Supply Chain Optimization Model

Once the Ambrox demand has been defined, the entire Ambrox supply chain system has to be optimized by means of a modeling framework. The proposed model is a mixed-integer linear programming (MILP) model, which includes all the related aspects of the supply chain, from raw materials to products in the final markets. In order to determine the total extension of land that has to be cultivated with A. jocotepecana (A ), a balance considering the area currently occupied by A jocotepecana () and the new area required (A ) is used  

The area cultivated has to be less than the total available area. This way, a maximum limit of the area in each municipahty can be defined to avoid excessive change of land or even change of crops this can be done with the following constraint  

The amount of stems per hectare is proportional to the cultivated area and the yield factor ( )  

The balance for the stems of A. jocotepecana indicates that the total flow of stems is equal to the stems sent to the preprocessing plants plus the stems sent to the central plant  

It can be seen that the preprocessing plants can receive stems only from the harvesting site associated with their location, while on the other hand the central plant can receive stems from any harvesting site, and thus the total flow in the central plant is equal to the summation of aU the flows sent from the different municipalities  

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The value objective function is oriented at the company s profit and loss definitions. Guiding principle is to only use value parameters that can be found in the cost controlling of the company signed by controlling. Penalty costs and without currency and weighting factors being applied to steer optimization results but having no actual financial impact - as it can be often found in supply chain optimization models - do not meet this requirement. 

Categorization schemes have been suggested both for facility location (e.g., Hamacher and Nickel 1998 Ballou 1992, pp. 323-324 Brandeau and Chiu 1989, pp. 647-650) and supply chain optimization models (e.g., Bankhofer 2003, pp. 27-34 Bestmann 2001, pp. 46-47) and many literature reviews contain classifications of the models they review. The following criteria (the abbreviations in brackets are used in Table 4), extending the classification introduced by Melo et al. (2005, p. 198), are used to classify the models from literature contained in Table 4 ... 

Typically, supply chain optimization models focus on minimizing costs, since the decisions of supply chain managers often involve choices that directly influence cosfs, while revenue may often be outside the scope of the supply chain manager s decisions. Some SCE models, however, may appropriately involve maximizing profit, to the extent that it is clear that the decision at hand has both cost and revenue implications. 

Risk recovery can be included in supply chain optimization models. One can first create risk scenarios based on previously computed risk values (using techniques described in Section 7.9). Then, assuming certain inventory levels, the p parameter for each scenario can be calculated and the sum of p parameters of each supplier can be minimized. 

Zeng AZ (2002) An optimization framework for evaluating logistics costs in a global supply chain an application to the commercial aviation industry. In Geunes J, Pardalos PM, Romeijn HE (eds) Supply Chain Management Models, Applications, and Research Directions. Kluwer Academic Publishers, Boston et al., pp 317-339... 

Fruit Industry Supply Chains (FISC) are interconnected networks conformed by production nodes (farms), processing plants (fruit packaging and concentrated juice plants), and storage facilities, along with clients and third party raw material and services suppliers. Although Supply Chain optimization is a mature field, very few contributions on FISC modeling with management purposes have appeared so far in the open literature. 

Keywords supply chain optimization, decision levels, MILP, model predictive control. 

Perea-Lopez E., Ydstie B.E. and Grossmaim I.E. 2003. A model predictive control strategy for supply chain optimization. Comp. Chem. Eng., 27, 1201-1218. 

The book starts with an Introduction and the second chapter deals with Supply Chain Management. This chapter discusses key decisions in supply chain management and considers planning operations for it. The third chapter introduces Scheduling Models in Supply Chain. The last chapter is Optimization in Supply Chain. Optimization problems and models reviewed are classified under transportation and facility location. 

The main goal of the model was to understand the impact of capacity changes in the system on the supply chain. The model solution recommended changes in the network—a 20% reduction in the number of distribution centers, an 8% increase in the return on assets, and an improvement in the customer service offered, while decreasing inventory. An interesting component of the model was its ability to quantify the impact of managerial choices on the supply chain that were different from the optimal solution. 

ABM has successfully been used in several scientific areas, e.g. economics (supply chain optimization and logistics, consumer behavior, etc.) and informatics (distributed computing, traffic congestion, etc.). Many ABM approaches for modeling and simulation... 

Given the heterogeneous nature of supply chains, optimization often cannot be performed with respect to a single objective. Multi-objective programming models seek an optimal solution with regard to multiple objectives. These models rely on judgmental assessment of the relative importance of each objective. 

The unified data source for supply chain configuration optimization, and simulation models in the form of the supply chain process model, which provides a business, user-friendly description of the supply chain. 

Tight integration between supply chain optimization and simulation models enabling comprehensive appraisal of supply chain configuration decisions. 

Nakano and Hirao (2011) used LCA and MFCA for data collection and proposed a supply chain collaboration model (SCCM) to promote improvement activity of product and environmental performance. Zhang and Huang (2013) have provided a fuzzy multi-objective model which optimizes economic. Hung et al. (2006) considered different qualitative and quantitative parameters and proposed a multi-objective model and used fuzzy AHP. 

In this work, a discrete event supply chain is modeled from the point of view of one of the members. The model takes into account uncertainty and it determines an optimal ordering policy so that profit is maximized and financial risk is controlled. Two cases are considered. In one case, uncertain the behavior of the other members of the chain is known while in the other they are not. 

Before proceeding to the specific models and solutions that are recommended, it is critical that we establish some points of importance. First, supply chain optimization is a concept that can only be approached. So much progress has been made and so many new innovations introduced that the end line becomes a moving target. Only a few firms come close to best practices across an entire extended enterprise. Nevertheless, the... 

Designing a distribution network involves making a large number of decisions. Different researchers, such as Bachlaus et al. (2008), Portillo-Bollat (2008), and Solo (2009), among others, have already considered many of these aspects in the distribution network design process, mostly based on the number of warehouses and plants needed and their locations, production and inventory levels, and optimal routing plans. Supply chain optimization or distribution network optimization is the term used for most models that pretend to solve these decision-making problems. The research in supply chain optimization is very broad nowadays, but a small review on research related to this chapter is presented in this section. 

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