Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review

Industry 4.0 has become a crucial part in the majority of processes, components, and related modelling, as well as predictive tools that allow a more efficient, automated and sustainable approach to industry. The availability of large quantities of data, and the advances in IoT, AI, and data-driven frameworks, have led to an enhanced data gathering, assessment, and extraction of actionable information, resulting in a better decision-making process. Product picking and its subsequent packing is an important area, and has drawn increasing attention for the research community. However, depending of the context, some of the related approaches tend to be either highly mathematical, or applied to a specific context. This article aims to provide a survey on the main methods, techniques, and frameworks relevant to product packing and to highlight the main properties and features that should be further investigated to ensure a more efficient and optimised approach

Tools for efficient Deep Learning

In the era of Deep Learning (DL), there is a fast-growing demand for building and deploying Deep Neural Networks (DNNs) on various platforms. This thesis proposes five tools to address the challenges for designing DNNs that are efficient in time, in resources and in power consumption. We first present Aegis and SPGC to address the challenges in improving the memory efficiency of DL training and inference. Aegis makes mixed precision training (MPT) stabler by layer-wise gradient scaling. Empirical experiments show that Aegis can improve MPT accuracy by at most 4\%. SPGC focuses on structured pruning: replacing standard convolution with group convolution (GConv) to avoid irregular sparsity. SPGC formulates GConv pruning as a channel permutation problem and proposes a novel heuristic polynomial-time algorithm. Common DNNs pruned by SPGC have maximally 1\% higher accuracy than prior work. This thesis also addresses the challenges lying in the gap between DNN descriptions and executables by Polygeist for software and POLSCA for hardware. Many novel techniques, e.g. statement splitting and memory partitioning, are explored and used to expand polyhedral optimisation. Polygeist can speed up software execution in sequential and parallel by 2.53 and 9.47 times on Polybench/C. POLSCA achieves 1.5 times speedup over hardware designs directly generated from high-level synthesis on Polybench/C. Moreover, this thesis presents Deacon, a framework that generates FPGA-based DNN accelerators of streaming architectures with advanced pipelining techniques to address the challenges from heterogeneous convolution and residual connections. Deacon provides fine-grained pipelining, graph-level optimisation, and heuristic exploration by graph colouring. Compared with prior designs, Deacon shows resource/power consumption efficiency improvement of 1.2x/3.5x for MobileNets and 1.0x/2.8x for SqueezeNets. All these tools are open source, some of which have already gained public engagement. We believe they can make efficient deep learning applications easier to build and deploy.Open Acces

A Hybrid Approach to the Optimization of Multiechelon Systems

In freight transportation there are two main distribution strategies: direct shipping and multiechelon distribution. In the direct shipping, vehicles, starting from a depot, bring their freight directly to the destination, while in the multiechelon systems, freight is delivered from the depot to the customers through an intermediate points. Multiechelon systems are particularly useful for logistic issues in a competitive environment. The paper presents a concept and application of a hybrid approach to modeling and optimization of the Multi-Echelon Capacitated Vehicle Routing Problem. Two ways of mathematical programming (MP) and constraint logic programming (CLP) are integrated in one environment. The strengths of MP and CLP in which constraints are treated in a different way and different methods are implemented and combined to use the strengths of both. The proposed approach is particularly important for the discrete decision models with an objective function and many discrete decision variables added up in multiple constraints. An implementation of hybrid approach in the ECLiPSe system using Eplex library is presented. The Two-Echelon Capacitated Vehicle Routing Problem (2E-CVRP) and its variants are shown as an illustrative example of the hybrid approach. The presented hybrid approach will be compared with classical mathematical programming on the same benchmark data sets

System level performance of ATM transmission over a DS-CDMA satellite link.

PhDAbstract not availableEuropean Space Agenc

Stochastic optimization model for coordinated operation of natural gas and electricity networks

Renewable energy sources will anticipate significantly in the future energy system paradigm due to their low cost of operation and low pollution. Considering the renewable generation (e.g., wind) intermittency, flexible gas-fired power plants will continue to play their essential role as the main linkage of natural gas and electricity networks, and hence coordinated operation of these networks is beneficial. Furthermore, uncertainty is always found in gas demand prediction, electricity demand prediction, and output power of wind generation. Therefore, in this paper, a two-stage stochastic model for operation of natural gas and electricity networks is implemented. In order to model uncertainty in these networks, Monte Carlo simulation is applied to generate scenarios representing the uncertain parameters. Afterwards, a scenario reduction algorithm based on distances between the scenarios is applied. Stochastic and deterministic models for natural gas and electricity networks are optimized and compared considering integrated and iterative operation strategies. Furthermore, the value of flexibility options (i.e., electricity storage systems) in dealing with uncertainty is quantified. A case study is presented based on a high pressure 15-node gas system and the IEEE 24-bus reliability test system to validate the applicability of the proposed approach. The results demonstrate that applying the stochastic model of gas and electricity networks as well as considering integrated operation strategy in the presence of flexibility provides different benefits (e.g., 14% cost savings) and enhances the system reliability in the case of contingency

Optimal Trading and Inventory Management in Electronic Markets

In this thesis three distinct trading scenarios are considered and stochastic optimal control models are proposed to derive the optimal strategy the agent/firm should follow. First, we consider an agent who needs to liquidate a large amount of an asset and can trade in both a ‘lit’ exchange and a dark pool. We find the optimal selling schedule by solving numerically the resulting Hamilton-JacobiBellman (HJB) equation. Next, we consider a customised liquidity pool (CLP) that offers a market-making service, by showing bid and ask prices to its clients. The CLP earns the spread from each transaction and it is subject to an inventory risk deriving from potential unfavourable price movements. The CLP can hedge its position in the ‘lit’ pool by means of limit and/or market orders so to rebalance its position on the asset. Finally, we consider a firm that offers mixed principal-versus-agency trading to its clients, and which earns the spread from the principal portion and a fixed fee for the brokerage service. We find the optimal proportion of principal/agency liquidity that should be displayed to clients and the optimal hedging strategy. We make specific reference to the foreign exchange market and consider the cases of one currency pair and three currency pairs. We provide the pseudo-codes, which have been written for solving numerically the models presented in this thesis, as well as a concise review of the dynamic programming principle (DPP) and the viscosity solution theory, specifically applied to the models discussed herein

Abductive knowledge induction from raw data

For many reasoning-heavy tasks with raw inputs, it is challenging to design an appropriate end-to-end pipeline to formulate the problem-solving process. Some modern AI systems, e.g., Neuro-Symbolic Learning, divide the pipeline into sub-symbolic perception and symbolic reasoning, trying to utilise data-driven machine learning and knowledge-driven problem-solving simultaneously. However, these systems suffer from the exponential computational complexity caused by the interface between the two components, where the sub-symbolic learning model lacks direct supervision, and the symbolic model lacks accurate input facts. Hence, they usually focus on learning the sub-symbolic model with a complete symbolic knowledge base while avoiding a crucial problem: where does the knowledge come from? In this paper, we present Abductive Meta-Interpretive Learning (MetaAbd) that unites abduction and induction to learn neural networks and logic theories jointly from raw data. Experimental results demonstrate that MetaAbd not only outperforms the compared systems in predictive accuracy and data efficiency but also induces logic programs that can be re-used as background knowledge in subsequent learning tasks. To the best of our knowledge, MetaAbd is the first system that can jointly learn neural networks from scratch and induce recursive first-order logic theories with predicate invention

Systematic mapping of power system models: Expert survey

The power system is one of the main subsystems of larger energy systems. It is a complex system in itself, consisting of an ever-changing infrastructure used by a large number of actors of very different sizes. The boundaries of the power system are characterised by ever-evolving interfaces with equally complex subsystems such as gas transport and distribution, heating and cooling, and, increasingly, transport. The situation is further complicated by the fact that electricity is only a carrier, able to fulfil demand for such things as lighting, heat or mobility. One specific and fundamental feature of the electricity system is that demand and generation must match at any time, while satisfying technical and economic constraints. In most of the world’s power systems, only relatively small quantities of electricity can be stored, and only for limited periods of time. A detailed analysis of supply and demand is thus needed for short time intervals. Mathematical models facilitate power system planning, operation, transmission and distribution, demonstrating problems that need to be solved over different timescales and horizons. The use of modelling to understand these processes is not only vital for the system’s direct actors, i.e. the companies involved in the generation, trade, transmission, distribution and use of electricity, but also for policy-makers and regulators. Power system models can provide evidence to support policy-making at European Union, Member State and Regional level. As a consequence of the growth in computing power, mathematical models for power systems have become more accessible. The number of models available worldwide, and the degree of detail they provide, is growing fast. A proper mapping of power system models is therefore essential in order to: - provide an overview of power system models and their applications available in, or used by, European organisations; - analyse their modelling features; - identify modelling gaps. Few reviews have been conducted to date of the power system modelling landscape. The mission of the Knowledge for the Energy Union Unit of the Joint Research Centre (JRC) is to support policies related to the Energy Union by anticipating, mapping, collating, analysing, quality checking and communicating all relevant data/knowledge, including knowledge gaps, in a systematic and digestible way. This report therefore constitutes: - From the energy modelling perspective, a useful mapping exercise that could help promote knowledge-sharing and thus increase efficiency and transparency in the modelling community. It could trigger new, unexplored avenues of research. It also represents an ideal starting point for systematic review activities in the context of the power system. - From the knowledge management perspective, a useful blueprint to be adopted for similar mapping exercises in other thematic areas. Finally, this report is aligned with the objectives of the European Commission's Competence Centre on Modelling, (1) launched on 26 October 2017 and hosted by the JRC, which aims to promote a responsible, coherent and transparent use of modelling to support the evidence base for European Union policies. In order to meet the objectives of this report, an online survey was used to collect detailed and relevant information about power system models. The participants’ answers were processed to categorise and describe the modelling tools identified. The survey, conducted by the Knowledge for the Energy Union Unit of the JRC, comprised a set of questions for each model to ascertain its basic information, its users, software characteristics, modelling properties, mathematical description, policy-making applications, selected references, and more. The survey campaign was organised in two rounds between April and July 2017. 228 surveys were sent to power system experts and organisations, and 82 questionnaires were completed. The answers were processed to map the knowledge objectively. (2) The main results of the survey can be summarised as follows: - Software-related features: about two thirds of the models require third-party software such as commercial optimisation solvers or off-the-shelf software. Only 14% of the models are open source, while 11% are free to download. - Modelling-related features: models are mostly defined as optimisation problems (78%) rather than simulation (33%) or equilibrium problems (13%). 71% of the models solve a deterministic problem while 41% solve probabilistic or stochastic problems. - Modelled power system problems: the economic dispatch problem is the most commonly modelled problem with a share of approximately 70%, followed by generation expansion planning, unit commitment, and transmission expansion planning, with around 40‒43% each. Most of the models (57%) have non-public input data while 31% of models use open input data. - Modelled technologies: hydro, wind, thermal, storage and nuclear technologies are widely taken into account, featuring in around 83‒94% of models. However, HVDC, wave tidal, PSTs, and FACTS (3) are not often found unless the analysis is specifically performed for those technologies. - Applicability in the context of European energy policy: more than half of the mapped models (56%) were used to answer a specific policy question. Of the five Energy Union strategic dimensions, integration of the European Union internal energy market was addressed the most often (27%), followed by climate action (23%), research, innovation and competitiveness (21%), and energy efficiency (15%). This report includes JRC recommendations based on the results of the survey, on future research avenues for power system modelling and its applicability within the Energy Union strategic dimensions. More attention should be paid, for example, to model uncertainty features, and collaboration among researchers and practitioners should be promoted to intensify research into specific power system problems such as AC (4) optimal power flow. The report includes factsheets for each model analysed, summarising relevant characteristics based on the participants’ answers. While this report represents a scientific result per se, one of the expected (and welcomed) outcomes of this mapping exercise is to raise awareness of power system modelling activities among European policy makers.JRC.C.7-Knowledge for the Energy Unio