Exploring Cognitive Sustainability Concerns in Public Responses to Extreme Weather Events: An NLP Analysis of Twitter Data

The United States has a long history of experiencing extreme weather events. Hurricanes are among the most devastating natural disasters that have significant economic and physical impacts on the country. By applying Natural Language Processing (NLP) to Twitter data for sentiment analysis, emotion detection, and topic modelling, this study provides a more thorough understanding of public response and concerns during five study cases of hurricanes that hit the United States: Harvey, Irma, Maria, Ida, and Ian. The findings on sentiment analysis revealed that 64.75% of the tweets were classified as Negative and 35.25% as Positive. For emotion detection, the predominant emotion was anger, with 39.91%. These results were centred around the main public concerns shown by the topic modelling: hurricane management, donation and support, and disaster impacts. Our future work will focus on understanding people’s responses to extreme weather events through the evolving concept of Cognitive Sustainability

Autonomous Vehicle and Pedestrian Interaction : Leveraging the Use of Model Predictive Control & Genetic Algorithm

Driving assistance systems and even autonomous driving have and will have an important role in sustainable mobility systems. Traffic situations where participants’ cognitive levels are different will cause challenges in the long term. When a pedestrian crosses the road, an autonomous vehicle may need to navigate safely while maintaining its desired speed. Achieving this involves using a predictive model to anticipate pedestrian movements and a strategy for the vehicle to adjust its speed proactively. This research combined model-based predictive control (MPC) with a social-force model (SFM) to effectively control the autonomous vehicle’s longitudinal speed. A genetic algorithm (GA) was also integrated into the approach to address the optimisation problem. A comparison between the proposed approach (MPC-GA) and the conventional MPC technique proved the outperformance of MPC-GA

A Computational Study for the Steiner Tree Problem with Revenue, Budget and Hop Constraints

We address the Steiner tree problem with revenues, budget and hop constraints (STPRBH), which is a generalization of the well-known Steiner tree problem. Given a connected undirected graph, a root node, edge costs and delays, nodes revenues, as well as a preset budget and hop, the STPRBH seeks to find a subtree that includes the root node, satisfies bound constraints on the total edge cost as well as the number of edges between any node and the root node, while maximizing the sum of the total node revenues. We focus on investigating polynomial-sized formulations. First, we propose an enhanced formulation based on the Miller-Tucker-Zemlin subtour constraints. Next, we investigate a nonlinear MIP formulation that is linearized using the Reformulation-Linearization Technique (RLT). We present the results of a comprehensive computational study of the proposed formulations. These result provide evidence that benchmark instances with up to 500 nodes can be effectively solved using the proposed RLT-based formulation

New Lagrangian Relaxation Approach for the Discrete Cost Multicommodity Network Design Problem

We aim to derive effective lower bounds for the Discrete Cost Multicommodity Network Design Problem (DCMNDP). Given an undirected graph, the problem requires installing at most one facility on each edge such that a set of point-to-point commodity flows can be routed and costs are minimized. In the literature, the Lagrangian relaxation is usually applied to an arc-based formulation to derive lower bounds. In this work, we investigate a path-based formulation and we solve its Lagrangian relaxation using several non-differentiable optimization techniques. More precisely, we devised six variants of the deflected subgradient procedures, using various direction-search and step-length strategies. The computational performance of these Lagrangian-based approaches are evaluated and compared on a set of randomly generated instances, and real-world problems

An exact approach for the multicommodity network optimization problem with a step cost function

We investigate the Multicommodity Network Optimization Problem with a Step Cost Function (MNOP-SCF) where the available facilities to be installed on the edges have discrete step-increasing cost and capacity functions. This strategic long-term planning problem requires installing at most one facility capacity on each edge so that all the demands are routed and the total installation cost is minimized. We describe a path-based formulation that we solve exactly using an enhanced constraint generation based procedure combined with columns and new cuts generation algorithms. The main contribution of this work is the development of a new exact separation model that identifies the most violated bipartition inequalities coupled with a knapsack-based problem that derives additional cuts. To assess the performance of the proposed approach, we conducted computational experiments on a large set of randomly generated instances. The results show that it delivers optimal solutions for large instances with up to 100 nodes, 600 edges, and 4950 commodities while in the literature, the best developed approaches are limited to instances with 50 nodes, 100 edges, and 1225 commodities

Pre-auction optimization for the selection of shared customers in the last-mile delivery

Companies are constantly looking for new strategies to improve their logistics performance and ensure their competitiveness in the global market. This article provides a new scheme for managing the selection of shared customers for a logistics company. The new mechanism proposes the use of the auction as a tool to manage the selection of shared clients through the coalition pool. Thus, all unprofitable shared customers will be pushed to the pool for outsourcing by the other collaborating carriers. Then, some profitable auctioned ones will be selected. The selection system is designed based on solving a vehicle routing problem that aims to maximize the carrier's profit in a decentralized context. At first, a mixed integer linear programing model is derived to solve the deterministic version of the problem. Then in order to efficiently address the stochastic version of the problem, a simulation-based optimization model is developed. This model is employed to solve a real case study of a parcel delivery company, considering the travel times as a bimodal distribution. A comparative study is conducted to demonstrate the effectiveness of the auction approach in managing shared customers. The results of our study reveal that the proposed auction approach efficiently manages the shared customers which leads to the substantial increase of 22.65% in profits for the delivery company. These findings have significant implications for logistics companies seeking to improve their performance and competitiveness in the global market

A simulation-optimization approach for the stochastic discrete cost multicommodity flow problem

This article addresses a variant of the Discrete Cost Multicommodity Flow (DCMF) problem with random demands, where a penalty is incurred for each unrouted demand. The problem requires finding a network topology that minimizes the sum of the fixed installation facility costs and the expected penalties of unmet multicommodity demands. A two-stage stochastic programming with recourse model is proposed. A simulation-optimization approach is developed to solve this challenging problem approximately. To be precise, the first-stage problem requires solving a specific multi-facility network design problem using an exact enhanced cut-generation procedure coupled with a column generation algorithm. The second-stage problem aims at computing the expected penalty using a Monte Carlo simulation procedure together with a hedging strategy. To assess the empirical performance of the proposed approach, a Sample Average Approximation (SAA) procedure is developed to derive valid lower bounds. Results of extensive computational experiments attest to the efficacy of the proposed approach.Scopu

Efficiency Analysis to evaluate a Breast Cancer Screening Campaign: A Case Study from Tunisia

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