ORIGINAL PAPER
Optimization of transport processes using the evolutionary solver
,
 
,
 
 
 
More details
Hide details
 
Journal of Research and Applications in Agricultural Engineering 2018;63(4):169-175
 
KEYWORDS
ABSTRACT
The algorithm for calculating the shortest route for three vehicles with the limitation of their capacity and number of places per one vehicle using Microsoft Office Excel with the addition of OpenSolver 2.9.0 is presented. The algorithm was designed mainly for small transport companies whose number is several dozen times bigger than large companies. The evolutionary method was used, which belongs to the group of exact methods that guarantee calculation of the shortest possible route. Improving the work organization of transport means that can be achieved by using the presented computerized transport management system will result in reduction of carbon dioxide emissions and measurable savings as a result of reducing the distances necessary to overcome. Presented algorithm provides a step-by-step procedure with snapshots for improved performance. Visualization of the route allows for transparent display of the data developed.
REFERENCES (16)
1.
Afshar A.; Haghani A. (2012). Modeling integrated supply chain logistics in real-time large-scale disaster relief operations. Socio-Economic Planning Sciences, 46(4), 327-338.
 
2.
Baj-Rogowska A. (2013). Planowanie tras z wykorzystaniem narzędzia Solver jako zadanie logistyczne w małej firmie. In R. Miler & T. Nowosielski & B. Pac (Eds.) Optymalizacja systemów i procesów logistycznych. Warszawa: Wydawnictwo CeDeWu.
 
3.
Balakrishnana A.; Karstenb C.V. (2017). Container shipping service selection and cargo routing with transshipment limits. European Journal of Operational Research, 263(2), 652-663.
 
4.
Barbu A.D.; Fernandez R. (2008). Energy and environment report 2008. Office for Official Publications of the European Communities, DOI 10.2800/10548.
 
5.
Brouer B.D.; Karsten C.V.; Pisinger D. (2017). Optimization in liner shipping. A Quarterly Journal of Operations Research, 15(1), 1-35.
 
6.
Hanczar P. (2010). Wspomaganie decyzji w obszarze wyznaczania tras pojazdów. Decyzje, 13, 55-83.
 
7.
Marczuk A., Misztal W. (2011). Optymalizacja transportu produktów rolniczych w warunkach nierównowagi rynkowej. Inżynieria Rolnicza, 4(129), 221-226.
 
8.
Mula J.; Peidro D.; Díaz-Madroñero M.; Vicens E. (2010). Mathematical programming models for supply chain production and transport planning. European Journal of Operational Research, 204, 377-390.
 
9.
Nossack J.; Pesch E. (2013). A truck scheduling problem arising in intermodal container transportation. European Journal of Operational Research, 230(3), 666-680.
 
10.
Notteboom T.E. (2006). The Time Factor in Liner Shipping Services. Maritime Economics & Logistics, 8(1), 19-39.
 
11.
Pollaris H. (2018). Loading constraints in vehicle routing problems: a focus on axle weight limits. A Quarterly Journal of Operations Research, 16(1), 105-106.
 
12.
Redmer A., Kiciński M., Rybak R. (2014). Zarządzanie samochodowym taborem ciężarowym - metody. Gospodarka Materiałowa i Logistyka, 4, 11-18.
 
13.
Reinhardt L.B., Pisinger D., Spoorendonk S., Sigurd M.M. (2016). Optimization of the drayage problem using exact methods. Information Systems and Operational Research, 54(1), 33-51.
 
14.
Reinhardt L.B.; Spoorendonk S.; Pisinger D. (2012) Solving vehicle routing with full container load and time windows. In Computational Logistics, Springer Berlin Heidelberg, LNCS 7555, 120-128.
 
15.
Tundys B.; Matuszczak A. (2014). Analiza zależności pomiędzy poziomem PKB a transportem i jego kosztami zewnętrznymi w wybranych krajach Unii Europejskiej. Logistyka, 2, 361-372.
 
16.
Węgrzyn J. (2014). Rozwiązywanie problemu komiwojażera za pomocą LP/Quadratic Solver z Analytic Solver Platform v12.5. Gospodarka Materiałowa i Logistyka, 10, 11-19.
 
eISSN:2719-423X
ISSN:1642-686X
Journals System - logo
Scroll to top