Robust planning

C. Daniel, App/ications of Statistics to lndustria/Experimentation, ]oE Wiley Sons, Inc., New York, 1976. This book is based on the personal experiences and insights of the author, an eminent practitioner of industrial appHcations of experimental design. It provides extensive discussions and concepts, especially in the areas of factorial and fractional factorial designs. "The book should be of use to experimenters who have some knowledge of elementary statistics and to statisticians who want simple explanations, detailed examples, and a documentation of the variety of outcomes that may be encountered." Some of the unusual features are chapters on "Sequences of fractional repHcates" and "Trend-robust plans," and sections entided, "What is the answer (what is the question )," and "Conclusions and apologies."... 

There is little or no capability to evaluate risk. The robustness of the plan to key uncertainties is rarely assessed. Important risks in R D develop-... 

Up to this point, it is assumed that prices are deterministic, which is true for contract demand and procurement but is not necessarily true for spot demand and procurement prices. Therefore, an important value chain planning requirement is the consideration of uncertain prices and price scenarios. Now, uncertain spot demand prices are under consideration and it is illustrated how price uncertainty can be integrated into the model in order to reach robust planning solutions. 

Robust planning is a specific research area within Operations Research (Scholl 2001). Generally, robustness can be defined as the insensitivity of an object or system against (stochastic) external influences (SchneeweiB 1992 reviewed by Scholl 2001, p. 93). A plan is robust, if the realization of the plan - also in a slightly modified form - leads to good and/or accept-... 

In the considered value chain planning problem, the uncertainty of spot sales prices impacts the profitability of the overall value chain plan, since volume decisions can lead to profit-suboptimal plans, if the average sales price cannot be realized as planned. Therefore, price volatility is considered as an external (stochastic) influence in the considered value chain planning problem. The following model extensions account for this uncertainty and try to derive methods to achieve more robust plans with respect to profit results with contributions from Habla (2006). The objective of the proposed modeling approach is to maximize profit for the entire value chain network. It is assumed that the company behaves risk-averse in face of the price uncertainty. 

Fig. 101 Value chain network excerpt used for robust planning testing The scale of this focused value chain network is shown in table 34. Table 34 Case data scale for testing robust planning model...

Fig. 103 shows all scenario profits in the one-phase and the two-phase optimization case as well as the sales quantity index the two-phase optimization results do not disperse as strong as the one-phase-optimization. Besides, the worst case scenario is comparably better than the worst case scenario of the one-phase-optimization strategy. The plan is more cautious supply quantities are reduced leading to lower expected profits but better minimum profits in the worst case scenario. Although robustness is not measured it get s visible in the numerical tests for the 2-phase optimization approach. 

Suh M, Lee T (2001) Robust Optimization Method for the Economic Term in Chemical Process Design and Planning. Industrial Engineering Chemistry Research 40 5950-5959... 

Ebdon et al. [88] have discussed a programme to improve the quality of analytical results in the environmental monitoring of organotin compounds. They discuss the evolution of a sensitive, reliable and robust analytical method for the determination of tributyltin, with emphasis on the difficulties of determining it at the ng per litre levels at which it was usually encountered, more especially as other forms of tin frequently occurred together at similar levels. The preparation of a standard reference sample, for use in interlaboratory comparative determinations, under the aegis of the Bureau of Community Reference of the EU is described, and plans for subsequent distributions of blank, artificially spiked, and genuinely affected sediments are sketched. 

Because of the vastness of the subject matter, we shall focus our attention on hydrogen bonding interactions between ions and on the possibilities and limitations of their use in the design and construction of molecular materials of desired architectures and/or destined to predetermined functions. Obviously, the crystal engineer (or supramolecular chemist) needs to know the nature of the forces s/he is planning to master, since molecular and ionic crystals, even if constructed with similar building blocks, differ substantially in chemical and physical properties (solubility, melting points, conductivity, mechanical robustness, etc.). 

Review of the quality of data, identification of data gaps, preparation of SIDS Dossiers including Robust Study Summaries and SIDS Testing Plans... 

An overview is provided of the North American PE Foam Market. Historical market growth rate and market dynamics are presented as well as a forecast to 2007. An analysis is also presented of the forces that impact PE foam demand and pricing based upon Michael Porter s well-known five-market forces model for analysing industries and markets. Application of this model will provide some insight into dynamics that should be considered in creating a robust business plan. 3 refs. 

The flexibility in the petrochemical industry production and the availability of many process technologies require adequate strategic planning and a comprehensive analysis of all possible production alternatives. Therefore, a model is needed to provide the development plan of the petrochemical industry. The model should account for market demand variability, raw material and product price fluctuations, process yield inconsistencies, and adequate incorporation of robustness measures. 

It is desirable to demonstrate that the proposed stochastic formulations provide robust results. According to Mulvey, Vanderbei, and Zenios (1995), a robust solution remains close to optimality for all scenarios of the input data while a robust model remains almost feasible for all the data of the scenarios. In refinery planning, model robustness or model feasibility is as essential as solution optimality. For example, in mitigating demand uncertainty, model feasibility is represented by an optimal solution that has almost no shortfalls or surpluses in production. A trade-off exists... 

In Chapter 3 of this book we discussed the problem of multisite refinery integration under deterministic conditions. In this chapter, we extend the analysis to account for different parameter uncertainty. Robustness is quantified based on both model robustness and solution robustness, where each measure is assigned a scaling factor to analyze the sensitivity of the refinery plan and integration network due to variations. We make use of the sample average approximation (SAA) method with statistical bounding techniques to generate different scenarios. 

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