27,571 research outputs found
Note on Combinatorial Engineering Frameworks for Hierarchical Modular Systems
The paper briefly describes a basic set of special combinatorial engineering
frameworks for solving complex problems in the field of hierarchical modular
systems. The frameworks consist of combinatorial problems (and corresponding
models), which are interconnected/linked (e.g., by preference relation).
Mainly, hierarchical morphological system model is used. The list of basic
standard combinatorial engineering (technological) frameworks is the following:
(1) design of system hierarchical model, (2) combinatorial synthesis
('bottom-up' process for system design), (3) system evaluation, (4) detection
of system bottlenecks, (5) system improvement (re-design, upgrade), (6)
multi-stage design (design of system trajectory), (7) combinatorial modeling of
system evolution/development and system forecasting. The combinatorial
engineering frameworks are targeted to maintenance of some system life cycle
stages. The list of main underlaying combinatorial optimization problems
involves the following: knapsack problem, multiple-choice problem, assignment
problem, spanning trees, morphological clique problem.Comment: 11 pages, 7 figures, 3 table
Improvement/Extension of Modular Systems as Combinatorial Reengineering (Survey)
The paper describes development (improvement/extension) approaches for
composite (modular) systems (as combinatorial reengineering). The following
system improvement/extension actions are considered: (a) improvement of systems
component(s) (e.g., improvement of a system component, replacement of a system
component); (b) improvement of system component interconnection
(compatibility); (c) joint improvement improvement of system components(s) and
their interconnection; (d) improvement of system structure (replacement of
system part(s), addition of a system part, deletion of a system part,
modification of system structure). The study of system improvement approaches
involve some crucial issues: (i) scales for evaluation of system components and
component compatibility (quantitative scale, ordinal scale, poset-like scale,
scale based on interval multiset estimate), (ii) evaluation of integrated
system quality, (iii) integration methods to obtain the integrated system
quality. The system improvement/extension strategies can be examined as
seleciton/combination of the improvement action(s) above and as modification of
system structure. The strategies are based on combinatorial optimization
problems (e.g., multicriteria selection, knapsack problem, multiple choice
problem, combinatorial synthesis based on morphological clique problem,
assignment/reassignment problem, graph recoloring problem, spanning problems,
hotlink assignment). Here, heuristics are used. Various system
improvement/extension strategies are presented including illustrative numerical
examples.Comment: 24 pages, 28 figures, 14 tables. arXiv admin note: text overlap with
arXiv:1212.173
Course on System Design (structural approach)
The article describes a course on system design (structural approach) which
involves the following: issues of systems engineering; structural models; basic
technological problems (structural system modeling, modular design,
evaluation/comparison, revelation of bottlenecks, improvement/upgrade,
multistage design, modeling of system evolution); solving methods
(optimization, combinatorial optimization, multicriteria decision making);
design frameworks; and applications. The course contains lectures and a set of
special laboratory works. The laboratory works consist in designing and
implementing a set of programs to solve multicriteria problems
(ranking/selection, multiple choice problem, clustering, assignment). The
programs above are used to solve some standard problems (e.g., hierarchical
design of a student plan, design of a marketing strategy). Concurrently, each
student can examine a unique applied problem from his/her applied domain(s)
(e.g., telemetric system, GSM network, integrated security system, testing of
microprocessor systems, wireless sensor, corporative communication network,
network topology). Mainly, the course is targeted to developing the student
skills in modular analysis and design of various multidisciplinary composite
systems (e.g., software, electronic devices, information, computers,
communications). The course was implemented in Moscow Institute of Physics and
Technology (State University).Comment: 22 pages, 14 figure
Composite Strategy for Multicriteria Ranking/Sorting (methodological issues, examples)
The paper addresses the modular design of composite solving strategies for
multicriteria ranking (sorting). Here a 'scale of creativity' that is close to
creative levels proposed by Altshuller is used as the reference viewpoint: (i)
a basic object, (ii) a selected object, (iii) a modified object, and (iv) a
designed object (e.g., composition of object components). These levels maybe
used in various parts of decision support systems (DSS) (e.g., information,
operations, user). The paper focuses on the more creative above-mentioned level
(i.e., composition or combinatorial synthesis) for the operational part (i.e.,
composite solving strategy). This is important for a search/exploration mode of
decision making process with usage of various procedures and techniques and
analysis/integration of obtained results. The paper describes methodological
issues of decision technology and synthesis of composite strategy for
multicriteria ranking. The synthesis of composite strategies is based on
'hierarchical morphological multicriteria design' (HMMD) which is based on
selection and combination of design alternatives (DAs) (here: local procedures
or techniques) while taking into account their quality and quality of their
interconnections (IC). A new version of HMMD with interval multiset estimates
for DAs is used. The operational environment of DSS COMBI for multicriteria
ranking, consisting of a morphology of local procedures or techniques (as
design alternatives DAs), is examined as a basic one.Comment: 24 pages, 28 figures, 5 table
Note on Evolution and Forecasting of Requirements: Communications Example
Combinatorial evolution and forecasting of system requirements is examined.
The morphological model is used for a hierarchical requirements system (i.e.,
system parts, design alternatives for the system parts, ordinal estimates for
the alternatives). A set of system changes involves changes of the system
structure, component alternatives and their estimates. The composition process
of the forecast is based on combinatorial synthesis (knapsack problem, multiple
choice problem, hierarchical morphological design). An illustrative numerical
example for four-phase evolution and forecasting of requirements to
communications is described.Comment: 8 pages, 8 figures, 9 table
Towards Decision Support Technology Platform for Modular Systems
The survey methodological paper addresses a glance to a general decision
support platform technology for modular systems (modular/composite
alterantives/solutions) in various applied domains. The decision support
platform consists of seven basic combinatorial engineering frameworks (system
synthesis, system modeling, evaluation, detection of bottleneck,
improvement/extension, multistage design, combinatorial evolution and
forecasting). The decision support platform is based on decision support
procedures (e.g., multicriteria selection/sorting, clustering), combinatorial
optimization problems (e.g., knapsack, multiple choice problem, clique,
assignment/allocation, covering, spanning trees), and their combinations. The
following is described: (1) general scheme of the decision support platform
technology; (2) brief descriptions of modular (composite) systems (or composite
alternatives); (3) trends in moving from chocie/selection of alternatives to
processing of composite alternatives which correspond to hierarchical modular
products/systems; (4) scheme of resource requirements (i.e., human,
information-computer); and (5) basic combinatorial engineering frameworks and
their applications in various domains.Comment: 10 pages, 9 figures, 2 table
Discrete Route/Trajectory Decision Making Problems
The paper focuses on composite multistage decision making problems which are
targeted to design a route/trajectory from an initial decision situation
(origin) to goal (destination) decision situation(s). Automobile routing
problem is considered as a basic physical metaphor. The problems are based on a
discrete (combinatorial) operations/states design/solving space (e.g.,
digraph). The described types of discrete decision making problems can be
considered as intelligent design of a route (trajectory, strategy) and can be
used in many domains: (a) education (planning of student educational
trajectory), (b) medicine (medical treatment), (c) economics (trajectory of
start-up development). Several types of the route decision making problems are
described: (i) basic route decision making, (ii) multi-goal route decision
making, (iii) multi-route decision making, (iv) multi-route decision making
with route/trajectory change(s), (v) composite multi-route decision making
(solution is a composition of several routes/trajectories at several
corresponding domains), and (vi) composite multi-route decision making with
coordinated routes/trajectories. In addition, problems of modeling and building
the design spaces are considered. Numerical examples illustrate the suggested
approach. Three applications are considered: educational trajectory
(orienteering problem), plan of start-up company (modular three-stage design),
and plan of medical treatment (planning over digraph with two-component
vertices).Comment: 25 pages, 34 figures, 16 table
Multiset Estimates and Combinatorial Synthesis
The paper addresses an approach to ordinal assessment of alternatives based
on assignment of elements into an ordinal scale. Basic versions of the
assessment problems are formulated while taking into account the number of
levels at a basic ordinal scale [1,2,...,l] and the number of assigned elements
(e.g., 1,2,3). The obtained estimates are multisets (or bags) (cardinality of
the multiset equals a constant). Scale-posets for the examined assessment
problems are presented. 'Interval multiset estimates' are suggested. Further,
operations over multiset estimates are examined: (a) integration of multiset
estimates, (b) proximity for multiset estimates, (c) comparison of multiset
estimates, (d) aggregation of multiset estimates, and (e) alignment of multiset
estimates. Combinatorial synthesis based on morphological approach is examined
including the modified version of the approach with multiset estimates of
design alternatives. Knapsack-like problems with multiset estimates are briefly
described as well. The assessment approach, multiset-estimates, and
corresponding combinatorial problems are illustrated by numerical examples.Comment: 30 pages, 24 figures, 10 table
Towards Integrated Glance To Restructuring in Combinatorial Optimization
The paper focuses on a new class of combinatorial problems which consists in
restructuring of solutions (as sets/structures) in combinatorial optimization.
Two main features of the restructuring process are examined: (i) a cost of the
restructuring, (ii) a closeness to a goal solution. Three types of the
restructuring problems are under study: (a) one-stage structuring, (b)
multi-stage structuring, and (c) structuring over changed element set.
One-criterion and multicriteria problem formulations can be considered. The
restructuring problems correspond to redesign (improvement, upgrade) of modular
systems or solutions. The restructuring approach is described and illustrated
(problem statements, solving schemes, examples) for the following combinatorial
optimization problems: knapsack problem, multiple choice problem, assignment
problem, spanning tree problems, clustering problem, multicriteria ranking
(sorting) problem, morphological clique problem. Numerical examples illustrate
the restructuring problems and solving schemes.Comment: 31 pages, 34 figures, 10 table
On balanced clustering with tree-like structures over clusters
The article addresses balanced clustering problems with an additional
requirement as a tree-like structure over the obtained balanced clusters. This
kind of clustering problems can be useful in some applications (e.g., network
design, management and routing). Various types of the initial elements are
considered. Four basic greedy-like solving strategies (design framework) are
considered: balancing-spanning strategy, spanning-balancing strategy, direct
strategy, and design of layered structures with balancing. An extended
description of the spanning-balancing strategy is presented including four
solving schemes and an illustrative numerical example.Comment: 15 pages, 15 figures, 9 table
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