CHN and Swap Heuristic to Solve the Maximum Independent Set Problem

We describe a new approach to solve the problem to find the maximum independent set in a given Graph, known also as Max-Stable set problem (MSSP). In this paper, we show how Max-Stable problem can be reformulated into a linear problem under quadratic constraints, and then we resolve the QP result by a hybrid approach based Continuous Hopfeild Neural Network (CHN) and Local Search. In a manner that the solution given by the CHN will be the starting point of the local search. The new approach showed a good performance than the original one which executes a suite of CHN runs, at each execution a new leaner constraint is added into the resolved model. To prove the efficiency of our approach, we present some computational experiments of solving random generated problem and typical MSSP instances of real life problem

Combined extreme learning machine and max pressure algorithms for traffic signal control

Nowadays, rush-hour traffic congestion problems persist in most major cities around the world, resulting in increased pollution, noise, and stress for citizens. Therefore, an optimal traffic light strategy is needed. For this purpose, several models have been proposed. However, these models often overlook the non-stationarity of traffic, which occurs due to changing traffic conditions over time. Additionally, these models are steady-state process models, leading to a decrease in their predictive power over time. To address these issues, this paper proposes the combination of two algorithms: a passive Extreme Learning Machine with periodic mini-batch learning (PB-ELM) for predicting traffic flow and the Max Pressure control algorithm (MPA) for signal control. In the first step, the passive periodic Extreme Learning Machine (PB-ELM) adjusts quickly and regularly based on new data, overcoming traffic non-stationarity and improving long-term performance. In the second step, the MPA is preferred for signal control due to its simplicity and speed. The PB-ELM-MPA model is a combination of predictive algorithms that takes the current road network conditions as input and predicts the flow of vehicles at intersections. The model utilizes learned characteristics of the source and destination roads to estimate the number of vehicles in each movement. The PB-ELM outputs serve as the starting point for the max-pressure algorithm, which reduces congestion by considering only the vehicles on road segments closest to the intersection and selecting the highest pressure at each time interval. The proposed PB-ELM-MPA model is evaluated on an isolated intersection simulated with the SUMO micro-simulator, demonstrating a significant improvement in avoiding traffic jams. The total staying time of all vehicles present at the intersection is reduced by 65% compared to the fixed configuration of traffic lights. Additionally, CO2 emissions and fuel consumption are reduced by approximately 34% compared to the classic MPA and Deep Q-Network approaches

Neural Network And Local Search To Solve Binary CSP

Continuous Hopfield neural Network (CHN) is one of the effective approaches to solve Constrain Satisfaction Problems (CSPs). However, the main problem with CHN is that it can reach stabilisation with outputs in real values, which means an inconsistent solution or an incomplete assignment of CSP variables. In this paper, we propose a new hybrid approach combining CHN and min-conflict heuristic to mitigate these problems. The obtained results show an improvement in terms of solution quality, either our approach achieves feasible soluion with a high rate of convergence, furthermore, this approach can also enhance theperformance more than conventional CHN in some cases, particularly, when the network crashes

Consumer Perceptions of Online Shopping and Willingness to Use Pick-Up Points: A Case Study of Morocco

The use of pick-up points by consumers is one of the most developed areas of research in the literature on last-mile logistics over the last decade. In this regard, several researchers have attempted to expose the factors that influence consumers’ online shopping behavior and their willingness to use pick-up points. However, no study has addressed this issue in African countries. The aim of this research is to examine the online shopping behavior of Moroccan consumers, focusing on their opinions about using pick-up points to receive/return goods purchased online. This research adopted a qualitative approach through focus group sessions with Moroccan consumers. The results indicate that temporal and spatial flexibility, competitive prices, and the quality of the retailer’s website are the main factors encouraging consumers to buy online. On the other hand, product risk, delivery risk, privacy, and security were identified as the factors that prevent consumers from buying online. In contrast, the location, density, security, and opening hours of pick-up points were considered to be the factors that influence the Moroccan consumer’s choice to use this delivery option when buying online. These findings are important both for parcel delivery companies that want to establish pick-up point networks in Casablanca and for public authorities and local communities that want to formulate policies and implement strategies leading to more sustainable urban environments

ICDS 2019 Preface

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record