Search results for: resource scheduling

Authors: Parampal Kaur, Deepak Aggarwal

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In the computational Grid, fault tolerance is an imperative issue to be considered during job scheduling. Due to the widespread use of resources, systems are highly prone to errors and failures. Hence, fault tolerance plays a key role in the grid to avoid the problem of unreliability. Scheduling the task to the appropriate resource is a vital requirement in computational Grid. The fittest resource scheduling algorithm searches for the appropriate resource based on the job requirements, in contrary to the general scheduling algorithms where jobs are scheduled to the resources with best performance factor. The proposed method is to improve the fault tolerance of the fittest resource scheduling algorithm by scheduling the job in coordination with job replication when the resource has low reliability. Based on the reliability index of the resource, the resource is identified as critical. The tasks are scheduled based on the criticality of the resources. Results show that the execution time of the tasks is comparatively reduced with the proposed algorithm using real-time approach rather than a simulator.

Keywords: computational grid, fault tolerance, task replication, job scheduling

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Authors: Ali Bagherinia

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Efficient task scheduling method can meet users' requirements, and improve the resource utilization, then increase the overall performance of the cloud computing environment. Cloud computing has new features, such as flexibility, virtualization and etc., in this paper we propose a two levels task scheduling method based on load balancing in cloud computing. This task scheduling method meet user's requirements and get high resource utilization, that simulation results in CloudSim simulator prove this.

Keywords: cloud computing, task scheduling, virtualization, SLA

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Authors: Yitao Lei, Xingxiang Zhai, Burra Venkata Durga Kumar

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As the quantity and complexity of computing in large-scale software systems increase, distributed system computing becomes increasingly important. The distributed system realizes high-performance computing by collaboration between different computing resources. If there are no efficient resource scheduling resources, the abuse of distributed computing may cause resource waste and high costs. However, resource scheduling is usually an NP-hard problem, so we cannot find a general solution. However, some optimization algorithms exist like genetic algorithm, ant colony optimization, etc. The large scale of distributed systems makes this traditional optimization algorithm challenging to work with. Heuristic and machine learning algorithms are usually applied in this situation to ease the computing load. As a result, we do a review of traditional resource scheduling optimization algorithms and try to introduce a deep reinforcement learning method that utilizes the perceptual ability of neural networks and the decision-making ability of reinforcement learning. Using the machine learning method, we try to find important factors that influence the performance of distributed system computing and help the distributed system do an efficient computing resource scheduling. This paper surveys the application of deep reinforcement learning on distributed system computing resource scheduling proposes a deep reinforcement learning method that uses a recurrent neural network to optimize the resource scheduling, and proposes the challenges and improvement directions for DRL-based resource scheduling algorithms.

Keywords: resource scheduling, deep reinforcement learning, distributed system, artificial intelligence

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Authors: Su Xiaohan, Jin Chicheng, Liu Yijing, Burra Venkata Durga Kumar

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Energy-aware scheduling in real-time systems aims to minimize energy consumption, but issues related to resource reservation and timing constraints remain challenges. This study focuses on analyzing two scheduling algorithms, Fair-Share Scheduling (FFS) and Priority-Driven Preemptive Scheduling (PDPS), for solving these issues and energy-aware scheduling in real-time systems. Based on research on both algorithms and the processes of solving two problems, it can be found that Fair-Share Scheduling ensures fair allocation of resources but needs to improve with an imbalanced system load, and Priority-Driven Preemptive Scheduling prioritizes tasks based on criticality to meet timing constraints through preemption but relies heavily on task prioritization and may not be energy efficient. Therefore, improvements to both algorithms with energy-aware features will be proposed. Future work should focus on developing hybrid scheduling techniques that minimize energy consumption through intelligent task prioritization, resource allocation, and meeting time constraints.

Keywords: energy-aware scheduling, fair-share scheduling, priority-driven preemptive scheduling, real-time systems, optimization, resource reservation, timing constraints

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Authors: R. Gupta, P. Jain, S. Das

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One way to approach construction scheduling optimization problem is to focus on the individual aspects of planning, which can be broadly classified as time scheduling, crew and resource management, and cost control. During the last four decades, construction planning has seen a lot of research, but to date, no paper had attempted to summarize the literature available under important heads. This paper addresses each of aspects separately, and presents the findings of an in-depth literature of the various planning techniques. For techniques dealing with time scheduling, the authors have adopted a rough chronological documentation. For crew and resource management, classification has been done on the basis of the different steps involved in the resource planning process. For cost control, techniques dealing with both estimation of costs and the subsequent optimization of costs have been dealt with separately.

Keywords: construction planning techniques, time scheduling, resource planning, cost control

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Authors: Busaba Phurksaphanrat

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This research proposes a pre-emptive fuzzy goal programming model for multi-objective multi-mode resource constrained project scheduling problem. The objectives of the problem are minimization of the total time and the total cost of the project. Objective in a multi-mode resource-constrained project scheduling problem is often a minimization of make-span. However, both time and cost should be considered at the same time with different level of important priorities. Moreover, all elements of cost functions in a project are not included in the conventional cost objective function. Incomplete total project cost causes an error in finding the project scheduling time. In this research, pre-emptive fuzzy goal programming is presented to solve the multi-objective multi-mode resource constrained project scheduling problem. It can find the compromise solution of the problem. Moreover, it is also flexible in adjusting to find a variety of alternative solutions.

Keywords: multi-mode resource constrained project scheduling problem, fuzzy set, goal programming, pre-emptive fuzzy goal programming

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Authors: Lars Laußat, Manfred Helmus, Kamil Szczesny, Markus König

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As one result of the project “Reactive Construction Project Scheduling using Real Time Construction Logistic Data and Simulation”, a procedure for using data about uncertain resource availability assumptions in reactive scheduling processes has been developed. Prediction data about resource availability is generated in a formalized way using real-time monitoring data e.g. from auto-ID systems on the construction site and in the supply chains. The paper focuses on the formalization of the procedure for monitoring construction logistic processes, for the detection of disturbance and for generating of new and uncertain scheduling assumptions for the reactive resource constrained simulation procedure that is and will be further described in other papers.

Keywords: auto-ID, construction logistic, fuzzy, monitoring, RFID, scheduling

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Authors: Anju Bala, Inderveer Chana

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Task scheduling is the key concern for the execution of performance-driven workflow applications. As efficient scheduling can have major impact on the performance of the system, task scheduling is often chosen for assigning the request to resources in an efficient way based on cloud resource characteristics. In this paper, priority based task scheduling algorithm has been proposed that prioritizes the tasks based on the length of the instructions. The proposed scheduling approach prioritize the tasks of Cloud applications according to the limits set by six sigma control charts based on dynamic threshold values. Further, the proposed algorithm has been validated through the CloudSim toolkit. The experimental results demonstrate that the proposed algorithm is effective for handling multiple task lists from workflows and in considerably reducing Makespan and Execution time.

Keywords: cloud computing, priority based scheduling, task scheduling, VM allocation

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Authors: Lin Yunuo, Seow Xing Quan, Burra Venkata Durga Kumar

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In this modern era, the cloud operating system is the world trend applied in various industries such as business, healthy, etc. In order to deal with the large capacity of requirements in cloud computing, research come up with multi-tenant cloud computing to maximize the benefits of server providers and clients. However, there are still issues in multi-tenant cloud computing especially regarding resource allocation. Issues such as inefficient resource utilization, large latency, lack of scalability and elasticity and poor data isolation had caused inefficient resource allocation in multi-tenant cloud computing. Without a doubt, these issues prevent multitenancy reaches its best condition. In fact, there are multiple studies conducted to determine the optimal resource allocation to solve these problems these days. This article will briefly introduce the cloud operating system, Multi-tenant cloud computing and resource allocation in cloud computing. It then discusses resource allocation in multi-tenant cloud computing and the current challenges it faces. According to the issue ‘ineffective resource utilization’, we will discuss an efficient dynamic scheduling technique for multitenancy, namely Multi-tenant Dynamic Resource Scheduling Model (MTDRSM). Moreover, there also have some recommendations to improve the shortcoming of this model in this paper’s final section.

Keywords: cloud computing, cloud operation system, multitenancy, resource allocation, utilization of cloud resources

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Authors: Zahra Ahmadi, Fazlollah Adibnia

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Scheduling of tasks and the optimal allocation of resources in the cloud are based on the dynamic nature of tasks and the heterogeneity of resources. Applications that are based on the scientific workflow are among the most widely used applications in this field, which are characterized by high processing power and storage capacity. In order to increase their efficiency, it is necessary to plan the tasks properly and select the best virtual machine in the cloud. The goals of the system are effective factors in scheduling tasks and resource selection, which depend on various criteria such as time, cost, current workload and processing power. Multi-criteria decision-making methods are a good choice in this field. In this research, a new method of work planning and resource allocation in a heterogeneous environment based on the modified AHP algorithm is proposed. In this method, the scheduling of input tasks is based on two criteria of execution time and size. Resource allocation is also a combination of the AHP algorithm and the first-input method of the first client. Resource prioritization is done with the criteria of main memory size, processor speed and bandwidth. What is considered in this system to modify the AHP algorithm Linear Max-Min and Linear Max normalization methods are the best choice for the mentioned algorithm, which have a great impact on the ranking. The simulation results show a decrease in the average response time, return time and execution time of input tasks in the proposed method compared to similar methods (basic methods).

Keywords: hierarchical analytical process, work prioritization, normalization, heterogeneous resource allocation, scientific workflow

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Authors: Gagandeep Kaur, Sonal Chawla

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In a Cloud Computing environment, resource provisioning, resource allocation and resource scheduling is the most complex issues these days. Cloud User expects the best resource utilization and Cloud Provider expects revenue maximization by considering budget and time constraints. In this research paper, Resource Management Framework has been proposed to allocate the resources to Cloud Users and Cloud Providers in Cloud environment. The main aim of the proposed work is to provide the resources and services to Cloud Providers and Cloud Users in an efficient and effective manner. The proposed framework has been simulated and tested using the CloudSim simulator tool.

Keywords: cloud computing, resource allocation, auction, provisioning

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Authors: Md Habibur Rahman, Jaeho Kim

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Efficient process scheduling is a crucial factor in ensuring optimal system performance and resource utilization in computer systems. While various algorithms have been proposed over the years, there are still limitations to their effectiveness. This paper introduces a new Wait-Optimized Scheduler (WOS) algorithm that aims to minimize process waiting time by dividing them into two layers and considering both process time and waiting time. The WOS algorithm is non-preemptive and prioritizes processes with the shortest WOS. In the first layer, each process runs for a predetermined duration, and any unfinished process is subsequently moved to the second layer, resulting in a decrease in response time. Whenever the first layer is free or the number of processes in the second layer is twice that of the first layer, the algorithm sorts all the processes in the second layer based on their remaining time minus waiting time and sends one process to the first layer to run. This ensures that all processes eventually run, optimizing waiting time. To evaluate the performance of the WOS algorithm, we conducted experiments comparing its performance with traditional scheduling algorithms such as First-Come-First-Serve (FCFS) and Shortest-Job-First (SJF). The results showed that the WOS algorithm outperformed the traditional algorithms in reducing the waiting time of processes, particularly in scenarios with a large number of short tasks with long wait times. Our study highlights the effectiveness of the WOS algorithm in improving process scheduling efficiency in computer systems. By reducing process waiting time, the WOS algorithm can improve system performance and resource utilization. The findings of this study provide valuable insights for researchers and practitioners in developing and implementing efficient process scheduling algorithms.

Keywords: process scheduling, wait-optimized scheduler, response time, non-preemptive, waiting time, traditional scheduling algorithms, first-come-first-serve, shortest-job-first, system performance, resource utilization

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Authors: Fahad Y. Al-dawish

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The current trend by a large number of organizations and individuals to use cloud computing. Many consider it a significant shift in the field of computing. Cloud computing are distributed and parallel systems consisting of a collection of interconnected physical and virtual machines. With increasing request and profit of cloud computing infrastructure, diverse computing processes can be executed on cloud environment. Many organizations and individuals around the world depend on the cloud computing environments infrastructure to carry their applications, platform, and infrastructure. One of the major and essential issues in this environment related to allocating incoming tasks to suitable virtual machine (cloud task scheduling). Cloud task scheduling is classified as optimization problem, and there are several meta-heuristic algorithms have been anticipated to solve and optimize this problem. Good task scheduler should execute its scheduling technique on altering environment and the types of incoming task set. In this research project a cloud task scheduling methodology based on ant colony optimization ACO algorithm, we call it ACO-TS Ant Colony Optimization for Task Scheduling has been proposed and compared with different scheduling algorithms (Random, First Come First Serve FCFS, and Fastest Processor to the Largest Task First FPLTF). Ant Colony Optimization (ACO) is random optimization search method that will be used for assigning incoming tasks to available virtual machines VMs. The main role of proposed algorithm is to minimizing the makespan of certain tasks set and maximizing resource utilization by balance the load among virtual machines. The proposed scheduling algorithm was evaluated by using Cloudsim toolkit framework. Finally after analyzing and evaluating the performance of experimental results we find that the proposed algorithm ACO-TS perform better than Random, FCFS, and FPLTF algorithms in each of the makespaan and resource utilization.

Keywords: cloud Task scheduling, ant colony optimization (ACO), cloudsim, cloud computing

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Authors: Junjie Peng, Jinbao Chen, Shuai Kong, Danxu Liu

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On the basis of traditional resource allocation strategies, the usage of resources on physical servers in cloud data center is great uncertain. It will cause waste of resources if the assignment of tasks is not enough. On the contrary, it will cause overload if the assignment of tasks is too much. This is especially obvious when the applications are the same type because of its resource preferences. Considering CPU intensive application is one of the most common types of application in the cloud, we studied the optimization strategy for CPU intensive applications on the same server. We used resource preferences to analyze the case that multiple CPU intensive applications run simultaneously, and put forward a model which can predict the execution time for CPU intensive applications which run simultaneously. Based on the prediction model, we proposed the method to select the appropriate number of applications for a machine. Experiments show that the model can predict the execution time accurately for CPU intensive applications. To improve the execution efficiency of applications, we propose a scheduling model based on priority for CPU intensive applications. Extensive experiments verify the validity of the scheduling model.

Keywords: cloud computing, CPU intensive applications, resource optimization, strategy

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Authors: Jian Zheng, Yoshiyasu Takahashi, Yuichi Kobayashi, Tatsuhiro Sato

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In the study, we present a conceptual framework for developing a scheduling system that can generate self-explanatory and easy-understanding schedules. To this end, a user interface is conceived to help planners record factors that are considered crucial in scheduling, as well as internal and external sources relating to such factors. A hybrid approach combining machine learning and constraint programming is developed to generate schedules and the corresponding factors, and accordingly display them on the user interface. Effects of the proposed system on scheduling are discussed, and it is expected that scheduling efficiency and system understandability will be improved, compared with previous scheduling systems.

Keywords: constraint programming, factors considered in scheduling, machine learning, scheduling system

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Authors: C. D. Papanikolaou, V. A. Giouvanis, E. A. Karatasiou, D. S. Dimakas, M. A. Sakellariou-Makrantonaki

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Irrigation is one of the most important cultivation practices for each crop, especially in areas where rainfall is enough to cover the crop water needs. In such areas, the farmers must irrigate in order to achieve high economical results. The precise irrigation scheduling contributes to irrigation water saving and thus a valuable natural resource is protected. Under this point of view, in the experimental field of the Laboratory of Agricultural Hydraulics of the University of Thessaly, a research was conducted during the growing season of 2012 in order to evaluate the growth, seed and oil production of sunflower as well as the water saving, by applying different methods of irrigation scheduling. Three treatments in four replications were organized. These were: a) surface drip irrigation where the irrigation scheduling based on the Penman-Monteith (PM) method (control); b) surface drip irrigation where the irrigation scheduling based on a soil moisture sensor (SMS); and c) surface drip irrigation, where the irrigation scheduling based on a soil potential sensor (WM).

Keywords: irrigation, energy production, soil moisture sensor, sunflower, water saving

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Authors: Martha Ndeley, John Ikome

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In this paper, we expand the scope of constraint-directed techniques to deal with the case of transportation schedule with capacity restrictions where the scheduling problem includes alternative activities. That is, not only does the scheduling problem consist of determining when an activity is to be executed, but also determining which set of alternative activities is to be executed at all level of transportation from input to output. Such problems encompass both alternative resource problems and alternative process plan problems. We formulate a constraint-based representation of alternative activities to model problems containing such choices. We then extend existing constraint-directed scheduling heuristic commitment techniques and propagators to reason directly about the fact that an activity does not necessarily have to exist in a final transportation schedule without being completed. Tentative results show that an algorithm using a novel texture-based heuristic commitment technique propagators achieves the best overall performance of the techniques tested.

Keywords: production, transportation, scheduling, integrated

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Authors: Salam Saudagar, Ankit Kamboj, Niraj Mohan, Satgounda Patil, Nilesh Powar

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Task Assignment and Scheduling is a challenging Operations Research problem when there is a limited number of resources and comparatively higher number of tasks. The Cost Management team at Cummins needs to assign tasks based on a deadline and must prioritize some of the tasks as per business requirements. Moreover, there is a constraint on the resources that assignment of tasks should be done based on an individual skill level, that may vary for different tasks. Another constraint is for scheduling the tasks that should be evenly distributed in terms of number of working hours, which adds further complexity to this problem. The proposed greedy approach to solve assignment and scheduling problem first assigns the task based on management priority and then by the closest deadline. This is followed by an iterative selection of an available resource with the least allocated total working hours for a task, i.e. finding the local optimal choice for each task with the goal of determining the global optimum. The greedy approach task allocation is compared with a variant of Hungarian Algorithm, and it is observed that the proposed approach gives an equal allocation of working hours among the resources. The comparative study of the proposed approach is also done with manual task allocation and it is noted that the visibility of the task timeline has increased from 2 months to 6 months. An interactive dashboard app is created for the greedy assignment and scheduling approach and the tasks with more than 2 months horizon that were waiting in a queue without a delivery date initially are now analyzed effectively by the business with expected timelines for completion.

Keywords: assignment, deadline, greedy approach, Hungarian algorithm, operations research, scheduling

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Authors: Babak H. Tabrizi, Seyed Farid Ghaderi

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Concurrent planning of the resource constraint project scheduling and material ordering problems have received significant attention within the last decades. Hence, the issue has been investigated here with the aim to minimize total project costs. Furthermore, the presented model considers different discount options in order to approach the real world conditions. The incorporated alternatives consist of all-unit and incremental discount strategies. On the other hand, a modified version of the genetic algorithm is applied in order to solve the model for larger sizes, in particular. Finally, the applicability and efficiency of the given model is tested by different numerical instances.

Keywords: genetic algorithm, material ordering, project management, project scheduling

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Authors: Shuen-Tai Wang, Ying-Chuan Chen, Yu-Ching Lin

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There has been renewal of interest in the relation between Green IT and cloud computing in recent years. Cloud computing has to be a highly elastic environment which provides stable services to users. The growing use of cloud computing facilities has caused marked energy consumption, putting negative pressure on electricity cost of computing center or data center. Each year more and more network devices, storages and computers are purchased and put to use, but it is not just the number of computers that is driving energy consumption upward. We could foresee that the power consumption of cloud computing facilities will double, triple, or even more in the next decade. This paper aims at resource allocation and scheduling technologies that are short of or have not well developed yet to reduce energy utilization in cloud computing platform. In particular, our approach relies on recalling services dynamically onto appropriate amount of the machines according to user’s requirement and temporarily shutting down the machines after finish in order to conserve energy. We present initial work on integration of resource and power management system that focuses on reducing power consumption such that they suffice for meeting the minimizing quality of service required by the cloud computing platform.

Keywords: cloud computing, energy utilization, power consumption, resource allocation

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Authors: Li Guo, Jianhong Wang, Dian Huang, Shengzhong Feng

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Computing networks as a new network architecture has shown great promise in boosting the utilization of different resources, such as computing, caching, and communications. To maximise the efficiency of resource orchestration in computing network control systems (CNCSs), this work proposes a dynamic orchestration strategy of a different resource based on task requirements from computing power requestors (CPRs). Specifically, computing power providers (CPPs) in CNCSs could share information with each other through communication channels on the basis of blockchain technology, especially their current idle resources. This dynamic process is modeled as a cooperative game in which CPPs have the same target of maximising long-term rewards by improving the resource utilization ratio. Meanwhile, the task requirements from CPRs, including size, deadline, and calculation, are simultaneously considered in this paper. According to task requirements, the proposed orchestration strategy could schedule the best-fitting resource in CNCSs, achieving the maximum long-term rewards of CPPs and the best quality of experience (QoE) of CRRs at the same time. Based on the EdgeCloudSim simulation platform, the efficiency of the proposed strategy is achieved from both sides of CPRs and CPPs. Besides, experimental results show that the proposed strategy outperforms the other comparisons in all cases.

Keywords: computing network control systems, resource orchestration, dynamic scheduling, blockchain, cooperative game

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Authors: Samia Dardouri, Ridha Bouallegue

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Scheduling is the process of dynamically allocating physical resources to User Equipment (UE) based on scheduling algorithms implemented at the LTE base station. Various algorithms have been proposed by network researchers as the implementation of scheduling algorithm which represents an open issue in Long Term Evolution (LTE) standard. This paper makes an attempt to study and compare the performance of PF, MLWDF and EXP/PF scheduling algorithms. The evaluation is considered for a single cell with interference scenario for different flows such as Best effort, Video and VoIP in a pedestrian and vehicular environment using the LTE-Sim network simulator. The comparative study is conducted in terms of system throughput, fairness index, delay, packet loss ratio (PLR) and total cell spectral efficiency.

Keywords: LTE, multimedia flows, scheduling algorithms, mobile computing

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Authors: Sahar Sohrabi

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The emergence of Cloud data centers has revolutionized the IT industry. Private Clouds in specific provide Cloud services for certain group of customers/businesses. In a real-time private Cloud each task that is given to the system has a deadline that desirably should not be violated. Scheduling tasks in a real-time private CLoud determine the way available resources in the system are shared among incoming tasks. The aim of the scheduling policy is to optimize the system outcome which for a real-time private Cloud can include: energy consumption, deadline violation, execution time and the number of host switches. Different scheduling policies can be used for scheduling. Each lead to a sub-optimal outcome in a certain settings of the system. A Bayesian Scheduling strategy is proposed for scheduling to further improve the system outcome. The Bayesian strategy showed to outperform all selected policies. It also has the flexibility in dealing with complex pattern of incoming task and has the ability to adapt.

Keywords: cloud computing, scheduling, real-time private cloud, bayesian

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Authors: Mohsen Ziaee

Abstract:

Almost all existing researches on the flowshop scheduling problems focus on the permutation schedules and there is insufficient study dedicated to the general flowshop scheduling problems in the literature, since the modeling and solving of the general flowshop scheduling problems are more difficult than the permutation ones, especially for the large-size problem instances. This paper considers the general flowshop scheduling problem with the objective function of the makespan (F//Cmax). We first find the optimal solution of the problem by solving a mixed integer linear programming model. An efficient heuristic method is then presented to solve the problem. An ant colony optimization algorithm is also proposed for the problem. In order to evaluate the performance of the methods, computational experiments are designed and performed. Numerical results show that the heuristic algorithm can result in reasonable solutions with low computational effort and even achieve optimal solutions in some cases.

Keywords: scheduling, general flow shop scheduling problem, makespan, heuristic

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Authors: Tabrej Khan

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Examination Scheduling System (ESS) is a scheduling system that targets as an exam committee in any academic institute to help them in managing the exams automatically. We present an algorithm for Examination Scheduling System. Nowadays, many universities have challenges with creating examination schedule fast with less confliction compared to hand works. Our aims are to develop a computerized system that can be used in examination scheduling in an academic institute versus available resources (Time, Hall, Invigilator and instructor) with no contradiction and achieve fairness among students. ESS was developed using HTML, C# language, Crystal Report and ASP.NET through Microsoft Visual Studio 2010 as developing tools with integrated SQL server database. This application can produce some benefits such as reducing the time spent in creating an exam schedule and achieving fairness among students

Keywords: examination scheduling system (ESS), algorithm, ASP.NET, crystal report

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Authors: Hong Kai, Zhong Jun Jie, Chen Lin Qi, Wang Chen Guang

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There are inefficient global scheduling parallelism and local scheduling parallelism prone to processor starvation in current scheduling algorithms. Regarding this issue, this paper proposed a load-aware queue partitioning scheduling strategy by first allocating the queues according to the number of processor cores, calculating the load factor to specify the load queue capacity, and it assigned the awaiting nodes to the appropriate perceptual queues through the precursor nodes and the communication computation overhead. At the same time, real-time computation of the load factor could effectively prevent the processor from being starved for a long time. Experimental comparison with two classical algorithms shows that there is a certain improvement in both performance metrics of scheduling length and task speedup ratio.

Keywords: load-aware, scheduling algorithm, perceptual queue, heterogeneous multi-core

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Authors: Majid Khalili, Hamed Tayebi

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This paper considers a stochastic flexible job-shop scheduling (SFJSS) problem in the presence of production disruptions, and reactive scheduling is implemented in order to find the optimal solution under uncertainty. In this problem, there are two main disruptions including machine failure which influences operation time, and modification or cancellation of the order delivery date during production. In order to decrease the negative effects of these difficulties, two derived strategies from reactive scheduling are used; the first one is relevant to being able to allocate multiple machine to each job, and the other one is related to being able to select the best alternative process from other job while some disruptions would be created in the processes of a job. For this purpose, a Mixed Integer Linear Programming model is proposed.

Keywords: flexible job-shop scheduling, reactive scheduling, stochastic environment, mixed integer linear programming

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Authors: Roshanak Khodabakhsh Jolfaei, Javad Akbari Torkestani

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As a cooperative environment for problem-solving, it is necessary that grids develop efficient job scheduling patterns with regard to their goals, domains and structure. Since the Grid environments facilitate distributed calculations, job scheduling appears in the form of a critical problem for the management of Grid sources that influences severely on the efficiency for the whole Grid environment. Due to the existence of some specifications such as sources dynamicity and conditions of the network in Grid, some algorithm should be presented to be adjustable and scalable with increasing the network growth. For this purpose, in this paper a job scheduling algorithm has been presented on the basis of learning automata in computational Grid which the performance of its results were compared with FPSO algorithm (Fuzzy Particle Swarm Optimization algorithm) and GJS algorithm (Grid Job Scheduling algorithm). The obtained numerical results indicated the superiority of suggested algorithm in comparison with FPSO and GJS. In addition, the obtained results classified FPSO and GJS in the second and third position respectively after the mentioned algorithm.

Keywords: computational grid, job scheduling, learning automata, dynamic scheduling

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Authors: M. Ndeley

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The performance of global manufacturing supply chains depends on the interaction of production and transport processes. Currently, the scheduling of these processes is done separately without considering mutual requirements, which leads to no optimal solutions. An integrated scheduling of both processes enables the improvement of supply chain performance. The integrated production and transport scheduling problem (PTSP) is NP-hard, so that heuristic methods are necessary to efficiently solve large problem instances as in the case of global manufacturing supply chains. This paper presents a heuristic scheduling approach which handles the integration of flexible production processes with intermodal transport, incorporating flexible land transport. The method is based on a graph that allows a reformulation of the PTSP as a shortest path problem for each job, which can be solved in polynomial time. The proposed method is applied to a supply chain scenario with a manufacturing facility in South Africa and shipments of finished product to customers within the Country. The obtained results show that the approach is suitable for the scheduling of large-scale problems and can be flexibly adapted to different scenarios.

Keywords: production and transport scheduling problem, graph based scheduling, integrated scheduling

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Authors: Midhun Paul, T. Radha Ramanan

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The objective of this work is to present a state-of-the-art literature review highlighting the challenges in the research of the scheduling of assembly job shop problem and providing an insight on how the future directions of the research would be. The number of work has been substantial that it requires a review to enable one to understand the origin of the research and how it is getting evolved. This review paper presents a comprehensive review of the literature dealing with various studies carried on assembly job shop scheduling. The review details the evolution of the AJS from the perspective of other scheduling problems and also presents a classification scheme. The work also identifies the potential directions for future research, which we believe to be worthwhile considering.

Keywords: assembly job shop, future directions, manufacturing, scheduling

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