(alphabetical by author)

118. Jones, C.V., "Attributed Graphs, Graph-Grammars, and Structured Modeling," Annals of Operations Research, 38 (1992), pp. 281-324. (Volume on Model Management in Operations Research edited by B. Shetty, H. Bhargava, and R. Krishnan.)

Chris originated the use of graph grammars as a modeling framework in his dissertation. This paper applies his work on Graph-Based Modeling Systems (GBMS) to SM. In particular, he explains how a syntax-directed editing environment for SM can be implemented as a GBMS. He has produced a prototype implementation called Networks/SM. This is a step toward the ultimate aim of producing a truly graph-based SM environment for workstations.

119. Kang, M., G. Wright, R. Chandrasekharan, R. Mookerjee and D. Worobetz, "The Design and Implementation of OR/SM: A Prototype Integrated Modeling Environment," Annals of Operations Research, 72 (1997), pp. 211-240.  (Volume on Information Systems and OR, part II, edited by R. Ramesh and H.R. Rao.)

This paper focuses on the computational implementation of the OR/SM (ORACLE/Structured Modeling) system using ORACLE Tools and Database. The functionality of OR/SM is described in the companion paper by G. Wright, N.D. Worobetz, M. Kang, R. Mookerjee and R. Chandrasekharan listed elsewhere in this Bibliography. Here the architecture is described in some detail, both internal and interfaces with other systems. More than 50,000 lines of C give this system its rich functionality.

120. Kemp, J., "Users, Needs, and Conceptual Models: the Next Step for Mathematical Programming," Working Paper 10, Dept. of Mathematics and Statistics, Brunel University, UK, 16 pages, 5 April, 1989.

This essay on research needs in mathematical programming comments on some of the ideas in four of my papers. There is also a companion paper, "Conceptual Modelling: The Necessary Alternative to Algebra for MP Practitioners."

121. Kendrick, D., "A Production Model Construction Program: Math Programming to Structured Modeling," in L.F. Pau, J. Motiwalla, Y.H. Pao, and H.H. Teh (eds.), Expert Systems in Economics, Banking and Management, Elsevier Science Publishers B.V. (North-Holland), 1989, pp. 351-360. See also the companion article by D. Kendrick, "A Production Model Construction System: PM Statement to Math Programming," J. Economic Dynamics and Control, 14:2 (May 1990), pp. 219-236.

This paper derives from Krishnan's dissertation (listed in Part IIA of this Bibliography). This excerpt from the cited companion article makes the role of both papers clear: "Krishnan's system consists of (1) a user interface complete with its own modeling language called PM, (2) a model construction program which transforms the PM representation of the model first to mathematical programming form and then to predicates which are elements of Geoffrion's (1987) Structured Modeling form, and (3) a back end which transforms the Structured Modeling predicates into the particular format required by Structured Modeling. This system makes heavy use of the concepts and terminology of knowledge-based systems and logic programming... Since many economists and management scientists have considerable experience with production and distribution modeling but limited familiarity as yet with knowledge-based systems and logic programming, a series of papers have been prepared to elucidate the elements of Krishnan's system to this community. The first of these papers, Krishnan, Kendrick, and Lee (1988) [listed in this Bibliography] describes the user interface and the PM language. The current paper [the cited companion article] ... covers the part of the model construction system from the PM representation to the mathematical programming form. The third paper [the current bibliographic item] ... covers the part from the mathematical programming form to the Structured Modeling form."

122. Kendrick, D., "Parallel Model Representations," Expert Systems with Applications, 1:4 (1990), pp. 383-389.

My 1987 Management Science article argued for a single model representation suitable for managerial communication, mathematical use, and direct computer execution. This article takes the apparently opposite view that multiple parallel representations should be maintained: graphical, knowledge-base, mathematical, and modeling language. These views are not really opposed, for I had in mind representations that were produced and kept in synch manually, while David has in mind that each representation would be derivable automatically from the others. I certainly agree that multiple views of a model are desirable if the modeling environment can ensure consistency and obviate the need for the special skills needed to perform translation. However, there is a possibly serious difficulty: not all representational formalisms are equally expressive, and so translation may not be invertible. For example, ordinary mathematics is not as expressive in some respects as mathematically extended semantic data models like SML.

123. Kendrick, D. and R. Krishnan, "A Comparison of Structured Modeling and GAMS," Computer Science in Economics and Management, 2:1 (1989), pp. 17-36. Presented at the Conference on Integrated Modeling Systems, held at the University of Texas at Austin in October 1986.

The first author is an expert in GAMS, and the second author is versed in SM. The simple transportation example is used to compare SM and GAMS. See also item 26 of this Bibliography.

123A. Kim, J.W., H.D. Kim and S.J. Park, "Multi-facetted Approach to Mathematical Model Representation and Management," Journal of the Korean Operations Research and Management Science Society, 23:2 (1998) [in Korean].

The paper's English abstract says, in part: "In this paper, a multi-facetted modeling approach is proposed as a basis for the development of an integrated modeling environment which provides for (1) independent management of modeling knowledge from individual models; (2) an object-oriented conceptual blackboard concept; (3) multi-facetted modeling; and (4) declarative representation of mathematical knowledge".

124. Kim, H.D. and S.J. Park, "Model Formulation Support in an Object-Oriented Structured Modeling Environment," Working Paper, Dept. of Management Science, KAIST, Taejon, Korea, 37 pages, 2/24/94.

The focus of this paper is on partially automating the process of formulating an SML schema. An object-oriented extension of the SM framework is proposed that relies on imposing certain constraints. Support for the model formulation process focuses mainly on the key roles of attribute elements and generic rules. The approach taken lends itself to a rapid prototyping method based on the constraint-based metaview approach explained in a Park-Kim paper mentioned later in this Bibliography. An illustration is given based on a corporate strategic planning application at a large Korean steel company.

125. Kottemann, J.E., "Applying the Jackson System Development and the Geoffrion Structured Modeling to Systems Description," Final Report, College of Business Administration, University of Hawaii, Honolulu, 9/86.

This work was done under Army contract in the context of combat simulation (cf. Dolk's 11/86 report and Stott's paper, both listed in this Bibliography). It presents an overview of JSD and SM, points out some of the shortcomings of each, and outlines a way to marry the two approaches. Of particular interest is the argument that SM properly subsumes the entity-attribute-set formalism of SIMSCRIPT (although the treatment is not rigorous).
The one shortcoming pointed out for SM was that it appears to be designed for "static" rather than dynamic models, and hence does not appear to directly support dynamic simulation. (Various authors have since examined SM for simulation carefully and fruitfully.)

126. Kottemann, J.E. and D.R. Dolk, "Model Integration and Modeling Languages: A Process Perspective," Information Systems Research, 3:1 (March 1992), pp. 1-16. An earlier version appeared as "Process-Oriented Model Integration," Proceedings of the Twenty-First Hawaii International Conference on System Sciences, Vol. III, IEEE Computer Society Press, Los Alamitos, CA, pp. 396-402, 1/88.

My work has assumed that languages for model manipulation will be natural extensions of model definition languages like SML, presumably with some simple procedural capability added so that solvers can be put through their paces more than one step at a time. This article indicates one possible approach to the design of such extensions. It also indicates that such extensions can accomplish some of the purposes of model integration via model interconnection.
The authors focus on the situation where multiple models (possibly from different modeling paradigms, and possibly written in SML) are used computationally in a coordinated way without having to integrate the individual model representations. They sketch a model manipulation language for such situations based on the idea of communicating sequential processes from computer science. The approach has a strongly procedural, rather than declarative, character.

127. Krishnan, R., "Automated Model Construction: A Logic Based Approach," Annals of Operations Research, 21 (1989), pp. 195-226. (Special volume on Linkages with Artificial Intelligence edited by F. Glover and H. Greenberg.)

Based on Krishnan's Ph.D. thesis. Compared to the Krishnan, Kendrick, and Lee article (see below), this paper has more detail on the PM language, problem-specific inferencing, and model construction.
Krishnan has two other papers in a similar vein. (1) "PDM: A Knowledge-Based Tool for Model Construction," Proceedings of the Twenty-Second Hawaii International Conference on System Sciences, IEEE Computer Society Press, Los Alamitos, CA, 1/89 and, in somewhat longer form, in Decision Support Systems, 7 (1991), pp. 301-314; and (2) "PM*: A Logic Modeling Language for Model Construction," which presents an extension of his dissertation research, in Decision Support Systems, 6 (1990), pp. 123-152.

128. Krishnan, R., D.A. Kendrick and R.M. Lee, "A Knowledge-Based System for Production and Distribution Economics," Computer Science in Economics and Management, 1:1 (1988), pp. 53-72.

This article gives an overview of Krishnan's dissertation. His system, called PM and implemented in Prolog, has three main parts. Its front end is a dialog based system that uses axiomatic knowledge about production-distribution models to guide the user toward a correct specification of the model in PM's internal first-order logic based language. PM's model construction program uses a two step rule-based procedure to produce an "equational form" of the model from the PM specification. This form, which consists of four kinds of predicates, is transformed syntactically by PM's back end into SML as it was some years ago. Fragments of the Mexican steel industry model, which happens to be the subject of one of my Informal Notes, is used illustratively in connection with each of the three parts.
PM can be viewed as a domain-specific system for building structured models. At least three advantages are claimed: (1) a powerful approach for posing queries concerning model structure, with Prolog as the query processor; (2) greatly facilitated model modification, thanks to explicitly represented knowledge of syntactic interdependencies among elements of the PM language; and (3) the potential to enable model building by users without technical training in either mathematical programming or modeling languages.

129. LeClaire, B., and E. Suh, "Object-Oriented Model Representation Scheme," College of Business Administration, Oklahoma State University, 25 pages, 1989.

This paper sketches the model representation basics of "object-oriented structured modeling" from a semantic data modeling perspective.
A closely related, subsequent paper is E. Suh, C. Suh and B. LeClaire, "Object-Oriented (O-O) Structured Modeling for an O-O DSS," Technical Report IE-TR-89-08, Dept. of Industrial Engineering, POSTECH, P.O. Box 125, Pohang, 790-600, Korea, 24 pages, 11/9/89. (See also C. Suh's M.S. Thesis in Part IIA of this Bibliography.)

130. LeClaire, B., R. Sharda and E. Suh, "An Object-Oriented Architecture for Decision Support Systems," Proceedings of the First ISDSS Conference, Austin, TX, September 1990, pp. 567-586. Also available as a working paper, 31 pages, 2/91; authors respectively at University of Wisconsin-Milwaukee, Oklahoma State Univ., and POSTECH (Korea).

This paper supersedes the previously listed LeClaire-Suh and Suh-Suh-LeClaire papers. It moves from model representation to the broader issues of DSS architecture, still from an O-O perspective. Emphasis is given to symmetry of treatment between data and models, and a diagrammatic technique based on Chen's entity-relationship diagrams is proposed in connection with so-called "Object-Oriented Structured Modeling" for model representation. Five classes are proposed for an O-O DSS: metamodels, entities, relationships, models, and relations. A prototype was implemented in Smalltalk/V 286, and some work has been done to convert to C++.

131. Lenard, M., "Representing Models as Data," Journal of Management Information Systems, 2:4 (1986), pp. 36-48.

This article gives a way to represent structured models in relational database form (cf. Dolk's "Model Management and Structured Modeling" Comm. ACM article). This article also points out the surprising compatibility between SM and Garth McCormick's theory of factorable functions.

132. Lenard, M., "Fundamentals of Structured Modeling," in G. Mitra (ed.), Mathematical Models for Decision Support, Springer-Verlag, Berlin, 1988. Presented at a NATO Advanced Study Institute in Val d'Isere, France, 1987.

A tutorial based on my 1987 Mgt. Sci. article, with editorial comment added.

133. Lenard, M., "Structured Model Management," in G. Mitra (ed.), Mathematical Models for Decision Support, Springer-Verlag, Berlin, 1988. Presented at a NATO Advanced Study Institute in Val d'Isere, France, 1987.

A sequel to an earlier version of Melanie's "An Object-Oriented Approach to Model Management" article listed below in this Bibliography. It includes a progress report on an implementation in KnowledgeMan.

134. Lenard, M., "An Implementation of Structured Modeling Using a Relational Database Management System," presented at the Second Annual Conference on Integrated Modeling Systems, University of Texas, Austin, 20 pages, October 1987.

This KnowledgeMan prototype targets very simple linear programs. Schemas are specified by filling in several standard relations, rather than by writing a text-based language. Functionality is sparse.

135. Lenard, M., "Mathematical Modeling Systems in a Database Environment," Proposal to NSF (DRMS), 51 page Project Description, 9/88.

This proposal, which was funded, focuses mainly on mathematical programming models. SM and the object-oriented paradigm provide the foundation. The proposal states that SM will be extended "so that it includes a means for expressing the operational (or dynamic) aspects of modeling as well as the representational (or static) aspects. This extension will make SM more compatible with the object-oriented paradigm by incorporating a key feature of 'Objects', namely that they are a combination of data and procedures." A prototype modeling system will be designed and built based on the extended framework, and on an object-oriented database language. The model itself -- schema as well as elemental detail -- will be stored in ONTOS, an object-oriented DBMS.

136. Lenard, M., "Extending the Structured Modeling Framework for Discrete-Event Simulation," Proceedings of the Twenty-Fifth Annual Hawaii International Conference on System Sciences, Vol. III, IEEE Computer Society Press, Los Alamitos, CA, pp. 494-503, 1/92.

This paper sketches 3 proposed SM extensions (new kinds of elements) designed to make it more useful for discrete-event simulation modeling: random attributes (whose values are generated by probability distributions), actions (which describe state transitions), and transactions (used to describe complex events in terms of a sequence of previously defined actions and transactions).

137. Lenard, M., "An Object-Oriented Approach to Model Management," Decision Support Systems, 9:1 (January 1993), pp. 67-73. An earlier version appeared in Proceedings of the Twentieth Hawaii International Conference on System Sciences, Vol. I, IEEE Computer Society Press, Los Alamitos, CA, pp. 509-515, 1/87. Presented at the Workshop on Structured Modeling, UCLA, 8/86.

This article was the first to point out that SM has a lot in common with the popular object-oriented programming paradigm. This observation is the basis for a design approach to object-oriented model management systems. With the addition of ideas from Melanie's earlier article "Representing Models as Data", this leads to the possibility of using a relational database system for O-O model management (cf. Dolk's "Model Management and Structured Modeling" Comm. ACM article).

138. Lenard, M.L., J.E. Burns, M.V. Kadyan, D.Z. Levy, M.J. Schement, and J.M. Wagner, "Structured Model Management System for Operational Planning: Installation and Users Guide, Prototype Version 1.0," prepared for U.S. Coast Guard Research & Development Center by Crystal Decision Systems, Brookline, MA, 120 pages, 7/1/92. Revised 3/31/93 (Version 2.0) and 10/6/93 (Version 3.0); now 207 pages.

This report, prepared under U.S. Coast Guard contract, delivers on the structured modeling extensions proposed by M. Lenard in "Extending the Structured Modeling Framework for Discrete-Event Simulation" (listed above in this Bibliography). The system addresses discrete-event simulation models, is built on top of a relational DBMS (ORACLE 6.0), and runs under MS-DOS. It makes extensive use of the ORACLE tools SQL*Forms and SQL*Menu; in particular, most user interaction is though forms selected from a set of pop-down menus. Among the system's features is the ability to convert extended structured models to SIMSCRIPT II.5 code. The code generation is done entirely in SQL*Plus (ORACLE's version of SQL).
Two prior reports specified the system documented here: (1) Lenard, M., "A Structured Model Management System, Phase I: Concept Study," 31 pages, 12/90; (2) "Extended Structured Modeling Tables and their Mapping to SIMSCRIPT," 23 pages, 11/91.

139. Lenard, M., "A Prototype Implementation of a Model Management System for Discrete-Event Simulation Models," Proceedings of the 1993 Winter Simulation Conference, IEEE, Piscataway, NJ, 560-568, 12/93.

This paper describes a database-based prototype implementation of the ideas in "Extending the Structured Modeling Framework for Discrete-Event Simulation" (see above). The database schema is not model-specific, as in SML, but rather is fixed for all models. Further documentation is provided in reports co-authored with the other implementors.

140. Liang, T., "On Structured Modeling," BEBR Faculty Working Paper No. 1385, College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, 15 pages, 8/87.

"Decomposition" and "integration" can be viewed as opposite poles of a single modeling axis. The first breaks a whole down into its parts and the second puts parts together into larger structures. This paper comments on my 1987 Management Science article from this perspective, and argues two main points. First, elemental structure may be too extreme a level of decomposition from the point of view of human cognitive limitations. Second, even higher levels of abstraction than those provided by SM are necessary to support automatic integration.
I could appreciate these points only after reading a prior paper by Liang and Chris Jones. My response to the first point: (a) nitty gritty detail cannot be avoided, (b) a black box model description is insufficient for many purposes, and (c) generic and modular structure help the modeler keep cognitive control even of a complex model. Concerning the second point: (d) modular structure already provides an appropriately high level of abstraction, and (e) the aim of automatic integration may be unrealistic. See item 8 of this Bibliography for in-depth material germane to both points.

141. Liang, T., "Analogical Reasoning and Case-Based Learning in Model Management Systems," Decision Support Systems, 10:2 (September 1993), pp. 137-160. See also "Modeling By Analogy: A Case-Based Approach to Automated Formulation of Linear Programs," Proceedings of the Twenty-Fourth Annual Hawaii International Conference on System Sciences, Vol. III, IEEE Computer Society Press, Los Alamitos, CA, pp. 276-283, 1/91. See also "Analogical Formulation of Linear Programs," Working Paper, Krannert School, Purdue University, 41 pages, 9/92 (due for revision).

This highly original work is on a topic that, although almost totally neglected to date by the modeling community, has considerable potential. It develops an analogical approach to new model formulation (or model revision) based on similarities to existing models. A model representation framework is used that, as the author notes, derives from SM ideas.

142. Liang, T., "SAM: A Language for Analogical Reasoning in Structured Modeling," Krannert School of Management, Purdue University, 28 pages, 5/91. Revision in progress. Related work was presented at the "New Directions in Structured Modeling" session at the INFORMS International Singapore, June 25-28, 1995.

This paper retargets the author's prior work on analogical modeling directly to SM.

143. Lin, S.E., D. Schuff and R.D. St. Louis, "Subscript-Free Modeling Languages: A Tool for Facilitating the Formulation and Use of Models," to appear in European Journal of Operational Research, 1999.  Earlier version presented at the ORSA/TIMS Meeting in Phoenix, 10/93.

This paper is based on Lin's dissertation (see Part IIA of this Bibliography).
143A.  Loh, Seow Yick and Gee Kin Yeo, “A Framework for Implementing Structured Modeling,” Technical Report TRB4/97, Dept. of Information Systems & Computer Science, The National University of Singapore, 18 pages, 4/97. A zipped 54 KB Word 6.0 file is available by ftp here.
This paper presents a general, non-technical discussion of the requirements for a useful implementation of structured modeling.

143B.  Loh, Seow Yick and Gee Kin Yeo, “VMS/SM: Implementing the Modeling Process,” Proceedings of IASTED International Conference on Modelling, Simulation and Optimization, Singapore, pp. 66-69, 8/97.  A 179 KB zipped Word 7.0 document is available by ftp here.

This paper explains how various phases of the modeling process are supported and implemented in VMS/SM (see items 62A and 184A of this Bibliography): model construction, model solution (one highly specialized solver is integrated), solution interpretation, model analysis, and model documentation.

144. Lustosa, L.J., "FW/SM-ANALYZE Integration: Research Proposal for a Prototype Computer-Based Environment for Model Schema and Instance Analyses," Technical Memorandum 09/93, Departmento de Engenharia Industrial, Pontificia Universidade Católica do Rio de Janeiro, Brazil, 9 pages, revised 1/94.

This project aims to integrate FW/SM with Harvey Greenberg's ANALYZE, a package that focuses almost entirely on LP model instances. The first phase seeks only a "loose" integration. ANALYZE will be invoked from within FW/SM, and will reformat the information contained in FW/SM-generated files so that most of FW/SM's functions will be immediately available. Therefore, users will always be in one of the two unmodified environments. In the second phase, an "external" integration will be attempted so that the user perceives a single coherent environment. Possible problems, and some interesting possibilities like using SML's interpretation sublanguage to generate the ANALYZE "syntax file", are briefly discussed. [Update 9/95: The first phase was finished, but the second phase was postponed.]
This work is partially supported by Brazil's Conselho Nacional de Desenvolvimento Cientifico e Tecnologico.

144A. Ma, J., Q. Tian and D. Zhou, "An Object-Oriented Approach to Structured Modeling," IRMA '98 Proceedings (1998 Information Resources Management Association International Conference, Boston, 17-20 May 1998), Idea Group Publishing, pp. 406-413.  On-line here.  See also the following companion paper: Q. Tian, J. Ma, D. Zhou, L. Huang, "Integration of Dynamic Models in Decision Support Systems," in B. Verma, Z. Liu, A. Sattar, R. Zurawski and J. You (eds.), Proceedings of the Second IEEE International Conference on Intelligent Processing Systems, Gold Coast, Australia, 4-7 August 1998, pp. 281-284.  Sponsored by IEEE Industrial Electronics Society. On-line here.

This paper points out that SM and object-oriented modeling are complementary fields, each strong in areas of the other's weakness.  In particular, SM could benefit by adapting dynamic models from object-oriented modeling.  The authors propose adapting an "action logic" (first-order and many-sorted) from artificial intelligence to capture system dynamics in conjunction with conventional SM (which most naturally captures system statics).  Their development and examples are in logical notation.  It remains to be seen how well this "extension" of SM can be integrated with existing SM theory, languages, and implementations.

The companion paper focuses more specifically on model integration.  It is not clear from the example whether the authors have a general model integration procedure.


145. Magel, K. and L. Shapiro, "JADE: An Object Oriented, Heterogeneous Model Management System," Computer Science Dept., North Dakota State University, 14 pages, 7/14/87.

The aim of this work is to create a model management environment in which users can have access to multiple modeling tools and languages (GAMS, IFPS, SML, etc.). The main integrating device is a second, canonical representation for each model that combines SM concepts with some ideas from knowledge representation. Encapsulation and peaceful coexistence of tools and models is facilitated by a consistently object-oriented approach. Some progress was made toward implementation in both the Microsoft Windows and SUN environments; this includes a limited SM interface.

146. Maturana, S., "Comparative Analysis of Mathematical Programming Systems," Working Paper 347, Western Management Science Institute, UCLA, 42 pages, 5/87. Presented at the TIMS/ORSA National Meeting, New Orleans, May, 1987.

The systems discussed are GAMS, GINO, GXMP, HEQS, and IFPS/OPTIMUM. The analysis criteria are: solver(s) used, system architecture, modeling language features, model management features, data management features, user interface, intermediate expression handling, and user control. This paper is not directly on SM, but was influenced by early papers on SM.

147. Maturana, S., "Some Extensions of Structured Modeling," Research Paper, John E. Anderson Graduate School of Management, UCLA, 30 pages, 12/23/87.

This paper develops two extensions of the core concepts of SM: (1) genera with a countable infinity of elements, and (2) attribute elements whose values are specified probabilistically. The first extension is useful, for example, for modeling discrete time with an unbounded horizon, and the second for some types of stochastic modeling and Monte Carlo simulation. Three other possible extensions are also discussed.

148. Maturana, S., "Integration of a Mathematical Programming Solver into a Modeling Environment," John E. Anderson Graduate School of Management, UCLA, 68 pages, 10/4/88.

Interfacing modeling languages with optimization engines requires converting a given language's description of general model structure, together with instantiating data, into an input file in some format that is intelligible to an optimizer. This paper lays out the generic problems associated with this task in the context of SML, and describes the design and implementation of software to accomplish it for FW/SM. The general architecture is that of a compiler. This document also serves as part of the technical documentation of FW/SM.

149. Maturana, S., "Issues in the Design of Modeling Languages for Mathematical Programming," European Journal of Operational Research, 72:2 (Jan 1994), pp. 243-261.

The issues discussed for algebraic modeling languages are: underlying conceptual framework, indexing, recursion, embedded special structures, error checking, and data specification. SML is one of the languages used for illustration.

150. Maturana, S. and Y. Eterovic, "Vehicle Routing and Production Planning Decision Support Systems: Designing Graphical User Interfaces," International Transactions in Operational Research, 2:3 (1995), pp. 233-247.  Presented at IFORS-SPC3 (Digital Technologies/Multimedia: OR/MS in Strategy, Operations and Decision Support), Santa Monica, 1/95. Also available as a working paper, Pontificia Universidad Católica de Chile, Dept. of Industrial and Systems Engineering, 17 pages, 4/4/95.

This paper falls under the GESCOPP project described in the item by Gazmuri, Maturana, Vicuña and Contesse earlier in this Bibliography. It reviews the general architecture of the implementation, which uses PowerBuilder for the graphic user interface, WATCOM SQL for the relational database, CPLEX for mixed integer optimization, and some of FW/SM's code for handling SML. The implementation has been specialized to two industrial applications: one for aggregate production planning for a Chilean home appliance manufacturer, and the other for vehicle routing for a Chilean candy manufacturer. The emphasis is on the user interface and related matters; more than a dozen screen shots are included.

150A. Maturana, S., P. Gazmuri and C. Villena, "Development and Implementation of a Production Planning DSS for a Manufacturing Firm," working paper, Dept. of Industrial and Systems Engineering, Pontificia Universidad Católica de Chile, 20 pages, 7/30/98.  Revised 3/29/99.

This paper falls under the GESCOPP project described in the item by Gazmuri, Maturana, Vicuña and Contesse earlier in this Bibliography, and follow-on work.  It describes in some depth the authors' practical experiences in developing and applying an optimization-based DSS for a leading Chilean appliance manufacturer.  Work continues on a consulting basis with this firm into 1998.  SML was the modeling language, and some of the programs developed for FW/SM were used.  The solver was CPLEX 3.0.

150B. Maturana, S., J. Ferrer and F. Barañao, "Design and Implementation of a Generator of Optimization-Based Decision Support Systems," working paper, Dept. of Industrial and Systems Engineering, Pontificia Universidad Católica de Chile, 26 pages, 6/16/99.

This paper falls under the GESCOPP project described in the item by Gazmuri, Maturana, Vicuña and Contesse earlier in this Bibliography, and follow-on work motivated by real production planning and truck dispatching applications. The emphasis is well-described by the title.  The generator uses SML, PowerBuilder, and CPLEX.  Experience with the generator has been very good.

151. Muhanna, W., "An Object-Oriented Framework for Model Management and DSS Development," Decision Support Systems, 9:2 (February, 1993), pp. 217-229. A preliminary version appeared in Proceedings of the First ISDSS Conference, Austin, TX, September 1990, pp. 553-565.

This article demonstrates the compatibility of the Muhanna-Pick systems framework for modeling-in-the-large with object-oriented ideas, and shows how this framework can coexist with the SM approach for modeling-in-the-small. The author is extending his earlier prototype model management system, SYMMS, to embody this synthesis [update 9/95: much remains to be done]. Muhanna-Pick citation: "Composite Models in SYMMS," Proc. Twenty-First Annual Hawaii Intern. Conf. on System Sciences, Vol. III, IEEE Computer Society Press, Los Alamitos, CA, 418-427, 1988.

152. Murphy, F., E.A. Stohr and A. Asthana, "Representation Schemes for Mathematical Programming Models," Management Science, 38:7 (July 1992), pp. 964-991.

This article describes and comments on 7 formalisms for representing mathematical programming models: block-schematic, algebraic, 3 different graphical styles, a database-oriented approach, and SM. A single running example is used throughout. For a complete SML rendering of that example, including data and a LINDO solution obtained by FW/SM, see FARMER5 in item 36 of this Bibliography.

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