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
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