Scheduling Subsystem Research Articles

A multi-population co-evolutionary algorithm for green integrated process planning and scheduling considering logistics system

Nowadays, green manufacturing is an increasingly significant theme in the worldwide industry fields. System integration can fully exploit the potential of subsystems to achieve satisfying green and low-carbon manufacturing. In particular, integrated process planning and scheduling (IPPS) can obtain better process routes and scheduling schemes to realize more efficient and less energy-consuming production by utilizing the complementary attributes of the process and scheduling subsystems. Additional consideration of the shop logistics system including task assignment of automated guided vehicles (AGVs) can improve shop productivity while ensuring the smooth running of the whole manufacturing system. Taking into account AGV transportation, this paper studies a green multi-objective IPPS problem considering logistics system (MO_IPPS_LS) to minimize the maximum completion time and energy consumption simultaneously. A multi-population co-evolutionary algorithm (MPCEA) is proposed with a novel integrated encoding method. The co-evolutionary framework can ensure the diversity of the populations through a backtracking mechanism and different evolutionary operators including modified critical-path based local searching. The proposed MPCEA is tested on the open benchmarks with different proportions of transport resources. The comparative experimental results show the effectiveness and superiority of MPCEA for solving MO_IPPS_LS problem.

Dynamic Scheduling, Operation Control and Their Integration in High-Speed Railways: A Review of Recent Research

Railway system performances depend on effective dynamic scheduling and train operation control. The fast expansion and increasing complexity of high-speed railway (HSR) networks raise new challenges in maintaining the punctuality and efficiency in daily operations, in particular, in the event of disruption. This paper aims to review the state-of-art in dynamic traffic scheduling, trains operation control, and their integration for safer, more punctuate, efficient, and resilient HSRs, whose origins may trace back to their counterparts in traditional railways. First, the existing two-tier hierarchy of scheduling and control in HSR’s daily operation is introduced. At the higher layer of scheduling, a general model of dynamic train scheduling is discussed, followed by reviewing the scheduling methodologies. At the lower layer of train operation control, recent progress in tracking control of high-speed trains is discussed, with focus on the latest advances in single train control and cooperative control for multiple trains. Then, as the trend of technological progress for future HSRs, the recent development of integrating dynamic scheduling and operation control is introduced, which is made possible by efficient information exchanges among the scheduling subsystem and the train control subsystem. A three-layer integration framework and associated co-optimization methodologies are presented by introducing a co-optimization layer that bridges the separated scheduling and control layers. Finally, this review is concluded with discussions on open questions and possible directions for future research.

A novel multiple objective whale optimization for time-cost-quality tradeoff in non-unit repetitive projects

Project managers are the ones who struggle to complete the project with the shortest possible time, the least amount of costs and, the highest quality. The primary objective in the planning stage of the project management process is to identify the most suitable schedule from provided time–cost-quality curve. Scheduling of construction repetitive projects assumes the same production and hard logic between units. This study proposes a free-parameter multiple objective whale optimization (MOWO) to tradeoff the time, cost and, quality factors in non-unit repetitive projects (NRP). An external scheduling system shall consider project activities dependencies, multiple crew assignments, and soft logic in units to calculate project duration, cost and, quality. A construction case study was used to examine MOWO performance. Furthermore, some broadly recognized multiple objective evolutionary algorithms are employed to compare with the proposed algorithm. Result comparison points out that MOWO attained the best optimization outcomes in handling the time-cost-quality tradeoff in non-unit repetitive construction projects. Highlights This study presents an advanced multiple optimization algorithm MOWO. A scheduling subsystem is proposed to enable TCQ tradeoff in non-unit repetitive projects. MOWO is applied to solve TCQ for non-unit repetitive construction projects. The model performance is demonstrated in the experimental results.

A Decentralized Informatics, Optimization, and Control Framework for Evolving Demand Response Services

This paper presents a decentralized informatics, optimization, and control framework to enable demand response (DR) in small or rural decentralized community power systems, including geographical islands. The framework consists of a simplified lumped model for electrical demand forecasting, a scheduling subsystem that optimizes the utility of energy storage assets, and an active/pro-active control subsystem. The active control strategy provides secondary DR services, through optimizing a multi-objective cost function formulated using a weight-based routing algorithm. In this context, the total weight of each edge between any two consecutive nodes is calculated as a function of thermal comfort, cost (tariff), and the rate at which electricity is consumed over a short future time horizon. The pro-active control strategy provides primary DR services. Furthermore, tertiary DR services can be processed to initiate a sequence of operations that enables the continuity of applied electrical services for the duration of the demand side event. Computer simulations and a case study using hardware-in-the-loop testing is used to evaluate the optimization and control module. The main conclusion drawn from this research shows the real-time operation of the proposed optimization and control scheme, operating on a prototype platform, underpinned by the effectiveness of the new methods and approach for tackling the optimization problem. This research recommends deployment of the optimization and control scheme, at scale, for decentralized community energy management. The paper concludes with a short discussion of business aspects and outlines areas for future work.

Optimizing non-unit repetitive project resource and scheduling by evolutionary algorithms

Repetitive project scheduling is a frequently encountered and challenging task in project planning. Researchers have developed numerous methods for the scheduling and planning of repetitive construction projects. However, almost all current repetitive scheduling methods are based on identical production units or they neglect the priorities of activities. This work presents a new hybrid evolutionary approach, called the fuzzy clustering artificial bee colony approach (FABC), to optimize resource assignment and scheduling for non-unit repetitive projects (NRP). In FABC, the fuzzy c-means clustering technique applies several multi-parent crossover operators to utilize population information efficiently and to improve convergence efficiency. The scheduling subsystem considers the following: (1) the logical relationships among activities throughout the project; (2) the assignment of multiple resources; and (3) the priorities of activities in groups to calculate project duration. Two numerical case studies are analyzed to demonstrate the use of the FABC-NRP model and its ability to optimize the scheduling of non-unit repetitive construction projects. Experimental results indicate that the proposed method yields the shortest project duration on average and deviation of optimal solution among benchmark algorithms considered herein and those considered previously. The outcomes will help project managers to prepare better schedules of repetitive projects.

Software for Crawling and Analysis of Ego-Network Graphs from Social Networking Services

Online social networks are one of the main platforms for arbitrary subjects of discussion. They are one of the main sources of data to analyse public opinion. For crawling and analysis of data from online social networks, are used data monitoring systems, which include a data collecting system. A typical system for collecting data from the Internet contains a crawler, parsers, a collection queue of tasks, a task scheduling subsystem, and a module for writing structured data to a storage system. The crawling from online social networks has a number of features. The paper considers methods of access to data from online social networks and a task planning subsystem. Formulates and underpins the requirements for a data collecting system to provide crawl results from online social networks, namely scalability, extensibility, and availability of a data storage subsystem and a queue of collection tasks.Describes main data accessing methods to have information from online social networks: API-based access, access through processing of HTML-pages and specialised interfaces for bots. Provides a description of main restrictions, which an online social network imposes, namely the need to register the application, the limited number of requests, the need to obtain user‘s permission to collect his (her) data. According to the analysis results, the anonymous download and processing of HTML pages were chosen, as a data access method.Formulates the task subsystem requirements, namely available types, hierarchy, and context of the task to be done. Describes the general architecture of the developed software system for crawling and analysis of data from online social networks, justifies its compliance with the earlier raised requirements.The problem of crawling and analysis of users’ ego-network graphs (sub-graphs of a social graph) are considered. Their collecting features are described and options of implementation are proposed depending on the amount of data collected.The results obtained can be used to build monitoring systems for online social networks and collect test data for experimentally estimated algorithms of social graphs analysis. Further development may be concerned with a detailed consideration of the problems of collecting other types of data from online social networks.

Genetic mechanism-based coupling algorithm for solving coordinated scheduling problems of yard systems in container terminals

This study develops models and methods utilized for solving the coordination scheduling problem in the yard of a container terminal. Based on the information shared by the yard storage subsystem and the YC scheduling subsystem, and the interaction between these subsystems, a coordination scheduling model, which is composed of a storage subsystem model, a YC scheduling subsystem and a coordinate controller model, is developed. A coupling algorithm, which is based on a genetic mechanism, is developed to solve the coordination scheduling problem. The algorithm adopts the genetic selection, crossover and mutation operations to adjust the yard storage plan and the YC scheduling plan. The performance of the coordination scheduling model and that of the proposed coupling algorithm are confirmed with reference to a numerical example.

Middleware design for high-speed railway integrated dispatching system based on SCA and SDO

In order to solve the system integration problems of high-speed railway integrated dispatching system in highly-distributed,highly heterogeneous environment,system integration framework based on Service Oriented Architecture(SOA) was proposed.The high-speed railway integrated dispatching system structure and its distributed SOA application were constructed based on Service Component Architecture(SCA) and Service Data Object(SDO) technology.The integration of power dispatching subsystem and other scheduling subsystems was achieved based on SCA and SDO technology on Java EE platform.The method fully embodies the openness and cross-platform features of SOA,and it is easy to implement.

ORGANIZATIONAL ASPECTS OF PLANNING AND SCHEDULING SUBSYSTEM

A comprehensive research for defining the organizational aspects of a construction contractor's subsystems was performed to provide these contractors with benchmarks for evaluating their subsystems. This paper describes the organizational aspects of Planning and Scheduling subsystems. The objective of this research was accomplished by surveying the literature, and by communicating with twenty one construction industry experts through a comprehensively structured questionnaire. The research results define the organizational aspects of a planning and scheduling department with respect to the key functions, location in the organization, key personnel, duties of chief planning and scheduling engineer, planning and scheduling tools, planning and scheduling techniques, links with other departments and most effective practices. These organizational aspects expound the contractors with the effective subsystem for bettering project effectiveness in largely cost oriented industry. The research reveals and recommends the various aspects and practices of the planning and scheduling subsystem which helps in organizing an ideal contractor system. The paper also presents an index mentioning all the identified organizational aspects of the planning and scheduling subsystem.

Temporal Reasoning for a Collaborative Planning Agent in a Dynamic Environment

We present a temporal reasoning mechanism for an individual agent situated in a dynamic environment such as the web and collaborating with other agents while interleaving planning and acting. Building a collaborative agent that can flexibly achieve its goals in changing environments requires a blending of real-time computing and AI technologies. Therefore, our mechanism consists of an Artificial Intelligence (AI) planning subsystem and a Real-Time (RT) scheduling subsystem. The AI planning subsystem is based on a model for collaborative planning. The AI planning subsystem generates a partial order plan dynamically. During the planning it sends the RT scheduling subsystem basic actions and time constraints. The RT scheduling subsystem receives the dynamic basic actions set with associated temporal constraints and inserts these actions into the agent's schedule of activities in such a way that the resulting schedule is feasible and satisfies the temporal constraints. Our mechanism allows the agent to construct its individual schedule independently. The mechanism handles various types of temporal constraints arising from individual activities and its collaborators. In contrast to other works on scheduling in planning systems which are either not appropriate for uncertain and dynamic environments or cannot be expanded for use in multi-agent systems, our mechanism enables the individual agent to determine the time of its activities in uncertain situations and to easily integrate its activities with the activities of other agents. We have proved that under certain conditions temporal reasoning mechanism of the AI planning subsystem is sound and complete. We show the results of several experiments on the system. The results demonstrate that interleave planning and acting in our environment is crucial.

Petri net based process scheduling: A model of the control system of flexible manufacturing systems

In this paper, we propose a class of algorithms for the sub-optimal solution of a particular class of problems of process scheduling, particularly focusing on a case study in the area of flexible manufacturing systems (FMSs). The general class of problems we face in our approach is characterized as follows: there is a set of concurrent processes, each formed by a number of temporally related tasks (segments). Tasks are executable by alternate resource sets, different both in performance and costs. Processes and tasks are characterized by release times, due dates, and deadlines. Time constraints are also present in the availability of each resource in resource sets. It has been proven that such a problem does not admit an algorithm for an optimal solution in polynomial time. Our proposed algorithm finds a sub-optimal schedule according to a set of optimization criteria, based on task and process times (earliness, tardiness), and/or time independent costs of resources. Our approach to process scheduling is based on Timed Coloured Petri Nets. We describe the structure of the coordination and scheduling algorithms, concentrating on (i) the general-purpose component, and (ii) the application-dependent component. In particular, the paper focuses on the following issues: (i) theautomatic synthesis of Petri net models of the coordination subsystem, starting from the problem knowledge base; (ii) the dynamic behavior of the coordination subsystem, whose kernel is a High Level Petri net executor, a coordination process based on an original, general purpose algorithm; (iii) the structure of the real-time scheduling subsystem, based on particular heuristic sub-optimal multi-criteria algorithms. Furthermore, the paper defines the interaction mechanisms between the coordination and scheduling subsystems. Our approach clearly distinguishes the mechanism of the net execution from the decision support system. Two conceptually distinct levels, which correspond to two different, interacting implementation modules in the prototype CASE tool, have been defined: theexecutor and thescheduler levels. One of the outstanding differences between these levels is that the executor is conceived as a fast, efficient coordination process, without special-purpose problem-solving capabilities in case of conflicts. The scheduler, on the other hand, is the adaptive, distributed component, whose behavior may heavily depend on the problem class. If the scheduler fails, the executor is, in any case, able to proceed with a general-purpose conflict resolution strategy. Experimental results on the real-time performance of the kernel of the implemented system are finally shown in the paper. The approach described in this paper is at the basis of a joint project with industrial partners for the development of a CASE tool for the simulation of blast furnaces.

Hierarchical scheduling in an intelligent environmental control system

This paper describes the design and implementation of a hierarchical scheduling subsystem and graphical user interface in an intelligent environmental control system. The hierarchical scheduling system is capable of managing all environmental events occurring in widely different time scales as specified by the user. We employ a knowledge representation scheme called a system entity structure to specify the environmental schedules in a hierarchical fashion. A system entity structure called TAL (Timed Action Language) is developed. TAL organizes a family of all possible long-/mid-/short-term schedules from which a specific schedule can be pruned by the user through graphical interface.

Computer-based scheduling of technical resources

This paper considers the design of a Scheduling Sub-System which could be attached to an existing computerised data processing system in order to enhance its overall operation. It draws heavily on the experience of the authors in designing a system to assist in the scheduling of video tape machines within BBC Television.