In this paper we present a methodology and simulation environment for solving multi-echelon supply chain planning and optimization problems for industries with batch and semi-batch processes. The introduced methodology is aimed to analyze efficiency of a specific planning policy over the product life cycle within the entire supply chain for automated switching from a non-cyclic to cyclic and to optimize the cyclic planning policy for products at the maturity phase. For optimization of a multi-echelon cyclic schedule, the simulation optimization algorithm developed is based on integration of the multi-objective genetic algorithm (GA) and response surface-based local search to improve GA solutions. The comparative analysis of planning policies is based on estimation of the difference between mean values of their total costs by using the Paired-t confidence interval method and evaluation of an additional cost of the cyclic schedule. The simulation environment allows one to describe input data to build the supply chain network and store it in an external file, computing effective planning policies, automatically generating and running a network simulation model, generating production rules for switching from one planning policy to another and optimizing parameters of a multi-echelon cyclic schedule. Finally, a business case is described that illustrates the practical application of the presented methodology.
Christopher M.Logistics and supply chain management, 3rd edn. Englewood Cliffs, NJ: Prentice Hall , 2005, p.321.
2.
Merkuryev Y., Merkuryeva G., Desmet B., Jacquet-Lagrèze E.Integrating Analytical and Simulation Techniques in Multi-Echelon Cyclic Planning . Proceedings of the First Asia International Conference on Modelling and Simulation, AMS2007, Prince of Songkla University, Phuket, Thailand, Al-Dabas D, Zobel R, Abraham A, Turner S (eds). Los Alamitos, CA: IEEE Computer Society Press, 2007, p.460-464.
3.
Chopra S., Meindl P.Supply chain management: Strategy, planning, and operation, 3rd edn. Pearson Prentice Hall, Upper Saddle River: New Jersey, USA, 2007, p.536.
4.
Sensi G., Longo F., and Mirabelli G.Inventory policies analysis under demand patterns and lead times constraints in a real supply chain. Int J Prod Res2008 ; 46: 6997-7016.
5.
Cambell GM, Mabert VACyclical schedules for capacitated lot sizing with dynamic demands. Manag . Sci1991; 37: 409-427.
6.
Roundy R.A 98%-effective lot-sizing rule for a multi-product, multi-stage production/inventory system. Math Oper Res1986; 11: 699-727.
7.
Desmet B.Collaborative processing in the extended supply chains. Project submitted in fulfillment of the degree of Master of Business Administration: General International Management. Autonomous Management School of Ghent University and Katholicke UniversiteitLeuven, 2004, p.134.
8.
Merkuryev Y., Merkuryeva G., Hatem J., Desmet B.Supply chain simulation in the ECLIPS Project. Proceedings of the Second Asia International Conference on Modelling and Simulation, AMS 2008, Kuala Lumpur, Malaysia, Al-Dabass D, Turner S, Tan G, Abraham A (eds). Los Alamitos, CA: IEEE Computer Society Press, 2008, p.684-690.
9.
Merkuryev Y., Merkurjeva G., Haes R., Desmet B., Wiespelaere A., Hatem J.Supply chain simulation in ECLIPS project: Real-life benefit. Proceedings of the Third Asia International Conference on Modelling and Simulation, AMS2009, Bandung/ Bali, Indonesia, Al-Dabas D, Triweko R, Susanto S, Abraham, A (eds). Los Alamitos, CA: IEEE Computer Society Press, 2009, p.526-532.
10.
Merkuryev Y., Hatem J., Merkuryeva G., Bikovska J.Business simulation game for teaching multi-echelon supply chain management . Proceedings of the 11th International Workshop on Harbour Maritime Multimodal Logistics Modelling & Simulation, HMS2008 , Bruzzone A, Longo F, Merkuryev Y, Mirabelli G, Piera MA (eds). Rende (CS), 2008, p.20-28.
11.
Sukov A.Recognition of transitions between different phases of the product life cycle . Proceedings of the 20th European Modelling and Simulation Symposium, EMSS-2008, Campora San Giovanni, Italy, Bruzzone A, Longo F, Piera MA, Aguilar R, Frydman C (eds.), 2008, p.81-86.
12.
Grégoire M., Jacquet-Lagrèze E., Merkuryeva G., et al. Cyclic planning in a multi-echelon network. Livre des résumés. Proceedings of the Conférence Scientifique Conjointe en Recherche Opérationnelle et Aide à la Décision FRANCORO V / ROADEF2007, Grenoble, 2007, p.221-222.
13.
Merkuryeva G. , Merkuryev Y., Napalkova, L.Simulation-based environment for multi-echelon cyclic planning and optimisation. Proceedings of the International Mediterranean Modelling Multiconference, I3M 2007, Bergeggi, Bruzzone A, Longo F, Merkuryev Y , Piera, MA (eds). Genoa: University of Genoa, 2007 , p.318-325.
14.
Merkuryeva G., Napalkova L.Supply chain cyclic planning and optimisation. In: Merkuryev Y, Merkuryeva G, Pierra MA, and Guash A (eds) Simulation-based studies in logistics: Education and applied research. Ch. 6. Berlin: Springer-Verlag, London, UK, 2009, p.89-111.
15.
Merkuryeva G., Napalkova L.Two-phase simulation optimisation procedure with applications to multi-echelon cyclic planning. Proceedings of the 20th European Modelling and Simulation Symposium, EMSS-2008, Campora San Giovanni, Italy, Bruzzone A, Longo F, Piera MA , Aguilar R, Frydman C (eds), 2008, p.51-58.
16.
Lee KY, El-Sharkawi MAModern heuristic optimization techniques: Theory and applications to power systems. New York: John Wiley & Sons, Inc, 2008, p.586.
17.
Abraham A., Jain L., and Goldberg R.Evolutionary multi-objective optimisation: Theoretical advances and applications . New York: Springer, 2005, p.302.
18.
Ghosh A., Dehuri S.Evolutionary algorithms for multi-criterion optimisation: A survey. Int J Comput Inform Sci2004; 2: 38-57.
19.
Azadivar F.A tutorial on simulation optimization. Proceedings of the 1992 Winter Simulation Conference, Swain JJ, Goldsman D, Crain RC, Wilson JR (eds). Piscataway, NJ: IEEE Press, 1992 , p.198-204.
20.
Merkuryev Y., Visipkov V.A survey of optimization methods in discrete systems simulation. Proceedings of the First Joint Conference of International Simulation Societies, Halin J, Karplus W, Rimane R (eds). Simulation Councils, Inc., 1994, p.104-110.
21.
Carson M., Maria A.Simulation optimisation: methods and applications. Proceedings of the 1997 Winter Simulation Conference, Anrdradottir S, Healy KJ, Witners DH, Nelson BL. Los Alamitos, CA: IEEE Computer Society Press, 1997, p.118-126.
22.
Fu MC, Glover FW, April J.Simulation optimisation: a review, new developments and applications. Proceedings of the 2005 Winter Simulation Conference, Kuhl ME, Steiger NM, Steiger F, Amstrong B, Joines JA (eds). Los Alamitos, CA : IEEE Computer Society Press, 2005 , p.83-95.
23.
Konak A., Coit DW, and Smooth AEMulti-objective optimization using genetic algorithms: A tutorial. Reliab Syst Saf2006; 91: 992-1007.
24.
Bruzzone AG , Giribone P., and Vio F.Genetic algorithms and simulation for supporting layout re-engineering of automotive component production facilities. Int J Flex Autom Intell Manuf1999; 7(3-4): 379-391.
25.
Hiroyasu T., Miki M., Watanabe S.Divided range genetic algorithms in multi-objective optimization problems . Proceedings of International Workshop on Emergent Synthesis, IWES’99, Ueda K (ed.). Kobe: Japan Society for the Promotion of Science, 1999, p.57-66.
26.
Deb K., Agrawal S., Meyarivan T.A Fast elitist non-dominated sorting genetic algorithm for multi-objective optimisation: NSGA-II. Proceedings of the 6th Parallel Problem Solving from Nature - PPSN VI, Schoenauer M, Deb K, Rudolph G, et al. (eds). Berlin: Springer, 2000, p.849-858.
27.
Harrell C., Ghosh BK, and Bowden ROSimulation using Promodel, 2nd edn. New York, USA: McGraw-Hill Higher Education, 2007, p.733.
28.
ProModel ‘SimRunner’, http://www.promodel.com/products/simrunner (accessed August 20, 2009).
29.
ProModel ‘OptQuest’, http://www.promodel.com/products/optquest (accessed August 20, 2009).
30.
Goldberg DEOptimal initial population size for binary-coded genetic algorithms. Technical Report TCGA-850001. University of Alabama, 1985, p.28.
31.
Napalkova L., Merkuryeva G.Theoretical framework of multi-objective simulation-based genetic algorithm for supply chain cyclic planning and optimisation. Proceedings of the 10th International Conference on Computer Modelling and Simulation EUROSIM/ UKsim-2008, Al-Dabas D, Orsoni A, Abraham A, Zobel R. (eds). Los Alamitos, CA: IEEE Computer Society Press, 2008, p.467-474.
32.
Merkuryeva G., Napalkova, and L. Two-phase simulation optimization algorithm with applications to multi-echelon cyclic planning. Int J Simulat Process Model2010; 6: 7-18.
33.
Merkuryeva G.Response surface-based simulation metamodelling methods with applications to optimisation problems. In: Dolgui A, Soldek J, and Zaikin O (eds) Supply chain optimisation product/process design, facility location and flow control. Ch. 15. New York : Springer Science-Business Media, 2005, p.205-215.
34.
Myers RH, Montgomery DC, and ChM Anderson-Cook .Response surface methodology: Process and product optimization using designed experiments, 3rd edn. New York: John Wiley & Sons, Inc, 2009, p.704.
35.
Merkuryeva G. , Timmermans S., Vecherinska O.Evaluating the ‘optimality gap’ between cyclic and non-cyclic planning policies in supply chains. Proceedings of the International Conference on Production Engineering , Chlebusa E (ed.). Wroclaw: Oficyna Wydawnicza Politechniki Wroclawskiej , 2006, p.155-162.
36.
Merkuryeva G. , Vecherinska O.Simulation-based approach for comparison of (s, Q) and (R, S) replenishment policies utilization efficiency in multi-echelon supply chains. Proceedings of the 10th International Conference on Computer Modelling and Simulation EUROSIM/UKsim-2008, Al-Dabas D, Orsoni A , Abraham A, Zobel R (eds). Los Alamitos, CA: IEEE Computer Society Press, 2008, p.434-440.
37.
Van Landeghem, H., and Vanmaele H.Robust planning: A new paradigm for demand chain planning. J Oper Manage2002; 20: 769-783.
