:: دوره 7، شماره 1 - ( 3-1398 ) ::
جلد 7 شماره 1 صفحات 191-169 برگشت به فهرست نسخه ها
رهیافتی کارا مبتنی بر الگوریتم جغرافیای زیستی بهبودیافته جهت حل مسئله مسیریابی موجودی
علی اصغر حیدری ، رحیم علی عباسپور*
دانشگاه تهران
چکیده:   (2605 مشاهده)
مسئله مسیریابی همواره به‌عنوان یکی از مراحل بنیادین توسعه سامانه‌های مدیریت مخاطرات موردتوجه پژوهشگران و مدیران شهری بوده است. در این پژوهش، یک مسئله مسیریابی با ارائه یک روش فرا اکتشافی بهبودیافته بر مبنای جغرافیای زیستی مورد بررسی و تحلیل قرار می‌گیرد. در این مسئله، برنامه‌ریزی تأمین در کنار مدیریت موجودی و برنامه‌ریزی توزیع کالاهای امدادی لحاظ گردیده و هدف کمینه‌سازی مجموع هزینه‌های راه‌اندازی سامانه، توزیع و نگه‌داری کالاهای امدادی است. سپس، به‌منظور جلوگیری از همگرایی زودرس به پاسخ‌های بهینه محلی و ارتقاء کارایی و سرعت همگرایی الگوریتم در مسائل مقید و با ابعاد بزرگ، یک الگوریتم بهینه‌سازی مبتنی بر جغرافیای زیستی جدید با عملگر دینامیک مهاجرت پیشنهاد می‌گردد. با در نظر گرفتن مسائل نمونه مسیریابی، عملکرد الگوریتم پیشنهادی نسبت به دیگر الگوریتم‌ها از دیدگاه زمان اجرا، سرعت همگرایی، استحکام، بهترین و میانگین و برتری آماری نتایج مقایسه شده است. ارزیابی آماری نتایج مبین بهبود کارایی و کسب نتایج برتر با استفاده از رهیافت پیشنهادی در مسیریابی زمان‌مند وسایل نقلیه امدادی است.
واژه‌های کلیدی: الگوریتم جغرافیای زیستی، زمان، مسیریابی، بهینه‌سازی، امداد.
متن کامل [PDF 2890 kb]   (764 دریافت)    
نوع مطالعه: پژوهشي | موضوع مقاله: سیستمهای اطلاعات مکانی (عمومی)
دریافت: 1396/3/6 | پذیرش: 1397/4/9 | انتشار: 1398/3/31
فهرست منابع
1. [1] E. Aivazidou, N. Tsolakis, E. Iakovou, and D. Vlachos, "The emerging role of water footprint in supply chain management: A critical literature synthesis and a hierarchical decision-making framework," Journal of Cleaner Production, vol. 137, pp. 1018-1037, 2016. [DOI:10.1016/j.jclepro.2016.07.210]
2. [2] C. Bode and S. M. Wagner, "Structural drivers of upstream supply chain complexity and the frequency of supply chain disruptions," Journal of Operations Management, vol. 36, pp. 215-228, 2015. [DOI:10.1016/j.jom.2014.12.004]
3. [3] B. Chang, C. Kuo, C.-H. Wu, and G.-H. Tzeng, "Using fuzzy analytic network process to assess the risks in enterprise resource planning system implementation," Applied Soft Computing, vol. 28, pp. 196-207, 2015. [DOI:10.1016/j.asoc.2014.11.025]
4. [4] S. Mohseni, M. S. Pishvaee, and H. Sahebi, "Robust design and planning of microalgae biomass-to-biodiesel supply chain: A case study in Iran," Energy, vol. 111, pp. 736-755, 2016. [DOI:10.1016/j.energy.2016.06.025]
5. [5] K. Xu and H. Gong, "Emergency logistics support capability evaluation model based on triangular fuzzy entropy and Choquet integral," Journal of Industrial and Production Engineering, pp. 1-8, 2016. [DOI:10.1080/21681015.2016.1155182]
6. [6] S. Zhang, C. K. Lee, H. Chan, K. L. Choy, and Z. Wu, "Swarm intelligence applied in green logistics: A literature review," Engineering Applications of Artificial Intelligence, vol. 37, pp. 154-169, 2015. [DOI:10.1016/j.engappai.2014.09.007]
7. [7] Y.-J. Zheng, S.-Y. Chen, and H.-F. Ling, "Evolutionary optimization for disaster relief operations: a survey," Applied Soft Computing, vol. 27, pp. 553-566, 2015. [DOI:10.1016/j.asoc.2014.09.041]
8. [8] Y.-J. Zheng and H.-F. Ling, "Emergency transportation planning in disaster relief supply chain management: a cooperative fuzzy optimization approach," Soft Computing, vol. 17, pp. 1301-1314, 2013. [DOI:10.1007/s00500-012-0968-4]
9. [9] K. Z. Zhou, C. Su, A. Yeung, and S. Viswanathan, "Supply chain management in emerging markets," Journal of Operations Management, 2016. [DOI:10.1016/j.jom.2016.07.007]
10. [10] G. C. Crişan, C.-M. Pintea, and V. Palade, "Emergency management using geographic information systems: application to the first romanian traveling salesman problem instance," Knowledge and Information Systems, vol. 50, pp. 265-285, 2017. [DOI:10.1007/s10115-016-0938-8]
11. [11] Z.-H. Hu, J.-B. Sheu, Y.-Q. Yin, and C. Wei, "Post-disaster relief operations considering psychological costs of waiting for evacuation and relief resources," Transportmetrica A: Transport Science, vol. 13, pp. 108-138, 2017. [DOI:10.1080/23249935.2016.1222559]
12. [12] C. Lin, K. L. Choy, G. T. Ho, S. Chung, and H. Lam, "Survey of green vehicle routing problem: past and future trends," Expert Systems with Applications, vol. 41, pp. 1118-1138, 2014. [DOI:10.1016/j.eswa.2013.07.107]
13. [13] R. F. Roldán, R. Basagoiti, and L. C. Coelho, "Robustness of inventory replenishment and customer selection policies for the dynamic and stochastic inventory-routing problem," Computers & Operations Research, vol. 74, pp. 14-20, 2016. [DOI:10.1016/j.cor.2016.04.004]
14. [14] Y.-B. Park, J.-S. Yoo, and H.-S. Park, "A genetic algorithm for the vendor-managed inventory routing problem with lost sales," Expert Systems With Applications, vol. 53, pp. 149-159, 2016. [DOI:10.1016/j.eswa.2016.01.041]
15. [15] H. Andersson, A. Hoff, M. Christiansen, G. Hasle, and A. Løkketangen, "Industrial aspects and literature survey: Combined inventory management and routing," Computers & Operations Research, vol. 37, pp. 1515-1536, 2010. [DOI:10.1016/j.cor.2009.11.009]
16. [16] M. Soysal, J. M. Bloemhof-Ruwaard, R. Haijema, and J. G. van der Vorst, "Modeling a green inventory routing problem for perishable products with horizontal collaboration," Computers & Operations Research, 2016.
17. [17] L. C. Coelho, J.-F. Cordeau, and G. Laporte, "Thirty years of inventory routing," Transportation Science, vol. 48, pp. 1-19, 2013. [DOI:10.1287/trsc.2013.0472]
18. [18] L. Bertazzi, M. Savelsbergh, and M. G. Speranza, "Inventory routing," in The vehicle routing problem: latest advances and new challenges, ed: Springer, 2008, pp. 49-72. [DOI:10.1007/978-0-387-77778-8_3]
19. [19] M. Chitsaz, A. Divsalar, and P. Vansteenwegen, "A two-phase algorithm for the cyclic inventory routing problem," European Journal of Operational Research, vol. 254, pp. 410-426, 2016. [DOI:10.1016/j.ejor.2016.03.056]
20. [20] E. E. Zachariadis, C. D. Tarantilis, and C. T. Kiranoudis, "An integrated local search method for inventory and routing decisions," Expert Systems with Applications, vol. 36, pp. 10239-10248, 2009. [DOI:10.1016/j.eswa.2009.01.069]
21. [21] Y. Yu, H. Chen, and F. Chu, "A new model and hybrid approach for large scale inventory routing problems," European Journal of Operational Research, vol. 189, pp. 1022-1040, 2008. [DOI:10.1016/j.ejor.2007.02.061]
22. [22] J. Li, H. Chen, and F. Chu, "Performance evaluation of distribution strategies for the inventory routing problem," European Journal of Operational Research, vol. 202, pp. 412-419, 2010. [DOI:10.1016/j.ejor.2009.05.018]
23. [23] A. M. Campbell and J. R. Hardin, "Vehicle minimization for periodic deliveries," European Journal of Operational Research, vol. 165, pp. 668-684, 2005. [DOI:10.1016/j.ejor.2003.09.036]
24. [24] Z. Pan, J. Tang, and R. Y. Fung, "Synchronization of inventory and transportation under flexible vehicle constraint: A heuristics approach using sliding windows and hierarchical tree structure," European Journal of Operational Research, vol. 192, pp. 824-836, 2009. [DOI:10.1016/j.ejor.2007.10.011]
25. [25] R. G. Van Anholt, L. C. Coelho, G. Laporte, and I. F. Vis, "An inventory-routing problem with pickups and deliveries arising in the replenishment of automated teller machines," Transportation Science, 2016. [DOI:10.1287/trsc.2015.0637]
26. [26] N. Aziz and N. Mom, "Genetic algorithm based approach for the mufti product multi period inventory routing problem," in 2007 IEEE International Conference on Industrial Engineering and Engineering Management, 2007, pp. 1619-1623. [DOI:10.1109/IEEM.2007.4419466]
27. [27] N. H. Moin, S. Salhi, and N. Aziz, "An efficient hybrid genetic algorithm for the multi-product multi-period inventory routing problem," International Journal of Production Economics, vol. 133, pp. 334-343, 2011. [DOI:10.1016/j.ijpe.2010.06.012]
28. [28] Q.-H. Zhao, S. Chen, and C.-X. Zang, "Model and algorithm for inventory/routing decision in a three-echelon logistics system," European Journal of Operational Research, vol. 191, pp. 623-635, 2008. [DOI:10.1016/j.ejor.2006.12.056]
29. [29] A. I. Esparcia-Alcazar, L. Lluch-Revert, M. Cardos, K. Sharman, and J. Merelo, "Configuring an evolutionary tool for the Inventory and Transportation Problem," in Proceedings of the 9th annual conference on Genetic and evolutionary computation, 2007, pp. 1975-1982. [DOI:10.1145/1276958.1277350]
30. [30] W. Zhu and H. Duan, "Chaotic predator-prey biogeography-based optimization approach for UCAV path planning," Aerospace science and technology, vol. 32, pp. 153-161, 2014. [DOI:10.1016/j.ast.2013.11.003]
31. [31] [31] R. Kumar, R. Gupta, and A. K. Bansal, "Economic analysis and power management of a stand-alone wind/photovoltaic hybrid energy system using biogeography based optimization algorithm," Swarm and Evolutionary Computation, vol. 8, pp. 33-43, 2013. [DOI:10.1016/j.swevo.2012.08.002]
32. [32] A. Hossein Mirzaei, I. Nakhai Kamalabadi, and S. H. Zegordi, "A new algorithm for solving the inventory routing problem with direct shipment," Journal of Production & Operations Management, vol. 2, pp. 1-28, 2012. [DOI:10.1109/IEEM.2011.6117911]
33. [33] M. Berghida and A. Boukra, "EBBO: an enhanced biogeography-based optimization algorithm for a vehicle routing problem with heterogeneous fleet, mixed backhauls, and time windows," The International Journal of Advanced Manufacturing Technology, vol. 77, pp. 1711-1725, 2015. [DOI:10.1007/s00170-014-6512-1]
34. [34] E. Emary and H. M. Zawbaa, "Impact of Chaos Functions on Modern Swarm Optimizers," PLOS ONE, vol. 11, p. e0158738, 2016. [DOI:10.1371/journal.pone.0158738]
35. [35] J. K. Lenstra and A. Kan, "Complexity of vehicle routing and scheduling problems," Networks, vol. 11, pp. 221-227, 1981. [DOI:10.1002/net.3230110211]
36. [36] S. Mirzapour Al-e-hashem and Y. Rekik, "Multi-product multi-period Inventory Routing Problem with a transshipment option: A green approach," International Journal of Production Economics, vol. 157, pp. 80-88, 2014. [DOI:10.1016/j.ijpe.2013.09.005]
37. [37] A. J. Kleywegt, V. S. Nori, and M. W. Savelsbergh, "The stochastic inventory routing problem with direct deliveries," Transportation Science, vol. 36, pp. 94-118, 2002. [DOI:10.1287/trsc.36.1.94.574]
38. [38] B. L. Golden, S. Raghavan, and E. A. Wasil, The vehicle routing problem: latest advances and new challenges vol. 43: Springer Science & Business Media, 2008. [DOI:10.1007/978-0-387-77778-8]
39. [39] M. Dror, M. Ball, and B. Golden, "A computational comparison of algorithms for the inventory routing problem," Annals of Operations Research, vol. 4, pp. 1-23, 1985. [DOI:10.1007/BF02022035]
40. [40] A. Federgruen and D. Simchi-Levi, "Analysis of vehicle routing and inventory-routing problems," Handbooks in operations research and management science, vol. 8, pp. 297-373, 1995. [DOI:10.1016/S0927-0507(05)80108-2]
41. [41] Q. Qin, S. Cheng, Q. Zhang, Y. Wei, and Y. Shi, "Multiple strategies based orthogonal design particle swarm optimizer for numerical optimization," Computers & Operations Research, vol. 60, pp. 91-110, 2015. [DOI:10.1016/j.cor.2015.02.008]
42. [42] S.-M. Guo and C.-C. Yang, "Enhancing differential evolution utilizing eigenvector-based crossover operator," IEEE Transactions on Evolutionary Computation, vol. 19, pp. 31-49, 2015. [DOI:10.1109/TEVC.2013.2297160]
43. [43] C. R. Reeves, "A genetic algorithm for flowshop sequencing," Computers & operations research, vol. 22, pp. 5-13, 1995. [DOI:10.1016/0305-0548(93)E0014-K]
44. [44] B. Liu and N. Jin, "An application of lingo software to solve dynamic programming problem in the field of environmental protection," in 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 2015, pp. 577-580. [DOI:10.1109/IAEAC.2015.7428619]
45. [45] K. Hammond and G. Michaelson, Research directions in parallel functional programming: Springer Science & Business Media, 2012.
46. [46] M. Boudia and C. Prins, "A memetic algorithm with dynamic population management for an integrated production-distribution problem," European Journal of Operational Research, vol. 195, pp. 703-715, 2009. [DOI:10.1016/j.ejor.2007.07.034]
47. [47] A. H. Gandomi and X.-S. Yang, "Chaotic bat algorithm," Journal of Computational Science, vol. 5, pp. 224-232, 2014. [DOI:10.1016/j.jocs.2013.10.002]
48. [48] V. Punnathanam and P. Kotecha, "Reduced Yin-Yang-Pair optimization and its performance on the CEC 2016 expensive case," in Evolutionary Computation (CEC), 2016 IEEE Congress on, 2016, pp. 2996-3002. [DOI:10.1109/CEC.2016.7744168]
49. [49] M. D. Li, H. Zhao, X. W. Weng, and T. Han, "A novel nature-inspired algorithm for optimization: Virus colony search," Advances in Engineering Software, vol. 92, pp. 65-88, 2016. [DOI:10.1016/j.advengsoft.2015.11.004]



XML   English Abstract   Print



بازنشر اطلاعات
Creative Commons License این مقاله تحت شرایط Creative Commons Attribution-NonCommercial 4.0 International License قابل بازنشر است.
دوره 7، شماره 1 - ( 3-1398 ) برگشت به فهرست نسخه ها