Integrating labor awareness to energy-efficient production scheduling under real-time electricity pricing : an empirical study

With the penetration of smart grid into factories, energy-efficient production scheduling has emerged as a promising method for industrial demand response. It shifts flexible production loads to lower-priced periods to reduce energy cost for the same production task. However, the existing methods only focus on integrating energy awareness to conventional production scheduling models. They ignore the labor cost which is shift-based and follows an opposite trend of energy cost. For instance, the energy cost is lower during nights while the labor cost is higher. Therefore, this paper proposes a method for energy-efficient and labor-aware production scheduling at the unit process level. This integrated scheduling model is mathematically formulated. Besides the state-based energy model and genetic algorithm-based optimization, a continuous-time shift accumulation heuristic is proposed to synchronize power states and labor shifts. In a case study of a Belgian plastic bottle manufacturer, a set of empirical sensitivity analyses were performed to investigate the impact of energy and labor awareness, as well as the production-related factors that influence the economic performance of a schedule. Furthermore, the demonstration was performed in 9 large-scale test instances, which encompass the cases where energy cost is minor, moderate, and major compared to the joint energy and labor cost. The results have proven that the ignorance of labor in existing energy-efficient production scheduling studies increases the joint energy and labor cost, although the energy cost can be minimized. To achieve effective production cost reduction, energy and labor awareness are recommended to be jointly considered in production scheduling. (C) 2017 Elsevier Ltd. All rights reserved

Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning

The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques

The 1990 progress report and future plans

This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers

Report of the In Situ Resources Utilization Workshop

The results of a workshop of 50 representatives from the public and private sector which investigated the potential joint development of the key technologies and mechanisms that will enable the permanent habitation of space are presented. The workshop is an initial step to develop a joint public/private assessment of new technology requirements of future space options, to share knowledge on required technologies that may exist in the private sector, and to investigate potential joint technology development opportunities. The majority of the material was produced in 5 working groups: (1) Construction, Assembly, Automation and Robotics; (2) Prospecting, Mining, and Surface Transportation; (3) Biosystems and Life Support; (4) Materials Processing; and (5) Innovative Ventures. In addition to the results of the working groups, preliminary technology development recommendations to assist in near-term development priority decisions are presented. Finally, steps are outlined for potential new future activities and relationships among the public, private, and academic sectors

Trade in environmental services: opportunities and constraints

The environmental services sector has been growing rapidly during the last two decades. Moreover, privatization and increasing outward-orientation of environmental services during the 1990s has made this sector an important service sector for negotiations under the new round of GATS.This study explores the nature and structure of the environmental services sector, both globally and in India, with particular focus on recent trends such as privatization and foreign investment in this sector. It also discusses issues of classification and definition which have occupied centre stage in the multilateral negotiations in this sector. The study assesses the implications of liberalizing environmental services in India, taking into account the country's strengths, weaknesses, and interests in this sector. Based on this assessment, it suggests a negotiating strategy for India in environmental services in the ongoing GATS 2000 negotiations. Given the special features of this sector, the study also suggests domestic reforms and regulations to ensure equity and sustainability along with economic efficiency in the provision of environmental services in India.

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

A dynamic systems engineering methodology research study. Phase 2: Evaluating methodologies, tools, and techniques for applicability to NASA's systems projects

A study of NASA's Systems Management Policy (SMP) concluded that the primary methodology being used by the Mission Operations and Data Systems Directorate and its subordinate, the Networks Division, is very effective. Still some unmet needs were identified. This study involved evaluating methodologies, tools, and techniques with the potential for resolving the previously identified deficiencies. Six preselected methodologies being used by other organizations with similar development problems were studied. The study revealed a wide range of significant differences in structure. Each system had some strengths but none will satisfy all of the needs of the Networks Division. Areas for improvement of the methodology being used by the Networks Division are listed with recommendations for specific action