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A particle swarm optimization approach for constraint joint single buyer-single vendor inventory problem with changeable lead time and (r,Q) policy in supply chain

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Abstract

In this paper, the chance-constraint joint single vendor-single buyer inventory problem is considered in which the demand is stochastic and the lead time is assumed to vary linearly with respect to the lot size. The shortage in combination of back order and lost sale is considered and the demand follows a uniform distribution. The order should be placed in multiple of packets, the service rate limitation on each product is considered a chance constraint, and there is a limited budget for the buyer to purchase the products. The goal is to determine the re-order point and the order quantity of each product such that the chain total cost is minimized. The model of this problem is shown to be an integer nonlinear programming type and in order to solve it, a particle swarm optimization (PSO) approach is used. To assess the efficiency of the proposed algorithm, the model is solved using both genetic algorithm and simulated annealing approaches as well. The results of the comparisons by a numerical example, in which a sensitivity analysis on the model parameters is also performed, show that the proposed PSO algorithm performs better than the other two methods in terms of the total supply chain costs.

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References

  1. Aarts EHL, Korst JHM (1989) Simulated annealing and Boltzmann machine; a stochastic approach to computing, 1st edn. Wiley, Chichester, UK

    Google Scholar 

  2. Al-Tabtabai H, Alex AP (1999) Using genetic algorithms to solve optimization problems in construction. Eng Con Arch Man 6:121–132

    Google Scholar 

  3. Banerjee A (1986) A joint economic lot size model for purchaser and vendor. Decis Sci 17:292–311

    Article  Google Scholar 

  4. Ben-Daya M, Raouf A (1994) Inventory models involving lead time as a decision variable. Journal of the Operational Research Society 45:579–582

    Google Scholar 

  5. Ben-Daya M, Hariga M (2004) Integrated single vendor single buyer model with stochastic demand and variable lead time. Int J Prod Econ 92:75–80

    Article  Google Scholar 

  6. Dorigo M, Stutzle T (2004) Ant colony optimization. MIT Press, Cambridge

    Book  MATH  Google Scholar 

  7. Dueck G, Scheuer T (1990) Threshold accepting: a general purpose algorithm appearing superior to simulated annealing. J Comput Phys 90:161–175

    Article  MATH  MathSciNet  Google Scholar 

  8. El-Sharkawi L (2008) Modern heuristic optimization techniques, 1st edn. Wiley Inter Science, New Jersey

    Google Scholar 

  9. Gaiduk AR, Vershinin YA, West MJ (2002) Neural networks and optimization problems. In Proceedings of IEEE 2002 International Conference on Control Applications, 1:37–41

  10. Geem ZW, Kim JH, Loganathan GV (2001) A new heuristic optimization algorithm: harmony search. Simulation 76:60–68

    Article  Google Scholar 

  11. Gen M, Cheng R (1997) Genetic algorithm and engineering design, 1st edn. Wiley, New York

    Google Scholar 

  12. Goyal SK (1988) Joint economic lot size model for purchaser and vendor: a comment. Decis Sci 19:236–241

    Article  Google Scholar 

  13. Goyal SK, Gupta YP (1989) Integrated inventory models: the vendor–buyer coordination. Eur J Oper Res 41:261–269

    Article  MathSciNet  Google Scholar 

  14. Guo YW, Li WD, Mileham AR, Owen GW (2008) Optimization of integrated process planning and scheduling using a particle swarm optimization approach. Int J Prod Res 40:1–22

    Google Scholar 

  15. Hadley G, Whitin TM (1963) Analysis of inventory systems. Prentice-Hall, Englewood Cliffs

    MATH  Google Scholar 

  16. Hariga M, Ben-Daya M (1999) Some stochastic inventory models with deterministic variable lead time. Eur J Oper Res 113:42–51

    Article  MATH  Google Scholar 

  17. Heydari J, Baradaran-Kazemzadeh R, Chaharsooghi SK (2009) A study of lead time variation impact on supply chain performance. Int J Adv Manuf Technol 40:1206–1215

    Article  Google Scholar 

  18. Hill R (1999) The optimal production and shipment policy for the single-vendor single-buyer integrated production–inventory problem. Int J Prod Res 37:2463–2475

    Article  MATH  Google Scholar 

  19. Hsiao JM, Lin C (2005) A buyer-vendor EOQ model with changeable lead time in supply chain. Int J Adv Manuf Technol 26:917–921

    Article  Google Scholar 

  20. Joo SJ, Bong JY (1996) Construction of exact D-optimal designs by Tabu search. Comput Stat Data Anal 21:181–191

    Article  MATH  Google Scholar 

  21. Kannan G, Noorul-Haq A, Devik M (2009) Analysis of closed loop supply chain using genetic algorithm and particle swarm optimization. Int J of Prod Res 47:1175–1200

    Article  Google Scholar 

  22. Kennedy J, Eberhart R (1995) Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, 1942–1945

  23. Kennedy J, Eberhart R (2001) Swarm intelligence. Academic, San Diego

    Google Scholar 

  24. Kim CO, Jun J, Baek JK, Smith RL, Kim YD (2005) Adaptive inventory control models for supply chain management. Int J Adv Manuf Technol 26:1184–1192

    Article  Google Scholar 

  25. Laumanns M, Thiele L, Deb K, Zitzler E (2002) Combining convergence and diversity in evolutionary multi-objective optimization. Evol Comput 10:263–282

    Article  Google Scholar 

  26. Lee KS, Geem ZW (2004) A new structural optimization method based on the harmony search algorithm. Comput Struct 82:781–798

    Article  Google Scholar 

  27. Liu B, Wang L, Jin Y (2008) An effective hybrid PSO-based algorithm for flow shop scheduling with limited buffers. Comput Oper Res 35:2791–2806

    Article  MATH  Google Scholar 

  28. Liu B (2004) Uncertainty theory: an introduction to its axiomatic foundations. Springer, Berlin

    MATH  Google Scholar 

  29. Lodree E, Jang W, Klein CM (2004) Minimizing response time in a two-stage supply chain system with variable lead time and stochastic demand. Int J of Prod Res 42:2263–2278

    Article  MATH  Google Scholar 

  30. Lu L (1995) A one-vendor multi-buyer integrated inventory model. Eur J Oper Res 81:312–323

    Article  MATH  Google Scholar 

  31. Naka S, Genji T, Yura T, Fukuyama Y (2001) Practical distribution state estimation using hybrid particle swarm optimization. Proceedings of the IEEE Power Engineering Society Winter Meeting

  32. Ouyang LY, Yeh NC, Wu KS (1996) Mixture inventory models with backorders and lost sales for variable lead time. J Oper Res Soc 47:829–832

    MATH  Google Scholar 

  33. Ouyang LY, Chuang BR (2000) A periodic review inventory model involving variable lead time with a service level constraint. Int J Sys Sci 31:1209–1215

    Article  MATH  Google Scholar 

  34. Rahimi-Vahed AR, Mirghorbani SM, Rabbani M (2007) A hybrid multi-objective particle swarm algorithm for a mixed-model assembly line sequencing problem. Eng Optim 39:877–898

    Article  MathSciNet  Google Scholar 

  35. Salman A, Ahmad I, Al-Madani S (2003) Particle swarm optimization for fast assignment problem. Microprocess Microsyst 26:363–371

    Article  Google Scholar 

  36. Sha DY, Hsu C (2008) A new particle swarm optimization for the open shop scheduling problem. Comput Oper Res 35:3243–3261

    Article  MATH  Google Scholar 

  37. Shi Y, Eberhart RC (1999) Empirical study of particle swarm optimization. Proceedings of the 1999 Congress on Evolutionary Computation, 1945–1950

  38. Siajadi H, Ibrahim RN, Lochert PB (2006) A single-vendor multiple-buyer inventory model with a multiple-shipment policy. Int J Adv Manuf Technol 27:1030–1037

    Article  Google Scholar 

  39. Su S, Zhan D, Xu X (2008) An extended state task network formulation for integrated production-distribution planning in supply chain. Int J Adv Manuf Technol 37:1232–1249

    Article  Google Scholar 

  40. Taleizadeh AA, Aryanezhad MB, Niaki STA (2008) Optimizing multi-product multi-constraint inventory control systems with stochastic replenishment. J Appl Sci 8:1228–1234

    Article  Google Scholar 

  41. Taleizadeh AA, Niaki STA, Hosseini V (2008) The multi-product multi-constraint newsboy problem with incremental discount and batch order. As J Appl Sci 1:110–122

    Google Scholar 

  42. Taleizadeh AA, Moghadasi H, Niaki STA, Eftekhari A (2008) An EOQ-joint replenishment policy to supply expensive imported raw materials with payment in advance. J Appl Sci 8:4263–4273

    Article  Google Scholar 

  43. Taleizadeh AA, Niaki STA, Aryanezhad MB (2009) Multi-product multi-constraint inventory control systems with stochastic replenishment and discount under fuzzy purchasing price and holding costs. Am J Appl Sci 6:1–12

    Google Scholar 

  44. Taleizadeh AA, Niaki STA, Hosseini V (2009) Optimizing multi-product multi-constraint bi-objective newsboy problem with discount by a hybrid method of goal programming and genetic algorithm. Eng Optim 41:437–457

    Article  MathSciNet  Google Scholar 

  45. Taleizadeh AA, Niaki STA, Aryanezhad MB (2009) A hybrid method of Pareto, TOPSIS and genetic algorithm to optimize multi-product multi-constraint inventory systems with random fuzzy replenishment. Math Comput Model 49:1044–1057

    Article  MATH  MathSciNet  Google Scholar 

  46. Taleizadeh AA, Niaki STA, Aryanezhad MB (2009d) Multi-product multi-constraint inventory control systems with stochastic period length and total discount under fuzzy purchasing price and holding costs. Int J Sys Sci (in press)

  47. Wang YC (2008) Evaluating flexibility on order quantity and delivery lead time for a supply chain system. Int J Sys Sci 39:1193–1202

    Article  MATH  Google Scholar 

  48. Wee HM, Yang PC (2007) A mutual beneficial pricing strategy of an integrated vendor-buyers inventory system. Int J Adv Manuf Technol 34:179–187

    Article  Google Scholar 

  49. Wu KS (2001) A mixed inventory model with variable lead time and random supplier capacity. Prod Plan Control 12:353–361

    Article  Google Scholar 

  50. Yapicioglu H, Smith AE, Dozier G (2007) Solving the semi-desirable facility location problem using bi-objective particle swarm. Eur J Oper Res 177:733–749

    Article  MATH  Google Scholar 

  51. Zahara E, Hu CH (2008) Solving constrained optimization problems with hybrid particle swarm optimization. Eng Optim 40:1031–1049

    Article  MathSciNet  Google Scholar 

  52. Zequeira RI, Durn A, Gutirrez G (2005) A mixed inventory model with variable lead time and random back-order rate. Int J Sys Sci 36:329–339

    Article  MATH  Google Scholar 

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Correspondence to Seyed Taghi Akhavan Niaki.

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Taleizadeh, A.A., Niaki, S.T.A., Shafii, N. et al. A particle swarm optimization approach for constraint joint single buyer-single vendor inventory problem with changeable lead time and (r,Q) policy in supply chain. Int J Adv Manuf Technol 51, 1209–1223 (2010). https://doi.org/10.1007/s00170-010-2689-0

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