American Institute of Mathematical Sciences

Advanced
January  2007, 3(1): 29-50. doi: 10.3934/jimo.2007.3.29

Taking market forces into account in the design of production-distribution networks: A positioning by anticipation approach

Didier Vila 1, , Alain Martel 2, and  Robert Beauregard 2,

1. 

Universiét Laval, FOR@C Research Consortium, Network Organization Technology Research Center (CENTOR), Sainte-Foy, Québec, G1K7P4, Canada
2. 

Université Laval, FOR@C Research Consortium, Network Organization Technology Research Center (CENTOR), Sainte-Foy, Québec, G1K7P4, Canada, Canada

Received  August 2005 Revised  April 2006 Published  January 2007

This paper presents an approach to take into account market opportunities when designing production-distribution networks. Three types of sub-markets found in several industrial contexts are analyzed: spot markets, contracts and Vendor Managed Inventory (VMI) agreements. For contracts and VMI agreements, customer preferences with respect to different logistics policies are considered. A price-supply function is proposed to model the spot market behavior. The production-distribution network design problem is formulated as a two-stage stochastic program with fixed recourse. Finally, a sample average approximation method (SAA), based on Monte Carlo sampling techniques, is used to solve the model.
Keywords: Mathematical Programming, Market Analysis, Monte-Carlo Sampling Methods, Production-distribution Network Design, Logistics Policy Selection..
Mathematics Subject Classification: Primary: 90B1.
Citation: Didier Vila, Alain Martel, Robert Beauregard. Taking market forces into account in the design of production-distribution networks: A positioning by anticipation approach. Journal of Industrial and Management Optimization, 2007, 3 (1) : 29-50. doi: 10.3934/jimo.2007.3.29
[1]

Chjan C. Lim, Joseph Nebus, Syed M. Assad. Monte-Carlo and polyhedron-based simulations I: extremal states of the logarithmic N-body problem on a sphere. Discrete and Continuous Dynamical Systems - B, 2003, 3 (3) : 313-342. doi: 10.3934/dcdsb.2003.3.313
[2]

Azam Moradi, Jafar Razmi, Reza Babazadeh, Ali Sabbaghnia. An integrated Principal Component Analysis and multi-objective mathematical programming approach to agile supply chain network design under uncertainty. Journal of Industrial and Management Optimization, 2019, 15 (2) : 855-879. doi: 10.3934/jimo.2018074
[3]

Michael B. Giles, Kristian Debrabant, Andreas Rössler. Analysis of multilevel Monte Carlo path simulation using the Milstein discretisation. Discrete and Continuous Dynamical Systems - B, 2019, 24 (8) : 3881-3903. doi: 10.3934/dcdsb.2018335
[4]

Michelle L.F. Cheong, Rohit Bhatnagar, Stephen C. Graves. Logistics network design with supplier consolidation hubs and multiple shipment options. Journal of Industrial and Management Optimization, 2007, 3 (1) : 51-69. doi: 10.3934/jimo.2007.3.51
[5]

Zhiyan Ding, Qin Li. Constrained Ensemble Langevin Monte Carlo. Foundations of Data Science, 2022, 4 (1) : 37-70. doi: 10.3934/fods.2021034
[6]

Jiuping Xu, Pei Wei. Production-distribution planning of construction supply chain management under fuzzy random environment for large-scale construction projects. Journal of Industrial and Management Optimization, 2013, 9 (1) : 31-56. doi: 10.3934/jimo.2013.9.31
[7]

Abdelghani Bouras, Ramdane Hedjar, Lotfi Tadj. Production planning in a three-stock reverse-logistics system with deteriorating items under a periodic review policy. Journal of Industrial and Management Optimization, 2016, 12 (3) : 1075-1089. doi: 10.3934/jimo.2016.12.1075
[8]

Abdelghani Bouras, Lotfi Tadj. Production planning in a three-stock reverse-logistics system with deteriorating items under a continuous review policy. Journal of Industrial and Management Optimization, 2015, 11 (4) : 1041-1058. doi: 10.3934/jimo.2015.11.1041
[9]

Ross Callister, Duc-Son Pham, Mihai Lazarescu. Using distribution analysis for parameter selection in repstream. Mathematical Foundations of Computing, 2019, 2 (3) : 215-250. doi: 10.3934/mfc.2019015
[10]

Giacomo Dimarco. The moment guided Monte Carlo method for the Boltzmann equation. Kinetic and Related Models, 2013, 6 (2) : 291-315. doi: 10.3934/krm.2013.6.291
[11]

Guillaume Bal, Ian Langmore, Youssef Marzouk. Bayesian inverse problems with Monte Carlo forward models. Inverse Problems and Imaging, 2013, 7 (1) : 81-105. doi: 10.3934/ipi.2013.7.81
[12]

Ajay Jasra, Kody J. H. Law, Yaxian Xu. Markov chain simulation for multilevel Monte Carlo. Foundations of Data Science, 2021, 3 (1) : 27-47. doi: 10.3934/fods.2021004
[13]

Theodore Papamarkou, Alexey Lindo, Eric B. Ford. Geometric adaptive Monte Carlo in random environment. Foundations of Data Science, 2021, 3 (2) : 201-224. doi: 10.3934/fods.2021014
[14]

Jiakou Wang, Margaret J. Slattery, Meghan Henty Hoskins, Shile Liang, Cheng Dong, Qiang Du. Monte carlo simulation of heterotypic cell aggregation in nonlinear shear flow. Mathematical Biosciences & Engineering, 2006, 3 (4) : 683-696. doi: 10.3934/mbe.2006.3.683
[15]

Gheorghe Craciun, Baltazar Aguda, Avner Friedman. Mathematical Analysis Of A Modular Network Coordinating The Cell Cycle And Apoptosis. Mathematical Biosciences & Engineering, 2005, 2 (3) : 473-485. doi: 10.3934/mbe.2005.2.473
[16]

Thomas Hillen, Peter Hinow, Zhi-An Wang. Mathematical analysis of a kinetic model for cell movement in network tissues. Discrete and Continuous Dynamical Systems - B, 2010, 14 (3) : 1055-1080. doi: 10.3934/dcdsb.2010.14.1055
[17]

Joseph Nebus. The Dirichlet quotient of point vortex interactions on the surface of the sphere examined by Monte Carlo experiments. Discrete and Continuous Dynamical Systems - B, 2005, 5 (1) : 125-136. doi: 10.3934/dcdsb.2005.5.125
[18]

Olli-Pekka Tossavainen, Daniel B. Work. Markov Chain Monte Carlo based inverse modeling of traffic flows using GPS data. Networks and Heterogeneous Media, 2013, 8 (3) : 803-824. doi: 10.3934/nhm.2013.8.803
[19]

Mazyar Zahedi-Seresht, Gholam-Reza Jahanshahloo, Josef Jablonsky, Sedighe Asghariniya. A new Monte Carlo based procedure for complete ranking efficient units in DEA models. Numerical Algebra, Control and Optimization, 2017, 7 (4) : 403-416. doi: 10.3934/naco.2017025
[20]

Juntao Yang, Viet Ha Hoang. Multilevel Markov Chain Monte Carlo for Bayesian inverse problem for Navier-Stokes equation. Inverse Problems and Imaging, , () : -. doi: 10.3934/ipi.2022033

2021 Impact Factor: 1.411

Tools

Metrics

  • PDF downloads (93)
  • HTML views (0)
  • Cited by (18)

Other articles
by authors

[Back to Top]

Copyright © 2022 American Institute of Mathematical Sciences | Privacy Policy