Multi-Stage Multi-Criteria Decision Analysis for Siting Electric Vehicle Charging Stations within and across Border Regions

Electric Vehicles (EVs) replace fossil fuel vehicles in effort towards having more sustainable transport systems. The battery of an EV is recharged at a charging point using electricity. While some recharging will be required at locations where vehicles are normally parked, other recharging could be necessary at strategic locations of vehicular travel. Certain locations are suitable for EV charging station deployment, others are not. A multi-stage decision analysis methodology for selecting suitable locations for installing EV charging station is presented. The multi-stage approach makes it possible to select critical criteria with respect to any defined objectives of the EV charging station and techno-physio-socio-economic factors without which the EV charging station could not be deployed or would not serve its designated purpose. In a case, the type of charging station is specified, and a purpose is defined: rapid EV charging stations intended for public use within and across border regions. Applied in siting real EV charging stations at optimal locations, stages in the methodology present additional techno-physio-socio-economic factors in deploying the type of EV charging stations at optimal locations and keep the EV charging stations operating within acceptable standards. Some locations were dropped at the critical analysis stage; others were dropped at the site-specific analysis stage and replacement sites were required in certain instances. Final locations included most optimal, less optimal, least optimal, and strategic or special need locations. The average distances between contiguous recharging locations were less than 60 miles. Using any specified separation standard, the number of additional EV charging stations required between EV charging stations were determinable with the Pool Box. The Overall Charging Station Availability quadrants suggest that the overall user experience could get worse as less-standardized additional EV charging stations are deployed

The role of Industry 4.0 enabling technologies for safety management: A systematic literature review

Abstract Innovations introduced during the Industry 4.0 era consist in the integration of the so called "nine pillars of technologies" in manufacturing, transforming the conventional factory in a smart factory. The aim of this study is to investigate enabling technologies of Industry 4.0, focusing on technologies that have a greater impact on safety management. Main characteristics of such technologies will be identified and described according to their use in an industrial environment. In order to do this, we chose a systematic literature review (SLR) to answer the research question in a comprehensively way. Results show that articles can be grouped according to different criteria. Moreover, we found that Industry 4.0 can increase safety levels in warehouse and logistic, as well as several solutions are available for building sector

Electric vehicle charging and routing management via multi-infrastructure data fusion

The introduction of Electric Vehicles (EVs) has placed a strain on the aged and already overworked electrical grid. With each EV requiring the same amount of power as 3 to 140 single family homes, depending on how fast the charge occurs, measures need to be taken in order to protect the electrical grid from serious damage. The electric grid renovations proposed by the U.S. department of energy, commonly referred to as the smart grid, could help accommodate an even greater EV penetration. The introduction of the smart grid and other cutting-edge technologies create the potential for applications which provide new consumer conveniences and aid in the preservation of the electrical grid. This thesis aims to create one such application through the production of a prototype system which takes advantage of current and in-development technologies in order to route an electric vehicle to the closest and least detrimental charge station based on current conditions. Traffic conditions are assessed based on data collected from both ITSs (Intelligent Transportation Systems) and VANETs (Vehicle Ad-hoc Networks), while grid information is gathered through the early stages of the Smart Grid. The system is hosted in a cloud environment base on the current trend of offloading Information Technology systems to the cloud ; this also allows for the advantages of a shared data space between sub-systems. As part of the thesis the prototype system was put through a stress test in a simulated environment in order to both establish system requirements and determine scalability for use with larger maps. The system requirements were compared with the technical specifications of an off-the-shelf GPS routing device. It was determined that such a device could not handle routing with such extensive underlying data, and will require hosting the prototype in a cloud environment. The system was also used to perform a case study on charging station placement in the Greater Rochester area. It was determined that the current charging stations are insufficient for a significant number of electric vehicles and that adding even six stations would provide a greater EV operational area and provide a more uniform distribution of charging station usage

The role of Industry 4.0 enabling technologies for safety management: A systematic literature review

Innovations introduced during the Industry 4.0 era consist in the integration of the so called "nine pillars of technologies" in manufacturing, transforming the conventional factory in a smart factory. The aim of this study is to investigate enabling technologies of Industry 4.0, focusing on technologies that have a greater impact on safety management. Main characteristics of such technologies will be identified and described according to their use in an industrial environment. In order to do this, we chose a systematic literature review (SLR) to answer the research question in a comprehensively way. Results show that articles can be grouped according to different criteria. Moreover, we found that Industry 4.0 can increase safety levels in warehouse and logistic, as well as several solutions are available for building sector

New Gateway Selection Algorithm Based on Multi-Objective Integer Programming and Reinforcement Learning

Connecting vehicles to the infrastructure and benefiting from the services provided by the network is one of the main objectives to increase safety and provide well-being for passengers. Providing such services requires finding suitable gateways to connect the source vehicles to the infrastructure. The major feature of using gateways is to decrease the load of the network infrastructure resources so that each gateway is responsible for a group of vehicles. Unfortunately, the implementation of this goal is facing many challenges, including the highly dynamic topology of VANETs, which causes network instability, and the deployment of applications with high bandwidth demand that can cause network congestion, particularly in urban areas with a high-density vehicle. This work introduces a novel gateway selection algorithm for vehicular networks in urban areas, consisting of two phases. The first phase identifies the best gateways among the deployed vehicles using multi-objective integer programming. While in the second phase, reinforcement learning is employed to select a suitable gateway for any vehicular node in need to access the VANET infrastructure. The proposed model is evaluated and compared to other existing solutions. The obtained results show the efficiency of the proposed system in identifying and selecting the gateways

An Allocation-Routing Optimization Model for Integrated Solid Waste Management

Integrated smart waste management (ISWM) is an innovative and technologically advanced approach to managing and collecting waste. It is based on the Internet of Things (IoT) technology, a network of interconnected devices that communicate and exchange data. The data collected from IoT devices helps municipalities to optimize their waste management operations. They can use the information to schedule waste collections more efficiently and plan their routes accordingly. In this study, we consider an ISWM framework for the collection, recycling, and recovery steps to improve the performance of the waste system. Since ISWM typically involves the collaboration of various stakeholders and is affected by different sources of uncertainty, a novel multi-objective model is proposed to maximize the probabilistic profit of the network while minimizing the total travel time and transportation costs. In the proposed model, the chance-constrained programming approach is applied to deal with the profit uncertainty gained from waste recycling and recovery activities. Furthermore, some of the most proficient multi-objective meta-heuristic algorithms are applied to address the complexity of the problem. For optimal adjustment of parameter values, the Taguchi parameter design method is utilized to improve the performance of the proposed optimization algorithm. Finally, the most reliable algorithm is determined based on the Best Worst Method (BWM)