The energy scheduling problem: Industrial case-study and constraint propagation techniques

This paper deals with production scheduling involving energy constraints, typically electrical energy. We start by an industrial case-study for which we propose a two-step integer/constraint programming method. From the industrial problem we derive a generic problem,the Energy Scheduling Problem (EnSP). We propose an extension of specific resource constraint propagation techniques to efficiently prune the search space for EnSP solving. We also present a branching scheme to solve the problem via tree search.Finally,computational results are provided

A Hierarchical Temporal Planning-Based Approach for Dynamic Hoist Scheduling Problems

Hoist scheduling has become a bottleneck in electroplating industry applications with the development of autonomous devices. Although there are a few approaches proposed to target at the challenging problem, they generally cannot scale to large-scale scheduling problems. In this paper, we formulate the hoist scheduling problem as a new temporal planning problem in the form of adapted PDDL, and propose a novel hierarchical temporal planning approach to efficiently solve the scheduling problem. Additionally, we provide a collection of real-life benchmark instances that can be used to evaluate solution methods for the problem. We exhibit that the proposed approach is able to efficiently find solutions of high quality for large-scale real-life benchmark instances, with comparison to state-of-the-art baselines

Hybrid modelling methodology for system design

In the face of rapid development in information technology coupled with a growing dynamism in global markets, manufacturing systems have to be re-constructed for short term or long term goal. Such innovations promise to lead to a new competitive stage, which typically involve design of function, information and behaviour of systems. In order to design the system, simulation has often been chosen. However, simulation has proved limited and fails to aid design of such a complex systems because of consuming much computing time and cost, especially when modelling larger systems. Thus, there is a need to seek a new approach, in a way that results in simulating such a large manufacturing system with less demand on computing time and cost. This study researches into a hybrid modelling approach to minimise these limitations. It includes proposing a hybrid modelling methodology and developing a hybrid modelling tool. The methodology integrates simulation and metamodelling techniques. The metamodel employed in the study possesses, not only characteristics of conventional metamodels in terms of representing relationships in quantity, but also in time lapse. This is the originality of the study and the significant distinction between this research and application of metamodelling in conventional ways. The hybrid modelling tool is developed to support and demonstrate the identified hybrid methodology. LISP has been used as the software language for the hybrid modelling tool. The result of this work concludes that the hybrid modelling approach is capable of simulating a complex manufacturing system with less demands on the computer. The work reported in this thesis has been carried out in conjunction with the EPSRC research project, Hierarchical Manufacturing System Modelling (HMSM) (GR/F96549), to produce an Integrated Design and Modelling Methodology (IDEM). The project was initially a collaborative research program including Loughborough University of Technology (LUT), Morris Crane Ltd., of Loughborough and GEC Large Machine, of Rugby. The experience of these collaborators has proved most valuable in supporting the research, and have provided a cross section of views and comments. The research reported in this thesis is set in the context of the HMSM Research group at Loughborough

Using Reduced Graphs for Efficient HLS Scheduling

High-Level Synthesis (HLS) is the process of inferring a digital circuit from a high-level algorithmic description provided as a software implementation, usually in C/C++. HLS tools will parse the input code and then perform three main steps: allocation, scheduling, and binding. This results in a hardware architecture which can then be represented as a Register-Transfer Level (RTL) model using a Hardware Description Language (HDL), such as VHDL or Verilog. Allocation determines the amount of resources needed, scheduling finds the order in which operations should occur, and binding maps operations onto the allocated hardware resources. Two main challenges of scheduling are in its computational complexity and memory requirements. Finding an optimal schedule is an NP-hard problem, so many tools use elaborate heuristics to find a solution which satisfies prescribed implementation constraints. These heuristics require the Control/Data Flow Graph (CDFG), a representation of all operations and their dependencies, which must be stored in its entirety and therefore use large amounts of memory. This thesis presents a new scheduling approach for use in the HLS tool chain. The new technique schedules operations using an algorithm which operates on a reduced representation of the graph, which does not need to retain individual dependency information in order to generate a schedule. By using the simplified graph, the complexity of scheduling is significantly reduced, resulting in improved memory usage and lower computational effort. This new scheduler is implemented and compared to the existing scheduler in the open source version of the LegUp HLS tool. The results demonstrate that an average of 16 times speedup on the time required to determine the schedule can be achieved, with just a fraction of the memory usage (1/5 on average). All of this is achieved with 0 to 6% of added cost on the final hardware execution time

Methodological study on technology integration for sustainable manufacturing in the surface finishing industry

Today, industries explore advanced techniques to enhance their development efforts to meet the goals of sustainability due to various challenges which is caused by industrial globalization, high energy and raw material costs, increased environmental regulations and social pressures, and new technological innovations. In order for an industrial process to become sustainable, it is essential to improve the process inputs efficiency from raw materials and energy while maintaining highest productivity and quality; in addition to, minimizing waste generation and the impact on the environment. Engaging in industrial sustainability requires major efforts from decision makers to implement advanced technologies to satisfy each triple bottom line of sustainability. Due to the complexity of industrial systems and lack of quantifiable mechanisms to assess sustainability triple bottom lines, decision makers are facing a very difficult task to solve. In this research a holistic methodology for sustainability assessment and decision-making is developed, which will assist in improving the sustainability level through implementing and integrating sustainable technologies in manufacturing systems through case studies, particularly on the electroplating industry. The methodology is general but our intent is to apply it to electroplating metal substrate processes. This research is valuable in its methodological contribution for sustainability assessment, decision-making, and technology quantification via known and well established sustainability metrics to assist decision makers to identify desired technologies needed for improving overall industrial sustainability development. This methodology is applicable for any type of industrial system of any complexity, and its efficacy is demonstrated in a case study identifying desired technologies and their implementation for achieving an overall sustainable level enhancement. Moreover, a computer aided computational tool is developed for industry forecasters to assess their current industrial sustainability and determine future sustainability goals in a quantitative manner using an interactive graphical user interface. To the best of our knowledge the introduced concept of technology integrated sustainability enhancement (TISE) holistic approach is the first to be used to effectively enhance the overall industrial system sustainability by evaluating each technology or suite of technologies based on strategically selected indicators and combined benefits methodology assessment. Furthermore, an optimization based approach was introduced for a proficient sustainability assessment of industrial systems via technology integration. Another major contribution in this research is the development of an industrial sustainability assessment program using LabView software and Matlab programming tools to assess the sustainability of various technology options. The assessment results from this program provide different technology integration options and alternatives which can be compared in terms of sustainability triple bottom lines, overall sustainability performance, and the optimum solution can be identified as the one yielding to the highest sustainability value depending on budget cost limitation to implement those technologies

Biopsychosocial Assessment and Ergonomics Intervention for Sustainable Living: A Case Study on Flats

This study proposes an ergonomics-based approach for those who are living in small housings (known as flats) in Indonesia. With regard to human capability and limitation, this research shows how the basic needs of human beings are captured and analyzed, followed by proposed designs of facilities and standard living in small housings. Ninety samples were involved during the study through in- depth interview and face-to-face questionnaire. The results show that there were some proposed of modification of critical facilities (such as multifunction ironing work station, bed furniture, and clothesline) and validated through usability testing. Overall, it is hoped that the proposed designs will support biopsychosocial needs and sustainability

The synergistic effect of operational research and big data analytics in greening container terminal operations: a review and future directions

Container Terminals (CTs) are continuously presented with highly interrelated, complex, and uncertain planning tasks. The ever-increasing intensity of operations at CTs in recent years has also resulted in increasing environmental concerns, and they are experiencing an unprecedented pressure to lower their emissions. Operational Research (OR), as a key player in the optimisation of the complex decision problems that arise from the quay and land side operations at CTs, has been therefore presented with new challenges and opportunities to incorporate environmental considerations into decision making and better utilise the ‘big data’ that is continuously generated from the never-stopping operations at CTs. The state-of-the-art literature on OR's incorporation of environmental considerations and its interplay with Big Data Analytics (BDA) is, however, still very much underdeveloped, fragmented, and divergent, and a guiding framework is completely missing. This paper presents a review of the most relevant developments in the field and sheds light on promising research opportunities for the better exploitation of the synergistic effect of the two disciplines in addressing CT operational problems, while incorporating uncertainty and environmental concerns efficiently. The paper finds that while OR has thus far contributed to improving the environmental performance of CTs (rather implicitly), this can be much further stepped up with more explicit incorporation of environmental considerations and better exploitation of BDA predictive modelling capabilities. New interdisciplinary research at the intersection of conventional CT optimisation problems, energy management and sizing, and net-zero technology and energy vectors adoption is also presented as a prominent line of future research

Smart Manufacturing

This book is a collection of 11 articles that are published in the corresponding Machines Special Issue “Smart Manufacturing”. It represents the quality, breadth and depth of the most updated study in smart manufacturing (SM); in particular, digital technologies are deployed to enhance system smartness by (1) empowering physical resources in production, (2) utilizing virtual and dynamic assets over the Internet to expand system capabilities, (3) supporting data-driven decision-making activities at various domains and levels of businesses, or (4) reconfiguring systems to adapt to changes and uncertainties. System smartness can be evaluated by one or a combination of performance metrics such as degree of automation, cost-effectiveness, leanness, robustness, flexibility, adaptability, sustainability, and resilience. This book features, firstly, the concepts digital triad (DT-II) and Internet of digital triad things (IoDTT), proposed to deal with the complexity, dynamics, and scalability of complex systems simultaneously. This book also features a comprehensive survey of the applications of digital technologies in space instruments; a systematic literature search method is used to investigate the impact of product design and innovation on the development of space instruments. In addition, the survey provides important information and critical considerations for using cutting edge digital technologies in designing and manufacturing space instruments