A Novel Grouping Genetic Algorithm for the One-Dimensional Bin Packing Problem on GPU

Abstract

Abstract. One-dimensional Bin Packing Problem (1D-BPP) is a challenging NP-Hard combinatorial problem which is used to pack finite number of items into minimum number of bins. Large problem instances of the 1D-BPP cannot be solved exactly due to the intractable nature of the problem. In this study, we propose an efficient Grouping Genetic Algorithm (GGA) by harnessing the power of the Graphics Processing Unit (GPU) using CUDA. The time consuming crossover and mutation processes of the GGA are executed on the GPU by increasing the evaluation times significantly. The obtained experimental results on 1,238 benchmark 1D-BPP instances show that our proposed algorithm has a high performance and is a scalable algorithm with its high speed fitness evaluation ability. Our proposed algorithm can be considered as one of the best performing algorithms with its 66 times faster computation speed that enables to explore the search space more effectively than any of its counterparts.

Genetic algorithm for scheduling of parcel delivery by drones

“…Hazama, Iima, Karuno and Mishima, Journal of Advanced Mechanical Design, Systems, and Manufacturing, Vol.15, No.6 (2021) (Cuadra et al, 2016), and they are often used for not only the grouping problems but also many others. Mutations include Elimination (Ramos-Figueroa et al, 2020;Ozcan et al, 2016;Moghaddam et al, 2015), Merge and split (Tucker et al, 2005), Swap (Mutingi and Mbohwa, 2014;Mutingi and Onwubolu, 2012), Insertion (Mutingi and Mbohwa, 2014;Cuadra et al, 2016;Chen et al, 2019), and Item elimination (Rossi et al, 2010).…”

“…A GA based on the characteristics of this type of problem was first proposed in (Falkenauer, 1994). Since then, many studies have proposed GAs for various grouping problems (Ramos-Figueroa et al, 2020Mutingi and Mbohwa, 2014;Mutingi and Onwubolu, 2012;Jawahar and Subhaa, 2017;Cuadra et al, 2016;Ozcan et al, 2016;Moghaddam et al, In this paper, we propose a GA for scheduling of parcel delivery by drones. Our scheduling problem is the same as (Karuno and Mishima, 2019).…”

Incorporating capacitative constraint to the preference-based conference scheduling via domain transformation approach

“…There are three types of methods under Bin Packing approach, which is First Fit, Next Fit and Worst Fit. For our study, we are using First Fit method because according to [20], First Fit is fast in allocating the papers to slot and has less possibility in adding a new slot.…”

“…First Fit to the best of our knowledge is more suitable to be used in our case. Based on [20], definition of first fit method is it will put each item (paper) as you come to it into the available slot (slots created from the previous process) into which it fits (has no conflict).…”

“…According to [29,20], the definition in allocating the conference paper is next fit will start the allocation process on where the previous searching is stopped and never go back and if there is no slots suitable slots available, it will open a new slot. Comparing First Fit with other two methods, Next Fit for example, it will not only ignore the slots the visited slots from before to schedule the respective paper, it will also add more slots if the paper is not fitted anywhere in the available slots, this will result in more cost in conducting the conference scheduling as the time of the conference held will be longer due to the newly created slots.…”