scholarly journals A New Ant Colony-Based Methodology for Disaster Relief

Mathematics ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 518 ◽  
Author(s):  
José M. Ferrer ◽  
M. Teresa Ortuño ◽  
Gregorio Tirado
Keyword(s):  
Large Scale ◽  
Humanitarian Aid ◽  
Decision Support Tool ◽  
Ant Colony ◽  
Haiti Earthquake ◽  
Distribution Problem ◽  
Support Tool ◽  
Last Mile ◽  
Research Gap ◽  
High Uncertainty

Humanitarian logistics in response to large scale disasters entails decisions that must be taken urgently and under high uncertainty. In addition, the scarcity of available resources sometimes causes the involved organizations to suffer assaults while transporting the humanitarian aid. This paper addresses the last mile distribution problem that arises in such an insecure environment, in which vehicles are often forced to travel together forming convoys for security reasons. We develop an elaborated methodology based on Ant Colony Optimization that is applied to two case studies built from real disasters, namely the 2010 Haiti earthquake and the 2005 Niger famine. There are very few works in the literature dealing with problems in this context, and that is the research gap this paper tries to fill. Furthermore, the consideration of multiple criteria such as cost, time, equity, reliability, security or priority, is also an important contribution to the literature, in addition to the use of specialized ants and effective pheromones that are novel elements of the algorithm which could be exported to other similar problems. Computational results illustrate the efficiency of the new methodology, confirming it could be a good basis for a decision support tool for real operations.

Optimization of Complex Engineered Systems Under Risk and Uncertainty

2007 ◽  
Author(s):  
Seung-Kyum Choi ◽  
Mervyn Fathianathan ◽  
Dirk Schaefer
Keyword(s):  
Large Scale ◽  
Optimization Procedure ◽  
Decision Support Tool ◽  
Primary Objective ◽  
Risk And Uncertainty ◽  
Support Tool ◽  
Structural Problems ◽  
Complex Engineered Systems ◽  
Engineered Systems ◽  
Karhunen Loeve Transform

The advances in information technology significantly impact the engineering design process. The primary objective of this research is to develop a novel probabilistic decision support tool to assist management of structural systems under risk and uncertainty by utilizing a stochastic optimization procedure and IT tools. The proposed mathematical and computational framework will overcome the drawbacks of the traditional methods and will be critically demonstrated through large-scale structural problems. The efficiency of the proposed procedure is achieved by the combination of the Karhunen-Loeve transform with the stochastic analysis of polynomial chaos expansion to common optimization procedures. The proposed technology, comprising new and adapted current capabilities, will provide robust and physically reasonable solutions for practical engineering problems.

Decision-support tool for coordination of volunteers in large-scale lockdowns

2021 ◽  
pp. 102420
Author(s):  
Aleksandra Krstikj ◽  
Moisés Gerardo Contreras Ruiz Esparza ◽  
Jaime Mora Vargas ◽  
Laura Hervert Escobar ◽  
Cecilia López de la Rosa ◽  
...  
Keyword(s):  
Decision Support ◽  
Large Scale ◽  
Decision Support Tool ◽  
Support Tool

scholarly journals Planning and Suitability Assessment of Large-scale Nature-based Solutions for Flood-risk Reduction

2021 ◽  
Author(s):  
Adam Mubeen ◽  
Laddaporn Ruangpan ◽  
Zoran Vojinovic ◽  
Arlex Sanchez Torrez ◽  
Jasna Plavšić
Keyword(s):  
Flood Risk ◽  
Large Scale ◽  
Decision Support Tool ◽  
Careful Consideration ◽  
Climate Projections ◽  
Suitable Alternative ◽  
Support Tool ◽  
Suitability Assessment ◽  
Suitability Maps

AbstractAdverse effects of climate change are increasing around the world and the floods are posing significant challenges for water managers. With climate projections showing increased risks of storms and extreme precipitation, the use of traditional measures alone is no longer an option. Nature-Based Solutions (NBS) offer a suitable alternative to reduce the risk of flooding and provide multiple benefits. However, planning such interventions requires careful consideration of various factors and local contexts. The present paper provides contribution in this direction and it proposes a methodology for allocation of large-scale NBS using suitability mapping. The methodology was implemented within the toolboxes of ESRI ArcMap software in order to map suitability for four types of NBS interventions: floodplain restoration, detention basins, retention ponds, and river widening. The toolboxes developed were applied to the case study area in Serbia, i.e., the Tamnava River basin. Flood maps were used to determine the volume of floodwater that needs to be stored for reducing flood risk in the basin and subsequent downstream areas. The suitability maps produced indicate the potential of the new methodology and its application as a decision-support tool for selection and allocation of large-scale NBS.

scholarly journals A Hierarchical Route Guidance Framework for Off-Road Connected Vehicles

2018 ◽  
Vol 140 (7) ◽  
Author(s):  
Judhajit Roy ◽  
Nianfeng Wan ◽  
Angshuman Goswami ◽  
Ardalan Vahidi ◽  
Paramsothy Jayakumar ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Optimal Path ◽  
Computational Cost ◽  
Grid Cell ◽  
Decision Support Tool ◽  
Route Guidance ◽  
Low Resolution ◽  
Support Tool ◽  
Optimal Cost

A new framework for route guidance, as part of a path decision support tool, for off-road driving scenarios is presented in this paper. The algorithm accesses information gathered prior to and during a mission which are stored as layers of a central map. The algorithm incorporates a priori knowledge of the low resolution soil and elevation information and real-time high-resolution information from on-board sensors. The challenge of high computational cost to find the optimal path over a large-scale high-resolution map is mitigated by the proposed hierarchical path planning algorithm. A dynamic programming (DP) method generates the globally optimal path approximation based on low-resolution information. The optimal cost-to-go from each grid cell to the destination is calculated by back-stepping from the target and stored. A model predictive control algorithm (MPC) operates locally on the vehicle to find the optimal path over a moving radial horizon. The MPC algorithm uses the stored global optimal cost-to-go map in addition to high resolution and locally available information. Efficacy of the developed algorithm is demonstrated in scenarios simulating static and moving obstacles avoidance, path finding in condition-time-variant environments, eluding adversarial line of sight detection, and connected fleet cooperation.

scholarly journals Supporting COVID-19 policy response with large-scale mobility-based modeling

2021 ◽  
Author(s):  
Serina Chang ◽  
Mandy L. Wilson ◽  
Bryan Lewis ◽  
Zakaria Mehrab ◽  
Komal K. Dudakiya ◽  
...  
Keyword(s):  
Decision Support ◽  
Large Scale ◽  
Decision Support Tool ◽  
Support Tool ◽  
Epidemiological Modeling ◽  
Fine Grained ◽  
Dynamic Mobility ◽  
Virginia Department ◽  
Analytical Tools ◽  
The Impact

ABSTRACTSocial distancing measures, such as restricting occupancy at venues, have been a primary intervention for controlling the spread of COVID-19. However, these mobility restrictions place a significant economic burden on individuals and businesses. To balance these competing demands, policymakers need analytical tools to assess the costs and benefits of different mobility reduction measures.In this paper, we present our work motivated by our interactions with the Virginia Department of Health on a decision-support tool that utilizes large-scale data and epidemiological modeling to quantify the impact of changes in mobility on infection rates. Our model captures the spread of COVID-19 by using a fine-grained, dynamic mobility network that encodes the hourly movements of people from neighborhoods to individual places, with over 3 billion hourly edges. By perturbing the mobility network, we can simulate a wide variety of reopening plans and forecast their impact in terms of new infections and the loss in visits per sector. To deploy this model in practice, we built a robust computational infrastructure to support running millions of model realizations, and we worked with policymakers to develop an intuitive dashboard interface that communicates our model’s predictions for thousands of potential policies, tailored to their jurisdiction. The resulting decision-support environment provides policymakers with much-needed analytical machinery to assess the tradeoffs between future infections and mobility restrictions.

scholarly journals Transportation Risk Control of Waste Disposal in the Healthcare System with Two-Echelon Waste Collection Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Haolin Li ◽  
Yi Hu ◽  
Junyan Lyu ◽  
Hao Quan ◽  
Xiang Xu ◽  
...  
Keyword(s):  
Healthcare System ◽  
Large Scale ◽  
Programming Model ◽  
Time Windows ◽  
Decision Support Tool ◽  
Mixed Integer ◽  
Waste Collection ◽  
Routing Problem ◽  
Support Tool ◽  
Transportation Risk

This paper investigates a vehicle routing problem arising in the waste collection of the healthcare system with the concern of transportation risk. Three types of facilities abstracted from the health system are investigated in this paper, namely, facilities with collection points, facilities without collection points, and small facilities. Two-echelon collection mode is applied in which the waste generated by small facilities is first collected by collection points, and then transferred to the recycling centre. To solve this problem, we propose a mixed-integer linear programming model considering time windows and vehicle capacity, and we use particle swarm optimisation (PSO) algorithm for solving large-scale problems. Numerical experiments show the capability of the proposed algorithm. Sensitivity analysis is conducted to investigate the influence of facilities with collection points and the collection routes. This research can provide a decision support tool for the routing of waste collection in the healthcare system.

scholarly journals Application of a model to assess aflatoxin risk in peanuts

2010 ◽  
Vol 148 (3) ◽  
pp. 341-351 ◽  
Author(s):  
Y. S. CHAUHAN ◽  
G. C. WRIGHT ◽  
R. C. N. RACHAPUTI ◽  
D. HOLZWORTH ◽  
A. BROOME ◽  
...  
Keyword(s):  
Large Scale ◽  
Production Systems ◽  
Risk Index ◽  
Temperature Response ◽  
Decision Support Tool ◽  
Fold Increase ◽  
Climatic Conditions ◽  
Aflatoxin Contamination ◽  
Climatic Data ◽  
Support Tool

SUMMARYWhen exposed to hot (22–35°C) and dry climatic conditions in the field during the final 4–6 weeks of pod filling, peanuts (Arachis hypogaeaL.) can accumulate highly carcinogenic and immuno-suppressing aflatoxins. Forecasting of the risk posed by these conditions can assist in minimizing pre-harvest contamination. A model was therefore developed as part of the Agricultural Production Systems Simulator (APSIM) peanut module, which calculated an aflatoxin risk index (ARI) using four temperature response functions when fractional available soil water was <0·20 and the crop was in the last 0·40 of the pod-filling phase. ARI explained 0·95 (P⩽0·05) of the variation in aflatoxin contamination, which varied from 0 toc. 800 μg/kg in 17 large-scale sowings in tropical and four sowings in sub-tropical environments carried out in Australia between 13 November and 16 December 2007. ARI also explained 0·96 (P⩽0·01) of the variation in the proportion of aflatoxin-contaminated loads (>15 μg/kg) of peanuts in the Kingaroy region of Australia during the period between the 1998/99 and 2007/08 seasons. Simulation of ARI using historical climatic data from 1890 to 2007 indicated a three-fold increase in its value since 1980 compared to the entire previous period. The increase was associated with increases in ambient temperature and decreases in rainfall. To facilitate routine monitoring of aflatoxin risk by growers in near real time, a web interface of the model was also developed. The ARI predicted using this interface for eight growers correlated significantly with the level of contamination in crops (r=0·95,P⩽0·01). These results suggest that ARI simulated by the model is a reliable indicator of aflatoxin contamination that can be used in aflatoxin research as well as a decision-support tool to monitor pre-harvest aflatoxin risk in peanuts.

Prioritizing high-risk sub-groups in a multi-manufacturer vaccine distribution program

2017 ◽  
Vol 28 (2) ◽  
pp. 311-331 ◽  
Author(s):  
Sharon Hovav ◽  
Avi Herbon
Keyword(s):  
Supply Chain ◽  
High Risk ◽  
Large Scale ◽  
Decision Support Tool ◽  
Added Value ◽  
Mixed Integer ◽  
Efficient Manner ◽  
Content Type ◽  
Support Tool ◽  
Medical Burden

Purpose Annual influenza epidemics cause great losses in both human and financial terms. The purpose of this paper is to propose a model for optimizing a large-scale influenza vaccination program (VP). The goal is to minimize the total cost of the vaccination supply chain while guaranteeing a sufficiently high level of population protection. From a practical point of view, the analysis returns the number of shipments and the quantity of vaccines in each periodic shipment that should be delivered from the manufacturers to the distribution center (DC), from the DC to the clinics, and from the clinics to each sub-group of customers during the vaccination season. Design/methodology/approach A mixed-integer programming optimization model is developed to describe the problem for a supply chain consisting of vaccine manufacturers, the healthcare organization (HCO) (comprising the DC and clinics), and the population being vaccinated (customers). The model suggests a VP that implemented by a nation-wide HCO. Findings The benefits of the proposed approach are shown to be particularly salient in cases of limited resources, as the model distributes demand backlogs in an efficient manner, prioritizing high-risk sub-groups of the population over lower-risk sub-groups. In particular, the authors show a reduction in direct medical burden of consumers, such as the need for doctors, hospitalization resources, and reduction of indirect, non-medical burden, such as loss of workdays. Practical implications Drawing from the extended enterprise paradigm, and, in particular, taking consumer benefits into account, the authors suggest an operational-strategic model that creates impressive added value in a highly constrained supply chain. The model constitutes a powerful decision tool for the deployment of large-scale seasonal products, and its implementation can yield multiple benefits for various consumer segments. Originality/value The model proposed herein constitutes a decision support tool comprising operational-tactical and tactical-strategic perspectives, which logistics managers can utilize to create an enterprise-oriented plan that takes into account medical and non-medical costs.

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