Network Design Analysis

The hospital s management team has made it clear that high service levels, high patient satisfaction, and low costs are important. Keeping these objectives in mind, analysts might consider adding an additional distribution facility. This would reduce response times, 

The first task is to understand the current situation. The supply chain group collects data from various departments such as sourcing, logistics, finance, and accounting. From these data, the current system s design, costs, and materials routing information can be ascertained. The data collected should include current and future demand for its supplies, facility capacities, fixed and variable cost related to inbound and outbound transportation, the operation of the distribution centers, and inventory. 

Maryland, Northern Virginia, and Southeast Virginia. There is one hospital located in Delaware, two in Maryland, and three in Virginia. Table 8.2 provides the weekly demand rates, supply capacities, and number of units shipped between the distribution center and the hospital. 

Based on the weekly capacity of the distribution centers and the demand rate for medical-surgical products by the hospitals, and the cost per unit to transport between any two points, the analysis shows, for example, that the Northeast Maryland distribution location has 

Given the capacities, demands, and costs, Mid-Atlantic Hospital System has aweekly cost of 58,300 with an annual cost of 3,031,600 to operate the current distribution network. They also find that by 

---

Some typical questions raised in pipeline network design and analysis are... 

Table 6 Analysis of Various Trimeprazine/Methotrimeprazine Mixtures Using the Optimal Network Design...

Kouvelis et al. (2004) present a relatively simple multi-period MILP plant location model for global production network design with investment decisions only allowed in the first period. The production system consists of component-dedicated manufacturing sites and final assembly sites. It is limited to two production levels and one final product. The objective function maximizes the NPV of the production network. The main purpose of the model is to analyze the effects financing subsidies, tax regimes, tariff structures and local content requirements have on optimal network design. The analysis is based on theoretical considerations and a numerical example. More complex aspects of international trade such as duty drawbacks are not considered. 

The objective of using a mathematical optimization model is to identify network design alternatives that best exploit structural cost differences between various locations and resolve trade-offs between different cost elements such as production cost advantages and additional transporta-tion/tariff costs. Therefore, relationships expressing the costs that will be incurred as a function of cost drivers (decision variables of the model) have to be established. A proper creation of these cost functions is a critical success factor of the overall analysis (cf. Shapiro 2001, p. 234 Vidal and Goetschalckx 1996, p. 13). 

The capability to interactively add or remove restrictions on the production network design without operations research skills is important for the acceptance of the decision support tool. Therefore, several features were integrated into the prototype to facilitate this kind of analysis (cf. Fig. 47). The tool allows to create multiple sets of configuration alternatives that can later be combined into scenarios for evaluation runs (cf. Chap. 5.4.3). 

In this study, the automatic network designer was utilized for 10 parallel runs incorporating the same performance parameters as above. All of the 10 parallel performances proposed a 14 1 1 architecture. Each of the MLP ANN named modeling network in this work performed pattern recognition analysis with a 100% accuracy rate. In order to confirm the pattern recognition ability and the robustness of the proposed MLP ANN model, leave-one-out cross validation (40) was also carried out (i.e., the sample to be classified was deleted from the data set for the training of MLP ANN). The MLP ANNs... 

The models used for Eq. 4.3 may range from simple to complicated. Most of these functions are already known from system design and from the detailed design of the sensing element (see Section 4.1.3). If there are no analytical models available or if the physical relationships are too complicated for analytical description, finite element analysis, network-type analysis, or empirical studies need to be used to obtain the relationships summarized in Eq. 4.3. 

As we proceed toward our ultimate objective of control loop analysis and compensation network design, we will be multiplying transfer functions of cascaded blocks to get the overall transfer function. That is because the output of one block forms the input for the next block, and so on. 

Depending on the type of analysis, a DSS may require collecting information from various parts of the company. For example, supply chain network design requires both static and dynamic information from different parts of the company. The static data include plant production rates, locations of the plants, warehouses, and customers as well as warehousing costs and transportation costs, and the dynamic data involve forecasts, orders, and current deliveries. This type of information will not usually be found in one database or one department in a company. 

---