Information logistics: A production-line approach to information services

Logistics can be defined as the process of strategically managing the acquisition, movement, and storage of materials, parts, and finished inventory (and the related information flow) through the organization and its marketing channels in a cost effective manner. It is concerned with delivering the right product to the right customer in the right place at the right time. The logistics function is composed of inventory management, facilities management, communications unitization, transportation, materials management, and production scheduling. The relationship between logistics and information systems is clear. Systems such as Electronic Data Interchange (EDI), Point of Sale (POS) systems, and Just in Time (JIT) inventory management systems are important elements in the management of product development and delivery. With improved access to market demand figures, logisticians can decrease inventory sizes and better service customer demand. However, without accurate, timely information, little, if any, of this would be feasible in today's global markets. Information systems specialists can learn from logisticians. In a manner similar to logistics management, information logistics is concerned with the delivery of the right data, to the ring customer, at the right time. As such, information systems are integral components of the information logistics system charged with providing customers with accurate, timely, cost-effective, and useful information. Information logistics is a management style and is composed of elements similar to those associated with the traditional logistics activity: inventory management (data resource management), facilities management (distributed, centralized and decentralized information systems), communications (participative design and joint application development methodologies), unitization (input/output system design, i.e., packaging or formatting of the information), transportations (voice, data, image, and video communication systems), materials management (data acquisition, e.g., EDI, POS, external data bases, data entry) and production scheduling (job, staff, and project scheduling)

Games and Mechanism Design in Machine Scheduling – An Introduction

In this paper, we survey different models, techniques, and some recent results to tackle machine scheduling problems within a distributed setting. In traditional optimization, a central authority is asked to solve a (computationally hard) optimization problem. In contrast, in distributed settings there are several agents, possibly equipped with private information that is not publicly known, and these agents need to interact in order to derive a solution to the problem. Usually the agents have their individual preferences, which induces them to behave strategically in order to manipulate the resulting solution. Nevertheless, one is often interested in the global performance of such systems. The analysis of such distributed settings requires techniques from classical Optimization, Game Theory, and Economic Theory. The paper therefore briefly introduces the most important of the underlying concepts, and gives a selection of typical research questions and recent results, focussing on applications to machine scheduling problems. This includes the study of the so-called price of anarchy for settings where the agents do not possess private information, as well as the design and analysis of (truthful) mechanisms in settings where the agents do possess private information.computer science applications;

Provably Efficient Adaptive Scheduling for Parallel Jobs

Scheduling competing jobs on multiprocessors has always been an important issue for parallel and distributed systems. The challenge is to ensure global, system-wide efficiency while offering a level of fairness to user jobs. Various degrees of successes have been achieved over the years. However, few existing schemes address both efficiency and fairness over a wide range of work loads. Moreover, in order to obtain analytical results, most of them require prior information about jobs, which may be difficult to obtain in real applications. This paper presents two novel adaptive scheduling algorithms -- GRAD for centralized scheduling, and WRAD for distributed scheduling. Both GRAD and WRAD ensure fair allocation under all levels of workload, and they offer provable efficiency without requiring prior information of job's parallelism. Moreover, they provide effective control over the scheduling overhead and ensure efficient utilization of processors. To the best of our knowledge, they are the first non-clairvoyant scheduling algorithms that offer such guarantees. We also believe that our new approach of resource request-allotment protocol deserves further exploration. Specifically, both GRAD and WRAD are O(1)-competitive with respect to mean response time for batched jobs, and O(1)-competitive with respect to makespan for non-batched jobs with arbitrary release times. The simulation results show that, for non-batched jobs, the makespan produced by GRAD is no more than 1.39 times of the optimal on average and it never exceeds 4.5 times. For batched jobs, the mean response time produced by GRAD is no more than 2.37 times of the optimal on average, and it never exceeds 5.5 times.Singapore-MIT Alliance (SMA

Cyber Physical System Based Proactive Collaborative Maintenance

The aim of the MANTIS project is to provide a proactive maintenance service platform architecture based on Cyber Physical Systems. The platform will allow estimating future performance, predicting and preventing imminent failures and scheduling proactive maintenance. Maintenance is an important element that creates added value in the business processes and it also creates new business models with a stronger service orientation. Physical systems and the environment they work in are continuously monitored by a range of intelligent sensors, resulting in massive amounts of data, which characterise the usage history, working condition, location, movement and other physical properties of the systems. These systems are part of a larger network of heterogeneous and collaborative systems (e.g. vehicle fleets) connected via robust communication mechanisms able to operate in challenging environments. MANTIS consists of distributed processing chains that efficiently transform raw data into knowledge, while minimising the need for bandwidth. Sophisticated distributed sensing and decision-making functions are performed at different levels collaboratively, ranging from local nodes to locally optimise performance, bandwidth and maintenance; to cloud-based platforms that integrate information from diverse systems and execute distributed processing and analytics algorithms for global decision-making

Multi-agent System Models for Distributed Services Scheduling

This thesis investigates the computational and modeling issues involved with developing solutions for distributed service scheduling problems. Compared with traditional manufacturing scheduling, service scheduling poses additional challenges due to the significant customer involvement in service processes. The first challenge is that the service scheduling environment is a distributed environment in which scheduling-related information is scattered among individual identities, such as service providers and customers. The second challenge is that the service scheduling environment is a dynamic environment. Uncertainty in customer demand, customer cancellations and no-shows make the scheduling of services a complex dynamic process. Service scheduling has to be robust and prepared to accommodate any contingencies caused by customer involvement in service production. The third challenge concerns customers’ private information. To compute optimal schedules, ideally, the scheduler should know the complete customer availability and preference information within the scheduling horizon. However, customers may act strategically to protect their private information. Therefore, service scheduling systems should be designed so that they are able to elicit enough of a customer’s private information that will make it possible to compute high quality schedules. The fourth challenge is that in a service scheduling environment, the objectives are complicated and they may even be in opposition. The distributed service scheduling environment enables each agent to have their own scheduling objectives. The objectives of these agents can vary from one to another. In addition to multiple objectives, since agents are self-interested, they are likely to behave strategically to achieve their own objectives without considering the global objectives of the system. Existing approaches usually deal with only a part of the challenges in a specific service domain. There is a need for general problem formulations and solutions that address service scheduling challenges in a comprehensive framework. In this thesis, I propose an integrated service scheduling framework for the general service scheduling problem. The proposed framework uses iterative auction as the base mechanism to tackle service scheduling challenges in distributed and dynamic environments. It accommodates customer’s private information by providing appropriate incentives to customers and it has the potential to accommodate dynamic events. This framework integrates customers’ preferences with the allocation of a provider’s capacity through multilateral negotiation between the provider and its customers. The framework can accommodate both price-based commercial settings and non-commercial service settings. Theoretical and experimental results are developed to verify the effectiveness of the proposed framework. The application of the framework to the mass customization of services and to appointment scheduling are developed to demonstrate the applicability of the general framework to specific service domains. A web-based prototype is designed and implemented to evaluate the scalability of the approach in a distributed environment

A Taxonomy of Workflow Management Systems for Grid Computing

With the advent of Grid and application technologies, scientists and engineers are building more and more complex applications to manage and process large data sets, and execute scientific experiments on distributed resources. Such application scenarios require means for composing and executing complex workflows. Therefore, many efforts have been made towards the development of workflow management systems for Grid computing. In this paper, we propose a taxonomy that characterizes and classifies various approaches for building and executing workflows on Grids. We also survey several representative Grid workflow systems developed by various projects world-wide to demonstrate the comprehensiveness of the taxonomy. The taxonomy not only highlights the design and engineering similarities and differences of state-of-the-art in Grid workflow systems, but also identifies the areas that need further research.Comment: 29 pages, 15 figure

A Novel Workload Allocation Strategy for Batch Jobs

The distribution of computational tasks across a diverse set of geographically distributed heterogeneous resources is a critical issue in the realisation of true computational grids. Conventionally, workload allocation algorithms are divided into static and dynamic approaches. Whilst dynamic approaches frequently outperform static schemes, they usually require the collection and processing of detailed system information at frequent intervals - a task that can be both time consuming and unreliable in the real-world. This paper introduces a novel workload allocation algorithm for optimally distributing the workload produced by the arrival of batches of jobs. Results show that, for the arrival of batches of jobs, this workload allocation algorithm outperforms other commonly used algorithms in the static case. A hybrid scheduling approach (using this workload allocation algorithm), where information about the speed of computational resources is inferred from previously completed jobs, is then introduced and the efficiency of this approach demonstrated using a real world computational grid. These results are compared to the same workload allocation algorithm used in the static case and it can be seen that this hybrid approach comprehensively outperforms the static approach

Analysis of Issues for Project Scheduling by Multiple, Dispersed Schedulers (distributed Scheduling) and Requirements for Manual Protocols and Computer-based Support

Although computerized operations have significant gains realized in many areas, one area, scheduling, has enjoyed few benefits from automation. The traditional methods of industrial engineering and operations research have not proven robust enough to handle the complexities associated with the scheduling of realistic problems. To address this need, NASA has developed the computer-aided scheduling system (COMPASS), a sophisticated, interactive scheduling tool that is in wide-spread use within NASA and the contractor community. Therefore, COMPASS provides no explicit support for the large class of problems in which several people, perhaps at various locations, build separate schedules that share a common pool of resources. This research examines the issue of distributing scheduling, as applied to application domains characterized by the partial ordering of tasks, limited resources, and time restrictions. The focus of this research is on identifying issues related to distributed scheduling, locating applicable problem domains within NASA, and suggesting areas for ongoing research. The issues that this research identifies are goals, rescheduling requirements, database support, the need for communication and coordination among individual schedulers, the potential for expert system support for scheduling, and the possibility of integrating artificially intelligent schedulers into a network of human schedulers