Decision Support System Prototype for Supply Network Configuration Planning and Operations Scheduling in the Machine Tool Industry: a case study

Transcription

1 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 1 *Corresponding author: Julien Maheut - juma2@upv.es

2 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 2

3 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 3

4 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 4

5 Literature Review (I) 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) A ij 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 U ir is also constituted, and this structure was considered by (Mize et al., 1982) 5

6 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) 6

7 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 7

8 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. 8

9 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 9

10 The DSS Brief Introduction 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 10

11 The DSS Brief Introduction Simulation SN Model Define Supply Strategies Simulation KPIs SNI1 Predefined Strategies SNI2 SNI3 Optimization and Simulation SN Model Optimize Order SNI KPIs C1 Optimal SNI C2 Optimization Criteria C1: Cost C2: Delivery Time Cn 11

12 The DSS Brief Introduction -A Tool for Collaborative Decision Making Centralized Coordinator DSS Plant 1 Plant 2 Plant n 12

13 The DSS A Data Base enable alternative operations 13

14 The DSS Optimization Tool 14

15 The DSS Optimization Tool - Steps I. Transformation II. III. IV. I. Generation of the Stroke Graph with the Data Base II. III. IV. Incorporation of Transport Operations between Locations Transformation of the Stroke Graph into an Hybrid Stroke Graph incorporating Selection Strokes Transformation into an Arcs-Nodes Hyper-graph Algorithm for Complete enumeration I. Generation of all feasible solutions II. Assessment in cost, lead time, operations schedule of all feasible solution Selection of the chosen solutions by the Coordinator 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 15

16 The DSS Optimization Tool Generation of the Stroke Graph 16

17 The DSS Optimization Tool Incorporating Transport Strokes between locations 17

18 The DSS Optimization Tool Incorporating Selection Strokes 18

19 The DSS Optimization Tool Generating all the feasible solutions 2 n solutions, initially. n = number of selection arcs Delete 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 19

20 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 20

21 The DSS The simulation tool 21

22 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 22

23 Some results Bimatec Soraluce case study Simulation and Optimization KPIs 23

24 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. 24

25 Thank your for your attention Any Question? Julien maheut PhD Candidat at Research Group ROGLE Universidad Politécnica de Valencia (Spain) The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/ ) under grant agreement no. NMP2-SL 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 DPI ". 25