Research on Hybrid Scheduling Algorithm Based on CAN bus

In view of the problem that the packet information preempted netw ork resources in the process of transmission in the CAN bus,w hich leads to the low utilization of netw ork resources and the low accuracy of information transmission. Thus,a hybrid scheduling algorithm NM TS based on CAN bus is proposed,in the NM TS hybrid scheduling algorithm,the dynamic scheduling algorithm EDF is used to schedule hard real-time messages to solve the problem of low utilization of netw ork resources; the static scheduling algorithm RM S is used to schedule soft real-time messages and non real-time messages,so as to solve the problem of low accuracy of information transmission. By using M ATLAB softw are,the CAN netw ork model can be built,the EDF algorithm,RM S algorithm and NM TS algorithm are simulated. The experimental results show that the netw ork resources utilization is 90%,the packet loss rate is 0% of the NM TS algorithm. Therefore,The hybrid scheduling algorithm based on CAN bus NM TS has the characteristics of high netw ork resource utilization and high accuracy of information transmission,w hich w ill be very helpful for further research of CAN bus.

An Elite-Class Teaching-Learning-Based Optimization for Reentrant Hybrid Flow Shop Scheduling with Bottleneck Stage

Bottleneck stage and reentrance often exist in real-life manufacturing processes;however,the previous research rarely addresses these two processing conditions in a scheduling problem.In this study,a reentrant hybrid flow shop scheduling problem(RHFSP)with a bottleneck stage is considered,and an elite-class teaching-learning-based optimization(ETLBO)algorithm is proposed to minimize maximum completion time.To produce high-quality solutions,teachers are divided into formal ones and substitute ones,and multiple classes are formed.The teacher phase is composed of teacher competition and teacher teaching.The learner phase is replaced with a reinforcement search of the elite class.Adaptive adjustment on teachers and classes is established based on class quality,which is determined by the number of elite solutions in class.Numerous experimental results demonstrate the effectiveness of new strategies,and ETLBO has a significant advantage in solving the considered RHFSP.

Differential evolution algorithm for hybrid flow-shop scheduling problems

Aiming at the hybrid flow-shop （HFS） scheduling that is a complex NP-hard combinatorial problem with wide engineering background, an effective algorithm based on differential evolution （DE） is proposed. By using a special encoding scheme and combining DE based evolutionary search and local search, the exploration and exploitation abilities are enhanced and well balanced for solving the HFS problems. Simulation results based on some typical problems and comparisons with some existing genetic algorithms demonstrate the proposed algorithm is effective, efficient and robust for solving the HFS problems.

A Heuristic for Two-Stage No-Wait Hybrid Flowshop Scheduling with a Single Machine in Either Stage

This paper studies the hybrid flow-shop scheduling problem with no-wait restrictions. The production process consists of two machine centers, one has a single machine and the other has more than one parallel machine. A greedy heuristic named least deviation algorithm is designed and its worst case performance is analyzed. Computational results are also given to show the algorithm's average performance compared with some other algorithms. The least deviation algorithm outperforms the others in most cases tested here, and it is of low computational complexity and is easy to carry out,thus it is of favorable application value.

Hybrid Flowshop Scheduling with Interstage Job Transportation

There are a variety of joint job production and transportation scheduling problems that arise in modern manufacturing systems.In this paper,we study one of such problems that arises in a flowshop environment where there are two processing stages and a single transporter that is available to deliver the finished jobs from the first stage to the second.There is a single machine in the first stage and two parallel machines in the second stage.The transporter can carry only one job in each shipment.Each job is first processed on the single machine at stage one,then transported to and processed on one of the two parallel machines at stage two.The objective is to minimize the makespan,i.e.,the completion time of the last job in the second stage.Since this problem is strongly NP-hard,we propose a fast heuristic and show that the heuristic has a worst-case bound of 5/2.We also conduct1 numerical experiments to evaluate the average performance of this heuristic.

Real-Time Hybrid Flow Shop Scheduling Approach in Smart Manufacturing Environment

Smart manufacturing in the“Industry 4.0”strategy promotes the deep integration of manufacturing and information technologies,which makes the manufacturing system a ubiquitous environment.However,the real-time scheduling of such a manufacturing system is a challenge faced by many decision makers.To deal with this challenge,this study focuses on the real-time hybrid flow shop scheduling problem(HFSP).First,the characteristic of the hybrid flow shop in a smart manufacturing environment is analyzed,and its scheduling problem is described.Second,a real-time scheduling approach for the HFSP is proposed.The core module is to employ gene expression programming to construct a new and efficient scheduling rule according to the real-time status in the hybrid flow shop.With the scheduling rule,the priorities of the waiting job are calculated,and the job with the highest priority will be scheduled at this decision time point.A group of experiments are performed to prove the performance of the proposed approach.The numerical experiments show that the real-time scheduling approach outperforms other single-scheduling rules and the back-propagation neural network method in optimizing most objectives for different size instances.Therefore,the contribution of this study is the proposal of a real-time scheduling approach,which is an effective approach for real-time hybrid flow shop scheduling in a smart manufacturing environment.

Q-Learning-Based Teaching-Learning Optimization for Distributed Two-Stage Hybrid Flow Shop Scheduling with Fuzzy Processing Time

Two-stage hybrid flow shop scheduling has been extensively considered in single-factory settings.However,the distributed two-stage hybrid flow shop scheduling problem(DTHFSP)with fuzzy processing time is seldom investigated in multiple factories.Furthermore,the integration of reinforcement learning and metaheuristic is seldom applied to solve DTHFSP.In the current study,DTHFSP with fuzzy processing time was investigated,and a novel Q-learning-based teaching-learning based optimization(QTLBO)was constructed to minimize makespan.Several teachers were recruited for this study.The teacher phase,learner phase,teacher’s self-learning phase,and learner’s self-learning phase were designed.The Q-learning algorithm was implemented by 9 states,4 actions defined as combinations of the above phases,a reward,and an adaptive action selection,which were applied to dynamically adjust the algorithm structure.A number of experiments were conducted.The computational results demonstrate that the new strategies of QTLBO are effective;furthermore,it presents promising results on the considered DTHFSP.