decision support system prototype for supply network ...decision support system prototype for supply...
TRANSCRIPT
Decision Support System Prototype for Supply Network
Configuration Planning and Operations Scheduling in
the Machine Tool Industry: a case study
Julien Maheut*, Juan Manuel Besga and Jone Uribetxeberria
4/13/2012 1 *Corresponding author: Julien Maheut - [email protected] Julien Maheut – [email protected]
Outline
I. Introduction
II. Literature Review
III. The “Stroke” concept
IV. The Decision Support System Prototype
I. Brief introduction
II. The Data Base enable alternative operations
III. The Algorithm for complete enumeration
IV. The Simulation Tool
V. Some results
VI. Conclusions & further works
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Outline
I. Introduction
II. Literature Review
III. The “Stroke” concept
IV. The Decision Support System Prototype
I. Brief introduction
II. The Data Base enable alternative operations
III. The Algorithm for complete enumeration
IV. The Simulation Tool
V. Some results
VI. Conclusions & further works
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Introduction
This presentation aims to introduced:
A decision support system prototype
A real supply network configuration problem and the operations scheduling problem
A case study of a company that assembles highly customised machine tools in several
European plants
Three relevant aspects of the DSS will be introduced:
1. A novel database structure is described able to consider :
Alternative operations (purchasing, production, routing and transport)
Alternative BOMs (upgrading, reconfiguring custom products)
2. An algorithm for complete enumeration to determine all the feasible solutions, and also
each solution cost and delivery time, is preliminary assessed.
3. A multi-agent-based simulator:
Evaluates the different KPIs handled by the company for each alternative solution (e.g., workload
plants, plants cost, SN lead time, SN total benefits, etc.)
Determines the optimum solution by collaborative decision making
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Literature Review (I)
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The basic structure of a conventional BOM has always been to
relate a parent item with one or several child items, which only
takes place in pure convergent product structures
The matrix linking each parent item with its child items required
for its assembly appears in this formulation according to the
Gozinto structure presented by Vazsonyi (1954)
The conventional way of representing the BOM is the Gozinto
Matrix (goes into) Aij in which products i (parent item) relate to
products j (child items)
In association with each product i, the quantity of resource r
required to produce a unit of product i by means of matrix Uir is
also constituted, and this structure was considered by (Mize et al.,
1982)
Literature Review (II)
Mathematical formulation of the MRP &
MRPII: Vazsonyi (1954), Mize, White & Brooks
(1982), Billington (1983), Staedtler(1993)
Alternative products and resources
CMIT and the alternate BOM: Escudero (1994)
RPS: Balakrishnan and Geunes (2000) Geunes (2003)
Lang y Domschke (2010)
Flexible BOM: Ram et al (2006)
Product Binning: Lin et al (2009)
Reverse Bill of Materials
Divergents structures: Segersted (1996)
Reverse MRP (Gupta y Taleb, 1994)
Alternative dismantling process (Spengler et al 1997)
Reverse LSP (Barba-Gutierrez et al, 2008)
BOM y rBOM: Schutz (2009)
Severals Inputs and outputs in the same
process
STN: Pantelides (1994)
RTN: Barbosa y Pantelides (1997)
Deliberated Co-producction (Vidal-Garcia et al. 2012)
Stroke (Maheut & Garcia-Sabater 2011; Maheut et al.
2012; Maheut and Garcia-Sabater 2012; Garcia-Sabater
et al. 2013)
Transport between plants
BOM y rBOM: Schutz (2009)
STN (Sousa et al., 2008)
Transport as an operation (Pires et al, 2008)
Packagings
Voss y Woodruff (2005)
Pinto et al. (2007)
Lang & Domschke (2010)
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Outline
I. Introduction
II. Literature Review
III. The “Stroke” concept
IV. The Decision Support System Prototype
I. Brief introduction
II. The Data Base enable alternative operations
III. The Algorithm for complete enumeration
IV. The Simulation Tool
V. Some results
VI. Conclusions & further works
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The “Stroke” concept Each stroke corresponds to a specific located operation. It is characterised by the use of located
resources (Garcia-Sabater et al. 2012; Garcia-Sabater et al. 2013; Maheut et al. 2012; Maheut & Garcia-
Sabater 2011)
Lead times, setup times and costs, time consumption and the costs of performing one stroke unit are
assigned to the stroke and not to the result of the operation.
Resources are associated with each stroke, but not with the product (or the series of products)
obtained.
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Outline
I. Introduction
II. Literature Review
III. The “Stroke” concept
IV. The Decision Support System Prototype
I. Brief introduction
II. The Data Base enable alternative operations
III. The Algorithm for complete enumeration
IV. The Simulation Tool
V. Some results
VI. Conclusions & further works
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The DSS –
Brief Introduction
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Spanish SME that designs, manufactures, transports, installs customized machine tool,
specifically milling machines and milling centres in Europe
Number of product variants theoretically includes around 2,5 billions possible combinations
The DSS –
Brief Introduction
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SimulationSimulation
SNI1
SNI2
SNI3
Predefined
Strategies
SN Model Define Supply Strategies Simulation KPIs
SN Model Optimize Order SNI KPIs
Optimal
SNI
OptimizationOptimization and and SimulationSimulation
C1
C2
C1: Cost Cn C2: Delivery Time OptimizationOptimization
CriteriaCriteria
The DSS – Brief Introduction -A Tool
for Collaborative Decision Making
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Centralized Coordinator
DSS
Plant 1 Plant 2 Plant n
The DSS –
Optimization Tool - Steps
I. Transformation
I. Generation of the Stroke Graph with the Data Base
II. Incorporation of Transport Operations between Locations
III. Transformation of the Stroke Graph into an Hybrid Stroke Graph incorporating Selection
Strokes
IV. Transformation into an Arcs-Nodes Hyper-graph
II. Algorithm for Complete enumeration
I. Generation of all feasible solutions
II. Assessment in cost, lead time, operations schedule of all feasible solution
III. Selection of the chosen solutions by the Coordinator
IV. Simulations for complete evaluation by the Simulation Tool
V. Selection by the Supply Network Coordinator of the Suitable Supply Network
Instance based on multi-criteria decision
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The DSS –
Optimization Tool Generation of the Stroke Graph
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The DSS –
Optimization Tool Incorporating Transport Strokes between locations
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The DSS –
Optimization Tool Incorporating Selection Strokes
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The DSS –
Optimization Tool Generating all the feasible solutions
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• 2n solutions, initially. ‘n’ = number of selection arcs
• Delete 0-0-0-0 solution
• Delete solutions with number of active arcs > m
m = number of selection nodes
• Delete solutions with more than one arc active in a
selection node
• For each solution:
• Activate the rest of arcs and nodes
• Update the graph:
• An arc not activated, deactivates his initial node.
• A node not activated, deactivates his entry
and his final node (not selection arcs)
arcs and his outing arc
• If the root node is not actived, the solution is not
possible.
• Delete redundant solutions
The DSS –
Optimization Tool
For each solution
Calculate earliness beginning and ending for each node and arc
Calculate tardiness beginning and ending for each node and arc
Calculate time needed to supply the order
Find the associated strokes that must be performed
The Coordinator Agent chooses the solution to assess in the Simulation Tool
Then the solution that satisfices the stakeholders is performed
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Outline
I. Introduction
II. Literature Review
III. The “Stroke” concept
IV. The Decision Support System Prototype
I. Brief introduction
II. The Data Base enable alternative operations
III. The Algorithm for complete enumeration
IV. The Simulation Tool
V. Some results
VI. Conclusions & further works
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Some results
Bimatec – Soraluce case study
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Simulation and Optimization KPIs
Conclusions & further works
A DSS to solve the SN configuration problem and the operations scheduling
problem of a company that assembles highly customised machine tools in several
European plants has been described.
A novel database structure that is able to consider alternative operations
(purchasing, production, routing and transport) and alternative BOMs (upgrading,
reconfiguring custom products) has been introduced.
The steps of an algorithm for complete enumeration to determine all the feasible
solutions have been presented.
Then a simulator based on multi-agent technology evaluates the different KPIs by
collaborative decision making.
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Thank your for your attention
Any Question?
Julien maheut
PhD Candidat at Research Group ROGLE
Universidad Politécnica de Valencia (Spain)
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The research leading to these results has received funding from the European Community's Seventh Framework
Programme (FP7/2007-2013) under grant agreement no. NMP2-SL-2009- 229333 and has been partially
supported by the Spanish Ministry of Science and Innovation within the "Proyectos de Investigación Fundamental
No Orientada Programme through Project "CORSARI MAGIC DPI2010-18243".