Abstract
The aim of this chapter is to introduce the modelling and optimization methods in engineering value chain decision-making and show the effectiveness and advances of solving management problems by information technologies. There are many critical problems related to decision-making along the engineering value chain. Based on the characteristics of a specific problem, different decision methods are developed and applied. In this chapter, we summarize several typical value chain decision problems and related decision methods and introduce the popular decision-making methods based on mathematic models and optimization algorithms. A complex engineering value chain construction decision problem is investigated, a multi-objective model is then developed, and the genetic algorithm-based solution procedure is proposed. Finally, numerical experiment and discussion are conducted to demonstrate the benefit of the method. Further, future development trends in engineering value chain modelling and optimization are illustrated.
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References
Abuali, F. N., Wainwright, R. L., & Schoenefeld, D. A. (1995, July). Determinant factorization: A new encoding scheme for spanning trees applied to the probabilistic minimum spanning tree problem. Proceedings of the Sixth International Conference on Genetic Algorithms, (pp. 470–477).
Altiparmak, F., Gen, M., Lin, L., & Karaoglan, I. (2009). A steady-state genetic algorithm for multi-product supply chain network design. Computers & Industrial Engineering, 56(2), 521–537.
Bakker, M., Riezebos, J., & Teunter, R. H. (2012). Review of inventory systems with deterioration since 2001. European Journal of Operational Research, 221(2), 275–284.
Başligil, H., Kara, S. S., Alcan, P., Özkan, B., & Çağlar, E. G. (2011). A distribution network optimization problem for third party logistics service providers. Expert Systems with Applications, 38(10), 12730–12738.
Beamish, P. W. (1987). Joint ventures in LDCs: Partner selection and performance. Management International Review, 23–37.
Boer, L. D., Labro, E., & Morlacchi, P. (2001). A review of methods supporting supplier selection. European Journal of Purchasing & Supply Management, 7(2), 75–89.
Boyd, S., & Vandenberghe, L. (2004). Convex optimization. Cambridge: Cambridge University Press.
Chai, J., Liu, J. N., & Ngai, E. W. (2013). Application of decision-making techniques in supplier selection: A systematic review of literature. Expert Systems with Applications, 40(10), 3872–3885.
Charnes, A., & Cooper, W. W. (1957). Management models and industrial applications of linear programming. Management Science, 4(1), 38–91.
Chou, H., Premkumar, G., & Chu, C. H. (2001). Genetic algorithms for communications network design-an empirical study of the factors that influence performance. IEEE Transactions on Evolutionary Computation, 5(3), 236–249.
Cooper, L. (1963). Location-allocation problems. Operations Research, 11(3), 331–343.
Creazza, A., Dallari, F., & Rossi, T. (2012). Applying an integrated logistics network design and optimisation model: The pirelli tyre case. International Journal of Production Research, 50(11), 3021–3038.
Dantzig, G. B., & Ramser, J. H. (1959). The truck dispatching problem. Management Science, 6(1), 80–91.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.
Eksioglu, B., Vural, A. V., & Reisman, A. (2009). The vehicle routing problem: A taxonomic review. Computers & Industrial Engineering, 57(4), 1472–1483.
Farahani, R. Z., Asgari, N., Heidari, N., Hosseininia, M., & Goh, M. (2012). Covering problems in facility location: A review. Computers & Industrial Engineering, 62(1), 368–407.
Fonseca, C. M., & Fleming, P. J. (1993, June). Genetic algorithms for multiobjective optimization: Formulation discussion and generalization. Proceedings of the Fifth International Conference on Genetic Algorithms, (pp. 416–423).
Gen, M., & Cheng, R. (2000). Genetic algorithms and engineering optimization, Vol. 7. Wiley.
Govindan, K., Rajendran, S., Sarkis, J., & Murugesan, P. (2015). Multi criteria decision making approaches for green supplier evaluation and selection: A literature review. Journal of Cleaner Production, 98, 66–83.
Gumus, A. T., Guneri, A. F., & Keles, S. (2009). Supply chain network design using an integrated neuro-fuzzy and MILP approach: A comparative design study. Expert Systems with Applications, 36(10), 12570–12577.
Horenbeek, A. V., Buré, J., Cattrysse, D., Pintelon, L., & Vansteenwegen, P. (2013). Joint maintenance and inventory optimization systems: A review. International Journal of Production Economics, 143(2), 499–508.
Horn, J., Nafpliotis, N., & Goldberg, D. (1994, June). A niched pareto genetic algorithm for multiobjective optimization.In Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence, (pp. 82–87).
Kiresuk, T. J., & Sherman, M. R. E. (1968). Goal attainment scaling: A general method for evaluating comprehensive community mental health programs. Community Mental Health Journal, 4(6), 443–453.
Knowles, J., & Corne, D. (1999, July). The pareto archived evolution strategy: A new baseline algorithm for paretomultiobjective optimisation. In Proceedings of the 1999 Congress on Evolutionary Computation.
Lee, J. H. (2014). Energy supply planning and supply chain optimization under uncertainty. Journal of Process Control, 24(2), 323–331.
Lin, C., Choy, K. L., Ho, G. T., Chung, S. H., & Lam, H. Y. (2014). Survey of green vehicle routing problem: Past and future trends. Expert Systems with Applications, 41(4), 1118–1138.
Melo, M. T., Nickel, S., & Saldanha-da-Gama, F. (2009). Facility location and supply chain management—A review. European Journal of Operational Research, 196(2), 401–412.
Monteiro, M. M., Leal, J. E., & Raupp, F. M. P. (2010). A four-type decision-variable MINLP model for a supply chain network design. Mathematical Problems in Engineering, 2010.
Nemhauser, G. L. (1989). Handbook in Operations Research and Management Science. North-Holland.
Pillac, V., Gendreau, M., Guéret, C., & Medaglia, A. L. (2013). A review of dynamic vehicle routing problems. European Journal of Operational Research, 225(1), 1–11.
Schaffer, J. D. (1985, July). Multiple objective optimization with vector evaluated genetic algorithms. In Proceedings of the First International Conference on Genetic Algorithms , (pp. 93–100).
Snyder, L. V. (2006). Facility location under uncertainty: A review. IIE Transactions, 38(7), 547–564.
Srinivas, N., & Deb, K. (1994). Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation, 2(3), 221–248.
Turban, E., & Meredith, J. R. (1998). Fundamentals of management science.New york: McGraw-Hill College.
Zitzler, E., & Thiele, L. (1999). Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation, 3(4), 257–271.
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Zhou, L., Xu, X. (2018). Engineering Value Chain Modelling and Optimization. In: Zhang, Y., Gregory, M. (eds) Value Creation through Engineering Excellence. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-319-56336-7_9
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DOI: https://doi.org/10.1007/978-3-319-56336-7_9
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