Papers and Reports:
A. Dolgui,
J. Soldek and O. Zaikin, (Editors), Supply Chain Optimisation –
Product/Process Design, Facility Location and Flow Control
The book
collects 20 papers, revised and extended versions of selected papers
from the
international conference on Production systems design, supply chain
management and logistics, Miedzyzdroje, Poland, 2002-10-23/25 and
additional contributions. It is organised in three parts: modelling
techniques,
optimisation methods and decision aid tools.
Part I:
Modelling techniques.
The 8 papers address a wide range of topics, from enterprise
integration and
the modelling of human roles to forecasting and simulation and
performance
evaluation.
The
problems in enterprise integration are addressed and areas for R&D
to
enhance enterprise and business process modelling to be employed in
enterprise
engineering and decision support are identified (Kosanke). With focus
on
knowledge logistics in agile SME networks a solution based on
competence models
as knowledge sources and knowledge supply networks as infrastructure
for
knowledge logistics is described (Sandkuhl et al). A modelling
framework is
presented that addresses the human aspects in business process
re-engineering
and aims at supporting process modelling. The framework is centred on
the
concepts of skills, role and knowledge. The authors identify four
classes of
roles (interpersonal, informational, operational); five competence
categories
((i) technical, (ii) organisational and decisional, (iii) adaptational,
(iv)
interpretational and formalisational, and (v) human and motivational);
and a
distinction in the knowledge domain between data, information, and
structured
and unstructured knowledge) (Worley et al). Demand forecasting through
modelling and simulation is the subject of two papers: a) simulation
and
analytical models are used together to create forecast for
service-sensitive
demands (Mercuryev et al); and b) new robust estimation and forecasting
algorithms for modelling the probability of customer response in data
base
marketing are developed and tested (Pashkevich and Dolgui). The use of
dioid
algebra for performance evaluation, sizing, cycle time and plant
control of an
industrial process in the car sector is demonstrated (Amari et al).
Deadlock
and starvation free control of concurrent processes competing for
shared
resources is achieved through determining an initial state and a set of
dispatching rules for the system of concurrent processes (Banaszak and
Polak).
Modelling of a supply chain in a distributed publishing enterprise for
total
cost minimisation is described and the results from a typical example
are
discussed (Zaikin et al).
Part II
Optimisation methods.
7 papers cover various applications of optimisation like line
balancing,
two-way product flows, delivery and operation cost, and planning.
Assembly
line balancing is addressed in two papers: a) employing a hybrid method
consisting of genetic algorithms together with heuristics is proposed
and has
been successfully tested with industrial data from the automotive
industry
(Boutevin et al); and b) investigation of the stability of an optimal
solution
has resulted in identification of necessary and sufficient conditions
and the
maximal value of simultaneous independent variations of operation times
(Sotskov et al). The characteristics of logistic systems with two-way
product
flows (product supply and return for recycling or disposal) is analysed
and a
facility location model is proposed (Lu et al). Particular optimisation
problems are discussed in the remaining four papers of this part: a)
optimisation
of product delivery cost has been investigated using a
pseudo-polynomial
algorithm and dynamic programming (Chauham et al); b) a linear
programming
model is used to optimise the operating cost in a concurrent
engineering approach
of product family and process design of an automotive supplier (Lamothe
et al);
c) sales and operation planning optimisation has been investigated
employing
linear programming. However, results provide the optimal strategy, but
do not
resist frequent parameter changes (Genin et al); and d) a
meta-modelling
procedure using response surface-based simulation has been applied to
the
optimisation of shop-floor production. Response surface methodology
(RSM) is a
collection of statistical and mathematical techniques for optimisation
of stochastic
functions (Merkuryeva).
Part
III: Decision aid tool.
Different methods and tools are described in the 5 papers of the last
part like discrete event simulation, process and resource planning and
multi-agent based simulation.
A modelling
and simulation framework to support Supply Chain Management (SCM) is
described
from which a discrete event simulation package has been developed. The
latter
has been employed in a case study of a distributed network in the
automotive
industry (Ding et al). A web-based integrated process and manufacturing
resource-planning system is presented, which allows predictions of
manufacturing
costs at the early stages of product design (Bargelis and
Mankuté). Visual
representation of material flows in chemical plants has been done
utilising a
software add-on (Schedule++) together with an SAP R/3 system (Sotskova
et al).
An identification-based technique for fault detection and condition
monitoring
of hydro- and electro-mechanical servomechanisms is proposed, which
employs
neural network analysis (Pashkevich et al). A Multi-Agent Methodology
Approach
for the Simulation of industrial systems (MaMA-S) is presented, which
facilitates modelling and simulation of decision-making processes
(Galland et
al).
Springer Science+Business Media, Inc. Applied
Optimization Series, Volume 94,
ISBN 0387-23566-3, e-ISBN 0-387-23581-7
Contact: http://www.springeronline.com