Storage assignment, the act of choosing what goods are placed in what locations in a warehouse, is a central problem of supply chain logistics. Past literature has shown that the optimal method to assign pallets is to arrange them in increasing duration of stay (DoS) in the warehouse (the DoS method), but the methodology requires perfect prior knowledge of DoS for each pallet, which is unknown and uncertain under realistic conditions. Attempts to predict DoS have largely been unfruitful due to the multi-valuedness nature (every shipment contains identical pallets with different DoS) and data sparsity induced by lack of matching historical conditions. In this paper, we introduce a new framework for storage assignment that provides a solution to the DoS prediction problem through a distributional reformulation and a novel neural network, ParallelNet. Through collaboration with a world-leading cold storage company, we show that the system is able to predict DoS with a MAPE of 29%, a decrease of ∼30% compared to a CNN-LSTM model, and suffers less performance decay into the future. The framework is then integrated into a first-of-its-kind Storage Assignment system, which is being deployed in warehouses across United States, with initial results showing up to 21% in labor savings. We also release the first publicly available set of warehousing records to facilitate research into this central problem.
The paper tackles the problem of assigning pallets to storage positions in a warehouse so as to minimize total travel time. Previous literature has shown that if durations of stay (DoS) is known in advance then the optimal storage position is inversely proportional to this. The paper proposes to predict a DoS distribution for each pallet using a 19 point CDF with a neural network. The authors show that the proposed network is better than a few other neural baselines and that it can predict the DoS reasonably well.
The authors report significant positive real life results in two warehouses and releases their dataset.
The paper is interesting and seems to make significant progress on an important real-life issue. The release of a large realistic dataset is a major contribution to this field.
Despite this I will not recommend this paper for publishing at ICLR, simply because I think it falls outside the scope of the conference.
I would recommend the authors seek to publish in a venue more focused on logistics or operational research (OR), where I think the paper could have a great impact.
I would have appreciated a more in-depth description of the problem. It seems that the general problem would be to minimize the expected cost (distance moved) over the lifetime of the warehouse. Under what simplifying assumptions does that reduce to minimizing the expected cost of each pallet individually as done here? 
Similarly, I would have preferred a more formal definition of the loss, e.g. start from the expected distance moved and derive the (approximate) loss from there. Approximations are fine as long as you tell the reader what you're approximating, why you need to do it, and what the biases are.
I would also move the detailed description and figures of the actual neural network to the appendix since it's not central to the understanding of the paper (aside from the CDF formulation).
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The area chair didn't get back to me, but I'll assume the paper is in scope and as such have changed my rating to weak reject. 
I'd prefer if the paper spend less space arguing that a specific network architecture is the right choice and prioritize introducing the problem more formally. I would move the simulations of different storage strategies (figure 2, blue and black lines) into the introduction, as part of introducing the reader to the problem and evaluation, and explain how figure 2 is related to what you really want to minimize: "total pallet distance moved over time x". I wonder if the pallet percentile distribution (figure 2) is actually interesting, other than as a way to compute the "total pallet distance moved over time x".
I fear the authors feel the need to introduce a "novel" NN in order to publish at ICLR and thus spend a lot of effort describing something that is really not very interesting (even giving it a name). Combining textual and non-textual features is not a new concept, and combining a RNN layer and a CNN layer is not novel. I'm skeptical that this exact network structure is needed. It's clear you should combine the textual and non-textual features, but is this *exact* network structure needed? I would find that very surprising. How about MLP(concat(CNN(text), RNN(text), non-text))?
Another gripe is the heuristic nature of the loss function. It's clear predicting the DoS distribution better is the right thing to do, but is the sum of L2 distances to the emprical log CDF the *right* measure or a heuristic? What is the right thing to do? What is it approximating if anything? Why?
I would expect the right thing to do is to maximize the probability of the observed duration of stay under the model, i.e max_\theta p(DoS|x, \theta), i.e. min_\theta \sum_i -log(p(DoS_i|x_i, \theta). If this is log-normally distributed, then a log-normal distribution is probably a good distribution. Or perhaps a mixture of log-normals. I think you can even use the 19 point CDF output layer and evaluate the data likelihood under this, but I *don't* think the 19 point L2 distance to the empirical probability distributions is the correct thing to do. Is that even defined if there's less than 19 pallets with the same inputs x?