addressing both Defender and Attacker perspectives for enhanced egress strategies. This model strategically allocates resources to fortify egresses, ensuring occupant safety during evacuations. Contributions further optimisation approaches, fortification strategies, and progressive enhancements in evacuation planning. These collectively address key challenges and gaps in existing literature, enhancing evacuation efficiency and public safety during emergencies. The research bridges gaps in existing approaches, providing a framework for future investigations into optimising evacuation strategies, enhanced disaster preparation, and further advancements in the field

Optimization for Decision Making II

In the current context of the electronic governance of society, both administrations and citizens are demanding the greater participation of all the actors involved in the decision-making process relative to the governance of society. This book presents collective works published in the recent Special Issue (SI) entitled “Optimization for Decision Making II”. These works give an appropriate response to the new challenges raised, the decision-making process can be done by applying different methods and tools, as well as using different objectives. In real-life problems, the formulation of decision-making problems and the application of optimization techniques to support decisions are particularly complex and a wide range of optimization techniques and methodologies are used to minimize risks, improve quality in making decisions or, in general, to solve problems. In addition, a sensitivity or robustness analysis should be done to validate/analyze the influence of uncertainty regarding decision-making. This book brings together a collection of inter-/multi-disciplinary works applied to the optimization of decision making in a coherent manner

Models and algorithms for trauma network design.

Trauma continues to be the leading cause of death and disability in the US for people aged 44 and under, making it a major public health problem. The geographical maldistribution of Trauma Centers (TCs), and the resulting higher access time to the nearest TC, has been shown to impact trauma patient safety and increase disability or mortality. State governments often design a trauma network to provide prompt and definitive care to their citizens. However, this process is mainly manual and experience-based and often leads to a suboptimal network in terms of patient safety and resource utilization. This dissertation fills important voids in this domain and adds much-needed realism to develop insights that trauma decision-makers can use to design their trauma network. In this dissertation, we develop multiple optimization-based trauma network design approaches focusing minimizing mistriages and, in some cases, ensuring equity in care among regions. To mimic trauma care in practice, several realistic features are considered in our approach, which include the consideration of: (i) both severely and non-severely injured trauma patients and associated mistriages, (ii) intermediate trauma centers (ITCs) along with major trauma centers (MTCs), (iii) three dominant criteria for destination determination, and (iv) mistriages in on-scene clinical assessment of injuries. Our first contribution (Chapter 2) proposes the Trauma Center Location Problem (TCLP) that determines the optimal number and location of major trauma centers (MTCs) to improve patient safety. The bi-objective optimization model for TCLP explicitly considers both types of patients (severe and non-severe) and associated mistriages (specifically, system-related under- and over-triages) as a surrogate for patient safety. These mistriages are estimated using our proposed notional tasking algorithm that attempts to mimic the EMS on-scene decision of destination hospital and transportation mode. We develop a heuristic based on Particle Swarm Optimization framework to efficiently solve realistic problem sizes. We illustrate our approach using 2012 data from the state of OH and show that an optimized network for the state could achieve 31.5% improvement in patient safety compared to the 2012 network with the addition of just one MTC; redistribution of the 21 MTCs in the 2012 network led to a 30.4% improvement. Our second contribution (Chapter 3) introduces a Nested Trauma Network Design Problem (NTNDP), which is a nested multi-level, multi-customer, multi-transportation, multi-criteria, capacitated model. The NTNDP model has a bi-objective of maximizing the weighted sum of equity and effectiveness in patient safety. The proposed model includes intermediate trauma centers (TCs) that have been established in many US states to serve as feeder centers to major TCs. The model also incorporates three criteria used by EMS for destination determination; i.e., patient/family choice, closest facility, and protocol. Our proposed ‘3-phase’ approach efficiently solves the resulting MIP model by first solving a relaxed version of the model, then a Constraint Satisfaction Problem, and a modified version of the original optimization problem (if needed). A comprehensive experimental study is conducted to determine the sensitivity of the solutions to various system parameters. A case study is presented using 2019 data from the state of OH that shows more than 30% improvement in the patient safety objective. In our third contribution (Chapter 4), we introduce Trauma Network Design Problem considering Assessment-related Mistriages (TNDP-AM), where we explicitly consider mistriages in on-scene assessment of patient injuries by the EMS. The TNDP-AM model determines the number and location of major trauma centers to maximize patient safety. We model assessment-related mistriages using the Bernoulli random variable and propose a Simheuristic approach that integrates Monte Carlo Simulation with a genetic algorithm (GA) to solve the problem efficiently. Our findings indicate that the trauma network is susceptible to assessment-related mistriages; specifically, higher mistriages in assessing severe patients may lead to a 799% decrease in patient safety and potential clustering of MTCs near high trauma incidence rates. There are several implications of our findings to practice. State trauma decision-makers can use our approaches to not only better manage limited financial resources, but also understand the impact of changes in operational parameters on network performance. The design of training programs for EMS providers to build standardization in decision-making is another advantage

A STOCHASTIC PROGRAMMING APPROACH TO ANALYZE DESIGN AND MANAGEMENT OF FLEXIBILITY IN INFRASTRUCTURE SYSTEMS OPERATING UNDER LONG-TERM UNCERTAINTY

Ph.DDOCTOR OF PHILOSOPH