Paper: Learning to solve Class-Constrained Bin Packing Problems via Encoder-Decoder Model (ICLR-2024)

Reviewer comment (Novelty):
The used heatmap method is not new and a similar method has been used in TSP and VRP.

Author response:
We appreciate the nice works [1,2] which leverage heat-map to boost the performance of TSP. Our heat-map is different from those applied in routing algorithm from two aspects.

* Firstly, our motivation to propose the heat-map is to solve complex vector bpp such as CCBPP. Learning-based packing problems often use sequential models to construct solutions and they can also get promising results on geometric BPPs. In this paper, we offer another perspective to solve complex vector BPP such as CCBPP. Concretely, we introduce the connection matrix as a label to represent whether different items are packed together in the optimal result, which can better represent the cluster information in the packing problems.
* Secondly, the training details are slightly different from [1,2]. Our heat-map consider connection information including modularity information while conventional research focus on node information and geometrical information. Besides, the ground-truth data generation via cutting stock algorithm mentioned in Appendix D.1 cost much lower time than the TSP labels generation by concorde.

[1] Joshi, Chaitanya K. et al. “An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem.” ArXiwo qianv abs/1906.01227 (2019): n. pag.

[2] Fu, Zhang-Hua et al. “Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instances.” ArXiv abs/2012.10658 (2020): n. pag.

Reviewer comment (Writing):
The method is not explained well and clearly in the main body such as cluster decode and active search.

Author response:
We are very sorry details of Cluster Decoder are not explained clearly in our paper.
The purpose of the Cluster Decoder is to map the trained heatmap to the final packing result. It contains two parts: Cluster Decode and Active search. The Cluster Decode strategy maps the heatmap to a packing sequence. The packing sequence can be transferred to the final packing result according to First Fit (FF) scheme which is commonly used in packing problem, as mentioned in section 4.3.1. For each selected item, FF will be placed to the smallest number of the bin if the constraints are satisfied.

Active Search fine-tunes the last layer of GCN based on the corresponding packing results obtained by the packing sequence along with the FF. In this way, the generated heatmap can be more consistent with the characteristics of the test instance itself and can also decode better results.

Reviewer comment (Novelty):
The active search seems to be fine-tuning with RL. I do not see anything novel.

Author response:
Active search is not the main contribution of this paper, it is combined with the  cluster decode strategy so that the instance-wise solution can be obtained by only changing the parameters of the last layer of Encoder. In the ablation experiments E.1.1 and E.1.2 provided in the appendix, we proved that the heat-map generated by the Encoder and the cluster decode strategy are also effective. These two parts also play greater roles in improving experimental results. 

Active Search is an excellent search strategy that can fine-tune the network according to the characteristics of each example. For combinatorial optimization problems, it is common to have differences between training samples and test samples, so there are many learning methods that combine certain search strategies to further improve the quality of the solution[1-5].

[1]	Fu, Zhang-Hua et al. “Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instances.” ArXiv abs/2012.10658 (2020): n. pag.

[2] Choo, Jinho et al. “Simulation-guided Beam Search for Neural Combinatorial Optimization.” Advances in neural information processing systems 35 (2022).

[3] Kool, Wouter et al. “Deep Policy Dynamic Programming for Vehicle Routing Problems.”
Integration of AI and OR Techniques in Constraint Programming (2021).

[4] Qiu, Ruizhong et al. “DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problems.” Advances in neural information processing systems 35 (2022).

[5] Son, Jiwoo et al. “Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization.” International Conference on Machine Learning (2023).

Reviewer comment (Reproducibility):
"we use class cosine similarities as edge feature" How to calculate the edge feature?

Author response:
The edge features are set as the cosine similarities corresponding to the one-hot form of class features, as mentioned in section 4.2.1.

Reviewer comment (Reproducibility):
What's the size number N set in the experiments?

Author response:
We are sorry we didn't make it clear in the text. When generating the ideal data set, we generated 20 bins with 100% utilization. Since the item size is 10~25 and the box capacity is 100, we can put a maximum of 10 items in each bin and a minimum of 4. In order to ensure the consistency of training, we set N = 20 * 10 = 200. If the number in one bin does not reach 10, we will add 0, as stated in Appendix D.1.

Reviewer comment (Experiments):
What's the dataset used for finetuning in experiments?

Author response:
The finetune is performed on **instance level**. The solution of each instance is further improved based on active search, which is denoted as 'finetune' in our paper. In other words, this finetuning procedure is performed on each test instance *separatedly*, and the dataset for finetuning is the instance itself with iterated costs $C(\pi) = -R(\pi)$. 

On the other hand, since active search is not the main contribution of this paper, we compare the performance of our model without finetune to these baselines (See experiments E.1.1 and E.1.2 provided in the appendix). These experiments demonstrate that our Encoder-Decoder model without finetune (GCN-Cluster-Greedy and GCN-Cluster-Sampling) also outperforms baselines (PointNet-Greedy and PointNet-Sampling), which validates the advantages of our model.

Reviewer comment (Experiments):
Is it a fair comparison with the baseline method without the finetuning dataset?

Author response:
The finetune is performed on **instance level**. The solution of each instance is further improved based on active search, which is denoted as 'finetune' in our paper. In other words, this finetuning procedure is performed on each test instance *separatedly*, and the dataset for finetuning is the instance itself with iterated costs $C(\pi) = -R(\pi)$. 

On the other hand, since active search is not the main contribution of this paper, we compare the performance of our model without finetune to these baselines (See experiments E.1.1 and E.1.2 provided in the appendix). These experiments demonstrate that our Encoder-Decoder model without finetune (GCN-Cluster-Greedy and GCN-Cluster-Sampling) also outperforms baselines (PointNet-Greedy and PointNet-Sampling), which validates the advantages of our model.

Reviewer comment (Theory):
Could other constraints be considered rather than just class numbers?

Author response:
The class-constrains in CCBPP changes the relationship between different items and it  can be reflected in problem formulation. If the mask is leveraged to judge the constraint in the form of a sequence, such as using seq2seq to construct the problem model, the results may not be so promising, as shown in our paper.

If there are some **unstable** and **personalized features** in the actual scene, which can not be encoded by the network, adding restrictions can be added to Cluster Decoder with specific mask function so that both the connectivity probabilities denoted by the heatmap and the complex constrains can be considered in the final result.

Reviewer comment (Novelty):
The constraints seem not as complex as the constraints in 3D-BPP. In the seq2seq problem, it could be easily handled by the mask function.

Author response:
The class-constrains in CCBPP changes the relationship between different items and it  can be reflected in problem formulation. If the mask is leveraged to judge the constraint in the form of a sequence, such as using seq2seq to construct the problem model, the results may not be so promising, as shown in our paper.

If there are some **unstable** and **personalized features** in the actual scene, which can not be encoded by the network, adding restrictions can be added to Cluster Decoder with specific mask function so that both the connectivity probabilities denoted by the heatmap and the complex constrains can be considered in the final result.

Reviewer comment (Novelty):
The encoder-decoder pipeline is quite common in machine learning solvers for combinatorial optimization problems. The authors make some specific adaptations for the bin packing problem, while in general, the results are not too surprising under such a framework.

Author response:
**Q1: The novelty of our proposed Encoder-Decoder framework**

We are very sorry we don't compare our encoder-decoder framework over other structures in our paper, the relative contents will be added to the revision of our manuscript. The encoder-decoder models for the combinatorial problems such as [1-3] are sequential models, which means the neural solvers are learned by the constructed sequences. In the context of CCBPP, an optimal packing result always corresponds to numerous packing sequences since shuffling the order of items within each bin does not affect the packing solution but will directly alter the packing sequence. As a result, sequential model may not be suitable.

Overall, the novelty of our proposed encoder-decoder framework are listed as follows:

* In the Encoder part, we introduce the connection matrix as a label to train the heat-map so that it can bring richer information than a packing sequence. 

* The Cluster-Decoder is designed to generate instance-wise packing solutions from heat-map, which successfully closes the gap between a heat-map and a final packing solution. 

* Our encoder and decoder are separated so that it is more flexible when applied in actual scene. The encoder is able to capture more problem-specific information, and the decoder can fine-tune the final solution at instance-wise level.

[1] Vinyals, Oriol, Meire Fortunato, and Navdeep Jaitly. "Pointer networks." Advances in neural information processing systems 28 (2015).

[2] Li, D., et al. "Solving Packing Problems by Conditional Query Learning." International Conference on Learning Representations (ICLR) Conference 2020.

[3] Zhang, Jingwei, Bin Zi, and Xiaoyu Ge. "Attend2pack: Bin packing through deep reinforcement learning with attention." arXiv preprint arXiv:2107.04333 (2021).

Reviewer comment (Writing):
It will be better to have more insights into the bin packing problem and what machine learning can help to inspire future researchers.

Author response:
**Q2:  More insights into the bin packing problem**

As far as we are concerned, our contributions to other BPPs can be listed as follows:

* Conventional learning-based COP algorithms mainly focus on solving geometric BPP like 3DBPP, In this paper, we bring another complex vector BPP, CCBPP into people's view and hope our work could bring more attention to these problems which are common encountered in practical applications along with great potential values.

* The Encoder-Decoder structure to deal with COPs are mainly end2end sequential models, they may  not catch the cluster information of the items. Our framework make good use of the connection matrix as a label to represent the cluster information of the bin packing problem, and it can be applied to other packing problems where the results are in the form of aggregation.

* The Encoder and Decoder mentioned in our framework are separated. The Encoder can better characterize the characteristics of the problem itself, while the Decoder can modify the solution at instance-wise level. In actual application scenarios, some personalized or customized constraints can be solved using the mask mechanism combined with the Cluster Decoder.

Reviewer comment (Evaluation):
Can you plot the gap vs time for different methods?

Author response:
**Q3: Can you plot the gap vs time for different methods?**

For all the baselines, only the heuristic methods ACO and GA are iterative algorithms during testing, the results of other algorithms do not change over time. We list several time v.s. the gap in the following table when $Q = 10, C = 5$.

| Method   |   Time=20s |  Time=40s |   Time=60s |  Time=80s| Time=100s| 
|:----------:|:------:|:----:|:------:|:----:|:------:|
|Random    |  5.03%|  5.03%|  5.03%| 5.03%|  5.03%|
|FFD    |  5.00%|  5.00%|  5.00%| 5.00%|  5.00%|
|GA    |  2.90%| 2.38%| 2.22%|2.10%| 2.05%|
|ACO  |   4.64%| 4.40%|4.175%| 4.10%|3.90%| 
|Ptr  |   4.95%| 4.95%|4.95%| 4.95%|4.95%| 
|Ours|2.27%| 2.125%|2.10%|2.075%| 2.00%|  

**Dear Reviewer Rj4P:**

   Thanks for replying our response! We rechecked the learning curve of random and found that we only listed the **random packing result** with sepecific random seed which is consistant with our paper since we conducted random packing only once in our experiment. After reading your responses, we recognized that we didn't conduct **random search**  which recorded the best solution during each iteration. We re-implemented the  **random search** experiment and found that the gap is 5%,  5%,  4.975%, 4.975%,  4.95% over Time=20s, 40s, 60s, 80s and 100s. 

A more precise result of Random packing and Random Search can be listed as :

| Method   |   Time=20s |  Time=40s |   Time=60s |  Time=80s| Time=100s| 
|:----------:|:------:|:----:|:------:|:----:|:------:|
|Random   Packing |  5.03%|  -|  -| -| -|
|Random  Search  |  5.00%|  5.00%|  4.975%| 4.975%|  4.95%|

Random search is indeed a good strategy to find better solutions given more random trails. However, the search space in our problem is very large.  Since the probability of searching for the optimal solution is very small, the curve of random search decreases very slowly.

Thanks again for your constructive suggestions in improving our paper during this rebuttal.

Reviewer comment (Novelty):
Justify the need of encoder architecture proposed by Fraughnaugh, 1997 used here what advantages it has in the proposed work implementation.

Author response:
The reference cited in section 4.2 Encoder (Fraughnaugh, 1997) is an introduction to graph theory. It is cited here to illustrate that the CCBPP we want to solve can form a graph structure according to the characteristics of the problem, so that it can be encoded with a graph neural network.

[1] Kathryn Fraughnaugh. Introduction to graph theory. Networks, 30:73, 1997.

Reviewer comment (Novelty):
This paper studies a typical example of the vector Bin Packing Problem. The authors may want to explain the significance and generality of the studied problem in detail.

Author response:
**Q1: the significance and generality of CCBPP**

We are sorry we don't claim the significance and generality of CCBPP clearly in our paper,  more about the CCBPP will be added into the revision of the manuscript. As far as we're concerned, the points are listed as follows:

1. **In the academic view, CCBPP as well as vector BPP are fully studied beyond neural combinatorial optimization community.** 

    BPP can be divided into two main categories, geometric BPP (2D and 3D BPP), and vector BPP [1]. The difference between these two problems is whether we should consider the geometrical constraints.
    In the vector BPP research field, there are plenty of works proposed to solve vector BPP with different constraints [1, 2, 3]. Among them, class-constrained BPP is a typical variants. These research mainly focus on approximation heuristics [4] or exact solutions [5]. Unfortunately, learning-based methods have not been applied in this field so far.
    
2. **In the application view, CCBPP is commonly encountered in BPP field with wide applications.** 

     In general, BPP can be viewed as a resource allocation problem with resources from two kinds, incompatible resources and compatible resources (shared resources). The vanilla vector BPP only considers incompatible resources. Actually, there are plenty of real-world applications corresponding to both kinds of resources. For example, an operation machine can only process 2 kinds of operation (compatible resource) with limited materials (incompatible resources) [6]. This is quite common in many real-world applications, *Automatic Scaling in Cloud Computing [7]*, *Data-Placement Problem in Video-on-Demand* [8], *Production Planning* [9], *Co-painting Problem* [10], *Steel Mill Slab Problem* [6] are all typical real-world applications of CCBPP.

[1] Christensen, Henrik I. , et al. "Approximation and online algorithms for multidimensional bin packing: A survey." Computer Science Review 24.May(2017):63-79.

[2] Epstein, Leah , Csanád Imreh, and A. Levin . "Class constrained bin packing revisited." THEORETICAL COMPUTER SCIENCE -AMSTERDAM- (2010).

[3] Kellerer, Hans and Ulrich Pferschy. “Cardinality constrained bin‐packing problems.” Annals of Operations Research 92 (1999): 335-348.

[4] Shachnai, Hadas and Tami Tamir. “Polynomial time approximation schemes for class‐constrained packing problems.” Journal of Scheduling 4 (2001): 313-338.

[5] Borges, Yulle G. F. et al. “Exact algorithms for class-constrained packing problems.” Comput. Ind. Eng. 144 (2020): 106455.

[6] Crévits, Igor et al. “A special case of Variable-Sized Bin Packing Problem with Color Constraints.” 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT) (2019): 1150-1154.

[7] Xiao, Zhen et al. “Automatic Scaling of Internet Applications for Cloud Computing Services.” IEEE Transactions on Computers 63 (2014): 1111-1123.

[8] Kochetov, Yury A. and A. Kondakov. “VNS matheuristic for a bin packing problem with a color constraint.” Electron. Notes Discret. Math. 58 (2017): 39-46.

[9] Jansen, Klaus et al. “Approximation Algorithms for Scheduling with Class Constraints.” Proceedings of the 32nd ACM Symposium on Parallelism in Algorithms and Architectures (2019): n. pag.

[10] Peeters, Marc and Zeger Degraeve. “The Co-Printing Problem: A Packing Problem with a Color Constraint.” Oper. Res. 52 (2004): 623-638.

Reviewer comment (Novelty):
This paper proposes an encoder-decoder model to solve Class-Constrained Bin Packing Problems. However, many existing learning-based methods for solving combinatorial optimization problems are based on Encoder-Decoder Models as well [1, 2, 3]. The authors may want to explain the novelty of their proposed method over existing works in detail.

Author response:
**Q2：the novelty of their proposed method over existing works, and the technical novelty of the proposed method.**

We are very sorry we don't compare our encoder-decoder frameworks over others in our paper. There are many learning-based encoder-decoder structures to solve combinatorial optimization problems [1-3] including bin packing problems, they are all end-to-end sequential models which means the both the encoder and the decoder are learned via sequences. However, an optimal packing result always corresponds to numerous packing sequences since shuffling the order of items within each bin does not affect the packing solution but will directly alter the packing sequence. Thus, it brings complexity when relying solely on learning from sequential models.

Overall, the novelty of our proposed encoder-decoder framework are from three aspects:

* In the encoder part, we introduce the connection matrix as a label to train the heat-map so that it can bring richer information than a packing sequence. 

* The Cluster-Decoder is designed to generate instance-wise packing solutions from heat-map, which successfully closes the gap between a heat-map and a final packing solution. 

* Our encoder and decoder are separated so that it is more flexible when applied in actual scene. The encoder is able to capture more problem-specific information, and the decoder can fine-tune the final solution at instance-wise level.

[1] Vinyals, Oriol, Meire Fortunato, and Navdeep Jaitly. "Pointer networks." Advances in neural information processing systems 28 (2015).

[2] Li, D., et al. "Solving Packing Problems by Conditional Query Learning." International Conference on Learning Representations (ICLR) Conference 2020.

[3] Zhang, Jingwei, Bin Zi, and Xiaoyu Ge. "Attend2pack: Bin packing through deep reinforcement learning with attention." arXiv preprint arXiv:2107.04333 (2021).

Reviewer comment (Theory):
The authors propose to use a graph neural network to generate a connection heatmap for classifying items into different packs. However, the motivation of using a heatmap to classify items rather than directly learning a node classification model is unclear.

Author response:
**Q3: the motivation of using a heatmap, why not use node classification?**

Learning-based packing problems often use sequential models to construct solutions and they can also get promising results. In this paper, we offer another perspective to introduce the connection matrix as a label to represent whether different items are packed together in the optimal result, which can better represent the cluster information in the packing problems. The results in our paper show that the learned heap-map can better obtain the relationship of different items.

Node classification may not be suitable for packing problems since there is no difference between different bins, which means the results reflect the aggregation of different items rather than categories. Clustering Algorithm such as knn is also not recommended since the packing constraints may not fully be satisfied, as mentioned in section 4.3.1.

Reviewer comment (Novelty):
Discussion on related work of other learning-based approaches for bin packing problems is missing.

Author response:
**Q4: related work of other learning-based approaches for bin packing problems is missing.**

We will add the corresponding related work to the revision of the manuscript.

Reviewer comment (Experiments):
The baselines are insufficient. Although the authors compare their method against the pointer network, recent work has proposed several improved models of the pointer network [4, 5]. The authors may want to compare their method with these baselines.

Author response:
**Q5: the baselines are insufficient.**

Thanks for reminding us the improved version of ptr! In [1], the encoder was changed to an attention structure while the decoder still used the rnn structure. In [2], both the encoder and decoder were changed to a transformer-styled multi-head attention structure.

We conduct experiments on the modified ptr[1,2]. Results below show that three different methods[1,2,3] do not make a big difference on CCBPP, which shows the limitation of sequence-to-sequence model when solving our problem. The rnn or the attention network may  not be able  to catch the constraints of different items as GCN does, and the  ptr cannot obtain the cluster information of different items as the heat-map does, as we stated in the third paragraph in section 1.

|Method| **Q = 10,**| **C = 3**  | |**Q = 20,**  | **C = 3**| |**Q = 30,** |**C = 3** | |
|:------:|:---------:|:------:|:----:|:----------:|:-------------:|:------:|:----------:|:----------:|:------:|
| | Bins|     Gap      |  Time |Bins|     Gap      |  Time |Bins|     Gap      |  Time |
|PointNetGreedy|21.41|7.05%|0.0181|21.88|9.40%|0.0181|22.11|10.55%|0.019|
|PointNet-Sampling|21.07|5.35%|0.4|21.03|5.15%|0.41|21.26|6.30%|0.625|
|Nazari. Etc - Greedy|21.44|7.20%|0.0135|21.82|9.10%|0.0157|22.12|10.60%|0.0179|
|Nazari. Etc - Sampling|21.06|5.30%|0.2068|21.03|5.15%|0.2353|21.24|6.20%|0.2826|
|Kool. Etc - Greedy|21.3|6.50%|0.0136|21.68|8.40%|0.0155|22.04|10.20%|0.0185|
|Kool. Etc - Sampling|21.03|5.15%|0.2794|21.03|5.15%|0.2454|21.1|5.50%|0.2994|
|GCN-Cluster,AS(Ours)|20.74|3.70%|10.14|20.91|4.55%|10.52|20.98|4.90%|14.05|

Reviewer comment (Experiments):
It would be more convincing if the authors could evaluate their method on large-scale benchmarks, such as bin packing problems with over 1,000 items.

Author response:
**Q6：experiments on large-scale benchmarks**

We conducted experiments on $N=300$ and $N=500$， as shown in the following tables. The results show that our proposed method still has advantages compared to other baselines, and the results of large-scale benchmarks will be added to the revision of our manuscript.
Larger-scale experiments when $N > 1000$  are not conducted at present due to the time and resource limitations. It brings us the inspiration to solve large-scale CCBPP with lower time complexity，which can be left to the future work.  There are some excellent works in the direction of combinatorial optimization that transplant models trained on small-scale problems to solve large-scale ones [1-3]， which will benefit us a lot.

* N = 300

| Method  |  N = 300| Q = 10   | C = 5 |  N = 300| Q = 20   | C = 5|
|:----------:|:-------------:|:------:|:----:|:-------------:|:------:|:----:|
|    | Bins|     Gap      |  Time |Bins|     Gap      |  Time |
|Random  |   31.25 |4.17% |0.0136| 31.07|3.57% |0.0134|
|FFD |   31.05|3.50%| 0.0141|31.125 |3.82%| 0.0145|
|GA    |   30.58| 1.93%|27.99|30.62|2.07%|29.753|
|ACO  |  30.625| 2.08%| 163.87|30.595|1.98%|179.704|
|PointNet-Greedy|  31.09| 3.63%| 0.034|31.19|3.97%|0.0305|
|PointNet-Sampling| 31.00|3.33%| 0.3537|31|3.33%|0.5056|
|GCN-Cluster,Greedy |31.00 |3.33%|0.0644|30.995|3.32%|0.0702|
|GCN-Cluster,Sampling |30.925|3.08%|0.6250|30.93|3.10%|0.6911|
|GCN-Cluster, AS(Ours)| **30.545**|**1.82%**| 21.0235|**30.57**|**1.90%**|20.16|

* N = 500

| Method  |  N = 500| Q = 10  | C = 5 |  N = 500| Q = 20   | C = 5|
|:----------:|:-------------:|:------:|:----:|:-------------:|:------:|:----:|
|    | Bins|     Gap      |  Time |Bins|     Gap      |  Time |
|Random  |   51.86 |3.72% |0.0409| 51.95 |3.90% |0.0409|
|FFD |   51.415 |2.83%| 0.0308|51.76 |3.52%|0.0315|
|GA    |   50.90| 1.80%|72.65| 51.00| 2.00%|76.65|
|ACO  |  50.97| 1.94%| 388.10| 51.00| 2.00%| 418.45|
|PointNet-Greedy| 51.91| 3.82%| 0.0509| 52.06| 4.12%| 0.0509|
|PointNet-Sampling| 51.22|2.44%| 0.7997|51.46|2.92%| 0.8793|
|GCN-Cluster,Greedy |51.465 |2.93%|0.1300|51.6450 |3.29%|0.1388|
|GCN-Cluster,Sampling |51.005|2.01%| 1.4969|51.02|2.04%| 1.5866|
|GCN-Cluster, AS(Ours)| **50.84**|**1.68%**| 63.72|**50.99**|**1.98%**| 64.52|


[1]	Li, S., Yan, Z., & Wu, C. (2021). Learning to Delegate for Large-scale Vehicle Routing. Neural Information Processing Systems.

[2]	Fu, Zhang-Hua et al. “Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instances.” ArXiv abs/2012.10658 (2020): n. pag

[3]	Son, Jiwoo et al. “Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization.” International Conference on Machine Learning (2023).

Reviewer comment (Novelty):
What is the technical novelty of the proposed method?

Author response:
**Q2：the novelty of their proposed method over existing works, and the technical novelty of the proposed method.**

We are very sorry we don't compare our encoder-decoder frameworks over others in our paper. There are many learning-based encoder-decoder structures to solve combinatorial optimization problems [1-3] including bin packing problems, they are all end-to-end sequential models which means the both the encoder and the decoder are learned via sequences. However, an optimal packing result always corresponds to numerous packing sequences since shuffling the order of items within each bin does not affect the packing solution but will directly alter the packing sequence. Thus, it brings complexity when relying solely on learning from sequential models.

Overall, the novelty of our proposed encoder-decoder framework are from three aspects:

* In the encoder part, we introduce the connection matrix as a label to train the heat-map so that it can bring richer information than a packing sequence. 

* The Cluster-Decoder is designed to generate instance-wise packing solutions from heat-map, which successfully closes the gap between a heat-map and a final packing solution. 

* Our encoder and decoder are separated so that it is more flexible when applied in actual scene. The encoder is able to capture more problem-specific information, and the decoder can fine-tune the final solution at instance-wise level.

[1] Vinyals, Oriol, Meire Fortunato, and Navdeep Jaitly. "Pointer networks." Advances in neural information processing systems 28 (2015).

[2] Li, D., et al. "Solving Packing Problems by Conditional Query Learning." International Conference on Learning Representations (ICLR) Conference 2020.

[3] Zhang, Jingwei, Bin Zi, and Xiaoyu Ge. "Attend2pack: Bin packing through deep reinforcement learning with attention." arXiv preprint arXiv:2107.04333 (2021).

Reviewer comment (Theory):
What is the motivation of using the heatmap to classify items?

Author response:
**Q3: the motivation of using a heatmap, why not use node classification?**

Learning-based packing problems often use sequential models to construct solutions and they can also get promising results. In this paper, we offer another perspective to introduce the connection matrix as a label to represent whether different items are packed together in the optimal result, which can better represent the cluster information in the packing problems. The results in our paper show that the learned heap-map can better obtain the relationship of different items.

Node classification may not be suitable for packing problems since there is no difference between different bins, which means the results reflect the aggregation of different items rather than categories. Clustering Algorithm such as knn is also not recommended since the packing constraints may not fully be satisfied, as mentioned in section 4.3.1.

Reviewer comment (Experiments):
Can the authors evaluate the proposed method on large-scale benchmarks?

Author response:
**Q6：experiments on large-scale benchmarks**

We conducted experiments on $N=300$ and $N=500$， as shown in the following tables. The results show that our proposed method still has advantages compared to other baselines, and the results of large-scale benchmarks will be added to the revision of our manuscript.
Larger-scale experiments when $N > 1000$  are not conducted at present due to the time and resource limitations. It brings us the inspiration to solve large-scale CCBPP with lower time complexity，which can be left to the future work.  There are some excellent works in the direction of combinatorial optimization that transplant models trained on small-scale problems to solve large-scale ones [1-3]， which will benefit us a lot.

* N = 300

| Method  |  N = 300| Q = 10   | C = 5 |  N = 300| Q = 20   | C = 5|
|:----------:|:-------------:|:------:|:----:|:-------------:|:------:|:----:|
|    | Bins|     Gap      |  Time |Bins|     Gap      |  Time |
|Random  |   31.25 |4.17% |0.0136| 31.07|3.57% |0.0134|
|FFD |   31.05|3.50%| 0.0141|31.125 |3.82%| 0.0145|
|GA    |   30.58| 1.93%|27.99|30.62|2.07%|29.753|
|ACO  |  30.625| 2.08%| 163.87|30.595|1.98%|179.704|
|PointNet-Greedy|  31.09| 3.63%| 0.034|31.19|3.97%|0.0305|
|PointNet-Sampling| 31.00|3.33%| 0.3537|31|3.33%|0.5056|
|GCN-Cluster,Greedy |31.00 |3.33%|0.0644|30.995|3.32%|0.0702|
|GCN-Cluster,Sampling |30.925|3.08%|0.6250|30.93|3.10%|0.6911|
|GCN-Cluster, AS(Ours)| **30.545**|**1.82%**| 21.0235|**30.57**|**1.90%**|20.16|

* N = 500

| Method  |  N = 500| Q = 10  | C = 5 |  N = 500| Q = 20   | C = 5|
|:----------:|:-------------:|:------:|:----:|:-------------:|:------:|:----:|
|    | Bins|     Gap      |  Time |Bins|     Gap      |  Time |
|Random  |   51.86 |3.72% |0.0409| 51.95 |3.90% |0.0409|
|FFD |   51.415 |2.83%| 0.0308|51.76 |3.52%|0.0315|
|GA    |   50.90| 1.80%|72.65| 51.00| 2.00%|76.65|
|ACO  |  50.97| 1.94%| 388.10| 51.00| 2.00%| 418.45|
|PointNet-Greedy| 51.91| 3.82%| 0.0509| 52.06| 4.12%| 0.0509|
|PointNet-Sampling| 51.22|2.44%| 0.7997|51.46|2.92%| 0.8793|
|GCN-Cluster,Greedy |51.465 |2.93%|0.1300|51.6450 |3.29%|0.1388|
|GCN-Cluster,Sampling |51.005|2.01%| 1.4969|51.02|2.04%| 1.5866|
|GCN-Cluster, AS(Ours)| **50.84**|**1.68%**| 63.72|**50.99**|**1.98%**| 64.52|


[1]	Li, S., Yan, Z., & Wu, C. (2021). Learning to Delegate for Large-scale Vehicle Routing. Neural Information Processing Systems.

[2]	Fu, Zhang-Hua et al. “Generalize a Small Pre-trained Model to Arbitrarily Large TSP Instances.” ArXiv abs/2012.10658 (2020): n. pag

[3]	Son, Jiwoo et al. “Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization.” International Conference on Machine Learning (2023).