Volume 43,Issue 1,2020 Table of Contents

1 An improved whale swarm algorithm for flexible job-shop scheduling problem

WANG Sihan , LI Yang , LI Xinyu

2020, 43(1):1-11. DOI: 10.11835/j.issn.1000-582X.2020.01.001

[Abstract](1434) [HTML](1309) [PDF 1.85 M](1349)

Abstract:
An improved whale swarm algorithm is proposed for solving flexible job shop schedule problem(FJSP) with the objective to minimize makespan based on whale swarm algorithm(WSA). First of all, the position representation and distance calculation method of individuals were well-designed based on processing sequence so that the WSA could solve discrete problem such as FJSP directly. Secondly, cooperating search was introduced to develop "better and near" whale swarm with quality and quantity, expanding the moving region of individuals. Finally, variable neighborhood search(VNS) based on critical path was embedded to enhance the local exploitation ability. Numerical experiments and comparisons were conducted against the best performing algorithms reported in the literature. The results validate the effectiveness and efficiency of proposed algorithm.

2 Optimism of job shop scheduling with multi-times

LI Zhengfeng , ZHAO Changchun , ZHANG Guohui , Ding Jianfei

2020, 43(1):12-18. DOI: 10.11835/j.issn.1000-582X.2020.01.002

[Abstract](833) [HTML](1190) [PDF 442.37 K](1235)

Abstract:
For the simplicity of model and theory study, the optimism of JSP(jpb shop sche duling problem) with focus only on processing time is the case in most job shop scheduling study. But non-processing takes up to 90% of the time in the manufacture process, and the auxiliary time including setup time,transportation time and failure time in production process plays a negligible important role in job shop scheduling. To solve this problem, this paper carried out a survey on the job shop scheduling with multi-time according to the practical production of job shop, with transportation time, setup time, waiting time, failure time and processing time taken into consideration. A scheduling model was established and the optimism algorithm based on improved GA(genetic algorithm) was contrived. Finally, classical examples were tested and contrasted. The results show it is very necessary to include multi-time factor in the optimism of JSP.

3 Research of optimal layout of public bike stations and bike lanes based on tabu search

FANG Yunfei , WANG Xiaoyuan , ZHOU Zhen , SONG Yan

2020, 43(1):19-27. DOI: 10.11835/j.issn.1000-582X.2020.01.003

[Abstract](776) [HTML](864) [PDF 472.14 K](1066)

Abstract:
To improve the attractiveness of public transportation system, optimal network layout of the public bike stations and bike lanes was studied from the point of view of transferring between buses and public bikes. In this paper, a nonlinear optimization model with the objective of maximizing the users' demand was formulated, and it was compared with the traditional location model. Based on the special designed neighborhood and its generation criterion, a tabu-search-based algorithm was proposed to solve the problem. Then simulation experiments by MATLAB program were conducted and the computational results show that the proposed algorithm efficiently solves different-sized instances and obtains high quality solutions for the network layout of public bicycle system. Furthermore, results of the sensitive experiments can provide useful information for planners' decision-making.

4 Optimization model and algorithm of location-routing for joint distribution

LI Zhenping , ZHAO Yuwei , ZHANG Yuwei

2020, 43(1):28-43. DOI: 10.11835/j.issn.1000-582X.2020.01.004

[Abstract](1006) [HTML](652) [PDF 3.40 M](1495)

Abstract:
Based on the multiple dairy products distribution in Beijing, the optimization of location-routing for joint distribution was studied. A mixed integer programming model was formulated for the joint distribution location-routing problem with two-echelon capacitated constraints. A three-phase algorithm was designed for solving it. First, all customers were divided into multiple customer sets by K-means clustering method based on genetic algorithm. Then, the optimal delivery routes and costs for each candidate distribution center serving each customer set were calculated, by which the joint distribution centers location and the secondary level delivery routing problem were simplified to that of joint distribution centers location and customer sets allocation.The mathematical model was formulated, and solved by Lingo software. Finally, the first level delivery routes from the logistics center to the selected joint distribution centers were determined. By comparing the costs of separate distribution mode and joint distribution mode of two brands of dairy products in Beijing, the rationality and effectiveness of the model and algorithm were verified. It provides the decision-making basis for solving the problems such as the joint distribution network optimization of multi-brand daily products.

5 On multi-load AGV green logistics scheduling in knitting workshop

WANG Zhen , WANG Chenxi , WANG Yuhao , DU Lizhen

2020, 43(1):44-52. DOI: 10.11835/j.issn.1000-582X.2020.01.005

[Abstract](773) [HTML](413) [PDF 691.04 K](951)

Abstract:
With the rapid development of modern manufacturing industry, enterprises are required to have higher production efficiency and lower production energy consumption, resulting in more improvement of the automation degree of intelligent production workshop. This paper mainly studied green intelligent logistics scheduling of automated guided vehicle(AGV) in job shop. AGV logistics scheduling optimization model was establised to reduce the energy consumption of AGV and optimize AGV path. A genetic particle swarm optimization (PSO) algorithm was proposed with task sequencing as the constraint condition. Finally, the actual logistics scheduling of a knitting shop was taken as example to verify the method proposed in this paper. The calculation results show that the AGV logistics scheduling model proposed can well simulate the AGV green scheduling energy consumption, and the improved genetic particle swarm optimization algorithm presents a faster convergence speed and a better optimization ability.

6 Research on group scheduling of optimal setup uncorrelated parallel machine based on GATS hybrid algorithm

SONG Haicao , YI Shuping , WU Changyou , ZHANG Shuntang , DENG Guanlong , LIU Pan , WEI Xuemeng

2020, 43(1):53-63. DOI: 10.11835/j.issn.1000-582X.2020.01.006

[Abstract](788) [HTML](735) [PDF 806.22 K](1301)

Abstract:
The uncorrelated parallel machine schedulNE.Cms_Inserting problem is a typical problem in the workshop scheduling, and the single piece small batch production mode leads to frequent job switching and a large number of setup times, which reduces equipment utilization and production efficiency. This dissertation presents a research on the scheduling of uncorrelated parallel machines based on the grouping technique, which is dependent on the setup time. According to the similarity of the resources required for workpiece processing, the workpieces are clustered and grouped, and with machine constraints condition met, the allocation of all the workpiece groups on the machines as well as the order of the workpiece groups and that within each group on the same machine is determined. In this paper, a mathematical model is constructed with the minimization of total delay time as the optimization goal and genetic tabu search (GATS) algorithm is applied to solve it. Artificial bee colony (ABC) algorithms and genetic simulated annealing (GASA) algorithms are used for case studies. The comparison results show that the proposed algorithm has better searching ability.

7 Research on big-data-driven green intelligent manufacturing mode and the implementation design

WANG Ting , LIAO Bin , YANG Chengcheng

2020, 43(1):64-73. DOI: 10.11835/j.issn.1000-582X.2020.01.007

[Abstract](920) [HTML](906) [PDF 9.30 M](1177)

Abstract:
In order to realize the effective integration of manufacturing process intelligence and green manufacturing, based on the analysis of intelligent manufacturing and green manufacturing synergy complementarity by system engineering theory, a new mode of green intelligent manufacturing driven by big data is proposed. With the product life cycle as the main line, the specific implementation of the new mode is expounded from the aspects of data-driven product green customized research and development, digital resource-based active resource allocation scheduling, service-oriented prior maintenance strategy, product recycling and remanufacturing process considering resource recycling. Finally, combined with the ideas of the business process reengineering and the matter-element extension method, the implementation plan and key technologies concerning the transition from traditional manufacturing mode to the new mode proposed in this paper are discussed. The feasibility and effectiveness of the operation mode are verified by an application case.

8 Research of data classification method based on multi-objective artificial bee colony algorithm

WANG Haiquan , HOU Yuliang , WEI Jianhua , XU Xiaobin , SU Menghao , ZHANG Shanshan

2020, 43(1):74-81. DOI: 10.11835/j.issn.1000-582X.2020.01.008

[Abstract](776) [HTML](427) [PDF 3.78 M](993)

Abstract:
In order to improve the classification accuracy of complex data on the premise of ensuring operation efficiency, a data classification algorithm based on multi-objective artificial bee colony algorithm and extreme learning machine is proposed, it takes the number of features and the classification accuracy as the optimization objectives, and improved artificial bee colony algorithm is introduced to optimize the parameters of the classifier and the selection of features of data. The simulation results based on six data sets verify the effectiveness of the proposed method.

9 Fault diagnosis of rolling bearing based on improved EEMD and convolutional neural network

HE Jiangjiang , LI Xiaoquan , ZHAO Yuwei , ZHANG Baoshan , DING Haibin

2020, 43(1):82-89. DOI: 10.11835/j.issn.1000-582X.2020.01.009

[Abstract](953) [HTML](521) [PDF 2.39 M](1460)

Abstract:
EEMD(ensemble empirical mode decomposition)is an analysis method for signal decomposition.However,there is serious divergence in the two endpoints of its modal function (IMF). If the decomposition results are directly applied to the fault diagnosis system, the diagnosis accuracy will decrease. In the paper, support vector machine (SVM) and EEMD algorithm were combined to decompose signal and the reliability analysis was conducted with simulation signal. After selecting the components of SVM-EEMD decomposition, the signal was decomposed further and the energy vector was constructed. Finally, with a combination of SVM-EEMD and convolutional neural network, rolling bearing fault diagnosis model was constructed and verified by experiment.The experimental comparison results show that the improved EEMD algorithm can effectively solve the problem of the endpoints divergence, and the fault diagnosis model constructed improves the fault diagnosis accuracy.

10 Energy management strategy of WSNs node based on photovoltaic capacitance

HE Jin , ZHONG Yuanchang , SUN Lili , MA Tianzhi

2020, 43(1):90-99. DOI: 10.11835/j.issn.1000-582X.2020.01.010

[Abstract](698) [HTML](635) [PDF 1.69 M](1080)

Abstract:
To solve the problem of the deficiency in energy allocation and management mechanism based on prediction algorithm in WSNs, energy consumption management in solar chargeable wireless sensor networks is studied, and energy neutral management mechanism based on historical capacity is proposed. A node energy acquisition model for adaptive tracking of sunlight is designed and an energy neutral management mechanism based on historical energy acquisition is constructed. According to the available energy converted from solar energy in the current operation cycle, the duty cycle of the node in the next operation cycle is adjusted to solve the optimization problem of node solar energy acquisition and node energy consumption. The experimental results show that the proposed energy neutral management mechanism based on historical energy acquisition achieves the best match between the size of solar panels and energy consumption of nodes, and provides a valuable solution for energy acquisition and energy consumption management in solar chargeable wireless sensor networks.

11 Queue-aware online power allocation strategy in wireless backhaul networks

HU Guangtao , TANG Lun

2020, 43(1):100-112. DOI: 10.11835/j.issn.1000-582X.2020.01.011

[Abstract](592) [HTML](498) [PDF 3.85 M](851)

Abstract:
Wireless backhaul technology is one of the promising solutions for next-generation mobile communication networks due to its merits of significantly reducing operator's cost, providing user equipment (UE) with fundamental flexibility and improving the network overall spectrum efficiency. By leveraging the Lyapunov stochastic optimization framework and convex optimization theory, this paper proposes a queue-aware power allocation algorithm for in-band full-duplex wireless backhaul networks. Specifically, in each discrete resource scheduling time slot, the algorithm dynamically allocates power for each user's downlink backhaul and access links by comprehensively taking channel state information (CSI) and queue state information (QSI) into consideration, so as to maximize the network average total spectrum efficiency (SE) while ensuring network stability and meeting the quality of service (QoS) requirements of users. In addition, the theoretical analysis and simulation results reveal that the proposed algorithm can flexibly strike a balance between SE and delay by simply tuning an introduced control parameter.

12 Reliability analysis of regional computer interlocking system based on dynamic Bayesian network

WU Guanghui , TAN Li , WEI Ziwen

2020, 43(1):113-122. DOI: 10.11835/j.issn.1000-582X.2020.01.012

[Abstract](779) [HTML](819) [PDF 1.79 M](1024)

Abstract:
Regional computer interlocking equipment is the core equipment to ensure the safety of regional traffic and transport efficiency, and the reliability research of it is of great significance. Combining the two existing regional interlocking schemes, a new interlocking scheme is proposed, that is, both the main control station and the slave control station (choose one or more stations) are equipped with interlocking equipment. Taking the common cause fault and maintainability of interlocking system into account, the dynamic Bayesian network is used to analyze the reliability. Firstly, from the perspective of system fault-safety and dangerous output, the dynamic fault tree of two interlocking units and triple interlocking units are established and then it is transformed into the corresponding dynamic Bayesian network models. By using the reasoning characteristic of dynamic Bayesian network, the reliability of regional interlocking equipment is analyzed. Finally, the results of this method are compared with those of static Bayesian network and dynamic fault tree analysis. The results show that it's the best way to set up interlocking equipment in the main control station and one of the slave control stations and the reliability analysis based on dynamic Bayesian network has obvious advantages over the above two methods in terms of calculation accuracy and time complexity. Through the diagnosis and reasoning of dynamic Bayesian network, it is known that common cause fault is the main cause of system fault, so we should focus on prevention of it to reduce the probability of accidents.

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