Research Directions in Information Systems for Humanitarian Logistics

This article systematically reviews the literature on using IT (Information Technology) in humanitarian logistics focusing on disaster relief operations. We first discuss problems in humanitarian relief logistics. We then identify the stage and disaster type for each article as well as the article’s research methodology and research contribution. Finally, we identify potential future research directions

Supply chain uncertainty:a review and theoretical foundation for future research

Supply-chain uncertainty is an issue with which every practising manager wrestles, deriving from the increasing complexity of global supply networks. Taking a broad view of supply-chain uncertainty (incorporating supply-chain risk), this paper seeks to review the literature in this area and develop a theoretical foundation for future research. The literature review identifies a comprehensive list of 14 sources of uncertainty, including those that have received much research attention, such as the bullwhip effect, and those more recently described, such as parallel interaction. Approaches to managing these sources of uncertainty are classified into: 10 approaches that seek to reduce uncertainty at its source; and, 11 approaches that seek to cope with it, thereby minimising its impact on performance. Manufacturing strategy theory, including the concepts of alignment and contingency, is then used to develop a model of supply-chain uncertainty, which is populated using the literature review to show alignment between uncertainty sources and management strategies. Future research proposed includes more empirical research in order to further investigate: which uncertainties occur in particular industrial contexts; the impact of appropriate sources/management strategy alignment on performance; and the complex interplay between management strategies and multiple sources of uncertainty (positive or negative)

An Individual-based Probabilistic Model for Fish Stock Simulation

We define an individual-based probabilistic model of a sole (Solea solea) behaviour. The individual model is given in terms of an Extended Probabilistic Discrete Timed Automaton (EPDTA), a new formalism that is introduced in the paper and that is shown to be interpretable as a Markov decision process. A given EPDTA model can be probabilistically model-checked by giving a suitable translation into syntax accepted by existing model-checkers. In order to simulate the dynamics of a given population of soles in different environmental scenarios, an agent-based simulation environment is defined in which each agent implements the behaviour of the given EPDTA model. By varying the probabilities and the characteristic functions embedded in the EPDTA model it is possible to represent different scenarios and to tune the model itself by comparing the results of the simulations with real data about the sole stock in the North Adriatic sea, available from the recent project SoleMon. The simulator is presented and made available for its adaptation to other species.Comment: In Proceedings AMCA-POP 2010, arXiv:1008.314

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

Human–agent collaboration for disaster response

In the aftermath of major disasters, first responders are typically overwhelmed with large numbers of, spatially distributed, search and rescue tasks, each with their own requirements. Moreover, responders have to operate in highly uncertain and dynamic environments where new tasks may appear and hazards may be spreading across the disaster space. Hence, rescue missions may need to be re-planned as new information comes in, tasks are completed, or new hazards are discovered. Finding an optimal allocation of resources to complete all the tasks is a major computational challenge. In this paper, we use decision theoretic techniques to solve the task allocation problem posed by emergency response planning and then deploy our solution as part of an agent-based planning tool in real-world field trials. By so doing, we are able to study the interactional issues that arise when humans are guided by an agent. Specifically, we develop an algorithm, based on a multi-agent Markov decision process representation of the task allocation problem and show that it outperforms standard baseline solutions. We then integrate the algorithm into a planning agent that responds to requests for tasks from participants in a mixed-reality location-based game, called AtomicOrchid, that simulates disaster response settings in the real-world. We then run a number of trials of our planning agent and compare it against a purely human driven system. Our analysis of these trials show that human commanders adapt to the planning agent by taking on a more supervisory role and that, by providing humans with the flexibility of requesting plans from the agent, allows them to perform more tasks more efficiently than using purely human interactions to allocate tasks. We also discuss how such flexibility could lead to poor performance if left unchecked

Optimal logistics scheduling with dynamic information in emergency response: case studies for humanitarian objectives

The mathematical model of infectious disease is a typical problem in mathematical modeling, and the common infectious disease models include the susceptible-infected (SI) model, the susceptible-infected-recovered model (SIR), the susceptible-infected-recovered-susceptible model (SIRS) and the susceptible-exposed-infected-recovered (SEIR) model. These models can be used to predict the impact of regional return to work after the epidemic. In this paper, we use the SEIR model to solve the dynamic medicine demand information in humanitarian relief phase. A multistage mixed integer programming model for the humanitarian logistics and transport resource is proposed. The objective functions of the model include delay cost and minimum running time in the time-space network. The model describes that how to distribute and deliver medicine resources from supply locations to demand locations with an efficient and lower-cost way through a transportation network. The linear programming problem is solved by the proposed Benders decomposition algorithm. Finally, we use two cases to calculate model and algorithm. The results of the case prove the validity of the model and algorithm