Supply chain network optimizing

Several simulation-based optimization models in the context of supply chain management can be found e.g. in the area of supply chain network optimization (Preusser et al. 2005) or to simulate rescheduling of production facing demand uncertainty or unplanned shut-downs (Tang/Grubbstrom 2002 Neuhaus/Giinther 2006). A basic approach of simulation-based optimization is presented by Preusser et al. 2005, p. 98 illustrated in fig. 24. 

L. Benyoucef, H. Ding, C. Hans, and X. Xie, On a New Tool for Supply Chain Network Optimization and Simulation, Proceedings of the 2004 Winter Simulation Conference, 2004. 

To put every scenario on a common basis, CGR Management Consultants and SFI developed a common cost model. Its purpose was to eliminate these differences when evaluating the underlying changes in location. The model, called SITELINK, used a structure that is adaptable for supply chain network optimization modeling. Figure 44.1 is a simplified stmcture of the SITELINK model. 

Next we apply these integer programming models to different supply chain network optimization problems, including warehouse location, network design, and distribution problems. The topic of risk pooling or inventory consolidation is presented next. In this portion of the chapter. 

Ferrio, J., Wassick, J. (2008). Chemical supply chain network optimization. Computers and Chemical Engineering, 32, 2481-2504. 

In literature simulation and simulation-based optimization is focused on supply chain management areas such as production (Smith 2003 Wullink et al. 2004), inventory (Siprelle et al. 2003), transportation or integrated supply chain networks (Preusser et al. 2005). 

Chen C, Lee W (2004) Multi-objective optimization of multi-echelon supply chain networks with uncertain product demands and prices. Computers Chemical Engineering 28 (6-7) 1131-1144... 

Chen C, Wang B, Lee W (2003) Multiobjective Optimization for a Multienterprise Supply Chain Network. Industrial Engineering Chemistry Research 42 1879-1889... 

C.L. Chen, B.W. Wang, W.C. Lee, Multi-objective Optimization for Multi-enterprise Supply Chain Networks, Ind. Eng. Chem. Res. 42 (2003) 1879-1889. 

Chen, C.-L., Wang, B.-W., and Lee, W.-C. Multiobjective optimization for a multienterprise supply chain network. Industrial Engineering Chemistry Research, 42(9) 1879-1889, 2003. 

Mele, F. D., Guillen, G., Espuna, A., and Puigjaner, L. A simulation-based optimization framework for parameter optimization of supply-chain networks. Industrial Engineering Chemistry Research, 45(9) 3133-3148, 2006. 

Watson M, Lewis S, Cacioppi P, Jayaraman J (2012) Supply chain network design applying optimization and tmalytics to the global supply chain. FT Press... 

A typical supply chain network is shown in Figure 2.3. Designing a supply chain involves choosing facilities, capacity, and deployment to maximize competitiveness. Steps to optimize a supply chain are described below. 

An important distinction, however, between Supply Chain Engineering (SCE) and Supply Chain Management (SCM) is the emphasis in SCE on the design of the supply chain network and the use of mathematical models and methods to determine the optimal strategies for managing the supply chain. 

Note also that the last example in the chapter essentially entails determining the placement of safety inventory in the supply chain (since the reorder point specifies inventory carried over and above the level required to meet expected demand over the replenishment lead time), albeit a simple, serial supply chain. Magnanti et al. (2006) present a general formulation of fhis problem, specifically a non-linear optimization formulation subject to linear constraints, to solve for the optimal placement of safety stocks in a general supply chain network. Magnanti et al. actually generalize the problem... 

The stage 2 analysis included capacity expansions in production and distribution facilities already considered by the management. Close to a dozen new production lines were planned within a 2 year horizon. Although management had already decided on their locations, multiple ophons were allowed in the model to confirm their choices. The results showed that the majority of the chosen locations were optimal. Although the locations were the best to maximize profit, the supply chain network was capable of meeting only 85% of the projected demand. 

The optimization models discussed in this chapter had a single objective— either to minimize supply chain costs or to maximize supply chain profitability, in case the product prices vary by location or customer. However, customer demand fulfillment and service are also important in designing a supply chain network. More recently, supply chain risk is emerging to be another important criterion (Supply chain risk is discussed in detail in Chapter 7). Hence, recent applications of optimization models have used multiple criteria optimization models for decision making. 

Portillo, R. C. 2009. Resilient global supply chain network design optimization. PhD dissertation. The Pennsylvania State University, University Park, PA. 

We will use RvaR( ) and RmitC ) this section to denote the combined risk values. Note that the term "supplier" could be easily replaced by "geographic region" in the context of supply chain network design optimization, where risk can be explicitly included as part of the criteria functions for locating plants and warehouses. 

Stage 2 Evaluate how the company s ability would be improved, considering the potential expansions of plants and DCs in the current supply chain. Stage 3 Optimize the global supply chain network that would deliver the best results for the entire 5 year time horizon. 

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