Title: Neural Combinatorial Optimization for Robust Routing Problem with Uncertain Travel Times
Venue: NeurIPS 2024
Accepted: yes
Keywords: Robust Routing Problem

Min-max Regret Criterion

Deep Reinforcement Learning

Attention Mechanism
Abstract: We consider the robust routing problem with uncertain travel times under the min-max regret criterion, which represents an extended and robust version of the classic traveling salesman problem (TSP) and vehicle routing problem (VRP). The general budget uncertainty set is employed to capture the uncertainty, which provides the capability to control the conservatism of obtained solutions and covers the commonly used interval uncertainty set as a special case. The goal is to obtain a robust solution that minimizes the maximum deviation from the optimal routing time in the worst-case scenario. Given the significant advancements and broad applications of neural combinatorial optimization methods in recent years, we present our initial attempt to combine neural approaches for solving this problem. We propose a dual multi-head cross attention mechanism to extract problem features represented by the inputted uncertainty sets. To tackle the built-in maximization problem, we derive the regret value by invoking a pre-trained model, subsequently utilizing it as the reward during the model training. Our experimental results on the robust TSP and VRP demonstrate the efficacy of our neural combinatorial optimization method, showcasing its ability to efficiently handle the robust routing problem of various sizes within a shorter time compared with alternative heuristic approaches.

Neural Combinatorial Optimization for Robust Routing Problem with Uncertain Travel Times

Abstract

We consider the robust routing problem with uncertain travel times under the min-max regret criterion, which represents an extended and robust version of the classic traveling salesman problem (TSP) and vehicle routing problem (VRP). The general budget uncertainty set is employed to capture the uncertainty, which provides the capability to control the conservatism of obtained solutions and covers the commonly used interval uncertainty set as a special case. The goal is to obtain a robust solution that minimizes the maximum deviation from the optimal routing time in the worst-case scenario. Given the significant advancements and broad applications of neural combinatorial optimization methods in recent years, we present our initial attempt to combine neural approaches for solving this problem. Wepropose a dual multi-head cross attention mechanism to extract problem features represented by the inputted uncertainty sets. To tackle the built-in maximization problem, we derive the regret value by invoking a pre-trained model, subsequently utilizing it as the reward during the model training. Our experimental results on the robust TSP and VRP demonstrate the efficacy of our neural combinatorial optimization method, showcasing its ability to efficiently handle the robust routing problem of various sizes within a shorter time compared with alternative heuristic approaches.

1 Introduction

The classic Traveling Salesman Problem (TSP) and Vehicle Routing Problem (VRP) represent fundamental NP-hard combinatorial optimization challenges. In these routing problems, an agent commences from a designated node and fulfills specific task requisites. The primary objective is to minimize the total travel time or cost of access. However, the classic routing problem is mathematically simplified and idealized, lacking the necessary robustness to handle the complexities of the real world. In actual scenarios, the existence of various uncertain factors may significantly impact the final outcome. For example, considering the travel time between two nodes, it is not only affected by objective factors such as traffic conditions, road quality, and weather, but also by subjective factors such as the traveler's status and mood. When planning a trip and estimating travel time, uncertainty about the actual travel conditions may arise. To ensure a satisfactory travel experience, it is sensible to adopt a robust approach, which involves seeking a solution that is less vulnerable to adverse conditions, even in the worst-case scenario.

While there are several robust routing formulations in the literature, this study primarily focuses on classic robust routing problems featuring a budget uncertainty set . The underlying idea is that the uncertainty support set is represented by a set of interval data, but usually not all arcs are affected to

achieve their worst values simultaneously in one realistic scenario. More concretely, the travel time between two nodes is given by a range with upper and lower values, and a parameter Γ is used to specify the number of affected arcs. In addition, the min-max regret criterion (also known as robust deviation criterion [1]) is generally considered, which avoids overconservatism while considering certain degree of robustness. It aims to minimize the maximum deviation from the optimal route over the realization of edge costs in all scenarios. Detailed definition of such criterion will be provided in Section 3.

To tackle the robust routing problem, traditional robust optimization methods have been employed, including both exact and heuristic approaches. These methods treat the problem instances individually and aim to generate optimal or near-optimal robust solutions within a reasonable computation time. However, their efficiency diminishes when dealing with a large set of instances that exhibit similar interval uncertainty structures. Specifically, the uncertain travel time of edges in different instances may share comparable upper and lower limits, following certain hourly or daily patterns. To overcome these limitations, we can leverage advanced neural techniques to exploit the underlying patterns and similarities in the data, thus enabling the derivation of more efficient solutions.

In summary, this paper attempts to explore the neural combination optimization methods to address the robust routing problem with uncertain travel times characterized by budget uncertainty set. The contributions of our work can be highlighted as follows.

We introduce the robust routing problem with the general budget uncertainty set, which covers the interval uncertainty set as a special case, and treat the problem from the perspective of deep reinforcement learning.

We propose an end-to-end neural model to capture the features of robust routing problem, and use a pre-trained neural model to efficiently calculate the reward with respect to the worst-case scenario during training.

We conduct extensive experiments on Robust Traveling Salesman Problem (RTSP) and Robust Capacitated Vehicle Routing Problem (RVCRP) instances. The results substantiate the efficacy of our approach in efficiently handling robust routing problems across diverse scales within shorter computation time.

The remainder of the paper is structured as follows. In Section 2, we provide an overview of existing works related to robust optimization and neural combinatorial optimization, respectively. In Section 3, taking the Robust Traveling problem as an example, we describe the problem definition of RTSP in detail. In Section 4, we elaborate the proposed neural combinatorial optimization method. In Section 5, we provide experimental results for different RTSP and RCVRP instances. In Section 6, we conclude our work with possible future directions. 1

2 Related Work

Robust Optimization. As early as the 1950s, Bellman and Zadeh [2] initiated research on uncertainty optimization. Robust optimization, as introduced by Ben-Tal et al. [3], adopts a conservative approach based on worst-case optimization, ensuring that the obtained solution remains feasible for any possible realization of uncertain parameters. The formulation of robust optimization models varies significantly depending on the choice of uncertainty sets and robustness standards. Uncertainty sets encompass various types, including interval sets [3], ellipsoid sets [4, 3], polyhedron sets [5], budget sets [6], and more. Robustness standards encompass criteria such as the normal min-max standard, min-max-regret standard [1], adjustable robustness [7], etc. Considering the literature of RTSP, Montemanni et al. [8] proposed exact algorithms for RTSP with interval uncertainty sets, employing the robust deviation criterion. Chassein and Goerigk [9] introduced a recoverable robust model that allows a tour to modify a limited number of edges once a scenario becomes known. Lu et al. [10] proposed a heuristic method based on simulated annealing and the LKH algorithm to solve RTSP with the min-max regret criterion and interval uncertainty sets. Additionally, Hasegawa and Wu [11] presented an intriguing heuristic edge generation algorithm to tackle this problem. On the issue of Robust VRP, Sungur et al. [12] applied the MTZ model to the traditional CVRP problem and

robustly modeled demand uncertainty. Subsequently, Solano-Charris et al. [13] introduced a metaheuristic algorithm based on local search to address the lexicographic min-max criterion. Eufinger et al. [14] proposed a heuristic algorithm for solving the k -adaptability min-max-min criterion.

Neural Combinatorial Optimization. Neural combinatorial optimization methods for end-to-end solving of the Vehicle Routing Problem (VRP) can be broadly categorized into supervised learning (SL) and deep reinforcement learning (DRL) approaches [15]. While SL requires high-quality solutions to the VRP as training labels [16-18], DRL methods are divided into value-based [1921] and policy-based techniques. Given the complexity of routing policies, policy-based DRL approaches have become the mainstream choice, achieving notable success in various combinatorial optimization problems [22-24], including routing [25-28]. In recent years, advancements have continued. For example, Zhang et al. [29] introduced a DRL method employing an edge-based graph attention network for solving the practical Vehicle Routing Problem with Time Windows (VRPTW). Additionally, Kwon et al. [30] designed a neural model utilizing cross attention to handle matrix inputs, while Zhou et al. [31] presented a general meta-learning framework that considers the generalization of size and distribution in vehicle routing problems. These studies collectively contribute to the advancement of neural combinatorial optimization methods, showcasing their ability to tackle various types of routing problems.

Neural Methods for Robust Optimization. In recent studies, there have been initial attempts to apply neural methods to the field of robust optimization. Jacobs et al. [32] employed reinforcement learning to solve min-max robust optimization problems. They precisely solved the inner minimization problem and utilized the DQN method to handle the outer problem. Dumouchelle et al. [33] proposed an efficient machine-learning-driven instantiation of the column-and-constraint generation algorithm for two-stage min-max-min robust optimization problems. Our approach differs in that we solely train a neural model capable of rapidly constructing high-quality solutions through inference. By focusing on training a neural model, we aim to provide a novel perspective on addressing the robust routing problem.

3 Problem Description

In the subsequent discussion, we employ RTSP as a paradigmatic illustration of the robust routing problem for comprehensive exposition. More detailed formulations of RTSP and RCVRP can be referenced in Appendix 7.3.

3.1 Nominal TSP

The traveling salesman problem is a classic combinatorial optimization problem mathematically defined as follows. Given a complete symmetric graph G = ( V, E ) , where V = { 1 , 2 ..., n } is the node set, t ij , as a nominal value, denote the expected travel time of edge ( i, j ) ∈ E . Here, x ij is a binary variable that indicates whether the edge ( i, j ) is selected for the route, with 1 for selection and 0 for non-selection. The objective is to find a close route with the minimum total travel time.

For simplicity, we hereinafter use S to represent the solution space, which is the set of all feasible TSP routes.

3.2 Robust TSP

Based on historical data, it is relatively straightforward to determine the fluctuation range [ t -ij , t + ij ] of travel time for each edge ( i, j ) , where t -ij and t + ij denote the lower and upper bounds of possible values, respectively. The interval uncertainty set is defined as U interval = { t | t ij ∈ [ t -ij , t + ij ] , ∀ ( i, j ) ∈ E } = { t | t ij = t -ij + ˆ t ij η ij , 0 ≤ η ij ≤ 1 , ∀ ( i, j ) ∈ E } , where ˆ t ij = t + ij -t -ij . To provide flexibility to control the conservatism of obtained solutions, the budget uncertainty set is usually considered, which is defined as U budget = { t | t ij = t -ij + ˆ t ij η ij , 0 ≤ η ij ≤ 1 , ∀ ( i, j ) ∈ E ; ∑ ( i,j ) ∈ E η ij ≤ Γ } . Here, the parameter Γ controls the number of affected edges. It is apparent that the budget uncertainty set simplifies to the interval uncertainty set when Γ ≥ ( n 2 ) and to the deterministic version when Γ = 0 . Hence, the budget uncertainty set demonstrates a certain level of generalization.

To specify the objective of RTSP, the min-max-regret criterion is adopted. For better understanding, we first introduce the concept of regret .

Definition 3.1 ( regret ) . Given a TSP solution x and a scenario u , the regret value denotes the difference between the objective value of x and the optimal solution under u .

where t u ij is the actual travel time of edge ( i, j ) under scenario u . y is the 0-1 decision variable of nominal TSP under scenario u .

Definition 3.2 ( min-max-regret criterion ) . It aims to find a TSP solution that minimizes the maximum regret over all realizations of edge costs.

where U is the uncertainty set including all possible scenarios.

From Equation (2), it is evident that RTSP is difficult to solve directly due to its triple optimality judgment. However, it is worth mentioning that in Montemanni et al. [8], a theorem regarding the derivation of the worst-case scenario in budget uncertainty set is proposed under the min-max-regret criterion. By leveraging this theorem, the complexity of RTSP can be significantly reduced.

Theorem 1. Given a TSP solution x , the worst-case scenario in budget uncertainty set for solution x (i.e., the scenario that maximizes the regret of solution x ) is the one where exactly Γ edges with the largest upper bound in x takes its upper bound (i.e., η ij = 1 and t ij = t -ij + ˆ t ij ), and the remaining edges take its lower bound (i.e., η ij = 0 and t ij = t -ij ) .

Given that the interval uncertainty set is a special case of the budget uncertainty set with Γ ≥ ( n 2 ) , a corollary can be outlined as follows.

Corollary 1. Given a TSP solution x , the worst-case scenario in interval uncertainty set for solution x (i.e., the scenario that maximizes the regret of solution x ) is the one where all the traversed edges of solution x reach their upper bound limit and the remaining edges reach their lower bound limit.

Taking the interval uncertainty for example, let wc ( x ) denote the worst-case scenario for solution x . Its associated travel time t wc ( x ) ij is given by:

Therefore, the formulation of RTSP can be simply expressed as:

In Figure 1, we give a specific example to illustrate the solution process of RTSP with interval uncertainty. There are 4 nodes and 6 edges. Each edge is associated with an uncertainty interval (see Figure 1a). Assuming that a feasible solution x is ' 1 → 2 → 3 → 4 → 1 ' represented by the green edges, its nominal total time is 5 + 9 + 4 + 4 = 22 . According to Corollary 1, we can obtain the worst-case scenario wc ( x ) with respect to x , as indicated by the edge values in Figure 1b. In this case, we can determine the optimal solution under the scenario wc ( x ) , represented by the red edges in Figure 1c, with a total time 5 + 1 + 4 + 4 = 14 . The max-regret value for x is then calculated as 22 -14 = 8 . By obtaining the max-regret for every feasible solution, the final optimal solution is the one with the minimum max-regret .

4 Methodology

The objective of the robust routing problem is to discover a robust route that minimizes the maxregret . Only when a route is complete can we calculate its max-regret value. This observation bears resemblance to the concept of delayed reward in deep reinforcement learning. We propose employing the neural combinatorial optimization method to construct such route in an end-to-end manner. To elaborate on our approach, we also employ RTSP with interval uncertainty for the study.

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4.1 Solution Framework

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The overall solution framework, as illustrated in Figure 2, is based on the encoder-decoder model. In this model, the encoder component receives the input data and exploits a series of neural networks to extract embeddings that represent the problem features. These embeddings are then utilized by the decoder component to compute probabilities for node selection and construct a complete solution in an autoregressive manner. Specifically, the model trains a stochastic policy p ( π | s ) to generate a solution x , or a permutation π equivalently, for an instance s . It is parameterized by θ as:

Note that the input data consists of a dual-weighted graph, where each edge is associated with an upper value and a lower value. Taking inspiration from MatNet [30], we treat the input as two matrices: one composed of the upper range values ('Up_Matrix'), and the other composed of the lower range values ('Low_Matrix'). These matrices are then fed into the encoder.

Furthermore, the model requires a reward signal for adjusting the network weight parameters. Once all the decoding steps are complete, we obtain a nominal solution π for the instance s . The loss function is defined as:

where R ( π ) is the expectation of the robustness of π , or its (negative) reward, given by

Equation (7) is originated from Equation (4), where t + ( π ) is the total travel time of solution π using the upper range value. TSP ∗ ( wc ( π )) calculates the optimal TSP route under the worst-case scenario

with respect to solution π . Because a large number of samples has to be generated to train the model, we need to find a solver to tackle TSP efficiently. In fact, it is not necessary to exactly solve TSP in the training stage due to the expectation of R ( π ) and high complexity of TSP. We turn to approximate optimal solutions by using a pre-trained TSP model detailed in Section 4.3.

4.2 Model Architecture

4.2.1 Encoder

The encoder takes two matrices ('Up_Matrix" and 'Low_Matrix") as the input. Each matrix can be regarded as a bipartite graph G = (Φ , Ψ , D ) , where Φ = ( ϕ 1 , . . . , ϕ n ) is the set of nodes with outgoing edges, Ψ = ( ψ 1 , . . . , ψ n ) is the set of nodes with incoming edges, and D = [ d ij ] n × n gives the edge values by the corresponding matrix.

We extract the feature embeddings of Φ and Ψ , denoted as h Φ and h Ψ , using MatNet [30] (detailed architecture can be found in Appendix 7.7). To achieve this, we initialize the initial embedding h 0 ϕ i for each ϕ i ∈ Φ as a zero vector, enabling the model to support variable-sized inputs. For each ψ j ∈ Ψ , the initial embedding h 0 ψ j is initialized as a one-hot vector to obtain discriminative first attention layer representations. Next, these initial embeddings are combined with the corresponding matrix D and passed through L attention layers to iteratively update the embeddings. It should be noted that the encoding process for sets Φ and Ψ is performed mutually. Specifically, for the encoding process of set Φ , h Φ serves as the query vector, h ψ acts as the key-value vector, and the matrix D is utilized for weighted fusion. Therefore, we obtain the updated embedding h ′ Φ as the output of the MatNet: h ′ Φ = MatNet ( h Φ , h Ψ , D ) . Conversely, for the encoding process of set Ψ , we swap the roles of h Φ and h Ψ and employ the transpose of matrix D . Consequently, we have h ′ Ψ = MatNet ( h Ψ , h Φ , D T ) .

Since there are two matrices involved, we use h + Φ and h + Ψ to denote the output embeddings for 'Up_Matrix'. Similarly, we use h -Φ and h -Ψ to denote the output embeddings for 'Low_Matrix'. Finally, the graph embeddings, denoted as a pair ( h Φ , h Ψ ) , are obtained by fusing the features of 'Up_Matrix' and 'Low_Matrix'. We simply perform the summation on their embeddings: h Φ = h + Φ + h -Φ , h Ψ = h + Ψ + h -Ψ .

4.2.2 Decoder

The decoder module generates a complete solution in an autoregressive manner based on the graph embeddings ( h Φ , h Ψ ) obtained from the encoder.

The key component of the decoder is the masked multi-head cross attention mechanism. At each decoding step t , it utilizes h ψ j ( j = 1 , . . . , n ) as the key/value vector ( k / v for short), and a context vector h c as the query vector ( q for short), which is formed by concatenating h Φ with the first and last nodes of the current solution, i.e., h c = [ h ϕ 1 , h ϕ πt ] . The multi-head cross attention operation is then performed and mask nodes that have already been visited.

After calculating the compatibilities u ( c ) j ( c symbolizes the current context node), the decoder proceeds to compute the probabilities for the remaining unmasked nodes. This probability computation is accomplished using a single-head attention mechanism. To scale the compatibility results within the range of [ -ζ, ζ ] ( ζ = 10 ), the tanh function is applied. Subsequently, a softmax function is utilized to calculate the final output probability vector.

At last, the decoder selects a node according to the probability vector and proceeds to the next decoding step.

4.3 Training

To train the model for RTSP, we adopt a combination of the classic REINFORCE [34] policy gradient algorithm and the POMO training approach [27], which primarily exploits the solution symmetry. We utilize Monte Carlo methods to sample n solution trajectories for each instance, with each trajectory using a different starting node. The reward is computed for each of these n trajectories, and the average reward of the n trajectories is used as a shared baseline within a mini-batch. To maximize the expected return J , we employ gradient ascent to approximate J , and the Adam optimizer [35] is used to optimize the policy network parameters.

In the following equations, π i represents the generated solution in the i -th sampled trajectory, and R ( π ) denotes the reward of the solution as defined in Equation (7).

To efficiently calculate the reward R ( π ) , a pretrained 'MatNet' is used as a TSP solver. Since a TSP instance can also be represented by a bipartite graph, it is inputted into the MatNet

Algorithm 1 Training process for the RTSP model

Input : Sample set S , number of epochs E , number of steps per epoch T , batch size B , total number of starting nodes per instance n

Initialization : policy network parameter θ

1: for epoch ← 1 to E do

2: for step ← 1 to T do

3: s i ← SampleInstance( S ) ∀ i ∈ { 1 , ..., B } .

4: { st 1 i , st 2 i , ..., st n i } ← StartNodes( s i ) ∀ i ∈ { 1 , ..., B } .

5: π j i ← SampleRollout( st j i , s i , p θ ) ∀ i ∈ { 1 , ..., B } , ∀ j ∈ { 1 , ..., n } .

6: wc j i ← WorstCaseTSP( π j i ) ∀ i ∈ { 1 , ..., B } , ∀ j ∈ { 1 , ..., n } .

7: R ( π j i ) ← ∑ wc j i π j i - PretrainedTSP( wc j i ) ∀ i ∈ { 1 , ..., B } , ∀ j ∈ { 1 , ..., n } .

8: b i ← 1 n ∑ n j =1 R ( π j i ) ∀ i ∈ { 1 , ..., B } .

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end for to construct a TSP route. Multiple greedy trajectories with different starting nodes and instance augmentation are utilized to generate TSP solutions.

Finally, Algorithm 1 outlines the training process for RTSP. In line 7, we feed the worst-case scenario corresponding to the current solution into the pre-trained TSP model, which provides us with a near-optimal TSP solution as feedback.

4.4 Inference

Once the RTSP model is trained, it can be used to make fast inference on a batch of instances. Algorithm 2 presents the overall inference process.

We use the POMO inference approach to sample multi-start solutions. Instance augmentation [30] during inference can also be adjusted to improve the solution quality. Concretely, we employ multiple independent randomizations on the initial one-hot node embeddings h 0 ψ j for each instance to explore various paths towards the optimal solution. Since two matrices are inputted to our model, they share the same randomized one-hot vector. Ultimately, the best solution is selected from the multiple generated solutions.

Recall that our proposed method is still an approximate approach. To ensure a fair and accurate comparison of the final results during the experimental sessions, we adopt a consistent approach by utilizing an exact TSP solver, namely Gurobi 2 , to determine the target value achieved through different methods (line 8).

Algorithm 2 Inference with the pre-trained TSP model

Input : instance s , augmentation factor K , policy p θ , total number of starting nodes per instance n

∗ ∗

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5 Experiments

Our proposed approach was programmed with Pytorch. All the experiments were conducted on a workstation with Intel(R) Core(TM) i5-4590 CPU @ 3.30GHz, 8.0 GB RAM and TITAN Xp GPU.

5.1 Experimental Setup of RTSP

Experimental Data. The experimental data in this study was generated following the work of Montemanni et al. [8]. The data generation process follows a specific rule: a 'RN -M ' type problem is considered, where N represents the number of nodes, and M is a range threshold (for the upper bounds). The control parameter is set to Γ ≥ ( n 2 ) . In this case, the upper bound travel time for each pair of nodes, denoted as t + ij , is randomly selected from set { 0 , 1 , 2 , ..., M } . The lower bound travel time, denoted as t -ij , is randomly chosen from set { 0 , 1 , ..., t + ij } . To maintain consistency, the distance values of the upper and lower bounds are then normalized using a scale factor of M . In addition, we ensure the upper and lower bound values to satisfy the triangle inequality, respectively.

Hyper-parameters. During the training process, we follow specific configurations based on the scale of the problem. For small-scale instances with 20 and 30 nodes, training is conducted on a single GPU. The batch size is set to 200 and 100, respectively. We use the Adam optimizer with a learning rate α = 4 × 10 -4 . Each epoch consists of training on 1000 instances. For larger-scale instances with 40 and 50 nodes, training is performed on two and three GPUs, respectively. The batch size is adjusted to 64 and 32, respectively, to accommodate the GPU memory limitations. The learning rate for the Adam optimizer is set to α = 2 × 10 -4 . Each GPU handles 400 training instances per epoch. For the built-in TSP models, they are trained for 2000 epochs to ensure their effectiveness and convergence.

Baselines. We have collected typical algorithms for solving RTSP as our baselines. These algorithms can be further categorized into exact and heuristic approaches.

Exact approaches: Branch-and-Cut (BC) and Benders Decomposition (BD) are exact algorithms proposed by Montemanni et al. [8] for solving RTSP. We have implemented these algorithms using the Python programming language and the Gurobi optimizer. For small-scale problems, such as those with 20 nodes, these two algorithms exhibit fast solution speeds. However, as the problem size increases, for example, with 50 nodes, the computation time required to obtain a solution is significantly increased.

Heuristic approaches: There are three heuristic baselines introduced for comparisons: heuristic algorithms based on simulated annealing (SA-based) [10], iterated dual substitution method (iDS) [36], and edge generation algorithm (EGA) [11]. To ensure fairness, we utilize the source codes provided by the authors, with the exception of SA-based, which we reimplemented ourselves. Furthermore, we maintain most of the original settings of these algorithms.

The time limit of all above algorithms are set to 3600 seconds for every instance, except that the SA-based method has a time limit of 15 n due to the limitation of repeated calls of the LKH algorithm. Nevertheless, some algorithms may stop earlier when reaching their termination criteria.

For the experimental setting of RCVRP, please refer to Appendix 7.5.

5.2 Results and Discussions

In the following reported tables, 'Obj' represents the average objective value accurately computed using Gurobi to solve the worst-case routing scenario. 'Gap' indicates the relative difference between the objective value obtained by each algorithm and the best-known optimal value, expressed as a percentage: Gap = Obj -Obj best Obj best × 100% . 'Time' represents the average solving time per instance.

Comparison Results of RTSP. Table 1 presents the main results of our trained model and other methods on randomly generated instances. The methods are categorized into three groups: exact methods, heuristic methods, and our proposed methods. The results of our methods are reported

for three versions: no augmentation, augmentation with 8 instances, and augmentation with 128 instances (except 64 instances for 50 nodes due to memory limitations).

From Table 1, it is evident that our approach consistently achieves high-quality solutions in significantly small time. Without instance augmentation, the gaps obtained by our method are consistently below 2.6%. When utilizing instance augmentation, our method can obtain approximately accurate or even exact solutions for small scales (such as 20 nodes). For larger scales, our method also produces solutions with gaps less than 0.7%. In terms of runtime, under both scenarios with and without instance augmentation, our approach has a large and significant advantage over exact methods and heuristics, taking only a fraction of a second. The results verify the effectiveness of our approach.

Generalization Results of RTSP.

We conduct experiments to evaluate the generalization ability of our model, specifically its performance on test instances that are outside the training distribution. To illustrate this, we consider an example where M = 100 and the augmentation involves 8 instances. We evaluate our models, trained with varying numbers of nodes, on test data with different node sizes. The results are depicted in Figure 3. As expected, the model trained on the corresponding node size achieves the best performance on the test data. Moreover, the model trained on 50 nodes demon-

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strong generalization capability. Conversely, the models trained on smaller scales exhibit weaker generalization capability.

Ablation Studies of RTSP. We investigate the impact of different designs for feature encoding of the uncertainty support set. We explore three encoding methods, namely 'ours", 'blended"(a blended matrix is computed by the weighted sum of the upper and lower bound matrices, which is then encoded by MatNet) and 'fusion"(two distance matrices and attention scores are fused through an MLP in the multi-head mixed score attention layer of the encoder), with their architectures depicted in Figure 5 in Appendix 7.4.

We compare these encoding methods on R-40-100 with 20 random instances. As shown in Table 2, our method outperforms both 'blended' and 'fusion' methods in all cases, including scenarios without instance augmentation, as well as those with × 8 and × 128 instance augmentation.

Comparison Results of RCVRP. Table 3 shows the comparison results between our method and the exact algorithm. The results are all obtained using the Gurobi solver in a uniform manner. To our knowledge, this specific type of RCVRP has never been discussed before. The results show that our algorithm still achieves high solution quality and has a clear advantage in the solution time of RCVRP. On a 20-node scale, our gap is around 0.5%. On a 50-node scale, our gap is below 4.5%. In terms of time consumption, we only need a few tenths of a second to dozens of seconds.

6 Conclusions

This paper focuses on addressing the robust routing problem with uncertain travel times, relevant to real-world scenarios. We introduce a neural combinatorial optimization approach, employing an end-to-end model with attention mechanism for automatic policy learning. Leveraging a pre-trained model, experimental results demonstrate that our method can generate near-optimal solutions much faster than exact and heuristic approaches. However, due to hardware memory limitations and constraints of the POMO algorithm used in training, our method is currently limited in scale and to problems satisfying the max-regret theorem. Moving forward, we aim to expand our approach to handle larger-scale problems and tackle a broader range of robust optimization problems.

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7 Appendix

7.1 Proof of Theorem 1

Proof. To provide a coherent description of the proof process, we first introduce a shorthand notation for certain equations.

The optimal tour under scenario u is denoted as y ∗ ( u ) .

The worst case scenario obtained by solution x according to the theorem is denoted as wc ( x ) .

The tour length of solution x under scenario u is denoted as C ( u, x ) . By the definition of regret, the value of regret for solution x under scenario u has the equation: regret ( u, x ) = C ( u, x ) -C ( u, y ∗ ( u )) .

We then outline a concise proof procedure. Assume that we have a solution x , our goal is to prove that the regret value under scenario wc ( x ) is greater than or equal to the regret value under any other scenario, i.e., regret ( u, x ) ≤ regret ( wc ( x ) , x ) . Here,

Based on the definition of wc ( x ) , it is evident that when ( i, j ) ∈ x \ y ∗ ( u ) , t wc ( x ) ij ≥ t u ij . Similarly, when ( i, j ) ∈ y ∗ ( u ) \ x, t wc ( x ) ij ≤ t u ij . We have:

Therefore, the proof of Theorem 1 is concluded.

7.2 RTSP Example with budget uncertainty

In Figure 4, we provide a specific example to illustrate the solution process of RTSP with budget uncertainty while Γ = ⌊ ( n 2 ) 4 ⌋ . Recall that the budget uncertainty set is defined as U budget = { t | t ij = t -ij + ˆ t ij η ij , 0 ≤ η ij ≤ 1 , ∀ ( i, j ) ∈ E ; ∑ ( i,j ) ∈ E η ij ≤ Γ } . where ˆ t ij = t + ij -t -ij . Figure 4 depicts a graph with 4 nodes and 6 edges. Suppose the values of t -ij of each edge are as shown in Figure 4a. For simplicity, let's assume that all ˆ t ij values are set to the same value 2, though in a real

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implementation, these values would differ. Consider a feasible solution ' 1 → 2 → 3 → 4 → 1 ' highlighted by the green edges in Figure 4b, under the given Γ ,the worst scenario corresponding to this solution is also shown in Figure 4b, with a nominal total time is 1 + 2 + 3 + 6 = 12 . The optimal solution in this scenario, highlighted in red in Figure 4c, is either the route 1 → 2 → 4 → 3 → 1 or 1 → 3 → 4 → 2 → 1 , both of which have a total cost of 10. The max-regret value for the original solution is calculated as 12 -10 = 2 . By evaluating the max-regret for each feasible solution, the final optimal solution is identified as the one with the smallest max-regret value.

7.3 Formulations of Robust Routing Problems

RTSP Formulation Based on the TSP formulation and Corollary 1, we present the following mathematical model for RTSP with the min-max-regret criterion. This modeling serves as the foundation for both exact algorithms and heuristic algorithms in this field.

The mathematical scenario for RTSP is defined as follows. Given a complete symmetric graph G = ( V, E ) , where V = { 1 , 2 , ..., n } is the set of nodes. The travel time (cost) of an edge ( i, j ) is defined by an interval uncertainty set [ t -ij , t + ij ] , where t -ij and t + ij denote the lower and upper bounds of possible values, respectively.

In the above model, x ij are the decision variables for the RTSP solution, while y ij serves as temporary auxiliary decision variables representing the decision variable for TSP in the worst-case scenario corresponding to each x . To establish the nonlinear interaction between x ij and y ij , a free variable r is introduced.

The objective function in Equation (13) aims to minimize the maximum regret value, while Constraint (14) ensures the maximization of regret. We define the set of all feasible TSP solutions as S , which is

bounded by the following constraints.

̸

̸

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Constraint (15) guarantee the feasibility of x ij as a TSP solution. It is important to note that y ij must also be a feasible and valid TSP solution, and thus we have y ∈ S in Equation (14).

RCVRP Formulation CVRP is an extension of TSP that additionally considers vehicle capacity constraints. Similar to RTSP, we can use the mathematical model of the nominal CVRP and Corollary 1 to extend the mathematical model of RCVRP.

The RCVRP scenario is defined as follows. We have a fleet of vehicles with a capacity of Q per vehicle, and n customers distributed geographically. Each customer has its own demand d i (where i = 1 , 2 , ..., n ). Each customer can only be served by one vehicle, and there is no limit on the number of available vehicles. Let V be the set of n demand nodes and a single depot, represented as node 0. The set E represents the edge connections between nodes. Similar to RTSP, we consider the robust scenario where the travel time is subject to interval uncertainty. The travel time (cost) of an edge ( i, j ) is within the interval [ t -ij , t + ij ] , where t -ij and t + ij represent the lower and upper bounds of possible values, respectively. Note that in our setting, we assume that t ij = t ji , meaning that the travel time matrix is symmetric.

The model is the same as the one described in Section 7.3. It should be noted that S is defined as the set of all feasible solutions for CVRP, subject to the following constraints.

̸

̸

̸

The main difference between CVRP and TSP lies in the consideration of capacity constraints. In CVRP, it is crucial to ensure that the total demand of customers served on each route does not exceed the vehicle's capacity. To address this, the Miller-Tucker-Zemlin (MTZ) constraint [37] introduces continuous variables u i for every i ∈ V \ { 0 } , which represent the flow in the vehicle after it visits customer i . Constraints (27)-(28) enforce both the capacity and connectivity requirements of feasible routes.

7.4 Additional Results of RTSP

Generalization on the Threshold Value. In addition to considering the generalization on dimension N , as depicted in Figure 3, we also test the generalization ability on dimension M . The models

trained with different ranges of M are respectively used to test the instances across all ranges of M . As shown in the Table 4, it can be seen that a model trained within a specific threshold value can achieve relatively ideal experimental results within other threshold values. This shows that under the premise of fixed N , our model can achieve good generalization results for different M values.

Effects of the Encoding Approaches. In Figure 5(a) (labeled as "ours"), our proposed approach is depicted. It involves encoding the upper and lower bound matrices separately using MatNet and then adding the embeddings of the corresponding nodes. In Figure 5(b) (labeled as "blended"), a blended matrix is computed by taking the weighted sum of the upper and lower bound matrices, represented as D = w ∗ D up +(1 . 0 -w ) ∗ D low . Here, w is initialized to 0.5 and serves as a learnable parameter. The blended matrix is subsequently encoded using MatNet.Figure 5(c) (labeled as "fusion") illustrates the third approach, which involves fusing the two matrices and attention scores through a multi-layer perceptron (MLP) in the multi-head mixed-score attention layer within the MatNet encoder.

Effects of the Built-in TSP Solvers. We also explore different built-in TSP solvers under the worst-case scenario. In addition to the pre-trained TSP model used in our method, we further consider the well-known heuristic algorithms like LKH [38] and CMA-ES [39]. The optimality gap and training time are reported in Table 5.

Even though LKH is the state-of-the-art TSP solver that can almost find the optimal solution in small-size cases, it has no superiority with regard to solution quality for training an RTSP model and even spends more training time. On the other hand, although CMA-ES evolutionary algorithm could be accelerated through parallelization techniques in a large number of model training processes, it faced a trade-off between solving speed and solution quality. As can be seen, the pre-trained TSP model used in our method exhibits almost the smallest optimality gap and the shortest training time compared with other methods.

Training Details. In Figure 6, we present the training progress curves that illustrate the loss and objective values for 'R-50-100' instances, serving as an illustrative example. The figure demonstrates that the training loss steadily converges, and the training score is minimized after 1000 epochs. These observations indicate that the model successfully learns the implicit features of RTSP during the training process.

7.5 Experimental Setup of RCVRP

Data Generation We generate random instances of RCVRP for two different sizes: n = 20 and n = 50 . The control parameter is set to Γ ≥ ( n 2 ) . The following settings are used.

The vehicle capacity is fixed at Q = 1 . 0 .

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The demand d i for each node i is determined by dividing a uniformly sampled value ˆ d i from the set { 1 , 2 , ..., 9 } by a value D . For n = 20 , D is set to 30, while for n = 50 , D is set to 40.

The uncertain variable t ij represents the travel time between nodes i and j . The upper bound travel time t + ij is randomly selected from the set { 0 , 1 , 2 , ..., M } , while the lower bound travel time t -ij is randomly chosen from the set { 0 , 1 , ..., t + ij } .

To ensure consistency, the distance values of the upper and lower bounds are normalized using a scale factor of M .

In the data generation process, we also consider the triangle inequality. This inequality is independently applied to both the upper bound and lower bound. For example, for each triplet ( i, j, k ) in the upper bound, we check if t + ik + t + kj > t + ij is violated. If the inequality is violated, we replace t + ij with t + ik + t + kj to ensure that the triangle inequality holds.

Hyper-parameters During the training process, we adopt specific configurations based on the problem scale. For small-scale instances with 20 nodes, training is conducted on a single GPU. The batch size is set to 200. We utilize the Adam optimizer with a learning rate of α = 4 × 10 -4 . Each epoch involves training on 1000 instances.

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As for the built-in CVRP models, they are trained for 5000 epochs to ensure their effectiveness and convergence.

7.6 Effectiveness of Budget Uncertainty Set

The hyperparameter Γ can be leveraged to adjust the robustness of the budget uncertainty set. Nevertheless, we conducted supplementary experiments to validate the effectiveness of our approach under general budget uncertainty conditions. Table 6 presents the comparative results for different values. Our method shows highly promising outcomes within a remarkably short time, especially when compared to the leading solver, EGA.

7.7 Architecture of MatNet

MatNet [30] is a graph attention network model designed for data objects that can be partitioned into two sets of nodes in a bipartite graph. To facilitate the description, we refer to the set of nodes acting as the heads of edges as Φ , and the set of nodes acting as the tails of edges as Ψ . The framework diagram of MatNet is depicted in Figure 7. MatNet consists of a stack of L attention layers. Each attention layer comprises two sub-layers: the multi-head mixed-score cross-attention layer and the fully connected feed-forward (FF) layer. Both sub-layers incorporate skip connections and batch normalization. In the FF sub-layer, a hidden layer dimension of 512 and a ReLU activation function are employed. The multi-head mixed-score cross-attention sub-layer calculates attention scores between each node in one set and all nodes in the other set. This mechanism enables the learning of association features between the two sets of nodes.

The update of two node sets Φ and Ψ is performed in a dual manner, where the roles of queries and keys/values are exchanged, accompanied by the transposition of the matrix. Taking the update of h l ϕi as an example, where l denotes the current encoder layer being updated, and h ϕi represents the embedding representation of node i in the node set Φ . We define the query q i for each node i in the set Φ by projecting the embedding h l -1 ϕi . Simultaneously, we obtain the key k j and value v j from the embedding h l -1 ψj .

The compatibility u ij is calculated using q i and k j :

where d k is the dimensions of the attention vector k and v .

During the feature update process of the nodes in the attention layer, edge weight information represented by a matrix is added with a multi-layer perceptron (MLP) in addition to the network output of the previous layer.

where scores ij = u ij ∥ d ij , and d ij denotes the corresponding distance matrix element.

Next, update the corresponding node embedding.

Then, proceed through the multi-head sub-layer and the feed-forward sub-layer sequentially to obtain the updated embedding representation for the next layer.

As depicted in Figure 7, the final node feature embedding representation for the two node sets can be obtained y repeating the above process L times.

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NeurIPS Paper Checklist

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Answer: [Yes]

Justification: We describe our problem scenario in the abstract and also describe our outstanding contributions to this problem. We consider the robust routing problem with uncertain travel times under the min-max regret criterion. The contributions of our work can be highlighted as follows: We introduce Robust routing problem with the general budget uncertainty set which covers the interval uncertainty set as a special case, and treat the problem from the perspective of deep reinforcement learning. We propose an end-to-end neural model to capture the features of Robust routing problem, and use a pre-trained routing model to efficiently calculate the reward with respect to the worst-case scenario during training. We conduct extensive experiments on RTSP and RCVRP instances. The results substantiate the efficacy of our approach in efficiently handling robust routing problems across diverse scales within shorter computation time.

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Justification: Our proposed approach was programmed with Pytorch. All the experiments were conducted on a246 workstation with Intel(R) Core(TM) i5-4590 CPU @ 3.30GHz, 8.0 GB RAM and TITAN Xp GPU.

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Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?

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Justification: The paper does not explicitly discuss both potential positive and negative societal impacts of the work performed. This could be due to a primary focus on the technical aspects of addressing the robust routing problem using neural combinatorial optimization, without delving into the broader societal implications.

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Peer reviews:
comments: SUMMARY:
The authors propose a dual multi-head cross attention mechanism to extract problem features represented by the inputted uncertainty sets for the robust vehicle routing problem with uncertain travel times under the min-max regret criterion. The experimental results on the robust TSP and VRP demonstrate the efficacy of our neural combinatorial optimization method, showcasing its ability to efficiently handle the robust routing approaches.

STRENGTHS:
The authors have proposed an end-to-end neural model to capture the features of the robust routing problem and have used a pre-trained neural model to efficiently calculate the reward with respect to the worst-case scenario during training. They conducted extensive experiments on Robust Traveling Salesman Problem (RTSP) and Robust Capacitated Vehicle Routing Problem (RCVRP) instances. The results confirm the effectiveness of their approach in efficiently handling robust routing problems across diverse scales within a shorter computation time.

WEAKNESSES:
[1] The algorithm has not been compared to other existing neural combinatorial optimization works.

[2] It is well known that the POMO algorithm is very effective in solving VRPs. What improvements have you made in the POMO network structure, and whether these improvements have brought improved results?

[3] The coding method of uncertain sets proposed in this paper is not very innovative.

QUESTIONS:
See the weaknesses.

LIMITATIONS:
yes
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 3
DATE: 2024-07-06

comments: SUMMARY:
The paper concerns robust routing problem with uncertain travel times under the min-max regret criterion, which represents an extended and robust version of the classic traveling salesman problem (TSP) and vehicle routing problem (VRP). The authors proposed a dual multi-head cross-attention mechanism to extract problem features represented by the inputted uncertainty sets and achieved very good results in experiments.

STRENGTHS:
The introduced model seems to be quite innovative, especially in the domain of routing problems. The paper is well-written and the quality of the review of related works, theoretical background, setup of experiments, and the presentation of results are very good. Taking into account that the authors tackle a more realistic variant of TSP/VRP and look for robust solutions, the methodology may have some practical applications.

WEAKNESSES:
I haven't identified significant weaknesses. The only one I have in mind is the fact that neither the code, nor the dataset are available, so it is not possible to verify the results. The authors promised to make the code publicly accessible once the paper is accepted. I am not sure about the datasets. I've also found a minor writing issue: it seems that in definition 3.1, it is not clear what exactly x_ij is (one can guess, but it would be good to clarify).

QUESTIONS:
I do not have any questions now.

LIMITATIONS:
Yes, the authors have addressed the limitations in the Conclusions section.
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 3
DATE: 2024-07-11

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe min-max regret criterion is employed, and the routing problems are solved when the moving cost of an edge varies (perhaps uniformly) within a certain range. The authors have discussed two (possibly) uncertain classes and proposed a theoretical analysis and a learning-based solver (on TSP and CVRP).\n\nSTRENGTHS:\n- [S1] The whole concept and problems are interesting. The interval-based uncertain is one of the standard formulations but it is applied to various real problems.\n- [S2] The learning method (i.e., REINFORCE + POMO training approach) achieved a promising performance, though the method is possibly a combination of existing concepts.\n- [S3] The proposed method can generate at least better solutions faster than simple solvers (e.g., SA-based).\n\nWEAKNESSES:\n- [W1] In contrast to [S1], there appear to be some gaps concerning the uncertainty of edge times (for example, another standard way is modeling them by some distributions).\n- [W2] The novelty of the solver is unclear.\n- [W3] To my understanding, the MatNet solver is used to compute the reward; however, the reward quality may not be high or unclear to me (of course, some solutions at high speed are essential for actual learning, but I wanted a discussion of this area).\n\nQUESTIONS:\n- [Augmentation] I'm curious what you are doing; please explain augmentation. If it is mentioned anywhere, please let me know where.\n- Two questions from [W1].\n    - I feel that it could be generalized to cases like following some normal distribution, not discrete intervals. I think the strength of the method can be used in a setting where the elapsed time of an edge is given by a sample from a certain distribution, as this seems to be a common setting in reality. In fact, I felt that it could be handled well in other locations and theoretical developments.\n    - From Def. 3.1 and the idea of using intervals, I feel that the $[t^-, t^+]$ interval is regarded everywhere as having the same importance. However, considering the actual application, it seems likely that the closer to $t^+$, the heavier the penalty may be. I was curious about the generalization and future development in this area.\n- I am unclear about the quality of the reward from MatNet [W3]. Can you explain this point more?\n- I did not understand the treatment of $\\Gamma$.\n    - $\\Gamma$ controls the amount of how much each side is inflated from $t^-$, but since $0 \\leq \\eta_{ij} \\leq 1$, I think it is a sum of real numbers. So I was confused by some explanations that seem to be natural number, for example l.133 so `exactly $\\Gamma$ edges`. Not related to the main point, but it bothers me.\n- [minor comment].\n    - I understand that this is a space issue, but the location of Table 1 came before the explanation, and it was difficult to understand at first what x8 and x128 meant.\n\n---\n\nAfter the rebuttal, I have updated my score.\n\nLIMITATIONS:\nIn my opinion, the authors have addressed the issue adequately and mentioned these points.", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 4, "DATE": "2024-07-12"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a neural combinatorial optimization (NCO) method to solve robust routing problems under uncertain travel times under the min-max regret criterion - a common occurrence in real-life scenarios in which one wants to minimize worst-case scenarios.  The proposed approach employs the transformer-based MatNet approach, which can model edge features such as uncertain travel times and project features into the latent space. Moreover, a secondary pre-trained model is employed to evaluate the optimal tour efficiently under the max-regret condition. The approach can obtain near-optimal solutions with much faster solution times compared to baselines.\n\nSTRENGTHS:\nTo my knowledge, this paper is the first to apply a neural approach to the specific application of uncertain travel times with min-max regret, which is an important practical application. This includes the use of edge features, which is very important in practice but understudied in NCO for VRPs. The proposed model is a reasonable approach and shows good performance while cutting down considerably on computational requirements both in training and inference.\n\nI have no major concerns regarding the paper's quality and experimental results. While the paper is specific to an application, I would recommend acceptance based on its quality, originality, and practical application.\n\nWEAKNESSES:\nI have spotted some weaknesses here and there, but no major ones.\n\n1. This paper's main weakness may be its specificity to a task. However, as described above, given its importance and overall quality, I believe this is not a major flaw.\n    \n2. Equation 6: why is the loss function minimizing the expected reward? Shouldn’t it maximize it? Moreover, the gradient of the loss is defined in Eq. 10, which is different from the loss (with a different notation).\n    \n3. No code is provided at the time of submission.\n\nQUESTIONS:\n1. Why did you choose MatNet instead of e.g. BQ-NCO [1r] as your backbone?\n    \n2. Could the uncertainty in travel times be seen as a sort of “adversarial attack” on the solution - in other words, connecting your approach to ROCO [2r]?\n    \n\n----\n### References\n\n[1r] Drakulic, Darko, et al. \"Bq-nco: Bisimulation quotienting for efficient neural combinatorial optimization.\" Advances in Neural Information Processing Systems 36 (2024).\n\n[2r] Lu, Han, et al. \"Roco: A general framework for evaluating robustness of combinatorial optimization solvers on graphs.\" The Eleventh International Conference on Learning Representations. 2023.\n\nLIMITATIONS:\nSee the above weaknesses. The authors describe the main limitation of the approach, namely that the scale is currently pretty small, although this is expected from a seminal paper.", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 4, "DATE": "2024-07-12"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a neural network based approach and applied it to the robust version of the traveling salesman problem (TSP) and the related vehicle routing problem (VRP) where interval uncertainty is taken into account and the goal is to minimise the max-regret value. The architecture (encoder, decoder) is described as well as the training process, and for the POMO approach is used for fast inference with a pre-trained model. Then experiments are conducted to compare the proposed approach to existing approaches from the literature.\n\nSTRENGTHS:\n- The robust version of TSP and VRP have been widely studied in the literature, so it is good to see a paper that continues the existing work \n\n- The proposed approach has been implemented and used to conduct experiments\n\n- The paper is written in good English\n\nWEAKNESSES:\n- The paper is not easy to follow; at the end of the introduction (i.e., Section 1) there should be an outline of the organisation of the paper to give the reader orientation\n\n- The discussion of related work is too brief to give a comprehensive overview of the state-of-the-art of ML-based approaches, including neural network based methods. The paper below is a recent survey paper that discusses supervised as well as reinforcement learning-based approaches:\nBogyrbayeva et al: Machine Learning Solve Vehicle Routing Problems: A Survey, IEEE Trans. on Intelligent Transportation Systems, 2023\n\n- As emphasised by the authors their proposed approach is a bit similar to the approach in MatNet described in reference [19]; so the novelty of the proposed method is limited; the paper's contribution is more the application to the robust TSP and VRP\n\n- The font size in Table , Table 3 and Fig. 2 are far too small\n\n- Careful proofreading is needed as the paper contains a range of typos, for example: page 3, line -2: \"budge\"\n\nQUESTIONS:\n- The formulation of the robust TSP presented in Section 3 seems to be the same to the one studied in reference [22]; so this just be made clear to the reader?\n\n- On page 9 what exactly is meant by blended and fusion in the ablation study?\n\n- The choice of the baseline methods (BS - branch & cut, BD - benders decomposition, SA - simulated annealing, iDS - iterated dual substitution, edge generation - EGA) used for the experiments is not well justified; why just these methods? why no others?\n\nLIMITATIONS:\n- The datasets are just chosen from reference [22]; but it is unclear whether the observations made in the paper still valid for other datasets; how well do they generalise?", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-12"}