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Solving decision problems in contemporary logistics : evaluation criteria and methods Part I. Decision programming
Języki publikacji
Abstrakty
W pracy dokonano przeglądu zagadnień programowania decyzji występujących we współczesnej logistyce, wykorzystującego najnowszą literaturę. Szczególną uwagę poświęcono metodom rozwiązywania zagadnień oraz kryteriom stosowanym do oceny ich rozwiązań. Przegląd ujawnił zróżnicowanie zagadnień i sposobów ich rozwiązywania. Pośród sposobów rozwiązywania dominują podejścia uniwersalne. Reprezentują one różne grupy: tradycyjne podejścia optymalizacyjne, heurystyki, metaheurystyki, metody sztucznej inteligencji i inne. Zróżnicowanie potrzeb związanych z oceną rozwiązań powoduje, że znajdują przy tym zastosowanie liczne i zróżnicowane kryteria. Obok tradycyjnych kryteriów o ilościowym charakterze np. kosztu i czasu są również stosowane kryteria jakościowe. Przy rozwiązywaniu standardowych zagadnień są stosowane typowe zestawy, złożone z nielicznych kryteriów. Wyznaczanie wiarygodnych rozwiązań zagadnień niestandardowych np. związanych z kontekstem zrównoważonego rozwoju wymaga jednak zastosowania bardziej licznych, a w tym również specyficznych kryteriów jakościowych.
A literature survey of decision programming problems in contemporary logistics is discussed in the paper. Special attention is drawn to methods applied while solving such problems and criteria utilized while evaluating possible problem solutions. It proves that there are diverse decision programming problems in logistics. Numerous approaches are applied to solve such problems. Universal approaches are utilized most often. They are applied in different ways. Such approaches represent diverse techniques: traditional optimization approaches, heuristics, metaheuristics, artificial intelligence and others. Diversity of actual needs results in application of different evaluation criteria while solving decision programming problems in logistics. Both traditional quantitative and less common qualitative criteria are considered. Solving typical decision programming problems usually relies on the utilization of typical sets consisting of a few evaluation criteria only. However, solving specific complex decision programming problems requires the application of numerous evaluation criteria including specific qualitative criteria.
Czasopismo
Rocznik
Tom
Strony
3076--3084, CD2
Opis fizyczny
Bibliogr. 33 poz.
Twórcy
autor
- AGH Akademia Górniczo-Hutnicza w Krakowie, Wydział Zarządzania, 30-067 Kraków, ul. Gramatyka 10
autor
- AGH Akademia Górniczo-Hutnicza w Krakowie, Wydział Zarządzania, 30-067 Kraków, ul. Gramatyka 10
Bibliografia
- 1. Aiello G., Enea M., Muriana C., The expected value of the traceability information. European Journal of Operational Research 2015 [http://dx.doi.org/10.1016/j.ejor.2015.01.028].
- 2.Akbari A.A., Karimi B., A new robust optimization approach for integrated multi-echelon, multiproduct, multi-period supply chain network design under process uncertainty. International Journal of Advanced Manufacturing Technology 2015 [doi: 10.1007/s00170-015-6796-9].
- 3. Avci M., Topaloglu S., An adaptive local search algorithm for vehicle routing problem with simultaneous and mixed pickups and deliveries. Computers & Industrial Engineering 2015, vol.83, pp.15-29.
- 4. Chern C.-C., Wang H.-M., Huang K.-L., A heuristic master planning algorithm for recycling supply chain management. Journal of Intelligent Manufacturing 2015 [doi: 10.1007/s10845-0151040-x].
- 5. De Armas J., Melián-Batista B., Constrained dynamic vehicle routing problems with time windows. Soft Computing 2015 [doi: 10.1007/s00500-014-1574-4].
- 6. Dechampai D., Tanwanichkul L., Sethanan K., Pitakaso R., A differential evolution algorithm for the capacitated VRP with flexibility of mixing pickup and delivery services and the maximum duration of a route in poultry industry. Journal of Intelligent Manufacturing 2015 [doi: 10.1007/s10845-015-1055-3].
- 7. Dumitrescu S., Steiner G., Zhang R., Optimal delivery time quotation in supply chains to minimize tardiness and delivery costs. Journal of Scheduling 2015, vol.18, pp.3-13.
- 8. Ekinci Y., Lu J.-C., Duman E., Optimization of ATM cash replenishment with group-demand forecasts. Expert Systems with Applications 2015, vol.42, pp.3480-3490.
- 9. Goeke D., Schneider M., Routing a Mixed Fleet of Electric and Conventional Vehicles. European Journal of Operational Research 2015 [doi: 10.1016/j.ejor.2015.01.049].
- 10. Guo J., Liu X., Jo J., Dynamic joint construction and optimal operation strategy of multi-period reverse logistics network: a case study of Shanghai apparel E-commerce enterprises. Journal of Intelligent Manufacturing 2015 [doi: 10.1007/s10845-015-1034-8].
- 11. Hu Z.-H., Zhao Y., Tao S., Sheng Z.-H., Finished-vehicle transporter routing problem solved by loading pattern discovery. Annals of Operational Research 2015 [10.1007/s10479-014-1777-1].
- 12. Jabali O., Leus R., Van Woensel T., de Kok T., Self-imposed time windows in vehicle routing problems. OR Spectrum 2015, vol.37, pp.331-352.
- 13. Jaehn F., Rieder J., Wiehl A., Minimizing delays in a shunting yard. OR Spectrum 2015, vol.37, pp.407-429.
- 14. Jin B., Zhu W., Lim A., Solving the container relocation problem by an improved greedy lookahead heuristic. European Journal of Operational Research 2015, vol.240, pp.837-847.
- 15. Karaoglan I., Altiparmak F., A memetic algorithm for the capacitated location-routing problem with mixed backhauls. Computers & Operations Research 2015, vol.55, pp.200-216.
- 16. Khalili-Damghani K., Abtahi A.-R., Ghasemi A., A New Bi-objective Location-routing Problem for Distribution of Perishable Products: Evolutionary Computation Approach. Journal of Mathematical Modelling and Algorithms 2015 [doi: 10.1007/s10852-015-9274-3].
- 17. Kostin A., Guillén-Gosálbez G., Jiménez L., Dimensionality reduction applied to the simultaneous optimization of the economic and life cycle environmental performance of supply chains. International Journal of Production Economics 2015, vol.159, pp.223-232.
- 18. Kumari S., Singh A., Mishra N., Garza-Reyes J.A., A multi-agent architecture for outsourcing SMEs manufacturing supply chain. Robotics and Computer-Integrated Manufacturing 2015 [http://dx.doi.org/10.1016/j.rcim.2014.12.009].
- 19. Küçükoğlu İ., Ene S., Aksoy A., Öztürk N., A memory structure adapted simulated annealing algorithm for a green vehicle routing problem. Environmental Science and Pollution Research 2015, vol.22, pp.3279-3297.
- 20. Liu W. Yang Y., Wang S., Bai E., A scheduling model of logistics service supply chain based on the time windows of the FLSP’s operation and customer requirement. Annals of Operational Research 2015 [doi: DOI 10.1007/s10479-015-1794-8].
- 21. Lloyd J., McCarney S., Ouhichi R., Lydon P., Zaffran M., Optimizing energy for a ‘green’ vaccine supply chain. Vaccine 2015, vol.33, pp.908-913.
- 22. Mandal S.K., Pacciarelli D., Løkketangen A., Hasle G., A memetic NSGA-II for the bi-objective mixed capacitated general routing problem. Journal of Heuristics 2015 [doi: 10.1007/s10732-0159280-7].
- 23. Momeni M., Sarmadi M., A Genetic Algorithm Based on Relaxation Induced Neighborhood Search in a Local Branching Framework for Capacitated Multicommodity Network Design. Networks & Spatial Economics 2015 [doi: 10.1007/s11067-015-9284-8].
- 24. Niknamfar A.H., Niaki S.T.A., Pasandideh S.H.R., Robust optimization approach for an aggregate production-distribution planning in a three-level supply chain. International Journal of Advanced Manufacturing Technology 2015, vol.76, pp.623-634.
- 25. Niknejad A., Petrovic D., Optimisation of integrated reverse logistics networks with different product recovery routes. European Journal of Operational Research, vol.238, pp.143-154.
- 26. Niu Y., Wang S., He J., Xiao J., A novel membrane algorithm for capacitated vehicle routing problem. Soft Computing 2015, vol.19, pp.471-482.
- 27. Rashid R., Bozorgi-Amiri A., Seyedhoseini S.M., Developing a new stochastic competitive model regarding inventory and price. Journal of Industrial Engineering International 2015 [doi: 10.1007/s40092-014-0097-z].
- 28. Schneider M., Stenger A., Hof J., An adaptive VNS algorithm for vehicle routing problems with intermediate stops. OR Spectrum 2015, vol.37, pp.353-387.
- 29. Veluscek M., Kalganova T., Broomhead P., Grichnik A., Composite goal methods for transportation network optimization. Expert Systems with Applications 2015, vol.42, pp.38523867.
- 30. Wang L., Song J., Shi L., Dynamic emergency logistics planning: models and heuristic algorithm. Optimization Letters 2015 [DOI 10.1007/s11590-015-0853-z].
- 31. Wang X., Kopfer H., Rolling horizon planning for a dynamic collaborative routing problem with full-truckload pickup and delivery requests. Flexible Services and Manufacturing Journal 2015 [doi: 10.1007/s10696-015-9212-8].
- 32. Yang S.S., Ong S.K., Nee A.Y.C., EOL strategy planning for components of returned products. International Journal of Advanced Manufacturing Technology 2015, vol.77, pp.991-1003.
- 33. Yao B., Yu B., Hu P., Gao J., Zhang M., An improved particle swarm optimization for carton heterogeneous vehicle routing problem with a collection depot. Annals of Operational Research 2015 [doi: 10.1007/s10479-015-1792-x].
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-519738b1-9adf-49a5-bb91-1d5af088c683