Journal articles: 'Size-based scheduling'

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DellAmico, Matteo, Damiano Carra, and Pietro Michiardi. "PSBS: Practical Size-Based Scheduling." IEEE Transactions on Computers 65, no. 7 (July 1, 2016): 2199–212. http://dx.doi.org/10.1109/tc.2015.2468225.

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Harchol-Balter, Mor, Bianca Schroeder, Nikhil Bansal, and Mukesh Agrawal. "Size-based scheduling to improve web performance." ACM Transactions on Computer Systems 21, no. 2 (May 2003): 207–33. http://dx.doi.org/10.1145/762483.762486.

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Shim, Euysup, and Byung-Cheol Kim. "Batch-Size Based Repetitive Scheduling Method (BRSM)." International Journal of Construction Education and Research 10, no. 2 (March 10, 2014): 140–56. http://dx.doi.org/10.1080/15578771.2013.826753.

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Pastorelli, Mario, Damiano Carra, Matteo DellAmico, and Pietro Michiardi. "HFSP: Bringing Size-Based Scheduling To Hadoop." IEEE Transactions on Cloud Computing 5, no. 1 (January 1, 2017): 43–56. http://dx.doi.org/10.1109/tcc.2015.2396056.

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Roberts, James, and Dario Rossi. "Size-based scheduling vs fairness for datacenter flows." ACM SIGMETRICS Performance Evaluation Review 50, no. 2 (August 30, 2022): 2–10. http://dx.doi.org/10.1145/3561074.3561076.

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Contrary to the conclusions of a recent body of work where approximate shortest remaining processing time first (SRPT) flow scheduling is advocated for datacenter networks, this paper aims to demonstrate that imposing fairness remains a preferable objective. We evaluate abstract queuing models by analysis and simulation to illustrate the non-optimality of SRPT under the reasonable assumptions that datacenter source-destination flows occur in batches and bursts and not, as usually assumed, individually at the instants of a Poisson process. Results for these models have significant implications for the design of bandwidth sharing strategies for datacenter networks. In particular, we propose a novel "virtual fair scheduling" algorithm that enforces fairness between batches and is arguably simple enough to be implemented in high speed devices.

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Down, Douglas G. "Open Problem—Size-Based Scheduling with Estimation Errors." Stochastic Systems 9, no. 3 (September 2019): 295–96. http://dx.doi.org/10.1287/stsy.2019.0041.

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Rai, Dipankar, Chien-Wei (Wilson) Lin, and Magdoleen T. Ierlan. "The Influence of Scheduling Style on Assortment Size." Management & Marketing 11, no. 4 (December 1, 2016): 553–65. http://dx.doi.org/10.1515/mmcks-2016-0016.

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Abstract People use two types of scheduling styles to schedule their daily activities, namely clock-time or event-time. When people use clock time, they organize tasks based on a clock. When they use event-time, they organize tasks based on their order of completion. This research shows that adopting different scheduling styles influence consumers’ assortment size preferences. We demonstrate, through two studies, that consumers using event-time scheduling style prefer a larger assortment size whereas consumers using clock-time scheduling style prefer a smaller assortment size. We also show that this effect is mediated by desirability-feasibility consideration. Specifically, event-time scheduling style leads consumers to focus on the desirability considerations, which leads them to prefer larger assortment size while shopping. On the other hand, clock-time scheduling style leads consumers to focus on the feasibility considerations, which leads them to prefer smaller assortment size while shopping. We also discuss the theoretical and managerial implications of our research.

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Mi, Ningfang, Giuliano Casale, and Evgenia Smirni. "ASIdE: Using Autocorrelation-Based Size Estimation for Scheduling Bursty Workloads." IEEE Transactions on Network and Service Management 9, no. 2 (June 2012): 198–212. http://dx.doi.org/10.1109/tnsm.2012.041712.100073.

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Verloop, Maaike, Sem Borst, and Rudesindo Núñez-Queija. "Stability of size-based scheduling disciplines in resource-sharing networks." Performance Evaluation 62, no. 1-4 (October 2005): 247–62. http://dx.doi.org/10.1016/j.peva.2005.07.008.

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Marin, Andrea, Sabina Rossi, and Carlo Zen. "Size-based scheduling for TCP flows: Implementation and performance evaluation." Computer Networks 183 (December 2020): 107574. http://dx.doi.org/10.1016/j.comnet.2020.107574.

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He, Zhiqiang, and Yongjun Lin. "CBSBS: A classification-based size-based scheduling and its application in industrial network." International Journal of Distributed Sensor Networks 13, no. 6 (June 2017): 155014771771417. http://dx.doi.org/10.1177/1550147717714170.

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Rai, I. A., E. W. Biersack, and G. Urvoy-Keller. "Size-based scheduling to improve the performance of short TCP flows." IEEE Network 19, no. 1 (January 2005): 12–17. http://dx.doi.org/10.1109/mnet.2005.1383435.

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Minagawa, Tadasuke, and Tokuhiro Kitami. "Packet size based dynamic scheduling for assured services in Diffserv network." Electronics and Communications in Japan (Part I: Communications) 88, no. 1 (2004): 12–20. http://dx.doi.org/10.1002/ecja.20151.

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Mohamad, Muhammad Afwan, Zulfan Adi Putra, M. Roil Bilad, Nik Abdul Hadi Md Nordin, and M. Dzul Hakim Wirzal. "An Excel Based Tool Development for Scheduling Optimization." ASEAN Journal of Science and Engineering Education 1, no. 1 (February 26, 2021): 7–14. http://dx.doi.org/10.17509/ajsee.v1i1.32398.

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Scheduling can be seen in many areas such as chemical processing, logistic, supply chain, and class. In this study, two different cases of scheduling problems are addressed which are batch reactor process scheduling and University Class Scheduling (UCS) problems. The aim for the batch reactor process scheduling is to minimize the total discrepancies between the size of the assigned reactors and their corresponding assigned capacities or reactor sizes. The approach taken into consideration includes some constraints such as desired products, the production capacities, and the reactor capacities. Meanwhile, for the UCS problem, the concern is on the number of needed courses assign to certain classrooms while at the same time noting the constraints such as the size of the classroom and the number students. The UCS problem also seeks to optimize the distribution of courses remarkably to classrooms refers to the ratio of classroom capacity to course enrolment. These issues will be resolved using Integer Linear Programming (ILP) in the form of Excel-based software. The ILP model tool for both scheduling problems, therefore, is developed and resolved using the Excel solver utilizing Visual Basic Applications (VBA) and Macro.

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Ma, Mingze, and Rizos Sakellariou. "Code-size-aware Scheduling of Synchronous Dataflow Graphs on Multicore Systems." ACM Transactions on Embedded Computing Systems 20, no. 3 (April 2021): 1–24. http://dx.doi.org/10.1145/3440034.

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Synchronous dataflow graphs are widely used to model digital signal processing and multimedia applications. Self-timed execution is an efficient methodology for the analysis and scheduling of synchronous dataflow graphs. In this article, we propose a communication-aware self-timed execution approach to solve the problem of scheduling synchronous dataflow graphs on multicore systems with communication delays. Based on this communication-aware self-timed execution approach, four communication-aware scheduling algorithms are proposed using different allocation rules. Furthermore, a code-size-aware mapping heuristic is proposed and jointly used with a proposed scheduling algorithm to reduce the code size of SDFGs on multicore systems. The proposed scheduling algorithms are experimentally evaluated and found to perform better than existing algorithms in terms of throughput and runtime for several applications. The experiments also show that the proposed code-size-aware mapping approach can achieve significant code size reduction with limited throughput degradation in most cases.

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Zhang, Zhi Cong, Kai Shun Hu, Hui Yu Huang, and Shuai Li. "Production Planning Considering Transfer Lot Size." Applied Mechanics and Materials 44-47 (December 2010): 552–56. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.552.

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Traditional methods conduct production planning and scheduling separately and solve transfer lot sizing problem between these two steps. Unfortunately, this may result in infeasibility in planning and scheduling. We take into account transfer lot size in production planning to obtain the consistency and to eliminate the gap between planning and real production. We present the detailed Transfer Lot-Based Model with mixed integer programming. Experiments show that performance measures of a production plan change remarkably with increasing of transfer lot size.

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Mashuri, Chamdan, Ahmad Heru Mujianto, Hadi Sucipto, and Rinaldo Yudianto Arsam. "Sistem Optimasi Penjadwalan Mesin Produksi Menggunakan Metode GUPTA Berbasis Android." JURNAL SISTEM INFORMASI BISNIS 10, no. 1 (March 12, 2020): 20–27. http://dx.doi.org/10.21456/vol10iss1pp20-27.

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Research has been carried out by developing an optimization information system for scheduling production machines by applying the Android-based GUPTA method. This android-based application is able to optimize production time, because in the android application it implements the GUPTA algorithm which uses the calculation of the comparative processing time on every machine in the company by prioritizing the smallest processing time for scheduling which aims to optimize production scheduling time, by paying attention to the value of makespan to produce product size 12 griddle, size 14 griddle, 16 size griddle, 18 size griddle and 20 size griddle so that an optimal makespan value is obtained. The GUPTA method can be used in problems with more than two machines, because this method combines the time of each process on the first and subsequent machines to find the minimum value and can only be used in pure flow shop scheduling. The advantage of this method is that it determines scheduling only on one machine group. This research resulted in an Android-based application that can schedule products to be produced by machines automatically. From the results of testing with a total of 12 pieces of production in each product with a total of 5 different sizes, the minimum value of makespan is obtained, namely 2054569 minutes with the sequence of product processing with work order 12 griddle, griddle 18, griddle 20, griddle 16, and griddle 14 The accuracy of the application test results shows 98.87% for the first time and 98.84% for the second time when compared with manual calculations.

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Shao, Lin, Yunxiao Zu, and Hanyong Shao. "A New Swap-Based Frequency-Domain Packet Scheduling Algorithm in OFDMA System with Data Queue Size Constraints." Mathematical Problems in Engineering 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/6087856.

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This paper aims at the frequency-domain packet scheduling (FDPS) problem in orthogonal frequency division multiple access (OFDMA) system. Under users’ data queue size constraints, a new swap-based FDPS algorithm is proposed to achieve further improvement in system throughput. In this algorithm, the swap of physical resource blocks (PRBs) between different users is introduced to give a comprehensive view of the overall scheduling process. Moreover, the proposed algorithm optimizes the choosing method of swap candidates and always tries to select the user who can maximize the throughput improvement. Simulation results demonstrate that this new algorithm can improve the system throughput significantly as well as reduce the resource waste effectively.

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Li, Jitong, Chao Wang, Daehee Seo, Xiaoman Cheng, Yunhua He, Limin Sun, and Ke Xiao. "Deep Learning-Based Service Scheduling Mechanism for GreenRSUs in the IoVs." Wireless Communications and Mobile Computing 2021 (September 30, 2021): 1–15. http://dx.doi.org/10.1155/2021/7018486.

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Green roadside units (RSUs), also called renewable energy-powered RSUs, are utilized recently rather than the traditional electric-powered RSUs with high power consumption and the large infrastructure deployment cost in the Internet of vehicles (IoVs). However, the power of the green RSUs is limited and unstable, which is affected by the battery size and charging environment. Therefore, a big challenge to deploy green RSUs in the IoVs is to schedule their service process properly, in order to extend the service efficiency of RSUs. In this paper, a deep learning-based communication scheduling mechanism is proposed regarding the service scheduling problem. In particular, a three-part scheduling algorithm consisting of RSU clustering, deep learning-based traffic prediction, and a vehicle access scheduling algorithm is presented to maximize the service number of vehicles and minimize the energy cost. An extensive simulation is done, and the simulation results indicate that our algorithm can serve more vehicles with less energy consumption compared with other scheduling mechanisms under different scenarios.

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Gokuldev, S., and Jathin R. "Range smart cluster monitor based guesstimate approach for resource scheduling in small size clusters." International Journal of Engineering & Technology 7, no. 2 (June 1, 2018): 837. http://dx.doi.org/10.14419/ijet.v7i2.9531.

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Performing scheduling of tasks with low energy consumption with high performance is one of the major concerns in distributed computing. Most of the existing systems have achieved improved energy efficiency but compromised with QoS metrics such as makespan and resource utilization. A resource scheduling strategy for wireless clusters is proposed by making careful considerations on decisions that would im-prove the battery life of nodes. The proposed strategy also incorporates monitoring system with in the clusters for optimizing the system performance as well as energy consumption. The system ensures “Any case zero loss" performance wherein each cluster will be monitored by at least one cluster monitor. This is implemented by using predictive calculation at each cluster monitor to communicate only if absolutely essential, during assigning jobs to resources, selecting optimal resources by assigning the jobs to the most power efficient resource among the available idle resources within the cluster. The experimental result ensures improved system performance with low power consumption in homogeneous computing environment. The resource sharing strategy is experimentally analyzed, considering the important performance metrics such as starvation deadline, turnaround time, miss hit count through simulations. Significant results were observed with improved efficiency.

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Zhou, Binghai, and Jiahui Xu. "An adaptive SVM-based real-time scheduling mechanism and simulation for multiple-load carriers in automobile assembly lines." International Journal of Modeling, Simulation, and Scientific Computing 08, no. 04 (December 2017): 1750048. http://dx.doi.org/10.1142/s1793962317500489.

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Multiple-load carriers are widely introduced for material delivery in manufacturing systems. The real-time scheduling of multiple-load carriers is so complex that it deserves attention to pursue higher productivity and better system performance. In this paper, a support vector machine (SVM)-based real-time scheduling mechanism was proposed to tackle the scheduling problem of parts replenishment with multiple-load carriers in automobile assembly plants under dynamic environment. The SVM-based scheduling mechanism was trained first and then used to make the optimal real-time decisions between “wait” and “deliver” on the basis of real-time system states. An objective function considering throughput and delivery distances was established to evaluate the system performance. Moreover, a simulation model in eM-Plant software was developed to validate and compare the proposed SVM-based scheduling mechanism with the classic minimum batch size (MBS) heuristic. It simulated both the steady and dynamic environments which are characterized by the uncertainty of demands or scheduling criteria. The simulation results demonstrated that the SVM-based scheduling mechanism could dynamically make optimal real-time decisions for multiple-load carriers and outperform the MBS heuristic as well.

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Peng, Fei, Xian Fan, Puxin Wang, and Mingan Sheng. "A Time-Space Network-Based Optimization Method for Scheduling Depot Drivers." Sustainability 14, no. 21 (November 3, 2022): 14431. http://dx.doi.org/10.3390/su142114431.

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The driver scheduling problem at Chinese electric multiple-unit train depots becomes more and more difficult in practice and is studied in very little research. This paper focuses on defining, modeling, and solving the depot driver scheduling problem which can determine driver size and driver schedule simultaneously. To solve this problem, we first construct a time-space network based on which we formulate the problem as a minimum-cost multi-commodity network flow problem. We then develop a Lagrangian relaxation heuristic to solve this network flow problem, where the upper bound heuristic is a two-phase method consisting of a greedy heuristic and a local search method. We conduct a computational study to test the effectiveness of our Lagrangian relaxation heuristic. The computational results also report the significance of the ratio of driver size to task size in the depot.

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Liu, Chin-I., and P. A. Ioannou. "A PETRI NET BASED APPROACH FOR AGV DISPATCH SCHEDULING AND FLEET SIZE DETERMINATION." IFAC Proceedings Volumes 35, no. 1 (2002): 19–24. http://dx.doi.org/10.3182/20020721-6-es-1901.00006.

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AGUILAR, JOSE, and ERNST LEISS. "PARALLEL LOOP SCHEDULING APPROACHES FOR DISTRIBUTED AND SHARED MEMORY SYSTEMS." Parallel Processing Letters 15, no. 01n02 (March 2005): 131–52. http://dx.doi.org/10.1142/s0129626405002118.

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In this paper, we propose different approaches for the parallel loop scheduling problem on distributed as well as shared memory systems. Specifically, we propose adaptive loop scheduling models in order to achieve load balancing, low runtime scheduling, low synchronization overhead and low communication overhead. Our models are based on an adaptive determination of the chunk size and an exploitation of the processor affinity property, and consider different situations (central or local queues, and dynamic or static loop partition).

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Dou, Wan Feng, Jing Zhao, Kun Yang, and Min Xu. "Data Parallel and Scheduling Mechanism Based on Petri Nets." Applied Mechanics and Materials 543-547 (March 2014): 3264–67. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.3264.

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Data-parallel and task-parallel methods are the basic methods frequently used for algorithm design in parallel computing. Data-parallel method as name means is used for partition data to be processed into some small blocks considering storage and computing capacity such as memory size of a computation node, node number to take part in parallel computing and total data size, and etc. On the other hand, data dispensing strategy is an important problem carefully considered to increase the efficiency of computation. According to the characteristics of analysis of digital terrain, petri nets is introduced to describe the parallel relationships within data partitions based on data granularity model considering two kinds of computing modes, shared memory and distributed memory respectively, and corresponding scheduling algorithms are proposed for load balance. The experimental results show that our method is very usable to data partition and dispensation, in particular to distributed memory mode.

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Kim, Juho, Haoqi Zhang, Paul André, Lydia Chilton, Anant Bhardwaj, David Karger, Steven Dow, and Robert Miller. "Cobi: Community-Informed Conference Scheduling." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 1 (November 3, 2013): 97–98. http://dx.doi.org/10.1609/hcomp.v1i1.13050.

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Creating a schedule for a large multi-track conference requires considering the preferences and constraints of organizers, authors, and attendees. Traditionally, a few dedicated organizers manage the size and complexity of the schedule with limited information and coverage. Cobi presents an alternative approach to conference scheduling by engaging the entire community to take active roles in the planning process. It consists of a collection of crowdsourcing applications that elicit preferences and constraints from the community, and software that enable organizers and other community members to take informed actions based on collected information.

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Zeng, Qiongfang, Yinggui Zhang, and Dingyou Lei. "An Experimental Analysis on Dispatching Rules for the Train Platforming Problem in Busy Complex Passenger Stations." PROMET - Traffic&Transportation 29, no. 4 (September 4, 2017): 411–23. http://dx.doi.org/10.7307/ptt.v29i4.2263.

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This paper presents the scheduling models for trainplatforming problem (TPP) by using mixed integer linear programming and job shop scheduling theory. First, the operation procedures and scheduled time adjustment costs of different train types specific to busy complex passenger stations are explicitly represented. Second, a multi-criteria scheduling model (MCS) for TPP without earliness and tardiness time window (ETTW) and a time window scheduling model (TWS) with ETTW for TPP are proposed. Third, various dispatching rules were designed by incorporating the dispatcher experiences with modern scheduling theory and a rule-based metaheuristic to solve the above model is presented. With solution improvement strategies analogous to those used in practice by dispatchers, the realistic size problems in acceptable time can be solved.

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Grzegorowski, Marek, Eftim Zdravevski, Andrzej Janusz, Petre Lameski, Cas Apanowicz, and Dominik Ślęzak. "Cost Optimization for Big Data Workloads Based on Dynamic Scheduling and Cluster-Size Tuning." Big Data Research 25 (July 2021): 100203. http://dx.doi.org/10.1016/j.bdr.2021.100203.

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Jin, Bin, Peter B. Luh, and L. S. Thakur. "An effective optimization-based algorithm for job shop scheduling with fixed-size transfer lots." Journal of Manufacturing Systems 18, no. 4 (January 1999): 284–300. http://dx.doi.org/10.1016/s0278-6125(00)86631-5.

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Li, Kun, and Huixin Tian. "An Improved Particle Swarm Optimization for Selective Single Machine Scheduling with Sequence Dependent Setup Costs and Downstream Demands." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/687968.

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This paper investigates a special single machine scheduling problem derived from practical industries, namely, the selective single machine scheduling with sequence dependent setup costs and downstream demands. Different from traditional single machine scheduling, this problem further takes into account the selection of jobs and the demands of downstream lines. This problem is formulated as a mixed integer linear programming model and an improved particle swarm optimization (PSO) is proposed to solve it. To enhance the exploitation ability of the PSO, an adaptive neighborhood search with different search depth is developed based on the decision characteristics of the problem. To improve the search diversity and make the proposed PSO algorithm capable of getting out of local optimum, an elite solution pool is introduced into the PSO. Computational results based on extensive test instances show that the proposed PSO can obtain optimal solutions for small size problems and outperform the CPLEX and some other powerful algorithms for large size problems.

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Liu, Bin. "Study on the Stochastic Chance-Constrained Fuzzy Programming Model and Algorithm for Wagon Flow Scheduling in Railway Bureau." Mathematical Problems in Engineering 2012 (2012): 1–13. http://dx.doi.org/10.1155/2012/602153.

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The wagon flow scheduling plays a very important role in transportation activities in railway bureau. However, it is difficult to implement in the actual decision-making process of wagon flow scheduling that compiled under certain environment, because of the interferences of uncertain information, such as train arrival time, train classify time, train assemble time, and flexible train-size limitation. Based on existing research results, considering the stochasticity of all kinds of train operation time and fuzziness of train-size limitation of the departure train, aimed at maximizing the satisfaction of departure train-size limitation and minimizing the wagon residence time at railway station, a stochastic chance-constrained fuzzy multiobjective model for flexible wagon flow scheduling problem is established in this paper. Moreover, a hybrid intelligent algorithm based on ant colony optimization (ACO) and genetic algorithm (GA) is also provided to solve this model. Finally, the rationality and effectiveness of the model and algorithm are verified through a numerical example, and the results prove that the accuracy of the train work plan could be improved by the model and algorithm; consequently, it has a good robustness and operability.

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Wang, Chun Xiao, Ying Guo, and Xiu Gang Guo. "Research of Public Service Platform Based on Cloud Computing for Middle Size Enterprises." Advanced Materials Research 989-994 (July 2014): 5498–503. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.5498.

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Based on the research in cross-regional resource scheduling and massive data storage, the article built public service platform including IaaS, PaaS and SaaS. It provided all kinds of management software and business software for middle size enterprise users, and provided the development, testing, deployment platform for software vendors, and provided unified resource management, monitoring and maintenance for platform operators which finally become cloud service platform to support enterprise management and software development lifecycle. This helps to form a self-loop and self-development cloud computing ecosystem, to form the linkage of cloud services production, cloud services consumption and cloud service management, and to provide comprehensive information support for the growth and development of middle size enterprise.

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Hazim Lokman, Muhamad, Ismail Musirin, Saiful Izwan Suliman, Hadi Suyono, Rini Nur Hasanah, Sharifah Azma Syed Mustafa, and Mohamed Zellagui. "Multi-verse optimization based evolutionary programming technique for power scheduling in loss minimization scheme." IAES International Journal of Artificial Intelligence (IJ-AI) 8, no. 3 (December 1, 2019): 292. http://dx.doi.org/10.11591/ijai.v8.i3.pp292-298.

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The growth of computational intelligence technology has witnessed its application in numerous fields. Power system study is not left behind as far as computational intelligence trend is concerned. In power system community, optimization process is one of the crucial efforts for most remedial action to maintain the power system security. Basically, power scheduling refers to prior to fact action(such as scheduling generators to generate certain powers for next week). Power scheduling process is one of the most important routines in power systems. Scheduling of generators in a power transmission system is an important scheme; especially its offline studies to identify the security status of the system. This determines the cost effectiveness in power system planning. This paper investigates the performance of multi-verse based evolutionary programming(lowest EP) technique in the application of power system scheduling to ensure loss is gained by the system. Losses in the system can be controlled through this implementation which can be realized through the validation on a chosen reliability test system as the main model. Validation on <span style="font-size: 10.0pt; font-family: 'Times New Roman','serif'; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;" lang="EN-US">IEEE 30-Bus Reliability Test System resulted that both techniques are reliable and robust in addressing this issue.

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SUN, WEI. "POPULATION SIZE MODELING FOR GA IN TIME-CRITICAL TASK SCHEDULING." International Journal of Foundations of Computer Science 22, no. 03 (April 2011): 603–20. http://dx.doi.org/10.1142/s0129054111008258.

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Genetic algorithms (GAs) have been well applied in solving scheduling problems and their performance advantages have also been recognized. However, practitioners are often troubled by parameters setting when they are tuning GAs. Population Size (PS) has been shown to greatly affect the efficiency of GAs. Although some population sizing models exist in the literature, reasonable population sizing for task scheduling is rarely observed. In this paper, based on the PS deciding model proposed by Harik, we present a model to represent the relation between the success ratio and the PS for the GA applied in time-critical task scheduling, in which the efficiency of GAs is more necessitated than in solving other kinds of problems. Our model only needs some parameters easy to know through proper simplifications and approximations. Hence, our model is applicable. Finally, our model is verified through experiments.

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Mahdavinejad, Ramezan Ali. "Job Shop-Scheduling Problems- Single Process." Applied Mechanics and Materials 44-47 (December 2010): 330–34. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.330.

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In this paper, single-processors jobshop scheduling problems are solved by a heuristic algorithm based on the hybrid of priority dispatching rules according to an ant colony optimization algorithm. The objective function is to minimize the makespan, i.e. total completion time, in which a simultanous presence of various kinds of ferons is allowed. The process of finding the best solution will be improved by using the suitable hybrid of priority dispatching rules. Ant colony optimization algorithm, not only promote the ability of this proposed algorithm, but also decreases the total working time because of decreasing in setup times and modifying the working production line. By solving some problems as samples (i.e. Fisher's & Thomson's problems), this algorithm is compared with the others. The results show that when the size of the problem becomes lorger, the deviation from lower limit increases, but its rate decreases with the size of the problems, so that it reaches to its limit.

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Lan, Zekang, Shiwei He, Rui Song, and Sijia Hao. "Optimizing Vehicle Scheduling Based on Variable Timetable by Benders-and-Price Approach." Journal of Advanced Transportation 2019 (January 6, 2019): 1–13. http://dx.doi.org/10.1155/2019/2781590.

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In practice, vehicle scheduling is planned on a variable timetable so that the departure times of trips can be shifted in tolerable ranges, rather than on a fixed timetable, to decrease the required fleet size. This paper investigates the vehicle scheduling problem on a variable timetable with the constraint that each vehicle can perform limited trips. Since the connection-based model is difficult to solve by optimization software for a medium-scale or large-scale instance, a designed path-based model is developed. A Benders-and-Price algorithm by combining the Benders decomposition and column generation is proposed to solve the LP-relaxation of the path-based model, and a bespoke Branch-and-Price is used to obtain the integer solution. Numerical experiments indicate that a variable timetable approach can reduce the required fleet size with a tolerable timetable deviation in comparison with a fixed timetable approach. Moreover, the proposed algorithm is greatly superior to GUROBI in terms of computational efficiency and guarantees the quality of the solution.

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Liu, Lihao, Zhenghong Dong, Haoxiang Su, and Dingzhan Yu. "A Study of Distributed Earth Observation Satellites Mission Scheduling Method Based on Game-Negotiation Mechanism." Sensors 21, no. 19 (October 7, 2021): 6660. http://dx.doi.org/10.3390/s21196660.

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While monolithic giant earth observation satellites still have obvious advantages in regularity and accuracy, distributed satellite systems are providing increased flexibility, enhanced robustness, and improved responsiveness to structural and environmental changes. Due to increased system size and more complex applications, traditional centralized methods have difficulty in integrated management and rapid response needs of distributed systems. Aiming to efficient missions scheduling in distributed earth observation satellite systems, this paper addresses the problem through a networked game model based on a game-negotiation mechanism. In this model, each satellite is viewed as a “rational” player who continuously updates its own “action” through cooperation with neighbors until a Nash Equilibria is reached. To handle static and dynamic scheduling problems while cooperating with a distributed mission scheduling algorithm, we present an adaptive particle swarm optimization algorithm and adaptive tabu-search algorithm, respectively. Experimental results show that the proposed method can flexibly handle situations of different scales in static scheduling, and the performance of the algorithm will not decrease significantly as the problem scale increases; dynamic scheduling can be well accomplished with high observation payoff while maintaining the stability of the initial plan, which demonstrates the advantages of the proposed methods.

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Sari, Rafika, Khairunnisa Fadhilla Ramdhania, and Rakhmat Purnomo. "Team-Teaching-Based Course Scheduling Using Genetic Algorithm." PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic 10, no. 1 (March 26, 2022): 55–66. http://dx.doi.org/10.33558/piksel.v10i1.4416.

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Scheduling problems occur in various fields, e.g., education, health institutions, transportation, sports, etc. Main scheduling problems in education is course scheduling which creates schedules for students and lecturers. In this study, course scheduling allocates the lecturers in the form of team teaching and courses into the class and a certain time to even out the workload of lecturers per day and a group of students per day in one week without breaking the constraint. The method used in this research is a genetic algorithm where Universitas Bhayangkara Jakarta Raya as the case study. The genetic algorithm process is done by getting several candidate solutions that undergo a process of selection, mutation, and crossing over to produce chromosomes with the best fitness values. The objective function in this research is minimizing the average variance of the workload of lecturers and students per day in one week. The parameters used in genetic algorithm are determined based on the Design of Experiments mechanism (DOE). The optimal parameter values used to run the program are as: population size = 50, with probability of crossing over = 0.4 and probability of mutation = 0.008. The results of scheduling with genetic algorithms show that the value of the workload variance lecturers and students by considering team teaching is better than actual scheduling. The application of the genetic algorithm method results in a decrease in the standard value deviation of the workload of lecturers and a group of students in one week is 0.114 (3.68%) and 3.11 (55.7%). In addition, course scheduling uses a genetic algorithm with consider team teaching better than genetic algorithm without considering team teaching because there is no class schedule that clashes in real conditions.

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Khan, Aimal, Assad Abbas, Hasan Ali Khattak, Faisal Rehman, Ikram Ud Din, and Sikandar Ali. "Effective Task Scheduling in Critical Fog Applications." Scientific Programming 2022 (March 31, 2022): 1–15. http://dx.doi.org/10.1155/2022/9208066.

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Information and technology have witnessed significant improvement with the introduction of Internet of things (IoT) applications, and most of the IoT applications are dependent on the cloud. Cloud computing is assisting IoT applications by providing storage, analysis, and processing services on the cloud. However, Fog computing is the new paradigm that supports the cloud by providing scheduling, resources optimization, and energy optimization services. Scheduling tasks based on MIPs size and prioritizing the tasks with smaller MIPs size first make critical tasks with larger MIPs wait, which ultimately increases the delay and may result in some serious problems. This paper proposes a methodology for critical tasks having large MIPs size by scheduling and prioritizing the tasks based on the nature of the task. The proposed methodology for latency-critical applications reduces latency, energy consumption, and network utilization. This paper proposed a scheduler “Critical task First Scheduler” (CTFS), which schedules tasks depending on the nature of the requests, which are classified as either critical or noncritical. The proposed methodology is implemented in a healthcare scenario, and the simulations are performed in iFogSim simulator. Critical requests, such as emergency notifications, are prioritized and designated as critical, requiring immediate processing. The environment was kept the same for all the approaches that are implemented to demonstrate the effectiveness of the proposed approach. The results of the proposed approach were compared with First Come First Served (FCFS), Shortest Job First (SJF), and cloud-only approaches to demonstrate the effectiveness of the proposed approach in terms of latency, energy consumption, and network utilization. Simulation results show that the proposed CTFS approach outperformed the compared techniques for all three comparison parameters.

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Oulad Nassar, Badr, Takuji Tachibana, and Kenji Sugimoto. "Random scheduling based on transmission delay and buffer size for hop-based burst-cluster transmission in OBS networks." Photonic Network Communications 19, no. 3 (December 11, 2009): 292–300. http://dx.doi.org/10.1007/s11107-009-0234-6.

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Bhargavi, K., and Sajjan G. Shiva. "Uncertainty Aware T2SS Based Dyna-Q-Learning Framework for Task Scheduling in Grid Computing." Cybernetics and Information Technologies 22, no. 3 (September 1, 2022): 48–67. http://dx.doi.org/10.2478/cait-2022-0027.

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Abstract Task scheduling is an important activity in parallel and distributed computing environment like grid because the performance depends on it. Task scheduling gets affected by behavioral and primary uncertainties. Behavioral uncertainty arises due to variability in the workload characteristics, size of data and dynamic partitioning of applications. Primary uncertainty arises due to variability in data handling capabilities, processor context switching and interplay between the computation intensive applications. In this paper behavioral uncertainty and primary uncertainty with respect to tasks and resources parameters are managed using Type-2-Soft-Set (T2SS) theory. Dyna-Q-Learning task scheduling technique is designed over the uncertainty free tasks and resource parameters. The results obtained are further validated through simulation using GridSim simulator. The performance is good based on metrics such as learning rate, accuracy, execution time and resource utilization rate.

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Jin, Shiyi, Mengzhuo Liu, Yujing Wu, Yihu Xu, Jingnan Zhang, and Yinan Xu. "Research of Message Scheduling for In-Vehicle FlexRay Network Static Segment Based on Next Fit Decreasing (NFD) Algorithm." Applied Sciences 8, no. 11 (October 26, 2018): 2071. http://dx.doi.org/10.3390/app8112071.

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FlexRay is becoming the in-vehicle communication network of the next generation. In this study, the main contents are the FlexRay network static segment scheduling algorithm and optimization strategy, improve the scheduling efficiency of vehicle network and optimize the performance of communication network. The FlexRay static segment characteristic was first analyzed, then selected bandwidth utilization as the performance metrics to scheduling problem. A signal packing method is proposed based on Next Fit Decreasing (NFD) algorithm. Then Frame ID (FID) multiplexing method was used to minimize the number of FIDs. Finally, experimental simulation by CANoe. FlexRay software, that shows the model can quickly obtain the message schedule of each node, effectively control the message payload size and reduced bus payload by 16.3%, the number of FID drops 53.8% while improving bandwidth utilization by 32.8%.

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Wightman, Rick A., and Emin Z. Baskent. "Forest neighbourhoods for timber harvest scheduling." Forestry Chronicle 70, no. 6 (December 1, 1994): 768–72. http://dx.doi.org/10.5558/tfc70768-6.

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Forest management involves exploring through time the scheduling opportunities for timber and non-timber values within a forest. The ability to identify and form neighbourhoods — areas of suitable stand conditions and locations — is critical to this endeavour. This paper presents a GIS-based algorithm for identifying and forming forest neighbourhoods suitable for timber harvest scheduling. The resulting neighbourhoods are contiguous and overlapping, composed of stands sharing similar attributes. Similarity is based on a definable similarity list where stand conditions closest to one another in the list are most similar to one another. The algorithm is demonstrated with a single stand example and then a small forest example. Control of neighbourhood size is limited using a vector data model, except in forests composed of small stands. The examples illustrate that neighbourhood inclusion is dependent on both a forest stand's condition and relative position in the forest. The paper concludes with suggestions for further development of the algorithm. Key words: timber harvest scheduling, forest management, spatial modelling, GIS, neighbourhoods

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Elazouni, Ashraf, Anas Alghazi, and Shokri Z. Selim. "Finance-based scheduling using meta-heuristics: discrete versus continuous optimization problems." Journal of Financial Management of Property and Construction 20, no. 1 (April 7, 2015): 85–104. http://dx.doi.org/10.1108/jfmpc-07-2014-0013.

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Purpose – The purpose of this paper is to compare the performance of the genetic algorithm (GA), simulate annealing (SA) and shuffled frog-leaping algorithm (SFLA) in solving discrete versus continuous-variable optimization problems of the finance-based scheduling. This involves the minimization of the project duration and consequently the time-related cost components of construction contractors including overheads, finance costs and delay penalties. Design/methodology/approach – The meta-heuristics of the GA, SA and SFLA have been implemented to solve non-deterministic polynomial-time hard (NP-hard) finance-based scheduling problem employing the objective of minimizing the project duration. The traditional problem of generating unfeasible solutions in scheduling problems is adequately tackled in the implementations of the meta-heuristics in this paper. Findings – The obtained results indicated that the SA outperformed the SFLA and GA in terms of the quality of solutions as well as the computational cost based on the small-size networks of 30 activities, whereas it exhibited the least total duration based on the large-size networks of 120 and 210 activities after prolonged processing time. Research limitations/implications – From researchers’ perspective, finance-based scheduling is one of the few domain problems which can be formulated as discrete and continuous-variable optimization problems and, thus, can be used by researchers as a test bed to give more insight into the performance of new developments of meta-heuristics in solving discrete and continuous-variable optimization problems. Practical implications – Finance-based scheduling discrete-variable optimization problem is of high relevance to the practitioners, as it allows schedulers to devise finance-feasible schedules of minimum duration. The minimization of project duration is focal for the minimization of time-related cost components of construction contractors including overheads, finance costs and delay penalties. Moreover, planning for the expedient project completion is a major time-management aspect of construction contractors towards the achievement of the objective of client satisfaction through the expedient delivery of the completed project for clients to start reaping the anticipated benefits. Social implications – Planning for the expedient project completion is a major time-management aspect of construction contractors towards the achievement of the objective of client satisfaction. Originality/value – SFLA represents a relatively recent meta-heuristic that proved to be promising, based on its limited number of applications in the literature. This paper is to implement SFLA to solve the discrete-variable optimization problem of the finance-based scheduling and assess its performance by comparing its results against those of the GA and SA.

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Kavoosi, Masoud, Maxim A. Dulebenets, Olumide Abioye, Junayed Pasha, Oluwatosin Theophilus, Hui Wang, Raphael Kampmann, and Marko Mikijeljević. "Berth scheduling at marine container terminals." Maritime Business Review 5, no. 1 (November 18, 2019): 30–66. http://dx.doi.org/10.1108/mabr-08-2019-0032.

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Purpose Marine transportation has been faced with an increasing demand for containerized cargo during the past decade. Marine container terminals (MCTs), as the facilities for connecting seaborne and inland transportation, are expected to handle the increasing amount of containers, delivered by vessels. Berth scheduling plays an important role for the total throughput of MCTs as well as the overall effectiveness of the MCT operations. This study aims to propose a novel island-based metaheuristic algorithm to solve the berth scheduling problem and minimize the total cost of serving the arriving vessels at the MCT. Design/methodology/approach A universal island-based metaheuristic algorithm (UIMA) was proposed in this study, aiming to solve the spatially constrained berth scheduling problem. The UIMA population was divided into four sub-populations (i.e. islands). Unlike the canonical island-based algorithms that execute the same metaheuristic on each island, four different population-based metaheuristics are adopted within the developed algorithm to search the islands, including the following: evolutionary algorithm (EA), particle swarm optimization (PSO), estimation of distribution algorithm (EDA) and differential evolution (DE). The adopted population-based metaheuristic algorithms rely on different operators, which facilitate the search process for superior solutions on the UIMA islands. Findings The conducted numerical experiments demonstrated that the developed UIMA algorithm returned near-optimal solutions for the small-size problem instances. As for the large-size problem instances, UIMA was found to be superior to the EA, PSO, EDA and DE algorithms, which were executed in isolation, in terms of the obtained objective function values at termination. Furthermore, the developed UIMA algorithm outperformed various single-solution-based metaheuristic algorithms (including variable neighborhood search, tabu search and simulated annealing) in terms of the solution quality. The maximum UIMA computational time did not exceed 306 s. Research limitations/implications Some of the previous berth scheduling studies modeled uncertain vessel arrival times and/or handling times, while this study assumed the vessel arrival and handling times to be deterministic. Practical implications The developed UIMA algorithm can be used by the MCT operators as an efficient decision support tool and assist with a cost-effective design of berth schedules within an acceptable computational time. Originality/value A novel island-based metaheuristic algorithm is designed to solve the spatially constrained berth scheduling problem. The proposed island-based algorithm adopts several types of metaheuristic algorithms to cover different areas of the search space. The considered metaheuristic algorithms rely on different operators. Such feature is expected to facilitate the search process for superior solutions.

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Yusuke Gotoh, Tomoki Yoshihisa, Hideo Taniguchi, and Masanori Kanazawa. "A Scheduling Method on Selective Contents Delivery with Node Relay-based Webcast Considering Data Size." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 2, no. 3 (September 30, 2010): 128–37. http://dx.doi.org/10.4156/aiss.vol2.issue3.17.

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Qiao, Xiuquan, Hongyi Wang, Pei Ren, Yukai Tu, Guoshun Nan, Junliang Chen, and M. Brian Blake. "Interest packets scheduling and size-based flow control mechanism for content-centric networking web servers." Future Generation Computer Systems 107 (June 2020): 564–77. http://dx.doi.org/10.1016/j.future.2020.02.004.

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Lakshmipathy, D., M. Chandrasekaran, T. Balamurugan, and P. Sriramya. "A New GT Heuristic for Solving Multi Objective Job Shop Scheduling Problems." Applied Mechanics and Materials 591 (July 2014): 184–88. http://dx.doi.org/10.4028/www.scientific.net/amm.591.184.

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The n-job, m-machine Job shop scheduling (JSP) problem is one of the general production scheduling problems in manufacturing system. Scheduling problems vary widely according to specific production tasks but most are NP-hard problems. Scheduling problems are usually solved using heuristics to get optimal or near optimal solutions because problems found in practical applications cannot be solved to optimality using reasonable resources in many cases. In this paper, optimization of three practical performance measures mean job flow time, mean job tardiness and makespan are considered. New Game theory based heuristic method (GT) is used for finding optimal makespan, mean flow time, mean tardiness values of different size problems. The results show that the GT Heuristic is an efficient and effective method that gives better results than Genetic Algorithm (GA). The proposed GT Heuristic is a good problem-solving technique for job shop scheduling problem with multi criteria.

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Wang, Yufei, Jun Liu, Yanhua Yin, Yu Tong, and Jiansheng Liu. "Space Information Network Resource Scheduling for Cloud Computing: A Deep Reinforcement Learning Approach." Wireless Communications and Mobile Computing 2022 (March 9, 2022): 1–16. http://dx.doi.org/10.1155/2022/1927937.

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With the development of satellite technology, space information networks (SINs) have been applied to various fields. SINs can provide more and more complex services and receive more and more tasks. The existing resource scheduling algorithms are difficult to play an efficient role in such a complex environment of resources and tasks. We propose a resource allocation scheme based on reinforcement learning. Firstly, according to the characteristics of the resources of SINs, we established the cloud computing architecture of SINs to manage the resources uniformly. Next, we adopt a variable granularity resources clustering algorithm based on fuzzy and hierarchical clustering algorithms. This algorithm can adaptively adjust the resource size and reduce the scheduling range. Finally, we model the resource scheduling process by a reinforcement learning algorithm to solve the joint resource scheduling problem. The simulation results show that the scheme can effectively reduce resources consumption, shorten the task execution time, and improve the resource utilization efficiency of SINs.

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Ali, Gheni. "Dynamic Task Scheduling in Multiprocessor Real Time Systems Using Genetic Algorithms." Journal of Al-Rafidain University College For Sciences ( Print ISSN: 1681-6870 ,Online ISSN: 2790-2293 ), no. 2 (October 26, 2021): 46–65. http://dx.doi.org/10.55562/jrucs.v23i2.478.

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The objective of the scheduling algorithm is to dynamically schedule as many tasks as possible such that each task meets its execution deadline while minimizing the total delay time of all of the tasks. The problem of scheduling of real-time tasks in multiprocessor systems is to determine when and on which processor a given task executes. In this paper we suggest a genetic algorithm for dynamic scheduling of real time tasks in multiprocessors system.The algorithm based on the use of a fixed size chromosome and repeatedly applying specific crossover (single point or double point) and mutation procedures with variable mutation rates (0.05 – 0.1) until all tasks are successfully scheduled.

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Journal articles on the topic 'Size-based scheduling'

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