Neural Speed Reading with Structural-Jump-LSTM

Recurrent neural networks (RNNs) can model natural language by sequentially 'reading' input tokens and outputting a distributed representation of each token. Due to the sequential nature of RNNs, inference time is linearly dependent on the input length, and all inputs are read regardless of their importance. Efforts to speed up this inference, known as 'neural speed reading', either ignore or skim over part of the input. We present Structural-Jump-LSTM: the first neural speed reading model to both skip and jump text during inference. The model consists of a standard LSTM and two agents: one capable of skipping single words when reading, and one capable of exploiting punctuation structure (sub-sentence separators (,:), sentence end symbols (.!?), or end of text markers) to jump ahead after reading a word. A comprehensive experimental evaluation of our model against all five state-of-the-art neural reading models shows that Structural-Jump-LSTM achieves the best overall floating point operations (FLOP) reduction (hence is faster), while keeping the same accuracy or even improving it compared to a vanilla LSTM that reads the whole text.Comment: 10 page

The Methodology of the Customers’ Operation from the Seaport Applying the “Simple Shuttle Problem”

This paper deals with one of the most significant fields of the application of mathematical calculations to optimize the operational research and traffic problems. This issue includes the distribution of goods from the source/sources (seaport in our case) to dispersed targets (customers).The application of the “simple shuttle problem” is one option of the transport problem solution

Applications of simulation and optimization techniques in optimizing room and pillar mining systems

The goal of this research was to apply simulation and optimization techniques in solving mine design and production sequencing problems in room and pillar mines (R&P). The specific objectives were to: (1) apply Discrete Event Simulation (DES) to determine the optimal width of coal R&P panels under specific mining conditions; (2) investigate if the shuttle car fleet size used to mine a particular panel width is optimal in different segments of the panel; (3) test the hypothesis that binary integer linear programming (BILP) can be used to account for mining risk in R&P long range mine production sequencing; and (4) test the hypothesis that heuristic pre-processing can be used to increase the computational efficiency of branch and cut solutions to the BILP problem of R&P mine sequencing. A DES model of an existing R&P mine was built, that is capable of evaluating the effect of variable panel width on the unit cost and productivity of the mining system. For the system and operating conditions evaluated, the result showed that a 17-entry panel is optimal. The result also showed that, for the 17-entry panel studied, four shuttle cars per continuous miner is optimal for 80% of the defined mining segments with three shuttle cars optimal for the other 20%. The research successfully incorporated risk management into the R&P production sequencing problem, modeling the problem as BILP with block aggregation to minimize computational complexity. Three pre-processing algorithms based on generating problem-specific cutting planes were developed and used to investigate whether heuristic pre-processing can increase computational efficiency. Although, in some instances, the implemented pre-processing algorithms improved computational efficiency, the overall computational times were higher due to the high cost of generating the cutting planes --Abstract, page iii

Service Network Design for Parcel Trucking

We develop a large-scale package express service network design methods using integer programming optimization models specified on flat network models that capture important timing constraints to ensure that package flows meet service constraints. In the first part, we focus on shuttle activities and develop optimization technology for the design of shuttle services using novel rate-based models to determine package flow paths as well as vehicle routes. A computational study using data from a large Chinese package company demonstrates that the technology produces a cost-effective service network design for shuttle schedules with excellent on-time performance. The second part presents a strategic hub selection problem developing a cost-effective greedy heuristic approach that solves tractable integer programming models to add a single intermediate hub on each iteration. A computational study shows that the greedy approach selects geographically-distributed and cost-effective hubs for package transfer, and moreover, the heuristic outperforms the full optimization model by a 20% gap difference for the relevant test instances. In the last part, we develop a new approach for solving the flow planning problem of service network design for large-scale networks with timing constraints. We introduce a so-called generalized in-tree, referred to as GIT, which has useful operational benefits. We demonstrate, via a computational study, that imposing a discretized GIT structure that groups remaining times into fixed-width buckets of 2 hours or 4 hours leads to solutions that are only 2% to 4% more costly than those that do not require GIT structure but significantly simpler to operationalize.Ph.D

k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)

Perhaps the most straightforward classifier in the arsenal or machine learning techniques is the Nearest Neighbour Classifier -- classification is achieved by identifying the nearest neighbours to a query example and using those neighbours to determine the class of the query. This approach to classification is of particular importance because issues of poor run-time performance is not such a problem these days with the computational power that is available. This paper presents an overview of techniques for Nearest Neighbour classification focusing on; mechanisms for assessing similarity (distance), computational issues in identifying nearest neighbours and mechanisms for reducing the dimension of the data. This paper is the second edition of a paper previously published as a technical report. Sections on similarity measures for time-series, retrieval speed-up and intrinsic dimensionality have been added. An Appendix is included providing access to Python code for the key methods.Comment: 22 pages, 15 figures: An updated edition of an older tutorial on kN

Public Transportation Systems:Basic Principles of System Design,Operations Planning and Real-TimeControl

This document is based on a set of lecture notes prepared in 2007-2010 for a University of California, Berkeley graduate course, Public Transportation Systems, a course targeted to first year graduate students with diverse academic backgrounds. Systems are examined in order of increased complexity so that generic insights evident in simple systems can be put to use as knowledge building blocks for the study of more complex systems. The document is organized in eight modules: five on planning (general, shuttle systems, corridors, two dimensional systems, and unconventional transit); two on management (vehicles and employees); and one on operations (how to stay on schedule)

4th Party Logistics Problem Optimizer

This thesis considers a pickup and delivery problem with multiple time windows, a complex cost structure and factory constraints. We formulated the problem as a mathematical model and created an instance generator based on real data. We also implemented a heuristic solution method for the problem and ran extensive statistical tests. The mathematical model shows the complexity of the problem and is implemented in AMPL to give a benchmark for the proposed solution method. The instance generator was created based on real anonymized data from a 4th party logistics (4PL) company. The proposed solution method, called the 4th Party Logis- tics Optimizer, is a meta-heuristic approach with industry specific implementations. The solution method is refined through extensive statistical experiments. The ex- periments determine which parts of the solution method have a significant positive impact on the objective value. This leads to a final composition of our solution method. The final solution method is robustly giving near optimal solutions to re- alistic sized instances in seconds, and is a powerful tool for companies facing the proposed adaptation of the pickup and delivery problem.Masteroppgave i informatikkINF399MAMN-PROGMAMN-IN

Maximizing space utilization in unit-load warehouses.

In a unit-load warehouse, products are stored and retrieved in pallet quantities. Examples include retail distribution centers (DC), third-party DCs, and transshipment hubs in freight transportation. Expenses related to space are a significant component of the operational cost of unit-load warehouses; therefore, maximizing space utilization is important. Moreover, the continuing revolution of retail e-commerce is changing the role and design of modern distribution centers (Boysen et al., 2018). An important trend with serious implications for design is the desire of many distributors to locate DCs in or near metropolitan areas in order to support same-day service or better (Thuermer, 2018). Land in these areas is very expensive, so there is a need to make the best use of existing space. The ability to store more products in the same space increases inventory availability and therefore service, and the ability to store the same inventory in a smaller footprint reduces costs. In this dissertation, we propose two strategies to improve space utilization in unit-load warehouses. We aim to minimize what we called loss of vertical space within slots (LVS)—the mismatch between the height of the pallet and the height of the slot where it is stored. LVS is a significant problem because it is standard practice to design storage racks in unit-load warehouses with all slots of equal height (maximum pallet height) such that every pallet can fit in every slot; however, pallet heights vary greatly. We propose the use of storage racks with multiple slot heights so that slot heights can better match the distribution of pallet heights. We analyzed historic (forecasted) inventory levels and the pallet heights to determine a robust design that guarantees a desired storage service level. Our method addresses the new warehouse design decisions that arise when having multiple slot heights: How to arrange the different slot heights in the rack-bays? How to organize the layout? How to avoid storage shortages? How do different slot heights affect travel times? We found that using multiple slot heights in unit-load warehouses has significant benefits in terms of footprint, expected travel time, and racking cost. For a typical warehouse, we expect space savings of 25–35 percent, depending on the number of slot types, and savings of 15–25 percent in annual operating cost. Although using multiple slot heights significantly decreases the loss of vertical space within slots, it does not completely eliminate it, and in warehouses where inventory levels are highly variable or product mixes change rapidly, this wasted space can still be significant. Examples of this situation in practice include warehouses with correlated order profiles, demands with seasonal peaks, new product launches, and distribution network consolidations. For such business environments, we propose pallet racks with dynamic heights as a way to maximize space utilization. Contrary to traditional pallet racks, the uprights and beams of pallet racks with dynamic heights are equipped with a mechanism to adjust slot heights easily. We found that pallet racks with dynamic heights have expected space savings of 16–30 percent when compared to traditional pallet racks

Exact and Heuristic Methods for Emerging Vehicle Routing Problems

The rise of global supply chains and e-commerce in recent decades have intensified the relevance of the transportation industry to both the individual and the economy. With rising consumer expectations and slim profit margins, the various sectors within the transportation industry rely on the development of carefully designed routes to remain competitive. Despite the wealth of research on route design, and the responsiveness of the research community to practical considerations, there remain gaps between the work done in practice and that appearing in the literature. Correspondingly, the work in this dissertation is directly in response to conversations had with contacts from real-world companies within the transportation domain. We consider problems presented, verbally, by companies representing three distinct segments of the industry: freight routing, last-mile delivery, and on-demand passenger transport. Each problem is centered around an innovative strategy with the potential to dramatically disrupt its corresponding domain. First, we consider the Shared Truckload (STL) freight shipping model, an alternative to the dominant Less-than-Truckload (LTL) model. Both models pool shipments from multiple customers into a single trailer, but, in the latter, consolidation is facilitated by a hub-and-spoke routing network, whereas, in the the former, freight moves directly from origin to destination. This strategy minimizes travel times and the risk of damage. We then investigate a novel strategy to facilitate last-mile, last-minute delivery, through coordinating a fleet of trucks and a fleet of smaller vehicles, referred to as shuttles. In order to accommodate requests which come in after trucks have been dispatched, shuttles are allowed to pick up packages from a depot and intercept trucks along their routes. This strategy can enable a shipper to make highly competitive service guarantees. Finally, we consider the emerging field of Urban Air Mobility (UAM), a vision of air taxis conveying passengers at lower altitudes throughout urban areas as an efficient alternative to gridlock traffic. In particular, we consider a UAM service company in the early stages of its development, where the chief goal is to maximize market share. These innovations represent significant deviations from the status quo in their respective fields, and, thus, the existing research for each is slim, if existent. Therefore, we introduce precise mathematical formulations of each of the problems to the research community. We then develop both exact and heuristic approaches to solve the problems, and carry out extensive computational studies comparing the solution methodologies. Furthermore, for each of the problems, we offer a sensitivity analysis and managerial insights. Among our contributions are original algorithms based on solving a set-partitioning formulations via column generation, a highly successful paradigm for solving large linear programs. Among the advantages of this approach is the ability to include highly general route costs and constraints. We illustrate this expressiveness by demonstrating its application to each of the three highly distinct problems we consider