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

On the dynamic inventory routing problem in humanitarian logistics: a simulation optimization approach using agent-based modeling

80 páginasIn the immediate aftermath of any disaster event, operational decisions are made to relieve the affected population and minimize casualties and human suffering. To do so, humanitarian logistics planners should be supported by strong decision-making tools to better respond to disaster events. One of the most important decisions is the delivery of the correct amount of humanitarian aid in the right moment to the right place. This decision should be made considering the dynamism of the disaster response operations where the information is not known beforehand and vary over time. For instance, the effect of the Word-of-Mouth and shortages in distribution points’ demand can impact the operational decisions. Therefore, the inventory and transportation decisions should be made constantly to better serve the affected people. This work presents a simulation-optimization approach to make disaster relief distribution decisions dynamically. An agent-based simulation model solves the inventory routing problem dynamically, considering changes in the humanitarian supply chain over the planning horizon. Additionally, the inventory routing schemes are made using a proposed mathematical model that aims to minimize the level of shortage and inventory at risk (associated to the risk of losing it). The computational proposal is implemented in the ANYLOGIC and CPLEX software. Finally, a case study motivated by the 2017 Mocoa-Colombia landslide is developed using real data and is presented to be used in conjunction with the proposed framework. Computational experimentations show the impact of the word-of-mouth and the frequency in decision making in distribution points’ shortages and service levels. Therefore, considering changes in demand over the planning horizon contributes to lowering the shortages and contributes to making better distributions plans in the response phase of a disaster.Después de la ocurrencia de cualquier desastre se deben tomar decisiones para aliviar a la población afectada minimizando las pérdidas humanas y el sufrimiento. Para ello, los responsables de la logística humanitaria deben contar con robustas herramientas para tomar decisiones acertadas que respondan adecuadamente ante esos eventos. Una de las decisiones más importantes es la entrega de ayuda humanitaria en el lugar, las cantidades y en el momento correcto. La anterior decisión debe ser tomada teniendo en cuenta el dinamismo de las operaciones de respuesta humanitaria en donde la información no es conocida de antemano y varía en el tiempo. Por ejemplo, el efecto del Voz a Voz y la escasez en los puntos de distribución de ayuda humanitaira pueden impactar las decisiones operacionales. Es por lo anterior, que las decisiones de transporte de ayuda humanitaria deben ser realizadas constantemente para servir de una mejor forma a la población afectada. Este trabajo presenta una propuesta de simulación-optimización para tomar las decisiones de ruteo de inventario de ayuda humanitaria de forma dinámica. A través de un modelo de simulación basado en agentes se resuelve dinámicamente el problema de ruteo de inventario considerando cambios en la cadena de suministro humanitaria. Adicionalmente, las decisiones de ruteo de inventario son tomadas mediante un modelo matemático propuesto que busca minimizar el nivel de inventario en riesgo y el nivel de escases simultáneamente. La propuesta computacional es implementada en los programas ANYLOGIC y CPLEX. Finalmente mediante un caso de estudio basado en la catastrofe de Mocoa-Colombia en 2017 se evaluará la propuesta. Experimentos computacionales muestran el impacto del voz-a-voz y frecuencia de toma de decisiones en la escasez y el nivel de servicio en los puntos de distribución. Por lo tanto, considerar cambios en la demanda contribuye a disminuir la escasez y hacer mejores esquemas de distribución de ayuda humanitaria.Maestría en Diseño y Gestión de ProcesosMagíster en Diseño y Gestión de Proceso

Chance-Constrained Equilibrium in Electricity Markets With Asymmetric Forecasts

We develop a stochastic equilibrium model for an electricity market with asymmetric renewable energy forecasts. In our setting, market participants optimize their profits using public information about a conditional expectation of energy production but use private information about the forecast error distribution. This information is given in the form of samples and incorporated into profit-maximizing optimizations of market participants through chance constraints. We model information asymmetry by varying the sample size of participants' private information. We show that with more information available, the equilibrium gradually converges to the ideal solution provided by the perfect information scenario. Under information scarcity, however, we show that the market converges to the ideal equilibrium if participants are to infer the forecast error distribution from the statistical properties of the data at hand or share their private forecasts

A Three-Step Methodology to Improve Domestic Energy Efficiency

Increasing energy prices and the greenhouse effect lead to more awareness of energy efficiency of electricity supply. During the last years, a lot of technologies have been developed to improve this efficiency. Next to large scale technologies such as windturbine parks, domestic technologies are developed. These domestic technologies can be divided in 1) Distributed Generation (DG), 2) Energy Storage and 3) Demand Side Load Management. Control algorithms optimizing a combination of these techniques can raise the energy reduction potential of the individual techniques. In this paper an overview of current research is given and a general concept is deducted. Based on this concept, a three-step optimization methodology is proposed using 1) offline local prediction, 2) offline global planning and 3) online local scheduling. The paper ends with results of simulations and field tests showing that the methodology is promising.\u

Energy sharing and trading in multi-operator heterogeneous network deployments

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.With a view to the expected increased data traffic volume and energy consumption of the fifth generation networks, the use of renewable energy (RE) sources and infrastructure sharing have been embraced as energy and cost-saving technologies. Aiming at reducing cost and grid energy consumption, in the present paper, we study RE exchange (REE) possibilities in late-trend network deployments of energy harvesting (EH) macrocell and small cell base stations (EH-MBSs, EH-SBSs) that use an EH system, an energy storage system, and the smart grid as energy procurement sources. On this basis, we study a two-tier network composed of EH-MBSs that are passively shared among a set of mobile network operators (MNOs), and EH-SBSs that are provided to MNOs by an infrastructure provider (InP). Taking into consideration the infrastructure location and the variety of stakeholders involved in the network deployment, we propose as REE approaches 1) a cooperative RE sharing, based on bankruptcy theory, for the shared EH-MBSs and 2) a non-cooperative, aggregator-assisted RE trading, which uses double auctions to describe the REE acts among the InP provided EH-SBSs managed by different MNOs, after an initial internal REE among the ones managed by a single MNO. Our results display that our proposals outperform baseline approaches, providing a considerable reduction in SG energy utilization and costs, with satisfaction of the participant parties.Peer ReviewedPostprint (author's final draft

Demand Prediction and Inventory Management of Surgical Supplies

Effective supply chain management is critical to operations in various industries, including healthcare. Demand prediction and inventory management are essential parts of healthcare supply chain management for ensuring optimal patient outcomes, controlling costs, and minimizing waste. The advances in data analytics and technology have enabled many sophisticated approaches to demand forecasting and inventory control. This study aims to leverage these advancements to accurately predict demand and manage the inventory of surgical supplies to reduce costs and provide better services to patients. In order to achieve this objective, a Long Short-Term Memory (LSTM) model is developed to predict the demand for commonly used surgical supplies. Moreover, the volume of scheduled surgeries influences the demand for certain surgical supplies. Hence, another LSTM model is adopted from the literature to forecast surgical case volumes and predict the procedure-specific surgical supplies. A few new features are incorporated into the adopted model to account for the variations in the surgical case volumes caused by COVID-19 in 2020. This study then develops a multi-item capacitated dynamic lot-sizing replenishment model using Mixed Integer Programming (MIP). However, forecasting is always considered inaccurate, and demand is hardly deterministic in the real world. Therefore, a Two-Stage Stochastic Programming (TSSP) model is developed to address these issues. Experimental results demonstrate that the TSSP model provides an additional benefit of $2,328.304 over the MIP model

Demand Prediction and Inventory Management of Surgical Supplies

Effective supply chain management is critical to operations in various industries, including healthcare. Demand prediction and inventory management are essential parts of healthcare supply chain management for ensuring optimal patient outcomes, controlling costs, and minimizing waste. The advances in data analytics and technology have enabled many sophisticated approaches to demand forecasting and inventory control. This study aims to leverage these advancements to accurately predict demand and manage the inventory of surgical supplies to reduce costs and provide better services to patients. In order to achieve this objective, a Long Short-Term Memory (LSTM) model is developed to predict the demand for commonly used surgical supplies. Moreover, the volume of scheduled surgeries influences the demand for certain surgical supplies. Hence, another LSTM model is adopted from the literature to forecast surgical case volumes and predict the procedure-specific surgical supplies. A few new features are incorporated into the adopted model to account for the variations in the surgical case volumes caused by COVID-19 in 2020. This study then develops a multi-item capacitated dynamic lot-sizing replenishment model using Mixed Integer Programming (MIP). However, forecasting is always considered inaccurate, and demand is hardly deterministic in the real world. Therefore, a Two-Stage Stochastic Programming (TSSP) model is developed to address these issues. Experimental results demonstrate that the TSSP model provides an additional benefit of $2,328.304 over the MIP model