A. Jamal, Hassan M. Al-Ahmadi, Farhan Muhammad Butt, Mudassir Iqbal, M. Almoshaogeh, Sajid Ali
{"title":"Metaheuristics for Traffic Control and Optimization: Current Challenges and Prospects","authors":"A. Jamal, Hassan M. Al-Ahmadi, Farhan Muhammad Butt, Mudassir Iqbal, M. Almoshaogeh, Sajid Ali","doi":"10.5772/intechopen.99395","DOIUrl":"https://doi.org/10.5772/intechopen.99395","url":null,"abstract":"Intelligent traffic control at signalized intersections in urban areas is vital for mitigating congestion and ensuring sustainable traffic operations. Poor traffic management at road intersections may lead to numerous issues such as increased fuel consumption, high emissions, low travel speeds, excessive delays, and vehicular stops. The methods employed for traffic signal control play a crucial role in evaluating the quality of traffic operations. Existing literature is abundant, with studies focusing on applying regression and probability-based methods for traffic light control. However, these methods have several shortcomings and can not be relied on for heterogeneous traffic conditions in complex urban networks. With rapid advances in communication and information technologies in recent years, various metaheuristics-based techniques have emerged on the horizon of signal control optimization for real-time intelligent traffic management. This study critically reviews the latest advancements in swarm intelligence and evolutionary techniques applied to traffic control and optimization in urban networks. The surveyed literature is classified according to the nature of the metaheuristic used, considered optimization objectives, and signal control parameters. The pros and cons of each method are also highlighted. The study provides current challenges, prospects, and outlook for future research based on gaps identified through a comprehensive literature review.","PeriodicalId":178865,"journal":{"name":"Search Algorithm - Essence of Optimization [Working Title]","volume":"225 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132040698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Search Algorithms on Logistic and Manufacturing Problems","authors":"Gladys Bonilla-Enriquez, S. Caballero-Morales","doi":"10.5772/INTECHOPEN.96554","DOIUrl":"https://doi.org/10.5772/INTECHOPEN.96554","url":null,"abstract":"The supply chain comprehensively considers problems with different levels of complexity. Nowadays, design of distribution networks and production scheduling are some of the most complex problems in logistics. It is widely known that large problems cannot be solved through exact methods. Also, specific optimization software is frequently needed. To overcome this situation, the development and application of search algorithms have been proposed to obtain approximate solutions to large problems within reasonable time. In this context, the present chapter describes the development of Genetic Algorithms (an evolutionary search algorithm) for vehicle routing, product selection, and production scheduling problems within the supply chain. These algorithms were evaluated by using well-known test instances. The advances of this work provide the general discussions associated to designing these search algorithms for logistics problems.","PeriodicalId":178865,"journal":{"name":"Search Algorithm - Essence of Optimization [Working Title]","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128829429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}