Multienterprise supply chain: Simulation and optimization

The advancements in connectivity among the entities belonging to industrial supply chain have given rise to more complex, global supply chain networks. These networks are often constituted of entities that belong to multiple such networks. Interactions among the entities in such networks are also influenced by whether they belong to the same enterprise or different ones. This work takes into consideration the effect of such interactions. The entities belonging to different enterprises are assumed to interact through auctions. An agent based simulation model that incorporates such auctions is used to represent multienterprise supply chain networks. The dynamics of the supply chain affected by the auction mechanism are investigated. Also a derivative free optimization methodology is proposed to find the optimal warehouse capacities for the minimization of total cost. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3392–3403, 2016

[1]  Jérémie Gallien,et al.  A Smart Market for Industrial Procurement with Capacity Constraints , 2005, Manag. Sci..

[2]  Fangruo Chen,et al.  Auctioning Supply Contracts , 2007, Manag. Sci..

[3]  Donald R. Jones,et al.  Efficient Global Optimization of Expensive Black-Box Functions , 1998, J. Glob. Optim..

[4]  Antonio Espuña Camarasa,et al.  Using mathematical knowledge management to support integrated decision-making in the enterprise , 2014, Comput. Chem. Eng..

[5]  Marianthi G. Ierapetritou,et al.  A Kriging-Based Approach to MINLP Containing Black-Box Models and Noise , 2008 .

[6]  Benoit Montreuil,et al.  Networked manufacturing:: The impact of information sharing , 1999 .

[7]  Edrisi Muñoz,et al.  Knowledge Management to Support the Integration of Scheduling and Supply Chain Planning using Lagrangean Decomposition , 2015 .

[8]  Juho Mäkiö,et al.  Towards Multi-Attribute Double Auctions for Financial Markets , 2006, Electron. Mark..

[9]  Ulla Lehtinen,et al.  Subcontractors in a partnership environment:: A study on changing manufacturing strategy , 1999 .

[10]  S. Gunn.  Support Vector Machines for Classification and Regression , 1998 .

[11]  Marianthi G. Ierapetritou,et al.  Derivative‐free optimization for expensive constrained problems using a novel expected improvement objective function , 2014 .

[12]  Antonio Espuña Camarasa,et al.  Scenario-based price negotiations vs. game theory in the optimization of coordinated supply chains , 2015 .

[13]  M. Ierapetritou,et al.  A kriging method for the solution of nonlinear programs with black‐box functions , 2007 .

[14]  William Samuelson,et al.  Selling procedures with private information and common values , 1996 .

[15]  Kenneth Holmström,et al.  The TOMLAB Optimization Environment , 2004 .

[16]  Michael Wooldridge,et al.  A supply chain as a network of auctions , 2010, Decis. Support Syst..

[17]  Achille Messac,et al.  Metamodeling using extended radial basis functions: a comparative approach , 2006, Engineering with Computers.

[18]  Emilio Bartezzaghi,et al.  The evolution of production models: is a new paradigm emerging? , 1999 .

[19]  René Bañares-Alcántara,et al.  A new integrated tool for complex decision making: Application to the UK energy sector , 2013, Decis. Support Syst..

[20]  S. Rippa,et al.  Numerical Procedures for Surface Fitting of Scattered Data by Radial Functions , 1986 .

[21]  J. Friedman.  Multivariate adaptive regression splines , 1990 .