Automatic Driving Material Handling Vehicle Station Location and Scheduling Mathematical Modeling and Analysis

Traditional material handling vehicles often use internal combustion engines as their power source, which results in exhaust emissions that pollute the environment. In contrast, automated material handling vehicles have the advantages of zero emissions, low noise, and low vibration, thus avoiding exhaust pollution and providing a more comfortable working environment for operators. In order to achieve the goals of “peaking carbon emissions by 2030 and achieving carbon neutrality by 2060”, the use of environmentally friendly autonomous material handling vehicles for material transportation is an inevitable trend. To maximize the amount of transported materials, consider peak-to-valley electricity pricing, battery pack procurement, and the construction of charging and swapping stations while achieving “minimum daily transportation volume” and “lowest investment and operational cost over a 3-year settlement period” with the shortest overall travel distance for all material handling vehicles, this paper examines two different scenarios and establishes goal programming models. The appropriate locations for material handling vehicle swapping stations and vehicle battery pack scheduling schemes are then developed using the NSGA-II algorithm and ant colony optimization algorithm. The results show that, while ensuring a daily transportation volume of no less than 300 vehicles, the lowest investment and operational cost over a 3-year settlement period is approximately 24.1 million Yuan. The material handling vehicles follow the shortest path of 119.2653 km passing through the designated retrieval points and have two shortest routes. Furthermore, the advantages and disadvantages of the proposed models are analyzed, followed by an evaluation, deepening, and potential extension of the models. Finally, future research directions in this field are suggested.

High Performance Priority Packets Scheduling Mechanism for Big Data in Smart Cities

Today,Internet of Things(IoT)is a technology paradigm which convinces many researchers for the purpose of achieving high performance of packets delivery in IoT applications such as smart cities.Interconnecting various physical devices such as sensors or actuators with the Internet may causes different constraints on the network resources such as packets delivery ratio,energy efficiency,end-to-end delays etc.However,traditional scheduling methodologies in large-scale environments such as big data smart cities cannot meet the requirements for high performance network metrics.In big data smart cities applications which need fast packets transmission ratio such as sending priority packets to hospitals for an emergency case,an efficient schedulingmechanism ismandatory which is the main concern of this paper.In this paper,we overcome the shortcoming issues of the traditional scheduling algorithms that are utilized in big data smart cities emergency applications.Transmission information about the priority packets between the source nodes(i.e.,people with emergency cases)and the destination nodes(i.e.,hospitals)is performed before sending the packets in order to reserve transmission channels and prepare the sequence of transmission of theses priority packets between the two parties.In our proposed mechanism,Software Defined Networking(SDN)with centralized communication controller will be responsible for determining the scheduling and processing sequences for priority packets in big data smart cities environments.In this paper,we compare between our proposed Priority Packets Deadline First scheduling scheme(PPDF)with existing and traditional scheduling algorithms that can be used in urgent smart cities applications in order to illustrate the outstanding network performance parameters of our scheme such as the average waiting time,packets loss rates,priority packets end-to-end delay,and efficient energy consumption.

Self-configuring scheduling scheme for IPv6 traffic with multiple QoS classes

This paper proposes a new queuing model and adaptive scheduling scheme which realizes multi-class QoS mechanism under DiffServ architecture. The queuing model is composed of two parallel output subqueues, each output sub-queue adopts random drop algorithm by setting different buffer threshold for different class traffic, so it can provide multi-class QoS. The new proposed scheduling scheme which adaptively changes the parameter A can guarantee the performance target of high class traffic, in the mean time, improve the QoS of low classes traffic.

Modeling and Solving Lot-Splitting Scheduling Problem Based on Process

In flexible job-shop batch scheduling problem, the optimal lot-size of different process is not always the same because of different processing time and set-up time. Even for the same process of the same workpiece, the choice of machine also affects the optimal lot-size. In addition, different choices of lot-size between the constrained processes will impact the manufacture efficiency. Considering that each process has its own appropriate lot-size, we put forward the concept of scheduling with lot-splitting based on process and set up the scheduling model of lot-splitting to critical path process as the core. The model could update the set of batch process and machine selection strategy dynamically to determine processing route and arrange proper lot-size for different processes, to achieve the purpose of optimizing the makespan and reducing the processing batches effectively. The experiment results show that, comparing with lot-splitting scheduling scheme based on workpiece, this model optimizes the makespan and improves the utilization efficiency of the machine. It also greatly decreases the machined batches （42%） and reduces the complexity of shop scheduling production management.

A flexible scheduling algorithm for the 5th-generation networks

At present,the 5th-Generation(5G)wireless mobile communication standard has been released.5G networks efficiently support enhanced mobile broadband traffic,ultra-reliable low-latency communication traffic,and massive machine-type communication.However,a major challenge for 5G networks is to achieve effective Radio Resource Management(RRM)strategies and scheduling algorithms to meet quality of service requirements.The Proportional Fair(PF)algorithm is widely used in the existing 5G scheduling technology.In the PF algorithm,RRM assigns a priority to each user which is served by gNodeB.The existing metrics of priority mainly focus on the flow rate.The purpose of this study is to explore how to improve the throughput of 5G networks and propose new scheduling schemes.In this study,the package delay of the data flow is included in the metrics of priority.The Vienna 5G System-Level(SL)simulator is a MATLAB-based SL simulation platform which is used to facilitate the research and development of 5G and beyond mobile communications.This paper presents a new scheduling algorithm based on the analysis of different scheduling schemes for radio resources using the Vienna 5G SL simulator.

Improving the real-time performance of Ethernet for plant automation(EPA) based industrial networks

Real-time Ethernet(RTE) control systems with critical real-time requirements are called fast real-time(FRT) systems.To improve the real-time performance of Ethernet for plant automation(EPA),we propose an EPA-FRT scheme.The minimum macrocycle of EPA networks is reduced by redefining the EPA network frame format,and the synchronization process is modified to acquire higher accuracy.A multi-segmented topology with a scheduling scheme is introduced to increase effective bandwidth utilization and reduce protocol overheads,and thus to shorten the communication cycle significantly.Performance analysis and practical tests on a prototype system show the effectiveness of the proposed scheme,which achieves the best performance at small periodic payload in large scale systems.