SciELO - Scientific Electronic Library Online

vol.22 número1Interactive System for the Analysis of Academic Achievement at the Upper-Middle Education in MexicoExperimental Platform for Intelligent Computing (EPIC) índice de autoresíndice de materiabúsqueda de artículos
Home Pagelista alfabética de revistas  

Servicios Personalizados




Links relacionados

  • No hay artículos similaresSimilares en SciELO


Computación y Sistemas

versión impresa ISSN 1405-5546

Comp. y Sist. vol.22 no.1 México ene./mar. 2018 

Articles of the Thematic Issue

A Storage Pattern-based Heuristic Algorithm for Solving Instances of Hard28 Datasets for the Bin Packing Problem

Joaquín Pérez1  * 

Rafael de la Rosa1 

Hilda Castillo2 

Darnes Vilariño2 

1 Centro Nacional de Investigación y Desarrollo Tecnológico, Cuernavaca, Morelos, Mexico.

2 Benemérita Universidad Autónoma de Puebla, Puebla, Mexico.,


In this paper, we propose a heuristic algorithm that obtains the optimal solution for 5 instances of the set of instances Hard28, for the problem of packing objects in containers of a dimension (1DBPP). This algorithm is based on storage patterns of objects in containers. To detect how objects are stored in containers, the HGGA-BP algorithm [8] was used. A tool for monitoring and analyzing the HGGA-BP algorithm was also developed. With the help of the user, this tool performs the monitoring and analysis of the intermediate solutions that are generated with the algorithm HGGA-BP [8]. The proposed algorithm uses the inherent characteristics of the objects, that is, the weight value of the objects of the set of instances Hard28 can be: a prime number, an even number or an odd number. As well as, the weights of some of the objects are bigger than half of the capacity of the containers. The set Hard28 consists of 28 instances and the optimal value was found in 5 of them. For 19 instances, a container is missing to reach the optimum solution. For 3 instances, two containers were missing to reach the optimal solution and in one of the obtained solutions, 3 containers were missing to reach the optimal solution. For each of the optimal solutions found, the calculated time is less or equal than one millisecond.

Keywords: 1DBPP; patterns; tool; heuristic; metaheuristic; container; instance; solution optimal

1 Introduction

In real situations, it is necessary to place elements, always looking for an optimal way to do it. For this, some storage medium is used, such as bags, boxes, containers, among others. These elements, commonly called objects, are of different size or type. This feature allows storage to be a complex problem, since it is always necessary to place them in the best way to save space. For example: storage problems, loading problems and containers, the problem of change, assembly lines, business problems, cutting patterns, among others. In general, these types of problems are known as cutting and packing problems. These problems, are included in a family of combinatorial problems, which can be applied to different areas such as: Health, Financial, Technological Development, Textile, Metal, among others, and this has given rise to disciplines such as: Computer science, Engineering, Operations Research, among others, propose or develop new algorithms that allow to obtain good solutions.

1.1 The One-Dimensional Bin Packing Problem (1DBPP)

The pioneers in working on this cutting problem were Gilmore and Gomory, in 1961 formulated this family of problems, known as cutting stock or trim loss problems. Subsequently, Garey and Johnson in [1], defined the one-dimensional Bin Packing problem (1DBPP).

The Bin Packing problem can be conceptually defined as follows:

Given a set of objects O (of different sizes), two constants B (container size) and N (number of containers), it is possible to place all the objects inside the N containers, such that the sum of the objects does not exceed the Capacity of each of the containers.

The problem of 1DBPP is classified as NP-hard, see [1]. Given this complexity, it is difficult to develop algorithms that obtain optimal results, such as those reported by the specialized literature [2, 3, 4, 5, 6], or close to the optimum, and even better, in a polynomial time. Proof of this are the different approaches that have been used to solve the 1DBPP, some of them are: Heuristic algorithms, Metaheuristics and Exact methods. Next, the works that have reported the best results to solve the 1DBPP are presented.

The section 2, describes the relevant works that solve some instances of the Hard28 set of 1DBPP and test sets that are difficult to solve. For its part, section 3, explains the characteristics of the analysis tool, through which the storage patterns of the objects are discovered. In section 4, the proposed algorithm that solves some instances of Hard28 of 1DBPP is detailed. The section 5, on the other hand, shows the experimental results and finally the conclusions and the work to be done are discussed in section 6.

2 State of the Art

To date, different algorithms have been proposed that solve the problem of 1DBPP, in this section, it evaluates those that have solved the largest number of instances of the Hard28 dataset, comparing the behavior of the algorithms and the quality of the solution.

2.1 Algorithms that Solve the Problem of 1DBPP

According to the specialized literature, the work developed in [12, 13], has reported a set of algorithms that have proven to be the best to obtain optimal solutions from some of the most referenced instances in the state of the art. Particularly, the instances of the Hard28 set have been reported as those that have been the most difficult to solve by metaheuristic algorithms.

The algorithm Hybrid Grouping Genetic Algorithm Bin Packing (HGGA-BP), discussed in [8], is a hybrid-genetic clustering algorithm. Which is inspired by the algorithm of Falkenauer, reported in [14]. The HGGA-BP uses heuristics (FFD, WFD, BFD, among others), to generate the initial population. Subsequently, it uses intensification and diversification to find the best individuals in the population, applying crosses and mutations to improve the quality of the solution. The HI-BP algorithm, reported in [7], is a hybrid metaheuristic algorithm, which has two stages: construction and improvement. In the first stage, it uses a hybrid heuristic and in the stage of improvement uses the tabu search algorithm reported in [11].

Weigth Anneling [9], is a metaheuristic algorithm that uses five steps to solve the 1DBPP problem. The first step generates an initial solution using the FFD heuristic. The second step distorts the sizes of objects; the third step performs local searches and exchanging objects between pairs of containers, in the fourth step reassigns weights and finally checks whether the stoppage criterion has been reached.

On the other hand, Pert-SAWMBS developed in [10], proposes a method of solution oriented to the container, this is done by controlling the average weight of the objects that are inside each container. As well as using reduction methods for each of the solutions generated.

The research work developed in [12], proposes a method based on patterns, data mining and iterative, to find the optimal values of the set of instances Hard28.

It is important to highlight the method of solution that is posed by in [13], in it a new strategy is proposed that allows finding optimal values reported in the literature [12]. This work proposes an exploration strategy that uses different approaches to solve the instances of the Bin Packing problem. To do this, it uses a hybrid algorithm that takes as its main elements: a mathematical model, a heuristic, reduction criteria and finally a metaheuristic algorithm, the latter calculates the lower limit of an instance. One of the limitations of this work is the computation time that it uses to obtain the optimal values.

2.2 1DBPP Instances

To date, there is no consensus to determine which sets of instances are difficult to resolve. In the work developed in [12], several investigations have been reported in which sets of instances considered by the authors as difficult to solve are proposed.

For example, the datasets proposed in [14]: Uniform [OR Library] and Triplets [OR Library]. In the first, the files binpack1 to binpack4 consist of objects whose size are uniformly distributed in (20,100), to be packed in containers of Size 150. And in the second, the files binpack5 to binpack8 consists of 'triplets' of objects distributed in (25, 50), to be packaged in 100 size containers.

The work reported in [15], proposes three sets of instances Data_Set_1, Data_Set_2 and Data_Set_3, each of these sets presents characteristics that make them difficult to solve.

The first set consists of 720 instances, each one of which is constructed similarly to those used in [19]. The objective of set number two is to obtain an average of 3, 5, 7 and 9 objects per container and the last set has instances considered as hard, since the weights of the objects are widely dispersed and the values are obtained in the interval from 20,000 to 35,000.

It is important to note that in [17], the data set Wae_Gau1 is proposed for the first time, which consists of 17, instances with different characteristics in each of them and which are considered as difficult to resolve by the authors.

In the work developed in [16], the data sets Was_1 and Was_2 are proposed, which are considered difficult to solve.

Finally, the research discussed in [18], proposes to the data set 53NIRUP and the data set Hard28. The set Hard28 consists of 28 instances, which have been considered as the most difficult to solve by the current metaheuristic algorithms.

The following explains the elements that are used to detect the different storage patterns of objects in containers.

3 Storage Patterns

The next section presents the visualization tool that was used to discover the storage patterns developed.

3.1 Visualization Tool

Given the large amount of information generated by the metaheuristic algorithms applied to the 1DBPP problem, the tool called MEVIZ was developed, which performs analysis and monitoring of the results generated by an algorithm. MEVIZ receives as input a set of instances and a metaheuristic algorithm.

The metaheuristic algorithm that to date has solved more instances of the Hard28 dataset is the HGGA-BP, proposed in [8]. These two elements are the input data to the visualization tool. To perform the monitoring, a listening agent is embedded in the metaheuristic algorithm.

Figure 1, shows an example of how objects are distributed within the containers by the HGGA-BP algorithm. And in Table 1, the total number of solutions generated by the same algorithm is shown.

Fig. 1 How objects are distributed in containers with the HGGA-BP metaheuristic algorithm [8

Table 1 Number of solutions generated by the HGGA-BP algorithm 

Instance name Number of solutions
hBPP14 537455
hBPP832 533409
hBPP40 842109
hBPP360 545123
hBPP645 831429
hBPP742 548413
hBPP766 538262
hBPP60 544368
hBPP13 536140
hBPP195 531753
hBPP709 534363
hBPP785 541316
hBPP47 539677
hBPP181 546009
hBPP359 546560
hBPP485 541622
hBPP640 543313
hBPP716 551829
hBPP119 532743
hBPP144 528376
hBPP561 528306
hBPP781 530417
hBPP900 531077
hBPP175 544497
hBPP178 539753
hBPP419 567358
hBPP531 553283
hBPP814 541937

As this algorithm is genetic, it considers many solutions, since it depends on the size of the initial population. With the tool developed in the present investigation, it is possible to visualize the whole set of solutions generated by the metaheuristic algorithm.

In Table 2, the segment of a solution generated by the HGGA-BP algorithm of the hBPP13 instance of the Hard28 set is shown and based on these results storage patterns can be obtained.

Table 2 Extract of a solution generated by the HGGA-BP algorithm applied to the HBPP13 instance 

Container Objects Total
15513 0 -596-332-72 1000
1 -85-444-471 1000
2 -194-555-251 1000
3 -649-36-315 1000
4 -652-51-297 1000
5 -385-426-189 1000
6 -128-572-300 1000
7 -499-341-160 1000
8 -662-122-216 1000
9 -674-28-298 1000
10 -641-169-190 1000
11 -690-281-29 1000
12 -378-126-496 1000
13 -635-62-303 1000
14 -660-167-173 1000
15 -316-628-56 1000
16 -651-181-168 1000
17 -691-95-214 1000
18 -458-541-1 1000
19 -626-144-230 1000
20 -634-84-282 1000

3.2 Pattern Discovery

Next, the results generated by the metaheuristic algorithm through the MEVIZ tool are analyzed and it is possible to discover storage patterns of objects in containers and that are the basis of the algorithm proposed in this research work.

Analyzing the solutions generated and taking as reference Table 2, container number

11 is filled with 3 objects, two of which have a weight whose value are prime numbers (281 and 29) and the other object has a weight whose value is an even number (690).

This is also true for container number 14, at its maximum capacity. In container number zero, the weight of the three objects are even numbers (596, 332 and 72), as well as container number six.

Another common pattern that has been discovered is that of 2 objects with weight whose value is an odd number and an object with a weight whose value is an even number.

It is important to mention that these patterns are meet whenever a container is full to its maximum capacity.

From the results obtained by the tool, it has been found that these patterns are presented in the different solutions that are obtained for an instance of Hard28, as well as for all instances of the same dataset. All this allows to generalize the storage patterns of the objects in the containers for the data set Hard28.

In the next section, we propose an algorithm that uses the properties of the natural numbers, this proposal reduces the dimensionality of the instances of the data set Hard28. Subsequently the FFD heuristic algorithm is applied and in this way the optimal is obtained for some of the instances of the Hard28, in practically negligible times.

4 Proposed Algorithm

After analyzing the results generated by the HGGA-BP algorithm and visualized with the MEVIZ tool, an algorithm is developed that reduces the dimensionality of the instances and, together with the FFD heuristic, allows to obtain the optimal value of some of the data set instances Hard28.

A general scheme of the proposed algorithm is shown in Figure 2, it uses the storage pattern (2 objects whose weight is a prime number and an object whose weight is an even number), to fill containers at their maximum capacity using only 3 objects.

Fig. 2 General scheme of the proposed algorithm 

One of the basic elements of the proposed algorithm is to divide the list of objects into sublists, this allows grouping objects that have common characteristics, without the need to perform excess combinations, as in the case of using a single list of objects. In addition, this reduces the dimensionality of instances. The general elements of the proposed algorithm are described below.

Phase 1. Fill a container with 2 objects. One of the characteristics of the set of instances of the Hard28 is that the values of some objects are greater than half the capacity of the container, this allows to traverse the list of objects and fix them in containers.

Subsequently, to be able to fill them to their maximum capacity the problem is reduced to using a search algorithm, i.e., takes the residual capacity of the container and searches that value in the list of free objects. This phase reduces the dimensionality of the instance.

Phase 2. Fill a container with 3 objects. Based on the detected pattern, containers are filled with 3 objects, of which the weight of 2 objects are prime numbers and the other object is an even number; this is valid because the capacity of the container, for the case of the set of instances Hard28, is an even number. Subsequently, the strategy of divide and conquer is used, this strategy is applied to the list of free objects of Phase 1.

For this, the reduced list is divided into 3 sublists; objects whose weight value is an even number and objects whose weight value are prime numbers. From this a combinatorial algorithm is developed, using the list of prime objects with the list of even objects. This fills some other containers and further reduces the dimensionality of the instance.

Phase 3. Heuristic algorithm. The three lists of objects left over from phase 2: primes, pairs and odd are concatenated, and the FFD heuristic algorithm is applied.

Next, the proposed algorithm is shown, which allows to obtain the optimum value of some instances of the dataset HARD28.

Step 1. The instance is reduced by filling the containers with 2 objects

For all objects in the instance:

Let O i and O j be the weights associated with the i-th and the j-th object of the instance and let C be the capacity of the container

If O i + O j = C then place them in a container and remove the O i and O j objects from the instance.

Step 2. The instance is reduced by filling the containers with 3 objects

Split the list into 3 sublists:

List of even objects,

List of prime objects,

List of odd objects.

For all objects in the list of primes and list of pairs.

Let O k , be an object that belongs to the list of even objects.

Let O i and O j , be two objects belonging to the list of prime objects and C the capacity of the containe.

If O i + O j + O k = C then place them in a container and delete the objects O i , O j and O k , from the instance.

Step 3. Join the lists of even, odd and prime objects.

Step 4. Apply to the First Fit Decreasing (FFD) algorithm.

In the next section, the experimental results are discussed.

5 Experimental Results

The tests were performed using the C# programming language and the VS.NET 2010 IDE on an Intel Core i7 2.80 GHz computer with 4GB of RAM, using the Windows 7 operating system.

Each of the instances of the Hard28 set contains different numbers of objects, 160, 180 or 200. The range of weights is from 1 to 800, with a container capacity equal to 1000.

Table 3 shows the percentage of objects that have a prime, even or odd value, as well as the percentage of objects that are above half the container capacity of the instances of the Hard28 dataset. This percentage corresponds to the objects that were left, after filling the containers with 2 objects.

Table 3 Percentage of objects with even values, odd, primes and total number of objects 

Name of
Percentage of
prime objects
Percentage of
odd objects
Percentage of
even objects
Percentage of
objects greater
than half of the
hbpp13 0.17 0.53 0.46 0.33 180
hbpp14 0.20 0.48 0.51 0.31 160
hbpp40 0.22 0.48 0.51 0.28 160
hbpp47 0.22 0.51 0.48 0.35 180
hbpp60 0.12 0.51 0.48 0.32 160
hbpp119 0.19 0.44 0.54 0.30 200
hbpp144 0.17 0.48 0.51 0.27 200
hbpp175 0.15 0.43 0.56 0.36 200
hbpp178 0.19 0.52 0.47 0.35 200
hbpp181 0.17 0.47 0.52 0.34 180
hbpp195 0.18 0.51 0.48 0.26 180
hbpp359 0.17 0.50 0.49 0.34 180
hbpp360 0.13 0.54 0.45 0.36 160
hbpp419 0.14 0.54 0.45 0.34 200
hbpp485 0.16 0.51 0.48 0.34 180
hbpp531 0.16 0.48 0.51 0.38 200
hbpp561 0.20 0.53 0.46 0.26 200
hbpp640 0.14 0.60 0.39 0.37 180
hbpp645 0.14 0.50 0.49 0.29 160
hbpp709 0.09 0.61 0.38 0.28 180
hbpp716 0.18 0.48 0.51 0.39 180
hbpp742 0.17 0.5 0.5 0.35 160
hbpp766 0.12 0.54 0.45 0.28 160
hbpp781 0.18 0.47 0.52 0.31 200
hbpp785 0.19 0.43 0.56 0.33 180
hbpp814 0.17 0.52 0.47 0.38 200
hbpp832 0.11 0.5 0.5 0.27 160
hbpp900 0.19 0.47 0.52 0.32 200

Of the 28 instances of the Hard28 data set the optimum was found in 5 of them: hBPP14, hBPP359, hBPP716, hBPP119, hBPP175, as shown in Table 4. These same instances were solved with the algorithms: HI-BP [7], WA [9] and Pert-SAWMBS [10], but the time it took the algorithm proposed in this paper is less than the algorithms mentioned above. In the case of the HGGA-BP algorithm [8], it solves 6 more, but in a much longer time, as shown in Table 5, it shows the total time consumed by the HGGA-BP algorithms [8], HI- BP [7], WA [9] and Pert-SAWMBS [10] in solving all set of instances Hard28.

Table 4 Results generated by the proposed strategy, compared with other algorithms 

Instance Optimal HI_BP WABP Pert-SAWMBS HGGA-BP Proposed Algorithm
hBPP14.txt 62 62 62 62 62 62
hBPP832.txt 60 61 61 61 61 61
hBPP40.txt 59 60 60 60 60 60
hBPP360.txt 62 63 63 63 62 63
hBPP645.txt 58 59 59 59 59 59
hBPP742.txt 64 65 65 65 64 65
hBPP766.txt 62 63 63 63 63 63
hBPP60.txt 63 64 64 64 64 64
hBPP13.txt 67 68 68 68 68 68
hBPP195.txt 64 65 65 65 65 65
hBPP709.txt 67 68 68 68 68 68
hBPP785.txt 68 69 69 69 69 69
hBPP47.txt 71 72 72 72 71 72
hBPP181.txt 72 73 73 73 73 73
hBPP359.txt 76 76 76 76 76 76
hBPP485.txt 71 72 72 72 72 72
hBPP640.txt 74 75 75 75 74 77
hBPP716.txt 76 76 76 76 76 76
hBPP119.txt 77 77 77 77 77 77
hBPP144.txt 73 74 74 74 74 74
hBPP561.txt 72 73 73 73 73 73
hBPP781.txt 71 72 72 72 72 73
hBPP900.txt 75 76 76 76 76 76
hBPP175.txt 84 84 84 84 84 84
hBPP178.txt 80 81 81 81 81 81
hBPP419.txt 80 81 81 81 81 81
hBPP531.txt 83 84 84 84 83 85
hBPP814.txt 81 82 82 82 81 83
Total 5 5 5 5 11 5

Table 5 Comparative table of the solution times 

Proposed Algorithm HGGA-BP HI_BP WA Pert-Saw MBS
10 ms. 4.31 seg 0.48 seg. 0.59 seg. 0.24 seg.

Although the optimal was not obtained for the remaining instances, the solution is close to the optimum (a container), except for instances hBPP814, hBPP531, hBPP640, hBPP781 whose difference is larger than a container. Particularly the instance hBPP640 is to 3 containers of the optimal value.

6 Conclusions

Many of the works that try to solve the problem 1DBPP have focused on giving solution to the set of instances Hard28, which have been reported in the specialized literature as the most difficult.

The algorithm proposed in this research is novel and promising. Unlike the ones reported in the literature, the algorithm uses the characteristics of each of the weights of the objects to be located, that is, the weight of the objects can be: a prime number, an even number or an odd number. Also used is the fact that the weights of some of the objects have a value higher than half the capacity of the container. This type of patterns allows to reduce the dimensionality of instances.

The first reduction of dimensionality is achieved by filling a container with two objects, depending on the size of the instance, but in general this allows to reduce the size of instances between 5% and 10%.

One of the results that is important to highlight is the discovery of storage patterns of objects in containers. A container is filled with 3 objects: 2 objects whose weight are prime numbers and an object whose weight is an even number, this is due to the implemented tool, MEVIZ, and that allowed to analyze the whole set of solutions generated by the algorithm HGGA-BP.

The algorithm proposed in this paper finds the optimal value for 5 instances, reported in the specialized literature [6, 7, 8, 9], and the computation time was in milliseconds, which is practically negligible, see Table 5.

The measured times for each of the instances are less than one millisecond, except for the instance hBPP14. But in instances hBPP716, hBPP119, hBPP175 and hBPP359 the computation time is one millisecond. For other instances, it is almost insignificant. In other related works, the reported times are in seconds and greater.

The results generated, see Table 4, allow to continue in this line, that is, to work with the characteristics of the objects. Find patterns of combinations for three objects and make it unique; two odd and one pair, or three objects whose weight is an even value.

As well as working with number theory and characterizing objects to determine three objects that must necessarily go together inside a container; two objects whose weight is a prime number and an object whose weight is an even number or three objects whose weight is an even number.


1. Garey, M.R., & Johnson, D.S. (1979). Computers and Intractability: A guide to the Theory of NP-Completeness. W.H. Freeman and Company, John Wiley & Sons. [ Links ]

2. Coffman, E.G., Garey, Jr. M.R., & Johnson, D.S. (1996). Approximation algorithms for bin packing: A survey. Approximation algorithms for NP-Hard problems, edited by D. Hochbaum, pp. 46-93. [ Links ]

3. Coffman, E.G., Galambos, G., Martello, S., & Vigo, D. (1999). Bin packing approximation algorithms: combinatorial analysis. Handbook of combinatorial optimization. Springer, DOI: 10.1007/ 978-1-4757-3023-4_3. [ Links ]

4. Csirik, J., Johnson, D.S., Kenyon, C., Shor, P.W., & Kenyon, R.R. (1974). Fast Algorithms for Bin Packing. Journal of computer and system sciences, Vol. 8, No. 3, pp. 272-314. DOI: 10.1016/S0022-0000(74)80026-7. [ Links ]

5. Reeves, C.R. (1996). Hybrid genetic algorithms for bin-packing and related problems. Annals of Operation Research, Vol. 63, No. 1, pp. 371-396. DOI: 10.1007/BF02125404. [ Links ]

6. Frederick, D., & John, L. (2004). Ant colony optimization and local search for bin packing and cutting stock problems. Journal of the Operational Research society, Vol. 55, No. 7, pp. 705-716. DOI: 10.1057/palgrave.jors.2601771. [ Links ]

7. Alvim, A.C.F., Ribeiro, C.C., Glover, F., & Aloise, D.J. (2004). A hybrid improvement heuristic for the one-dimensional bin packing problem. Journal of Heuristics, Vol. 10, No. 2, pp. 205-229. DOI: 10.1023/B:HEUR.0000026267.44673.ed. [ Links ]

8. Reyes, L.C., Quiroz, M., Alvim, A.C.F., Fraire, H.J., Gómez, C., & Jiménez, J.T. (2012). Efficient Hybrid Grouping Heuristics for the Bin Packing Problem. Computación y Sistemas, Vol. 16, No. 3, pp. 349-360. [ Links ]

9. Loh, K.H., Golden, B., & Wasil, E. (2008). Solving the one-dimensional bin packing problem with a weight annealing heuristic. Computers & Operations Research, Vol. 35, No. 7, pp. 2283- 2291. DOI: 10.1016/j.cor.2006.10.021. [ Links ]

10. Fleszar, K., & Charalambous, C. (2011). Average-weight-controlled bin-oriented heuristics for the one-dimensional bin-packing problem. European Journal of Operational Research, Vol. 210, No. 2, pp. 176-184. DOI: 10.1016/j.ejor.2010.11.004. [ Links ]

11. Glover, F., & Laguna, M. (1997). Tabu Search. Kluwer Academic Publishers. [ Links ]

12. Mexicano, S.A. (2012). Caracterización de conjuntos de instancias difíciles del problema de bin packing orientada a la mejora de algoritmos metaheurísticos mediante el uso de técnicas de minería de datos. [ Links ]

13. Pérez, O.J., Castillo, Z.H., Villarino, A.D., Mexicano, S.A., Zabala, D.J.C., Martínez, R.A., & Estrada, E.H. (2016). Una nueva estrategia heurística para el problema de bin packing. Revista Ingeniería Investigación y Tecnología, Vol. 17, No. 2, pp. 155-168. DOI: 10.1016/j.riit.2016.06.001. [ Links ]

14. Falkenauer, E. (1996). A hybrid grouping genetic algorithm for bin packing. Journal of Heuristics, Vol. 2, No. 1, pp. 5-30. DOI: 10.1007/BF00226291. [ Links ]

15. Scholl, A., Klein, R., & Jurgens, C. (1997). Bison: A fast hybrid procedure for exactly solving the one-dimensional bin packing problem. Computers Operations. Research, Vol. 24, No. 7, pp. 627-645. DOI: 10.1016/S0305-0548(96)00082-2. [ Links ]

16. Wascher, G., & Gau, T. (1996). Heuristic for the integer one dimensional cutting stock problem: A computational study. Operations Research-Spektrum, Springer-Verlag, Vol. 18, No. 3, pp. 131-144. DOI: 10.1007/BF01539705. [ Links ]

17. Schwerin, P., & Wascher, G. (1998). A new lower bound for the bin packing problem and its integration into MTP. Springer-Verlag. [ Links ]

18. Schwerin, P., & Wäscher, G. (1997). The bin-packing problem: A problem generator and some numerical experiments with FFD packing and MTP. International Transactions in Operational Research, Vol. 4, No. 5-6, pp. 337-389. DOI: 10.1111/j.1475-3995.1997.tb00093.x. [ Links ]

19. Martello, S., & Toth, P. (1990). Knapsack problems, algorithms and computer implementations. John Wiley & Sons. [ Links ]

Recibido: 12 de Agosto de 2016; Aprobado: 11 de Octubre de 2016

* Corresponding author: Joaquin Perez, e-mail:

Creative Commons License This is an open-access article distributed under the terms of the Creative Commons Attribution License