April  2005, 1(2): 251-273. doi: 10.3934/jimo.2005.1.251

A network simplex algorithm for simple manufacturing network model

1. 

School of Science, Xian Jiaotong University, Xian, Shanxi, 710049, China

2. 

Department of Applied Mathematics, Hong Kong Polytechnic University, Kowloon, Hong Kong, China

3. 

School of Mathematics and Information Science, Guangxi University, Guangxi, 53004, China

Received  June 2004 Revised  December 2004 Published  April 2005

In this paper, we propose a network model called simple manufacturing network. Our model is a combined version of the ordinary multicommodity network and the manufacturing network flow model. It can be used to characterize the complicated manufacturing scenarios. By formulating the model as a minimum cost flow problem plus several bounded variables, we present a modified network simplex method, which exploits the special structure of the model and can perform the computation on the network. A numerical example is provided for illustrating our method.
Citation: Jiangtao Mo, Liqun Qi, Zengxin Wei. A network simplex algorithm for simple manufacturing network model. Journal of Industrial & Management Optimization, 2005, 1 (2) : 251-273. doi: 10.3934/jimo.2005.1.251
[1]

I-Lin Wang, Shiou-Jie Lin. A network simplex algorithm for solving the minimum distribution cost problem. Journal of Industrial & Management Optimization, 2009, 5 (4) : 929-950. doi: 10.3934/jimo.2009.5.929

[2]

Junjie Peng, Ning Chen, Jiayang Dai, Weihua Gui. A goethite process modeling method by asynchronous fuzzy cognitive Network based on an improved constrained chicken swarm optimization algorithm. Journal of Industrial & Management Optimization, 2017, 13 (5) : 0-0. doi: 10.3934/jimo.2020021

[3]

Haodong Chen, Hongchun Sun, Yiju Wang. A complementarity model and algorithm for direct multi-commodity flow supply chain network equilibrium problem. Journal of Industrial & Management Optimization, 2017, 13 (5) : 0-0. doi: 10.3934/jimo.2020066

[4]

Ángela Jiménez-Casas, Aníbal Rodríguez-Bernal. Linear model of traffic flow in an isolated network. Conference Publications, 2015, 2015 (special) : 670-677. doi: 10.3934/proc.2015.0670

[5]

Li Gang. An optimization detection algorithm for complex intrusion interference signal in mobile wireless network. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1371-1384. doi: 10.3934/dcdss.2019094

[6]

Artyom Nahapetyan, Panos M. Pardalos. A bilinear relaxation based algorithm for concave piecewise linear network flow problems. Journal of Industrial & Management Optimization, 2007, 3 (1) : 71-85. doi: 10.3934/jimo.2007.3.71

[7]

Qun Lin, Antoinette Tordesillas. Towards an optimization theory for deforming dense granular materials: Minimum cost maximum flow solutions. Journal of Industrial & Management Optimization, 2014, 10 (1) : 337-362. doi: 10.3934/jimo.2014.10.337

[8]

Qiong Liu, Ahmad Reza Rezaei, Kuan Yew Wong, Mohammad Mahdi Azami. Integrated modeling and optimization of material flow and financial flow of supply chain network considering financial ratios. Numerical Algebra, Control & Optimization, 2019, 9 (2) : 113-132. doi: 10.3934/naco.2019009

[9]

Deena Schmidt, Janet Best, Mark S. Blumberg. Random graph and stochastic process contributions to network dynamics. Conference Publications, 2011, 2011 (Special) : 1279-1288. doi: 10.3934/proc.2011.2011.1279

[10]

R.L. Sheu, M.J. Ting, I.L. Wang. Maximum flow problem in the distribution network. Journal of Industrial & Management Optimization, 2006, 2 (3) : 237-254. doi: 10.3934/jimo.2006.2.237

[11]

Yuhe Du, Jianwei Ji, Yu Liao, Yichu Liu. Design of energy storage coordination optimization algorithm for distributed power distribution network operation planning. Discrete & Continuous Dynamical Systems - S, 2018, 0 (0) : 0-0. doi: 10.3934/dcdss.2020206

[12]

Yang Chen, Xiaoguang Xu, Yong Wang. Wireless sensor network energy efficient coverage method based on intelligent optimization algorithm. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 887-900. doi: 10.3934/dcdss.2019059

[13]

David J. Aldous. A stochastic complex network model. Electronic Research Announcements, 2003, 9: 152-161.

[14]

Ji Zhang, Hongxia Lv, Boer Deng, Wenxian Wang. An adaptive genetic algorithm for solving the optimization model of car flow organizat. Discrete & Continuous Dynamical Systems - S, 2018, 0 (0) : 0-0. doi: 10.3934/dcdss.2020200

[15]

King Hann Lim, Hong Hui Tan, Hendra G. Harno. Approximate greatest descent in neural network optimization. Numerical Algebra, Control & Optimization, 2018, 8 (3) : 327-336. doi: 10.3934/naco.2018021

[16]

Alberto Bressan, Khai T. Nguyen. Conservation law models for traffic flow on a network of roads. Networks & Heterogeneous Media, 2015, 10 (2) : 255-293. doi: 10.3934/nhm.2015.10.255

[17]

Chun Zong, Gen Qi Xu. Observability and controllability analysis of blood flow network. Mathematical Control & Related Fields, 2014, 4 (4) : 521-554. doi: 10.3934/mcrf.2014.4.521

[18]

Francesco Sanna Passino, Nicholas A. Heard. Modelling dynamic network evolution as a Pitman-Yor process. Foundations of Data Science, 2019, 1 (3) : 293-306. doi: 10.3934/fods.2019013

[19]

Philippe Michel, Suman Kumar Tumuluri. A note on a neuron network model with diffusion. Discrete & Continuous Dynamical Systems - B, 2017, 18 (11) : 0-0. doi: 10.3934/dcdsb.2020085

[20]

Weifu Sun, Xu Yang, Yijun Chen. Elimination algorithm of complex network redundant data stream based on information theory. Discrete & Continuous Dynamical Systems - S, 2018, 0 (0) : 0-0. doi: 10.3934/dcdss.2020256

2018 Impact Factor: 1.025

Metrics

  • PDF downloads (17)
  • HTML views (0)
  • Cited by (3)

Other articles
by authors

[Back to Top]