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Grid computing is gaining more significance in the high‐performance computing world. This concept leads to the discovery of solutions for complicated problems regarding the diversity of available resources among different jobs in the grid. However, the major problem is the optimal job scheduling for heterogeneous resources, in which each job needs to be allocated to a proper grid's node with the appropriate resources. An important challenge is to solve optimally the scheduling problem, because the capability and availability of resources vary dynamically and the complexity of scheduling increases with the size of the grid. The purpose of this paper is to present a framework which combines the fuzzy C‐mean (FCM) clustering with an ant colony optimization (ACO) algorithm to improve the scheduling decision when the grid is heterogeneous.

In the proposed model, the FCM algorithm classifies the jobs into appropriate classes, and the ACO algorithm maps the jobs to the appropriate resources. The ACO is characterized by ant‐like mobile agents that cooperate and stochastically explore a network, iteratively building solutions based on their own memory and on the traces (pheromone levels) left by other agents.

The simulation is done by using historical information on jobs in a grid. The experimental results show that the proposed algorithm can allocate jobs more efficiently and more effectively than the traditional algorithms for scheduling policies.

The paper provides a scheduling model based on FCM clustering and ACO algorithm for grid scheduling. The authors compared the performance of the proposed algorithm with the performance of various job‐scheduling algorithms in the grid computing environment. The comparison results show that the proposed algorithm outperforms other algorithms and gives optimal results.

In general, cloud computing is a model of on-demand business computing that grants a convenient access to shared configurable resources on the internet. With the increment of workload and difficulty of tasks that are submitted by cloud consumers; “how to complete these tasks effectively and rapidly with limited cloud resources?” is becoming a challenging question. The major point of a task scheduling approach is to identify a trade-off among user needs and resource utilization. However, tasks that are submitted by varied users might have diverse needs of computing time, memory space, data traffic, response time, etc. This paper aims to proposes a new way of task scheduling.

To make the workflow completion in an efficient way and to reduce the cost and flow time, this paper proposes a new way of task scheduling. Here, a self-adaptive fruit fly optimization algorithm (SA-FFOA) is used for scheduling the workflow. The proposed multiple workflow scheduling model compares its efficiency over conventional methods in terms of analysis such as performance analysis, convergence analysis and statistical analysis. From the outcome of the analysis, the betterment of the proposed approach is proven with effective workflow scheduling.

The proposed algorithm is more superior regarding flow time with the minimum value, and the proposed model is enhanced over FFOA by 0.23%, differential evolution by 2.48%, artificial bee colony (ABC) by 2.85%, particle swarm optimization (PSO) by 2.46%, genetic algorithm (GA) by 2.33% and expected time to compute (ETC) by 2.56%. While analyzing the make span case, the proposed algorithm is 0.28%, 0.15%, 0.38%, 0.20%, 0.21% and 0.29% better than the conventional methods such as FFOA, DE, ABC, PSO, GA and ETC, respectively. Moreover, the proposed model has attained less cost, which is 2.14% better than FFOA, 2.32% better than DE, 3.53% better than ABC, 2.43% better than PSO, 2.07% better than GA and 2.90% better than ETC, respectively.

This paper presents a new way of task scheduling for making the workflow completion in an efficient way and for reducing the cost and flow time. This is the first paper uses SA-FFOA for scheduling the workflow.

This study develops a model and algorithm to solve the decentralized resource-constrained multi-project scheduling problem (DRCMPSP) and provides a suitable priority rule (PR) for coordinating global resource conflicts among multiple projects.

This study addresses the DRCMPSP, which respects the information privacy requirements of project agents; that is, there is no single manager centrally in charge of generating multi-project scheduling. Accordingly, a three-stage model was proposed for the decentralized management of multiple projects. To solve this model, a three-stage solution approach with a repeated negotiation mechanism was proposed.

The experimental results obtained using the Multi-Project Scheduling Problem LIBrary confirm that our approach outperforms existing methods, regardless of the average utilization factor (AUF). Comparative analysis revealed that delaying activities in the lower project makespan produces a lower average project delay. Furthermore, the new PR LMS performed better in problem subsets with AUF < 1 and large-scale subsets with AUF > 1.

A solution approach with a repeated-negotiation mechanism suitable for the DRCMPSP and a new PR for coordinating global resource allocation are proposed.

Considers the resource‐constrained project scheduling problem where cash inflows and outflows are tied to the occurrence of events. The objective is the maximization of the project net present value (NPV) as well as the minimization of project tardiness in the presence of a project due date. Develops hybrid scheduling rules with both NPV and tardiness considerations to enhance both objectives. Experiments extensively with a set of benchmark problems originally designed for the objective of minimizing the project duration. Demonstrates that thje hybrid rules developed here are superior in performance with respect to both objectives when compared with well known rules which are developed for the two objective of minimizing the project duration. Demonstrates that the hybrid rules developed here are superior in performance with respect to both objectives when compared with well‐known rules which are developed for the two objectives taken individually. Furthermore, the iterative algorithm improves the performance of all tested rules significantly.

The traditional network scheduling methods such as the Critical Path Method (CPM), Programme Evaluation Review Technique (PERT), and bar charting are typically not effective for the planning of linear construction projects. Linear scheduling methods, on the other hand, model the progress of repetitive activities in sloping lines and are more effective for linear modelling and analysis. Nonetheless, their use in the construction industry has so far been very limited. Among other reasons for this is the unfamiliarity of construction personnel with these techniques, which plays a major role in hampering their application. This paper introduces a graphically based approach to assist in the linear programming (LP) modelling of linear scheduling analysis. The Planning & Optimization for Linear Operations (POLO) system provides a graphic LP modelling environment in which model formulation can be easily accomplished in a graphic and interactive fashion. Thus, the application of linear scheduling methods can be facilitated. The Isle of Palms Connector Bridge project in Mount Pleasant, South Carolina is used to demonstrate the use of the system.

Basic project control through traditional methods is not sufficient to manage the majority of real-time events in most construction projects. The purpose of this paper is to propose a Dynamic Scheduling (DS) model that utilizes multi-objective optimization of cost, time, resources and cash flow, throughout project construction.

Upon reviewing the topic of DS, a worldwide internet survey with 364 respondents was conducted to define end-user requirements. The model was formulated and solution algorithms discussed. Verification was reported using predefined problem sets and a real-life case. Validation was performed via feedback from industry experts.

The need for multi-objective dynamic software optimization of construction schedules and the ability to choose among a set of optimal alternatives were highlighted. Model verification through well-known test cases and a real-life project case study showed that the model successfully achieved the required dynamic functionality whether under the small solved example or under the complex case study. The model was validated for practicality, optimization of various DS schedule quality gates, ease of use and software integration with contemporary project management practices.

Optimized real-time scheduling can provide better resources management including labor utilization and cost efficiency. Furthermore, DS contributes to optimum materials procurement, thus minimizing waste.

Optimized real-time scheduling can provide better resources management including labor utilization and cost efficiency. Furthermore, DS contributes to optimum materials procurement, thus minimizing waste.

The paper illustrates the importance of DS in construction, identifies the user needs and overviews the development, verification and validation of a model that supports the generation of high-quality schedules beneficial to large-scale projects.

Integrated modular avionics (IMA) aims to provide highly flexible, reliable and integrated solutions for future aircraft systems which can readily exploit new advances in processor and networking technologies. In this paper, we discuss the problems involved in the provision of appropriate scheduling and timing analysis techniques for IMA systems and suggest a potential solution.

Proposes a heuristic iterative scheduling algorithm for the resource constrained project scheduling problem. Considers a general model where activities are represented by multiple operating modes and each operating mode is constituted of different activity durations and resource requirements. The performance measures considered are the minimization of project duration and the maximization of net present value (NPV). In the cash flow model assumed, activity expenditures take place at their start times and the project payment is made on its completion. The iterative scheduling algorithm consists of forward/ backward scheduling passes, where consecutive scheduling passes are linked by updated activity time windows. The iterative algorithm is supported by a conflict‐based activity selection technique called local constraint based analysis (LCBA). A considerable amount of improvement in both performance criteria is observed when the results of the iterative algorithm are compared with the results given by the initial forward schedule.

Discusses the twofold purpose of this study, first to introduce a number of measures devised to be used in comparing and evaluating two schedules for the same project, second to present and evaluate a heuristic procedure developed in this research for solving the resource constrained project scheduling problem. Posits that the new procedure is based on the notion that a project can be partitioned into a number of sections and there is no obligation to use a single policy all the way through all the sections of project. Shows that this research is also an attempt to bridge the gap between the theory and practice by eliminating the question of which priority rule to use for which project. Develops a new model showing that selecting from a number of priority rules is not required and, using the measures of performance introduced, decides on a suitable schedule from the schedules produced under varied assumptions is shown to be much easier. Concludes with suggestions for further research.

Project scheduling/rescheduling occurs in all stages of projects, from feasibility stage to monitoring stage to completion. Since the late 1950s, network‐based techniques CPM (critical path method) and PERT (programme evaluation review technique) are the techniques commonly used for project management. However, there are limitations in working with these tools that need to be overcome. Also, the computing ef. ciency of classic CPM/PERT analysis needs to be enhanced. Substantial research has been carried out globally in this field covering all areas of project scheduling: time scheduling, resource scheduling, cost scheduling, modern project management techniques, advanced mathematical models used for construction scheduling, and so on. To understand and document this research status, the authors have carried out an extensive study of various journals, published and unpublished research papers, and present this literature review.