the coverability of the safe space S s by the policy-admissible subspace, S(P). More formally, consider the polynomial-kernel policy defined by property H and let that is, the policy admissible subspace. Then, a viable policy efficiency measure is provided by the ratio (4.12) where denotes the cardinality number of set S. Because of the typically large size of the S(H) and S s subspaces, their explicit enumeration will not be possible and, therefore, we must resolve simulation and statistical sampling techniques. Such a technique, known as the co-space simulation technique, is developed in Reference [62]. Briefly, this approach recog- nizes that the set of safe states of a given SU-RAS, Q corresponds to the reachability set of a “co-system,” QЈ, which is defined from the original RAS Q by reversing its job routes. Hence, the operation of the co- system QЈ is simulated until a sufficiently large sample of states is obtained. According to the previous remark, this sample set consists of safe states of the original system. In continuation, the condition H defining the evaluated DAP is applied on the extracted sample set and the portion of the sample states admitted by the policy is determined. This portion expresses the policy coverability of the extracted sample set, and constitutes a point estimate for index I. Application of this technique to the polynomial-kernel DAPs of Section 4.5, and experimental evaluation results can be found in References [58], [59], and [62]. In the rest of this section, we discuss some properties of polynomial-kernel DAPs which can be used to enhance the operational flexibility of these policies when implemented on any given FMS configuration. Policy Disjunctions and Essential Difference The first way to improve the efficiency of an FMS structural controller employing polynomial-kernel DAPs, with respect to the metric of Eq. (4.12) is based on the following proposition. Proposition 4.4 Given two conditions H 1 ( ) and H 2 ( ) defining correct polynomial-kernel DAPs, the policy defined by the disjunction H 1 ( ) H 2 ( ) is another correct polynomial-kernel DAP. To see this, simply notice that acceptance of a state s by the policy disjunction implies that at least one of the two policy defining conditions, H 1 ( ), H 2 ( ), evaluates to TRUE at s and, therefore, state s is safe. Further- more, if state s ʦ S(H i ), i ʦ {1, 2}, then the correctness of the corresponding policy implies the existence of at least one feasible event e, which is enabled by that policy, and ␦ (e, s) sЈ ʦ S(H i ) (cf. Theorem 4.2). Then, sЈ ʦ S (H 1 H 2 ), and according to Theorem 4.2, the policy defined by H 1 ( ) H 2 ( ) is correct. It is also easy to see that the subspace admitted by the policy disjunction is the union of the subspaces admitted by the two constituent policies. If it happens that (4.13) then S(H 1 ) S(H 2 ) is richer in states than any of its constituents. Therefore, the resulting policy is more efficient with respect to index I. Two polynomial-kernel policies based on conditions H 1 and H 2 that satisfy Eq. (4.13) are characterized as essentially different. The essential difference of the polynomial-kernel policies presented in Section 4.5 is analyzed in Reference [59]. It turns out that RUN and the FMS Banker’s algorithm are essentially different, while RO is subsumed by Banker’s. Optimal and Orthogonal Orderings for RUN and RO DAPs A second opportunity for improving the efficiency of RUN and RO DAPs is provided by the fact that the defining logic of these two policies essentially leads to entire families of policies for a given FMS configu- ration. As we saw in Section 4.5, each member of these families is defined by a distinct ordering of the system resource set. Hence, a naturally arising question is which of these orderings leads to the most efficient SH() s i S : ʦ H(s i ) is TRUE{},ϵ I SH() S s ϭ S ∨ ϵ ∨∨ SH 1 () SH 2 ()()SH 2 () SH 1 ()()∧ ʜ © 2001 by CRC Press LLC 5 The Design of Human-Centered Manufacturing Systems 5.1 Introduction 5.2 Concept, Implementation, and Evaluation of Human-Centered Systems The Concept of Human-Centered Systems • Human-Centered Systems in Practice: Some Observations • Designing and Evaluating Human-Centered Systems • Simulation as an Evaluation Strategy 5.3 Shop Floor Control: NC Technology for Machining of Complex Shapes The Scope of the MATRAS Project • NC Kernel Improvement • A New NC Programming Data Interface • Conclusions 5.4 Shop Floor Information Support: Developing User-Oriented Shop Floor Software The Concept of Shop Floor Software Development • The Software System to Support Work Planning • The Software Tool to Support Group Communication • Conclusion 5.5 Shop Floor Cooperation Networks: A Shop Floor Production Planning System for Groupwork The Need for Computer–Supported Cooperative Work • Human-Centered CIM • Workflows for Shop Floor PPC • Conclusions 5.6 Process Control: Human-Process Communication and Its Application to the Process Industry The Concept of Human-Process Communication • Characteristics of Operational Situations • Presenting Human-Process Communication • Integration of Operational Experience • Conclusions 5.7 Enterprise Networks: The Reengineering of Complex Software Systems Problems of Software Reengineering: The Example of a Tourism Network • The Reengineering of a Tourism Booking and Information Software System • The Methodological Approach to the Software Reengineering Project • Conclusions 5.8 Assessing the Human Orientation of New Control Technology: The Example of Slovenia The Concept of Success Factors • The Importance of Human Orientation as a Success Factor • How to Integrate New Dietrich Brandt University of Technology (RWTH) Inga Tschiersch University of Technology (RWTH) Klaus Henning University of Technology (RWTH) © 2001 by CRC Press LLC . Networks: The Reengineering of Complex Software Systems Problems of Software Reengineering: The Example of a Tourism Network • The Reengineering of a Tourism Booking and Information Software. LLC 5 The Design of Human-Centered Manufacturing Systems 5.1 Introduction 5.2 Concept, Implementation, and Evaluation of Human-Centered Systems The Concept of Human-Centered Systems. subspace. Then, a viable policy efficiency measure is provided by the ratio (4.12) where denotes the cardinality number of set S. Because of the typically large size of the S(H) and S s subspaces, their