Skip to main content
SpringerLink
Log in

Decision Support in Automotive Supply Chain Management: Declarative and Operational Research Approach

  • Chapter
  • First Online:
Environmental Issues in Automotive Industry

Part of the book series: EcoProduction ((ECOPROD))

  • 1948 Accesses

Abstract

This chapter presents the two-phase decision support concepts in automotive supply chain. The proposed concept applied two environments-mathematical programming (operation research approach) and constraint logic programming (declarative approach). We present an approach that allows modeling and solution of the same decision making model in both environments independently but also to communicate between them. In this chapter the decision making model as a problem of optimizing the cost in supply chain under resources, multimodal and environmental constraints in the form of MILP (Mixed Integer Linear Programming) has been presented. The numerical experiments were carried out using sample data to show the possibilities of practical decision support and optimization of the automotive supply chain.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Apt K, Wallace M (2006) Constraint logic programming using eclipse, Cambridge University Press, Cambridge

    Google Scholar 

  • Beamon BM, Chen VCP (2001) Performance analysis of conjoined supply chains. Int J Prod Res 39:3195–3218

    Article  Google Scholar 

  • Chern CC, Hsieh JS (2007) A heuristic algorithm for master planning that satisfies multiple objectives. Comput Oper Res 34:3491–3513

    Article  Google Scholar 

  • Dietz F, Lang NS, Maurer A (2004) Beyond cost reduction: reinventing the automotive oem-supplier interface, The Boston Consulting Group, Boston

    Google Scholar 

  • Drucker P (2003) The essential drucker: the best of sixty years of peter drucker’s essential writings on management. Harper Business, Harper Collins, New York

    Google Scholar 

  • Heywood J (2006) Fueling Our Transportation Future. Sci Am 295(3):60–63

    Article  Google Scholar 

  • Huang GQ, Lau JSK, Mak KL (2003) The impacts of sharing production information on supply chain dynamics: a review of the literature. Int J Prod Res 41:1483–1517

    Article  Google Scholar 

  • Hugo WMJ, Badenhorst-Weiss JA, Van Biljon EHB (2004) Supply chain management: logistics in perspective, 3rd edn. Van Schaik, Pretoria

    Google Scholar 

  • Jang YJ, Jang SY, Chang BM, Park J (2002) A combined model of network design and production/distribution planning for a supply network. Comput Ind Eng 43:263–281

    Article  Google Scholar 

  • Jung H, Jeong B, Lee CG (2008) An order quantity negotiation model for distributor-driven supply chains. Int J Prod Econ 111:147–158

    Article  Google Scholar 

  • Kanyalkar AP, Adil GK (2005) An integrated aggregate and detailed planning in a multisite production environment using linear programming. Int J Prod Res 43:4431–4454

    Article  Google Scholar 

  • Kelly T (2012) The automotive supply chain in the new normal: analysis of the industry and its supply chain opportunities. JDA Software Group, Inc, Ontario Institute of PMAC

    Google Scholar 

  • Lee YH, Kim SH (2000) Optimal production–distribution planning in supply chain management using a hybrid simulation-analytic approach. In: Proceedings of the 2000 winter simulation conference 1 and 2, pp 1252–1259

    Google Scholar 

  • Park YB (2005) An integrated approach for production and distribution planning in supply chain management. Int J Prod Res 43:1205–1224

    Article  Google Scholar 

  • Perea-lopez E, Ydstie BE, Grossmann IE (2003) A model predictive control strategy for supply chain optimization. Comput Chem Eng 27:1201–1218

    Article  CAS  Google Scholar 

  • Rizk N, Martel A, D’amours S (2006) Multi-item dynamic production–distribution planning in process industries with divergent finishing stages. Comput Oper Res 33:3600–3623

    Article  Google Scholar 

  • Schrijver A (1998) Theory of linear and integer programming. John Wiley & Sons, Chichester. ISBN 0-471-98232-6

    Google Scholar 

  • Selim H, Am C, Ozkarahan I (2008) Collaborative production–distribution planning in supply chain: a fuzzy goal programming approach. Transp Res Part E-Logistics Transp Rev 44:396–419

    Article  Google Scholar 

  • Shapiro JF (2001) Modeling the supply chain. Duxbury Press, New York. ISBN 978-0-534-37741

    Google Scholar 

  • Simchi-Levi D, Kaminsky P, Simchi-Levi E (2003) Designing and managing the supply chain: concepts, strategies, and case studies. McGraw-Hill, New York. ISBN 978-0-07-119896-7

    Google Scholar 

  • Sitek P, Wikarek J (2008) In: Nguyen NT et al (eds) A Declarative Framework for Constrained Search Problems. New Frontiers in Applied Artificial Intelligence, Lecture Notes in Artificial Intelligence, vol 5027. Springer-Verlag, Berlin-Heidelberg: 728–737

    Google Scholar 

  • Tang D, Qian X (2008) Product lifecycle management for automotive development focus-ing on supplier integration. Comput Ind 59:288–295

    Article  Google Scholar 

  • Timpe CH, Kallrath J (2000) Optimal planning in large multi-site production networks. Eur J Oper Res 126:422–435

    Article  Google Scholar 

  • Torabi SA, Hassini E (2008) An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy Sets Syst 159:193–214

    Article  Google Scholar 

  • Venkat K (2007) Analyzing and optimizing the environmental performance of supply chains. In: Proceedings from the ACEEE summer studies on energy efficiency in industry

    Google Scholar 

  • Venkat K, Wakeland W (2006). Is lean necessarily green? In: Proceedings of the 50th annual meeting of the ISSS. International Society for the Systems Sciences, York

    Google Scholar 

  • Williams HP (2009) Logic and Integer Programming. Springer, Berlin

    Google Scholar 

  • www.lindo.com

  • www.eclipseclp.org

Download references

Author information

Authors and Affiliations

  1. Kielce University of Technology, Institute of Management Control Systems, Al. 1000-lecia PP 7, 25-314, Kielce, Poland

    Paweł Sitek & Jarosław Wikarek

Authors
  1. Paweł Sitek
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jarosław Wikarek
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Paweł Sitek .

Editor information

Editors and Affiliations

  1. Poznan University of Technology, Poznan, Poland

    Paulina Golinska

Appendix A

Appendix A

  • !P - the area/volume occupied by product k;

  • 10 15 15 10 20~

  • !F - the fixed cost of distributor/distribution center s;

  • 1200 1500 1000~

  • !V - distributor s maximum capacity/volume for E3 1200 1500 1200;

  • 2000 2500 1500~

  • !Pt - the capacity of transport unit using mode of transport d;

  • 60 180 600~

  • !Zt - the number of transport units using mode of transport d;

  • !E2 20 20 20;

  • !E3

  • 12 6 3 ~

  • !Z - customer j demand/order for product k;

  • 10 10 15 20 15 10 0 10 10 15 10 20 0 20 0 10 0 10 0 20~

  • !Tcm - the cut-off time of delivery to the customer j of product k;

  • 10 10 10 10 20 10 10 10 10 20 10 10 10 10 20 10 10 10 10 20~

  • !C - the cost of product k at factory i;

  • 100 200 200 300 300 150 210 150 250 350~

  • !w - production capacity at factory i for product k;

  • 100 100 100 100 100 100 100 100 100 100~

  • !R - if distributor s can deliver product k;

  • 1 1 1 1 0 1 1 0 1 1 1 1 1 0 1~

  • !Tp - the time needed for distributor;

  • !to prepare the shipment of product k;

  • 2 2 2 2 2 1 1 1 1 1 3 3 3 3 3~

  • !A - the fixed cost of delivery from manufacturer i;

  • !to distributor s using mode of transport d;

  • 10 20 40 12 24 42 5 10 25 5 10 20 10 20 40 15 25 35 ~

  • !R1 - if manufacturer i can deliver to distributor s;

  • !using mode of transport d;

  • 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 1 0 ~

  • !Tf - the time of delivery from manufacturer i to distributor s;

  • !using mode of transport d;

  • 2 3 4 1 2 3 1 2 3 4 6 7 4 6 7 4 6 7 ~

  • !G - the fixed cost of delivery from distributor s;

  • !to customer j using mode of transport d;

  • 2 4 10 2 5 12 14 12 20 15 13 30 4 8 16 3 6 15 5 10 15 2 4 10

  • 2 4 11 3 6 14 6 10 20 4 8 20 ~

  • !R2 - if distributor s can deliver to customer j;

  • !using mode of transport d;

  • 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 0 1 1 0 1 1 1

  • 1 1 1 1 1 1 ~

  • !Tm - the time of delivery from distributor s;

  • !to customer j using mode of transport d;

  • 1 1 2 1 1 2 1 1 2 1 1 2 1 1 2 1 1 2 1 1 2 1 1 2 1 1 2 1 1

  • 2 1 1 2 1 1 2 ~

  • !K1 - the variable cost of delivery of product k;

  • !from manufacturer i to distributor s using mode of transport d;

  • 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1 4 2 1 4 2 1 4 2 1 4 2 1 4 2 1

  • 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1 4 2 1 4 2 1 4 2 1 4 2 1 4 2 1

  • 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1 4 2 1 4 2 1 4 2 1 4 2 1 4 2 1 ~

  • !K2 - the variable cost of delivery of product k;

  • !from manufacturer i to distributor s using mode of transport d;

  • 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1

  • 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1

  • 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1

  • 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1

  • 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1

  • 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1 3 2 1 ~

  • !Od – the environmental cost of using mode of transport d;

  • 10 30 400 ~

  • !CW - Arbitrarily large constant;

  • 10000~

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Sitek, P., Wikarek, J. (2014). Decision Support in Automotive Supply Chain Management: Declarative and Operational Research Approach. In: Golinska, P. (eds) Environmental Issues in Automotive Industry. EcoProduction. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23837-6_7

Download citation

Publish with us

Policies and ethics

Search

Navigation