Multi-Criteria 3-Dimension Bin Packing Problem

• Autorzy: Kacprzak Ł. , Rudy J. , Żelazny D.
• Języki publikacji: EN

Abstrakty

EN

In this paper a multi-criteria approach to the 3-dimensions bin packing problem is considered. The chosen maximization criteria are the number and the total volume of the boxes loaded into the container. Existing solution representation and decoding method are applied to the problem. Next, two metaheuristic algorithms, namely simulated annealing and genetic algorithm are developed using the TOPSIS method for solution evaluation. Both algorithms are then used to obtain approximations of the Pareto front for a set of benchmarks from the literature. Despite the fact that both criteria work in favor of each other, we managed to obtain multiple solutions in many cases, proving that lesser number of boxes can lead to better utilization of the container volume and vice versa. We also observed, that the genetic algorithms performs slightly better in our test both in the terms of hyper-volume indicator and number of non-dominated solutions.

Słowa kluczowe

EN

bin packing problem; multi-criteria; genetic algorithm; simulated annealing; discrete optimization; Pareto efficiency

• Wydawca: Publishing House of Poznan University of Technology
• Czasopismo: Research in Logistics & Production
• Rocznik: 2015
• Tom: Vol. 5, No. 1
• Strony: 85--94
• Opis fizyczny: Bibliogr. 9 poz., rys., tab.

Twórcy

autor

Kacprzak Ł.

• Institute of Computer Engineering, Control and Robotic, Wrocław University of Technology, Wrocława, dolnyśląsk, 50-370, Poland

autor

Rudy J.

• Institute of Computer Engineering, Control and Robotic, Wrocław University of Technology, Wrocława, dolnyśląsk, 50-370, Poland

autor

Żelazny D.

• Institute of Computer Engineering, Control and Robotic, Wrocław University of Technology, Wrocława, dolnyśląsk, 50-370, Poland

Bibliografia

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• 2. Dahmania N., Clautiauxb F., Krichena S., Talbib E.-G., (2014), Self-adaptive metaheuristics for solving a multi-objective 2-dimensional vector packing problem, Applied Soft Computing, Volume 16, pp. 124-136.
• 3. Fernándeza A., Gila C., Bañosb R., Montoyaa M.G., (2013), A parallel multi-objective algorithm for two-dimensional bin packing with rotations and load balancing, Expert Systems with Applications, Volume 40, Issue 13, pp. 5169-5180.
• 4. Golbabaie F., Seyedalizadeh Ganji S.R., Arabshahi N., (2012), Multi-criteria evaluation of stacking yard configuration, Journal of King Saud University - Science, Volume 24, Issue 1, pp. 39-46.
• 5. Gonçalvesa J.F., Resendeb M.G.C., (2012), A parallel multi-population biased random-key genetic algorithm for a container loading problem, Computers & Operations Research, Volume 39, Issue 2, pp. 179-190.
• 6. Hwang C.L., Yoon K., (1981), Multiple Attribute Decision Making: Methods and Applications, Springer-Verlag, New York.
• 7. Leung S.Y.S., Wong W.K., Mok P.Y., (2008), Multiple-objective genetic optimization of the spatial design for packing and distribution carton boxes, Computers & Industrial Engineering, Volume 54, Issue 4, pp. 889-902.
• 8. Liu D.S., Tan K.C., Huang S.Y., Goh C.K., Ho W.K., (2008), On solving multi-objective bin packing problems using evolutionary particle swarm optimization, European Journal of Operational Research, Volume 190, Issue 2, pp. 357-382.
• 9. Zitzler E., Brockhoff D., Thiele L., (2006), The Hypervolume Indicator Revisited: On the Design of Pareto-compliant Indicators Via Weighted Integration, Proceedings of EMO 2006, pp. 862-876.