Journal articles on the topic 'Static scheduling problems'
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Ma, Yuliang, Yinghua Han, Jinkuan Wang, and Qiang Zhao. "A Constrained Static Scheduling Strategy in Edge Computing for Industrial Cloud Systems." International Journal of Information Technologies and Systems Approach 14, no. 1 (January 2021): 33–61. http://dx.doi.org/10.4018/ijitsa.2021010103.
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With the development of industrial internet, attention has been paid for edge computing due to the low latency. However, some problems remain about the task scheduling and resource management. In this paper, an edge computing supported industrial cloud system is investigated. According to the system, a constrained static scheduling strategy is proposed to over the deficiency of dynamic scheduling. The strategy is divided into the following steps. Firstly, the queue theory is introduced to calculate the expectations of task completion time. Thereupon, the task scheduling and resource management problems are formulated and turned into an integer non-linear programming (INLP) problem. Then, tasks that can be scheduled statically are selected based on the expectation of task completion and constrains of various aspects of task. Finally, a multi-elites-based co-evolutionary genetic algorithm (MEB-CGA) is proposed to solve the INLP problem. Simulation result shows that the MEB-CGA significantly outperforms the scheduling quality of greedy algorithm.
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Kämpke, Thomas. "Necessary optimality conditions for priority policies in stochastic weighted flowtime scheduling problems." Advances in Applied Probability 19, no. 3 (September 1987): 749–50. http://dx.doi.org/10.2307/1427418.
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Kämpke, Thomas. "Necessary optimality conditions for priority policies in stochastic weighted flowtime scheduling problems." Advances in Applied Probability 19, no. 03 (September 1987): 749–50. http://dx.doi.org/10.1017/s0001867800016876.
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Sharma, Manik, and Smriti Smriti. "STATIC AND DYNAMIC BNP PARALLEL SCHEDULING ALGORITHMS FOR DISTRIBUTED DATABASE." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 1, no. 1 (December 30, 2011): 10–15. http://dx.doi.org/10.24297/ijct.v1i1.2601.
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Parallel processing is a technique of executing the multiple tasksconcurrently on different processors. Parallel processing is usedto solve the complex problems that require vast amount ofprocessing time. Task scheduling is one of the major problemsof parallel processing. The objective of this study is to analyzethe performance of static (HLFET) and dynamic (DLS) BNPparallel scheduling algorithm for allocating the tasks ofdistributed database over number of processors. In the wholestudy the focus will be given on measuring the impact ofnumber of processors on different metrics of performance likemakespan, speed up and processor utilization by using HLFETand DLS BNP task scheduling algorithms.
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Terekhov, Daria, Tony Tran, Douglas Down, and J. Christopher Beck. "Long-Run Stability in Dynamic Scheduling." Proceedings of the International Conference on Automated Planning and Scheduling 22 (May 14, 2012): 261–69. http://dx.doi.org/10.1609/icaps.v22i1.13524.
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Stability analysis consists of identifying conditions under which the number of jobs in a system is guaranteed to remain bounded over time. To date, such long-run performance guarantees have not been available for periodic approaches to dynamic scheduling problems. However, stability has been extensively studied in queueing theory. In this paper, we introduce stability to the dynamic scheduling literature and demonstrate that stability guarantees can be obtained for methods that build the schedule for a dynamic problem by periodically solving static deterministic sub-problems. Specifically, we analyze the stability of two dynamic environments: a two-machine flow shop, which has received significant attention in scheduling research, and a polling system with a flow-shop server, an extension of systems typically considered in queueing. We demonstrate that, among stable policies, methods based on periodic optimization of static schedules may achieve better mean flow times than traditional queueing approaches.
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Hui, Ji Zhuang, Xiang Ding, and Kai Gao. "A FMS Dynamic Scheduling Optimization Strategy and Simulation Research." Applied Mechanics and Materials 389 (August 2013): 692–97. http://dx.doi.org/10.4028/www.scientific.net/amm.389.692.
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This paper studied the FMS dynamic scheduling problem which was based on Petri net FMS static scheduling optimization algorithm, which in accorder to solve the FMS actual production scheduling problems. A rolling window dynamic re-scheduling strategy was proposed which based on event driven and cycle driven. Then take the emergency machine failure often appearing in the actual workshop for example, this scheduling strategy was analyzed and applied to dynamic simulation and finally the effectiveness of the dynamic scheduling strategy was verified.
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Yu, Bin, Keming Wang, Can Wang, and Baozhen Yao. "Ship scheduling problems in tramp shipping considering static and spot cargoes." International Journal of Shipping and Transport Logistics 9, no. 4 (2017): 391. http://dx.doi.org/10.1504/ijstl.2017.084825.
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Yao, Baozhen, Can Wang, Keming Wang, and Bin Yu. "Ship scheduling problems in tramp shipping considering static and spot cargoes." International Journal of Shipping and Transport Logistics 9, no. 4 (2017): 391. http://dx.doi.org/10.1504/ijstl.2017.10005461.
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Hart, Emma, and Kevin Sim. "A Hyper-Heuristic Ensemble Method for Static Job-Shop Scheduling." Evolutionary Computation 24, no. 4 (December 2016): 609–35. http://dx.doi.org/10.1162/evco_a_00183.
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We describe a new hyper-heuristic method NELLI-GP for solving job-shop scheduling problems (JSSP) that evolves an ensemble of heuristics. The ensemble adopts a divide-and-conquer approach in which each heuristic solves a unique subset of the instance set considered. NELLI-GP extends an existing ensemble method called NELLI by introducing a novel heuristic generator that evolves heuristics composed of linear sequences of dispatching rules: each rule is represented using a tree structure and is itself evolved. Following a training period, the ensemble is shown to outperform both existing dispatching rules and a standard genetic programming algorithm on a large set of new test instances. In addition, it obtains superior results on a set of 210 benchmark problems from the literature when compared to two state-of-the-art hyper-heuristic approaches. Further analysis of the relationship between heuristics in the evolved ensemble and the instances each solves provides new insights into features that might describe similar instances.
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Boutekkouk, Fateh. "Real Time Scheduling Optimization." Journal of Information Technology Research 12, no. 4 (October 2019): 132–52. http://dx.doi.org/10.4018/jitr.2019100107.
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This article deals with real time embedded multiprocessor systems scheduling optimization using conventional and quantum inspired genetic algorithms. Real time scheduling problems are known to be NP-hard. In order to resolve it, researchers have resorted to meta-heuristics instead of exact methods. Genetic algorithms seem to be a good choice to solve complex, non-linear, multi-objective and multi-modal problems. However, conventional genetic algorithms may consume much time to find good solutions. For this reason, to minimize the mean response time and the number of tasks missing their deadlines using quantum inspired genetic algorithms for multiprocessors architectures. Our proposed approach takes advantage of both static and dynamic preemptive scheduling. This article has the developed algorithms on a typical example showing a big improvement in research time of good solutions in quantum genetic algorithms with comparison to conventional ones.
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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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Chen, Cong, Yibai Li, Guangqiao Cao, and Jinlong Zhang. "Research on Dynamic Scheduling Model of Plant Protection UAV Based on Levy Simulated Annealing Algorithm." Sustainability 15, no. 3 (January 17, 2023): 1772. http://dx.doi.org/10.3390/su15031772.
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The plant protection unmanned aerial vehicle (UAV) scheduling model is of great significance to improve the operation income of UAV plant protection teams and ensure the quality of the operation. The simulated annealing algorithm (SA) is often used in the optimization solution of scheduling models, but the SA algorithm has the disadvantages of easily falling into local optimum and slow convergence speed. In addition, the current research on the UAV scheduling model for plant protection is mainly oriented to static scenarios. In the actual operation process, the UAV plant protection team often faces unexpected situations, such as new orders and changes in transfer path costs. The static model cannot adapt to such emergencies. In order to solve the above problems, this paper proposes to use the Levi distribution method to improve the simulated annealing algorithm, and it proposes a dynamic scheduling model driven by unexpected events, such as new orders and transfer path changes. Order sorting takes into account such factors as the UAV plant protection team’s operating income, order time window, and job urgency, and prioritizes job orders. In the aspect of order allocation and solution, this paper proposes a Levy annealing algorithm (Levy-SA) to solve the scheduling strategy of plant protection UAVs in order to solve the problem that the traditional SA is easy to fall into local optimum and the convergence speed is slow. This paper takes the plant protection operation scenario of “one spray and three defenses” for wheat in Nanjing City, Jiangsu Province, as an example, to test the plant protection UAV scheduling model under the dynamic conditions of new orders and changes in transfer costs. The results show that the plant protection UAV dynamic scheduling model proposed in this paper can meet the needs of plant protection UAV scheduling operations in static and dynamic scenarios. Compared with SA and greedy best first search algorithm (GBFS), the proposed Levy-SA has better performance in static and dynamic programming scenarios. It has more advantages in terms of man-machine adjustment distance and total operation time. This research can provide a scientific basis for the dynamic scheduling and decision analysis of plant protection UAVs, and provide a reference for the development of an agricultural machinery intelligent scheduling system.
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Zhou, Yong Liang. "GAP-Like Scheduling Model of Byproduct Gas in Iron and Steel Process." Applied Mechanics and Materials 217-219 (November 2012): 505–10. http://dx.doi.org/10.4028/www.scientific.net/amm.217-219.505.
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Gas is a key byproduct of the iron and steel process, and the scheduling of gas is the most valuable one in Energy Management System. The production and consumption of the byproduct gas will be related to many sub-processes and tends to encounter imbalance problems. One GAP-like optimization model of gas scheduling is setup, where there are 3 key objectives, minimization of emission, external energy purchasing and instability of the byproduct gas system. The model is NP-Hard and can be find the solution by using intelligent optimization algorithm to realize the static and dynamic scheduling.
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Aizawa, Akiko N., and Benjamin W. Wah. "Scheduling of Genetic Algorithms in a Noisy Environment." Evolutionary Computation 2, no. 2 (June 1994): 97–122. http://dx.doi.org/10.1162/evco.1994.2.2.97.
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In this paper, we develop new methods for adjusting configuration parameters of genetic algorithms operating in a noisy environment. Such methods are related to the scheduling of resources for tests performed in genetic algorithms. Assuming that the population size is given, we address two problems related to the design of efficient scheduling algorithms specifically important in noisy environments. First, we study the durution-scheduling problem that is related to setting dynamically the duration of each generation. Second, we study the sample-allocation problem that entails the adaptive determination of the number of evaluations taken from each candidate in a generation. In our approach, we model the search process as a statistical selection process and derive equations useful for these problems. Our results show that our adaptive procedures improve the performance of genetic algorithms over that of commonly used static ones.
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Aramon Bajestani, M., and J. C. Beck. "Scheduling a Dynamic Aircraft Repair Shop with Limited Repair Resources." Journal of Artificial Intelligence Research 47 (May 21, 2013): 35–70. http://dx.doi.org/10.1613/jair.3902.
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We address a dynamic repair shop scheduling problem in the context of military aircraft fleet management where the goal is to maintain a full complement of aircraft over the long-term. A number of flights, each with a requirement for a specific number and type of aircraft, are already scheduled over a long horizon. We need to assign aircraft to flights and schedule repair activities while considering the flights requirements, repair capacity, and aircraft failures. The number of aircraft awaiting repair dynamically changes over time due to failures and it is therefore necessary to rebuild the repair schedule online. To solve the problem, we view the dynamic repair shop as successive static repair scheduling sub-problems over shorter time periods. We propose a complete approach based on the logic-based Benders decomposition to solve the static sub-problems, and design different rescheduling policies to schedule the dynamic repair shop. Computational experiments demonstrate that the Benders model is able to find and prove optimal solutions on average four times faster than a mixed integer programming model. The rescheduling approach having both aspects of scheduling over a longer horizon and quickly adjusting the schedule increases aircraft available in the long term by 10% compared to the approaches having either one of the aspects alone.
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Branco, Rogério M., Antônio S. Coelho, and Sérgio F. Mayerle. "Hybrid genetic algorithms: solutions in realistic dynamic and setup dependent job-shop scheduling problems." International Journal of Production Management and Engineering 4, no. 2 (July 13, 2016): 75. http://dx.doi.org/10.4995/ijpme.2016.5780.
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<p>This paper discusses the application of heuristic-based evolutionary technique in search for solutions concerning the dynamic job-shop scheduling problems with dependent setup times and alternate routes. With a combinatorial nature, these problems belong to an NP-hard class, with an aggravated condition when in realistic, dynamic and therefore, more complex cases than the traditional static ones. The proposed genetic algorithm executes two important functions: choose the routes using dispatching rules when forming each individual from a defined set of available machines and, also make the scheduling for each of these individuals created. The chromosome codifies a route, or the selected machines, and also an order to process the operations. In essence , each individual needs to be decoded by the scheduler to evaluate its time of completion, so the fitness function of the genetic algorithm, applying the modified Giffler and Thomson’s algorithm, obtains a scheduling of the selected routes in a given planning horizon. The scheduler considers the preparation time between operations on the machines and can manage operations exchange respecting the route and the order given by the chromosome. The best results in the evolutionary process are individuals with routes and processing orders optimized for this type of problema.</p>
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SCHIEX, THOMAS, and GÉRARD VERFAILLIE. "NOGOOD RECORDING FOR STATIC AND DYNAMIC CONSTRAINT SATISFACTION PROBLEMS." International Journal on Artificial Intelligence Tools 03, no. 02 (June 1994): 187–207. http://dx.doi.org/10.1142/s0218213094000108.
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Many AI synthesis problems such as planning, scheduling or design may be encoded in a constraint satisfaction problems (CSP). A CSP is typically defined as the problem of finding any consistent labeling for a fixed set of variables satisfying all given constraints between these variables. However, for many real tasks, the set of constraints to consider may evolve because of the environment or because of user interactions. The notion of dynamic CSP (DCSP) [DD88] has been proposed to represent such evolutions. The problem we consider here is the solution maintenance problem in a DCSP. Naively applying usual satisfaction algorithms to this problem results in redundant search and inefficiency. A general approach to suppress redundancies in case of both restrictions and relaxations is to concisely represent the frontier of the solution space and justifications of this frontier in terms of set of constraints. This paper proposes a new class of constraint recording algorithms called nogood recording that may be used for solving both dynamic CSPs and usual CSP (called static CSPs here). It offers an interesting compromise, polynomially bounded in space, between an ATMS-like (Assumption-based Truth Maintenance System) approach, that would give a precise and exhaustive description of the frontier, and the usual constraint satisfaction algorithms (that discover a new frontier at each execution). We first introduce the principles used for nogood generation and then examine various recording schemes, on top of different tree search algorithms, characterizing the tradeoffs between the amount of recorded constraints and the pruning achieved. We then present experimental results and comparisons with various existing techniques for solving static or dynamic CSP.
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Lu, Lin Lin, Xin Ma, and Ya Xuan Wang. "An E2GPGP-GASA-Based Multi-Agent Job Shop Scheduling System." Advanced Materials Research 505 (April 2012): 65–74. http://dx.doi.org/10.4028/www.scientific.net/amr.505.65.
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In this paper, a job shop scheduling model combining MAS (Multi-Agent System) with GASA (Simulated Annealing-Genetic Algorithm) is presented. The proposed model is based on the E2GPGP (extended extended generalized partial global planning) mechanism and utilizes the advantages of static intelligence algorithms with dynamic MAS. A scheduling process from ‘initialized macro-scheduling’ to ‘repeated micro-scheduling’ is designed for large-scale complex problems to enable to implement an effective and widely applicable prototype system for the job shop scheduling problem (JSSP). Under a set of theoretic strategies in the GPGP which is summarized in detail, E2GPGP is also proposed further. The GPGP-cooperation-mechanism is simulated by using simulation software DECAF for the JSSP. The results show that the proposed model based on the E2GPGP-GASA not only improves the effectiveness, but also reduces the resource cost.
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Dai, Yanyan, and Xiangli Zhang. "A Synthesized Heuristic Task Scheduling Algorithm." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/465702.
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Aiming at the static task scheduling problems in heterogeneous environment, a heuristic task scheduling algorithm named HCPPEFT is proposed. In task prioritizing phase, there are three levels of priority in the algorithm to choose task. First, the critical tasks have the highest priority, secondly the tasks with longer path to exit task will be selected, and then algorithm will choose tasks with less predecessors to schedule. In resource selection phase, the algorithm is selected task duplication to reduce the interresource communication cost, besides forecasting the impact of an assignment for all children of the current task permits better decisions to be made in selecting resources. The algorithm proposed is compared with STDH, PEFT, and HEFT algorithms through randomly generated graphs and sets of task graphs. The experimental results show that the new algorithm can achieve better scheduling performance.
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Saydam, Berkay, Cem Orhan, Niyazi Toker, and Mansur Turasan. "Optimisation of scheduled tasks by real-time measurement and correlation." New Trends and Issues Proceedings on Advances in Pure and Applied Sciences, no. 12 (April 30, 2020): 36–43. http://dx.doi.org/10.18844/gjpaas.v0i12.4984.
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For functional safety, the scheduler should perform all time critical tasks in an order and within predefined deadlines in embedded systems. Scheduling of time critical tasks is determined by estimating their worst-case execution times. To justify the model design of task scheduling, it is required to simulate and visualise the task execution and scheduling maps. This helps to figure out possible problems before deploying the schedule model to real hardware. The simulation tools which are used by companies in an industry perform scheduling simulation and visualisation of all time critical tasks to design and verify the model. All of them lack the capability of comparing simulation results versus real results to achieve the optimised scheduling design. This sometimes leads the overestimated worst-case execution times and increased system cost. The aim of our study is to decrease the system cost with optimisation of scheduled tasks via using the static analysing method.
Keywords: Schedule visualisation, scheduler optimisation, functional safety, real-time systems, scheduler.
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Elhossini, Ahmed, Shawki Areibi, and Robert Dony. "Architecture Exploration Based on GA-PSO Optimization, ANN Modeling, and Static Scheduling." VLSI Design 2013 (September 26, 2013): 1–22. http://dx.doi.org/10.1155/2013/624369.
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Embedded systems are widely used today in different digital signal processing (DSP) applications that usually require high computation power and tight constraints. The design space to be explored depends on the application domain and the target platform. A tool that helps explore different architectures is required to design such an efficient system. This paper proposes an architecture exploration framework for DSP applications based on Particle Swarm Optimization (PSO) and genetic algorithms (GA) techniques that can handle multiobjective optimization problems with several hybrid forms. A novel approach for performance evaluation of embedded systems is also presented. Several cycle-accurate simulations are performed for commercial embedded processors. These simulation results are used to build an artificial neural network (ANN) model that can predict performance/power of newly generated architectures with an accuracy of 90% compared to cycle-accurate simulations with a very significant time saving. These models are combined with an analytical model and static scheduler to further increase the accuracy of the estimation process. The functionality of the framework is verified based on benchmarks provided by our industrial partner ON Semiconductor to illustrate the ability of the framework to investigate the design space.
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Ahmad, Ishfaq. "Editorial: Resource management of parallel and distributed systems with static scheduling: Challenges, solutions and new problems." Concurrency: Practice and Experience 7, no. 5 (August 1995): 339–47. http://dx.doi.org/10.1002/cpe.4330070502.
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SEN, SANDIP, and EDMUND H. DURFEE. "THE ROLE OF COMMITMENT IN COOPERATIVE NEGOTIATION." International Journal of Cooperative Information Systems 03, no. 01 (March 1994): 67–81. http://dx.doi.org/10.1142/s0218215794000053.
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Cooperative information agents need mechanisms that enable them to work together effectively while solving common problems. We investigate the use of commitment by agents to proposed actions as a mechanism that allow agents to work concurrently on interdependent problems. Judicious use of commitment can not only increase the throughput of cooperative information systems, but also allow them to deal flexibly with dynamically changing environments. We use the domain of distributed scheduling to demonstrate that static commitment strategies are ineffective. Results from simulated experiments are used to identify the environmental features on which an adaptive commitment strategy should be predicated.
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Carretero, Javier, and Fatos Xhafa. "USE OF GENETIC ALGORITHMS FOR SCHEDULING JOBS IN LARGE SCALE GRID APPLICATIONS." Technological and Economic Development of Economy 12, no. 1 (March 31, 2006): 11–17. http://dx.doi.org/10.3846/13928619.2006.9637716.
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In this paper we present the implementation of Genetic Algorithms (GA) for job scheduling on computational grids that optimizes the makespan and the total flowtime. Job scheduling on computational grids is a key problem in large scale grid‐based applications for solving complex problems. The aim is to obtain an efficient scheduler able to allocate a large number of jobs originated from large scale applications to grid resources. Several variations for GA operators are examined in order to identify which works best for the problem. To this end we have developed a grid simulator package to generate large and very large size instances of the problem and have used them to study the performance of GA implementation. Through extensive experimenting and fine tuning of parameters we have identified the configuration of operators and parameters that outperforms the existing implementations in the literature for static instances of the problem. The experimental results show the robustness of the implementation, improved performance of static instances compared to reported results in the literature and, finally, a fast reduction of the makespan making thus the scheduler of practical interest for grid environments.
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Expósito-Izquierdo, Christopher, Eduardo Lalla-Ruiz, Belén Melian-Batista,, and J. Marcos Moreno-Vega. "A Study of Rescheduling Strategies for the Quay Crane Scheduling Problem under Random Disruptions." Inteligencia Artificial 17, no. 54 (December 18, 2014): 35. http://dx.doi.org/10.4114/intartif.vol17iss54pp35-47.
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Providing a suitable answer to different types of unforeseen changes in optimization problems is one challenging goal. This paper addresses the Quay Crane Scheduling Problem under random disruptions, whose goal is to determine the sequences of transshipment operations performed by a set of quay cranes in order to load and unload containers onto/from a berthed container vessel. An evolutionary algorithm is used to find an initial solution of the problem with completely deterministic data, whereas several rescheduling strategies are integrated into a dynamism management system aimed at keeping a proper quality level after a random disruption. Computational experiments indicate that using knowledge about previous static problems can largely improve the performance of the implemented schedule.
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Tran, Tony, Daria Terekhov, Doug Down, and J. Beck. "Hybrid Queueing Theory and Scheduling Models for Dynamic Environments with Sequence-Dependent Setup Times." Proceedings of the International Conference on Automated Planning and Scheduling 23 (June 2, 2013): 215–23. http://dx.doi.org/10.1609/icaps.v23i1.13552.
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Classically, scheduling research in artificial intelligence has concentrated on the combinatorial challenges arising in a large, static domain where the set of jobs, resource capacities, and other problem parameters are known with certainty and do not change. In contrast, queueing theory has focused primarily on the stochastic arrival and resource requirements of new jobs, de-emphasizing the combinatorics. We study a dynamic parallel scheduling problem with sequence-dependent setup times: arriving jobs must be assigned (online) to one of a set of resources. The jobs have different service times on different resources and there exist setup times that are required to elapse between jobs, depending on both the resource used and the job sequence. We investigate four models that hybridize a scheduling model with techniques from queueing theory to address the dynamic problem. We demonstrate that one of the hybrid models can significantly reduce observed mean flow time performance when compared to the pure scheduling and queueing theory methods. More specifically, at high system loads, our hybrid model achieves a 15% to 60% decrease in mean flow time compared to the pure methodologies. This paper illustrates the advantages of integrating techniques from queueing theory and scheduling to improve performance in dynamic problems with complex combinatorics.
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Bettinger, Pete, Kevin Boston, and John Sessions. "Intensifying a heuristic forest harvest scheduling search procedure with 2-opt decision choices." Canadian Journal of Forest Research 29, no. 11 (December 1, 1999): 1784–92. http://dx.doi.org/10.1139/x99-160.
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Forest management problems with even-flow and adjacency considerations are difficult to solve optimally. A heuristic search intensification process, which uses two types of decision procedures, changes to single-decision choices (1-opt moves) and changes to two-decision choices simultaneously (2-opt moves), was used in an attempt to locate feasible and efficient solutions to these problems. One-opt moves involve changing the timing of timber harvests for a single land unit and are commonly used in heuristic techniques. Two-opt moves involve swapping the harvest timing between two land units, which intensify the search process. We apply the procedures to two management problems, one with 40 land units and the other with 700 land units. The goal is to achieve the highest, and most even, flow of timber volume over five time periods, with adjacent units being unavailable for harvest in the same period. One-opt moves, used alone, allowed the search process to produce good feasible solutions to these management problems and to generate a relatively even spread (number) of harvests over the planning horizon. The use of 2-opt moves resulted in better solutions, although the number of harvests per time period remained static. These procedures, used alone, may not be appropriate for all problems, because of their nature and limitations.
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Abdelfattah, A., H. Anzt, J. Dongarra, M. Gates, A. Haidar, J. Kurzak, P. Luszczek, S. Tomov, I. Yamazaki, and A. YarKhan. "Linear algebra software for large-scale accelerated multicore computing." Acta Numerica 25 (May 1, 2016): 1–160. http://dx.doi.org/10.1017/s0962492916000015.
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Many crucial scientific computing applications, ranging from national security to medical advances, rely on high-performance linear algebra algorithms and technologies, underscoring their importance and broad impact. Here we present the state-of-the-art design and implementation practices for the acceleration of the predominant linear algebra algorithms on large-scale accelerated multicore systems. Examples are given with fundamental dense linear algebra algorithms – from the LU, QR, Cholesky, and LDLT factorizations needed for solving linear systems of equations, to eigenvalue and singular value decomposition (SVD) problems. The implementations presented are readily available via the open-source PLASMA and MAGMA libraries, which represent the next generation modernization of the popular LAPACK library for accelerated multicore systems.To generate the extreme level of parallelism needed for the efficient use of these systems, algorithms of interest are redesigned and then split into well-chosen computational tasks. The task execution is scheduled over the computational components of a hybrid system of multicore CPUs with GPU accelerators and/or Xeon Phi coprocessors, using either static scheduling or light-weight runtime systems. The use of light-weight runtime systems keeps scheduling overheads low, similar to static scheduling, while enabling the expression of parallelism through sequential-like code. This simplifies the development effort and allows exploration of the unique strengths of the various hardware components. Finally, we emphasize the development of innovative linear algebra algorithms using three technologies – mixed precision arithmetic, batched operations, and asynchronous iterations – that are currently of high interest for accelerated multicore systems.
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Joo, Cheol Min, and Byung Soo Kim. "Variable Neighborhood Search Algorithms for an Integrated Manufacturing and Batch Delivery Scheduling Minimizing Total Tardiness." Applied Sciences 9, no. 21 (November 4, 2019): 4702. http://dx.doi.org/10.3390/app9214702.
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This article addresses an integrated problem of one batching and two scheduling decisions between a manufacturing plant and multi-delivery sites. In this problem, two scheduling problems and one batching problem must be simultaneously determined. In the manufacturing plant, jobs ordered by multiple customers are first manufactured by one of the machines in the plant. They are grouped to the same delivery place and delivered to the corresponding customers using a set of delivery trucks within a limited capacity. For the optimal solution, a mixed integer linear programming model is developed and two variable neighborhood search algorithms employing different probabilistic schemes. We tested the proposed algorithms to compare the performance and conclude that the variable neighborhood search algorithm with dynamic case selection probability finds better solutions in reasonable computing times compared with the variable neighborhood search algorithm with static case selection probability and genetic algorithms based on the test results.
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Li, Bo, Shiyang Liang, Linyu Tian, Daqing Chen, and Ming Zhang. "An Adaptive Task Scheduling Method for Networked UAV Combat Cloud System Based on Virtual Machine and Task Migration." Mathematical Problems in Engineering 2020 (April 28, 2020): 1–12. http://dx.doi.org/10.1155/2020/5391479.
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This paper presents a systematic work aiming to improve the efficiency of task processing in a networked UAV combat cloud system. The work consists of three major aspects: (1) an architecture of UAV combat cloud systems—such a system provides the necessary resource pool for powerful computing and storage facilities and defines the attributes of the entities in the resource pool in detail; (2) an online adaptive task redistribution and scheduling algorithm—the algorithm involves task migration being performed on virtual machines on the cloud system and aims to address the problems caused by static task scheduling approaches; and (3) an online virtual machine and task migration algorithm—the algorithm considers collectively the priority type and quantity of the tasks to be migrated on virtual machines along with time constraints to determine the migration of virtual machine or task and optimize resource usages. Experimental simulation results have demonstrated that the proposed system and the relevant algorithms can significantly improve the efficiency of task schedule.
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Zhang, Xiaohui, Yuyan Han, Grzegorz Królczyk, Marek Rydel, Rafal Stanislawski, and Zhixiong Li. "Rescheduling of Distributed Manufacturing System with Machine Breakdowns." Electronics 11, no. 2 (January 13, 2022): 249. http://dx.doi.org/10.3390/electronics11020249.
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This study attempts to explore the dynamic scheduling problem from the perspective of operational research optimization. The goal is to propose a rescheduling framework for solving distributed manufacturing systems that consider random machine breakdowns as the production disruption. We establish a mathematical model that can better describe the scheduling of the distributed blocking flowshop. To realize the dynamic scheduling, we adopt an “event-driven” policy and propose a two-stage “predictive-reactive” method consisting of two steps: initial solution pre-generation and rescheduling. In the first stage, a global initial schedule is generated and considers only the deterministic problem, i.e., optimizing the maximum completion time of static distributed blocking flowshop scheduling problems. In the second stage, that is, after the breakdown occurs, the rescheduling mechanism is triggered to seek a new schedule so that both maximum completion time and the stability measure of the system can be optimized. At the breakdown node, the operations of each job are classified and a hybrid rescheduling strategy consisting of “right-shift repair + local reorder” is performed. For local reorder, we designed a discrete memetic algorithm, which embeds the differential evolution concept in its search framework. To test the effectiveness of DMA, comparisons with mainstream algorithms are conducted on instances with different scales. The statistical results show that the ARPDs obtained from DMA are improved by 88%.
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Punyakum, Voravee, Kanchana Sethanan, Krisanarach Nitisiri, and Rapeepan Pitakaso. "Hybrid Particle Swarm and Whale Optimization Algorithm for Multi-Visit and Multi-Period Dynamic Workforce Scheduling and Routing Problems." Mathematics 10, no. 19 (October 6, 2022): 3663. http://dx.doi.org/10.3390/math10193663.
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This paper focuses on the dynamic workforce scheduling and routing problem for the maintenance work of harvesters in a sugarcane harvesting operation. Technician teams categorized as mechanical, hydraulic, and electrical teams are assumed to have different skills at different levels to perform services. The jobs are skill-constrained and have time windows. During a working day, a repair request from a sugarcane harvester may arrive, and as time passes, the harvester’s position may shift to other sugarcane fields. We formulated this problem as a multi-visit and multi-period dynamic workforce scheduling and routing problem (MMDWSRP) and our study is the first to address the workforce scheduling and routing problem (WSRP). A mixed-integer programming formulation and a hybrid particle swarm and whale optimization algorithm (HPSWOA) were firstly developed to solve the problem, with the objective of minimizing the total cost, including technician labor cost, penalty for late service, overtime, travel, and subcontracting costs. The HPSWOA was developed for route planning and maintenance work for each mechanical harvester to be provided by technician teams. The proposed algorithm (HPSWOA) was validated against Lingo computational software using numerical experiments in respect of static problems. It was also tested against the current practice, the traditional whale optimization algorithm (WOA), and traditional particle swarm optimization (PSO) in respect of dynamic problems. The computational results show that the HPSWOA yielded a solution with significantly better quality. The HPSWO was also tested against the traditional genetic algorithm (GA), bat algorithm (BA), WOA, and PSO to solve the well-known CEC 2017 benchmark functions. The computational results show that the HPSWOA achieved more superior performance in most cases compared to the GA, BA, WOA, and PSO algorithms.
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Kwatra, Harpreet S., Francis J. Doyle, Ilya A. Rybak, and James S. Schwaber. "A Neuro-Mimetic Dynamic Scheduling Algorithm for Control: Analysis and Applications." Neural Computation 9, no. 3 (March 1, 1997): 479–502. http://dx.doi.org/10.1162/neco.1997.9.3.479.
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A simple neuronal network model of the baroreceptor reflex is analyzed. From a control perspective, the analysis suggests a dynamic scheduled control mechanism by which the baroreflex may perform regulation of the blood pressure. The main objectives of this work are to investigate the static and dynamic response characteristics of the single neurons and the network, to analyze the neuromimetic dynamic scheduled control function of the model, and to apply the algorithm to nonlinear process control problems. The dynamic scheduling activity of the network is exploited in two control architectures. Control structure I is drawn directly from the present model of the baroreceptor reflex. An application of this structure for level control in a conical tank is described. Control structure II employs an explicit set point to determine the feedback error. The performance of this control structure is illustrated on a nonlinear continuous stirred tank reactor with van de Vusse kinetics. The two case studies validate the dynamic scheduled control approach for nonlinear process control applications.
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Cui, Jing, and Patrik Haslum. "Dynamic Controllability of Controllable Conditional Temporal Problems with Uncertainty." Journal of Artificial Intelligence Research 64 (February 28, 2019): 445–95. http://dx.doi.org/10.1613/jair.1.11375.
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Dynamic Controllability (DC) of a Simple Temporal Problem with Uncertainty (STPU) uses a dynamic decision strategy, rather than a fixed schedule, to tackle temporal uncertainty. We extend this concept to the Controllable Conditional Temporal Problem with Uncertainty (CCTPU), which extends the STPU by conditioning temporal constraints on the assignment of controllable discrete variables. We define dynamic controllability of a CCTPU as the existence of a strategy that decides on both the values of discrete choice variables and the scheduling of controllable time points dynamically. This contrasts with previous work, which made a static assignment of choice variables and dynamic decisions over time points only. We propose an algorithm to find such a fully dynamic strategy. The algorithm computes the "envelope" of outcomes of temporal uncertainty in which a particular assignment of discrete variables is feasible, and aggregates these over all choices. When an aggregated envelope covers all uncertain situations of the CCTPU, the problem is dynamically controllable. However, the algorithm is complete only under certain assumptions. Experiments on an existing set of CCTPU benchmarks show that there are cases in which making both discrete and temporal decisions dynamically it is feasible to satisfy the problem constraints while assigning the discrete variables statically it is not.
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Cui, Jing, and Patrik Haslum. "Dynamic Controllability of Controllable Conditional Temporal Problems with Uncertainty." Proceedings of the International Conference on Automated Planning and Scheduling 27 (June 5, 2017): 61–69. http://dx.doi.org/10.1609/icaps.v27i1.13820.
Full textAbstract:
Dynamic Controllability (DC) of a Simple Temporal Problem with Uncertainty (STPU) uses a dynamic decision strategy, rather than a fixed schedule, to tackle temporal uncertainty. We extend this concept to the Controllable Conditional Temporal Problem with Uncertainty (CCTPU), which extends the STPU by conditioning temporal constraints on the assignment of controllable discrete variables. We define dynamic controllability of a CCTPU as the existence of a strategy that decides on both the values of discrete choice variables and the scheduling of controllable time points dynamically. This contrasts with previous work, which made a static assignment of choice variables and dynamic decisions over time points only. We propose an algorithm to find such a fully dynamic strategy. The algorithm computes the ''envelope'' of outcomes of temporal uncertainty in which a particular assignment of discrete variables is feasible, and aggregates these over all choices. When an aggregated envelope covers all uncertain situations of the CCTPU, the problem is dynamically controllable. However, the algorithm is not complete. Experiments on an existing set of CCTPU benchmarks show that there are cases in which making both discrete and temporal decisions dynamically it is feasible to satisfy the problem constraints, while assigning the discrete variables statically it is not.
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Abdesselam, M., A. N. Mustafizul Karim, H. M. Emrul Kays, Mohamed Abdul Rahman, and R. A. Sarker. "Formulation of an IP-Based Model for Reactive Flow-Shop Scheduling Problem Subject to Arrival of New Orders." Advanced Materials Research 1115 (July 2015): 616–21. http://dx.doi.org/10.4028/www.scientific.net/amr.1115.616.
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In order to survive in a competitive environment, industries are required to adopt strategies that ensure their abilities to provide their customers with a product featured by good quality, low cost and short delivery time. Short term scheduling plays a pivotal role in this context by ensuring the operations to be executed and monitored in an optimal or sub-optimal manner which guarantees the product shipping within the customers’ due dates at lower cost and/or higher utilization of resources. However, the dynamic nature of the shop floor environment causes the predictive schedules to be no longer optimal or even feasible. Frequent disruptions occurring during the execution of the predictive schedule require the operations managers to be reactive to make appropriate decision considering the new situation. Adequate research works based on integer programming are available in literature to cope with static scheduling problems, but there is a dearth in integer programming based approaches for dynamic or reactive situations. The aim of this work is to formulate a model that solves the reactive flow-shop scheduling problem subject to arrival of new orders. Objective function for makespan minimization and the comprehensive equations for predictive and reactive schedules are presented with the necessary elaboration.
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Kadri, Walid, and Belabbas Yagoubi. "Optimized Scheduling Approach for Scientific Applications Based on Clustering in Cloud Computing Environment." Scalable Computing: Practice and Experience 20, no. 3 (September 22, 2019): 527–40. http://dx.doi.org/10.12694/scpe.v20i3.1548.
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Cloud Computing refers to the use of the computing capabilities of remote computers, where the user has considerable computing power without having powerful units. Scientific applications, usually represented as Directed Acyclic Graphs (DAGs), are an important class of applications that lead to challenging problems for resource management in distributed computing. With the advent of Cloud Computing, particularly the IaaS offers for on demand virtual machines leasing, multiple jobs execution, consisting of a large number of DAGs, needs an elaborated scheduling and resource provisioning policies, for efficient use of resources. Only few works exists that consider this problem in the context of clouds environment. In goal of optimization and fault tolerance, DAGs applications are generally partitioned into multiple parallel DAGs using clustering algorithm and assigned to VM (Virtual Machine) resources independently. In this work, we investigate through simulation, the impact of clustering for both provisioning and scheduling policies in the total makespan and financial costs for execution of user's application. We implemented four scheduling policies well-known in grid computing systems, and adapted clustering algorithm to our resource management policy that leases and destroys dynamically VMs. We show that dynamic policies can achieve equal or even better performance than static management policies.
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Fang, Cheng, Andrew J. Wang, and Brian C. Williams. "Chance-constrained Static Schedules for Temporally Probabilistic Plans." Journal of Artificial Intelligence Research 75 (December 7, 2022): 1323–72. http://dx.doi.org/10.1613/jair.1.13636.
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Time management under uncertainty is essential to large scale projects. From space exploration to industrial production, there is a need to schedule and perform activities. given complex specifications on timing. In order to generate schedules that are robust to uncertainty in the duration of activities, prior work has focused on a problem framing that uses an interval-bounded uncertainty representation. However, such approaches are unable to take advantage of known probability distributions over duration. In this paper we concentrate on a probabilistic formulation of temporal problems with uncertain duration, called the probabilistic simple temporal problem. As distributions often have an unbounded range of outcomes, we consider chance-constrained solutions, with guarantees on the probability of meeting temporal constraints. By considering distributions over uncertain duration, we are able to use risk as a resource, reason over the relative likelihood of outcomes, and derive higher utility solutions. We first demonstrate our approach by encoding the problem as a convex program. We then develop a more efficient hybrid algorithm whose parent solver generates risk allocations and whose child solver generates schedules for a particular risk allocation. The child is made efficient by leveraging existing interval-bounded scheduling algorithms, while the parent is made efficient by extracting conflicts over risk allocations. We perform numerical experiments to show the advantages of reasoning over probabilistic uncertainty, by comparing the utility of schedules generated with risk allocation against those generated from reasoning over bounded uncertainty. We also empirically show that solution time is greatly reduced by incorporating conflict-directed risk allocation.
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Kerkad, Amira, Ladjel Bellatreche, Pascal Richard, Carlos Ordonez, and Dominique Geniet. "A Query Beehive Algorithm for Data Warehouse Buffer Management and Query Scheduling." International Journal of Data Warehousing and Mining 10, no. 3 (July 2014): 34–58. http://dx.doi.org/10.4018/ijdwm.2014070103.
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Analytical queries, like those used in data warehouses and OLAP, are generally interdependent. This is due to the fact that the database is usually modeled with a denormalized star schema or its variants, where most queries pass through a large central fact table. Such interaction has been largely exploited in query optimization techniques such as materialized views. Nevertheless, such approaches usually ignore buffer management and assume queries have a fixed order and are known in advance. We believe such assumptions are too strong and thus they need to be revisited and simplified. In this paper, we study the combination of two problems: buffer management and query scheduling, in both static and dynamic scenarios. We present an NP-hardness study of the joint problem, highlighting its complexity. We then introduce a new and highly efficient algorithm inspired by a beehive. We conduct an extensive experimental evaluation on a real DBMS showing the superiority of our algorithm compared to previous ones as well as its excellent scalability.
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Wang, Li Ming, and Xiao Ling Yan. "The Research of Response Time and Scheduling Algorithm in the TTCAN Bus Control Networks." Applied Mechanics and Materials 678 (October 2014): 461–67. http://dx.doi.org/10.4028/www.scientific.net/amm.678.461.
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By analyzing the priority arbitration bit by bit without destroy in can bus rule, the article elicits the conclusion that if static priority based on the affirmatory system model is used, the lower priority’s messages will be much more delayed, even lose some data when the bus’s bandwidth is widely used and scheduling can’t be modified during the system. The dynamic priority promoting method and math model of SQSA and SQMA is achieved in the article, it analyses the model’s rate of taking in and sending out in large quantities, the largest delay, the problems and solutions when using SQMA. In the end, It is validated that the method of improved dynamic in the article has good performances on the network rate of taking in and sending out in large quantities, the average delay and the rate of network usage by emulational experiments.
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Fazlollahtabar, Hamed. "Parallel autonomous guided vehicle assembly line for a semi-continuous manufacturing system." Assembly Automation 36, no. 3 (August 1, 2016): 262–73. http://dx.doi.org/10.1108/aa-08-2015-065.
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Purpose This paper aims to propose a parallel automated assembly line system to produce multiple products in a semi-continuous system. Design/methodology/approach The control system developed in this research consists of a manufacturing system for two-level hierarchical dynamic decisions of autonomous/automated/automatic-guided vehicles (AGVs) dispatching/next station selection and machining schedules and a station control scheme for operational control of machines and components. In this proposed problem, the assignment of multiple AGVs to different assembly lines and the semi-continuous stations is a critical objective. AGVs and station scheduling decisions are made at the assembly line level. On the other hand, component and machining resource scheduling are made at the station level. Findings The proposed scheduler first decomposes the dynamic scheduling problems into a static AGV and machine assignment during each short-term rolling window. It optimizes weighted completion time of tasks for each short-term window by formulating the task and resource assignment problem as a minimum cost flow problem during each short-term scheduling window. A comprehensive decision making process and heuristics are developed for efficient implementation. A simulation study is worked out for validation. Originality/value Several assembly lines are configured to produce multiple products in which the technologies of machines are shared among the assembly lines when required. The sequence of stations is pre-specified in each assembly line and the components of a product are kept in machine magazine. The transportation between the stations in an assembly line (intra assembly line) and among stations in different assembly lines (inter assembly line) are performed using AGVs.
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Badr-El-Din, Amr. "Object-Oriented Petri Nets Virtual Organization Structure." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 13, no. 7 (July 30, 2014): 4663–70. http://dx.doi.org/10.24297/ijct.v13i7.2510.
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Managing a mega organization has become an extremely complex task, especially if the organization is virtual. The operating structure of such a multi-faceted organization is very difficult to construct, and traditional organization structure models seem to fall short in coping with the demands imposed by such huge and complex entities. The object-oriented (organic) structure has many attributes that are suitable for solving the complexity of such organization structure and seems successful in catering for the needs of big organizations. However, the organic structure is by nature a static model that does not allow for predicting dynamic operation problems before they occur. The goal of this paper is thereforto design a generic model, using a Petri net-based framework, to simulate the workflow of virtual organizations that follow the organic structure in essential tasks like scheduling, monitoring and supervising.This model is useful in predicting problems before they occur and is therefore quite a useful tool for resource allocation, business planning and overall monitoring. Because of its capability in predicting problems like deadlock, priority inversion and starvation, a Petri net may become an essential tool for organization management.
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Yoshimoto, Atsushi, and Patrick Asante. "Inter-Temporal Aggregation for Spatially Explicit Optimal Harvest Scheduling under Area Restrictions." Forest Science 67, no. 5 (September 17, 2021): 587–606. http://dx.doi.org/10.1093/forsci/fxab025.
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Abstract We propose a new approach to solve inter-temporal unit aggregation issues under maximum opening size requirements using two models. The first model is based on Model I formulation with static harvest treatments for harvest activities. This model identifies periodic harvest activities using a set of constraints for inter-temporal aggregation. The second model is based on Model II formulation, which uses dynamic harvest treatments and incorporates periodic harvest activities directly into the model formulation. The proposed approach contributes to the literature on spatially constrained harvest scheduling problems as it allows a pattern of unit aggregation to change across multiple harvests over time, as inter-temporal aggregation under a maximum opening size requirement over period-specific duration. The main idea of the proposed approach for inter-temporal aggregation is to use a multiple layer scheme for a set of spatial constraints, which is adapted from a maximum flow specification in a spatial forest unit network and a sequential triangle connection to create fully connected feasible clusters. By dividing the planning horizon into period-specific durations for different spatial aggregation patterns, the models can complete inter-temporal spatial aggregation over the planning horizon under a maximum opening size requirement per duration.
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Et. al., B. Vijaya Laxmi,. "A Review of Dynamic Resource Allocation Framework for Large Amount of Cloud Enterprises." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 11, 2021): 1280–84. http://dx.doi.org/10.17762/turcomat.v12i2.1191.
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Cloud computing is an on-demand service because it offers dynamic flexible resource allocation for reliable and guaranteed services in pay as-you-use manner. Because of the consistently increasing demands of the clients for services or resources, it gets hard to allocate resources accurately to the client demands to satisfy their solicitations and also to take care of the Service Level Agreements (SLA) gave by the service suppliers. Dynamic resource allocation problem is one of the most challenging problems in the resource management problems. The dynamic resource allocation in cloud computing has attracted attention of the research network in the last couple of years. Many researchers around the world have thought of new ways of facing this challenge. Ad-hoc parallel data handling has arisen to be one of the executioner applications for Infrastructure-as-a-Service (IaaS) cloud. Number of Cloud supplier companies has started to incorporate frameworks for parallel data handling in their item which making it easy for clients to access these services and to convey their programs. The handling frameworks which are at present utilized have been intended for static and homogeneous bunch arrangements. So the allocated resources may be inadequate for large parts of the submitted tasks and unnecessarily increase preparing cost and time. Again because of opaque nature of cloud, static allocation of resources is conceivable, yet the other way around in dynamic situations. The proposed new generic data handling framework is expected to expressly misuse the dynamic resource allocation in cloud for task scheduling and execution.
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BARBA, IRENE, CARMELO DEL VALLE, BARBARA WEBER, and ANDRÉS JIMÉNEZ. "AUTOMATIC GENERATION OF OPTIMIZED BUSINESS PROCESS MODELS FROM CONSTRAINT-BASED SPECIFICATIONS." International Journal of Cooperative Information Systems 22, no. 02 (June 2013): 1350009. http://dx.doi.org/10.1142/s0218843013500093.
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Business process (BP) models are usually defined manually by business analysts through imperative languages considering activity properties, constraints imposed on the relations between the activities as well as different performance objectives. Furthermore, allocating resources is an additional challenge since scheduling may significantly impact BP performance. Therefore, the manual specification of BP models can be very complex and time-consuming, potentially leading to non-optimized models or even errors. To overcome these problems, this work proposes the automatic generation of imperative optimized BP models from declarative specifications. The static part of these declarative specifications (i.e. control-flow and resource constraints) is expected to be useful on a long-term basis. This static part is complemented with information that is less stable and which is potentially unknown until starting the BP execution, i.e. estimates related to (1) number of process instances which are being executed within a particular timeframe, (2) activity durations, and (3) resource availabilities. Unlike conventional proposals, an imperative BP model optimizing a set of instances is created and deployed on a short-term basis. To provide for run-time flexibility the proposed approach additionally allows decisions to be deferred to run-time by using complex late-planning activities, and the imperative BP model to be dynamically adapted during run-time using replanning. To validate the proposed approach, different performance measures for a set of test models of varying complexity are analyzed. The results indicate that, despite the NP-hard complexity of the problems, a satisfactory number of suitable solutions can be produced.
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Medeiros, Marcio Lindemberg Bezerra de, Antonio Martins de Oliveira Junior, and Rodolpho Rodrigues Fonseca. "Gain scheduling control applied to oil and gas separator level loop." Research, Society and Development 10, no. 4 (April 23, 2021): e55010414397. http://dx.doi.org/10.33448/rsd-v10i4.14397.
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Modeling and simulation applied to level control of oil and gas separators in production facilities is a very important tool because makes possible to perform tests that probably could not be viable due to operation and safety constraints. Asides the level dynamics can be well understood regarding the physical model, there will always be non-linearities to approach using a system identification procedure, requiring reasonable care on linear model identification. In order to assure a desired control performance, an adaptive control strategy has been proposed for level control for an oil and gas separator using the gain scheduling technique. Based on a first order process without time delay, the static gain and time period were determined for each point inside the operational space range of the equipment and by Internal Model Control (IMC), the tuning matrix found and converted into a function of operational parameters using polynomial interpolation methodology for future application in a real commercial PI controller. The horizontal separator was simulated using MATLAB/SIMULINK® and data from a real separator vessel were used to identify and validate the proposed process modeling in attempt to test an adaptive control strategy for practical applications. Once the GSC was implemented, simulations were performed over the non-linear system and results have shown better performance indexes for GSC while compared to the conventional PI controller for both servo and regulatory problems with reductions up to 17.65% for IAE, 29.88% for ISE, 16.38% for ITAE, 29.00% for ITSE and 13.20% for Control Effort (CE).
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Nahas, Nabil, Mohamed Noomane Darghouth, and Mohammed Abouheaf. "A Non-Linear-Threshold-Accepting Function Based Algorithm for the Solution of Economic Dispatch Problem." RAIRO - Operations Research 54, no. 5 (June 12, 2020): 1269–89. http://dx.doi.org/10.1051/ro/2019043.
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This article introduces a novel heuristic algorithm based on Non-Linear Threshold Accepting Function to solve the challenging non-convex economic dispatch problem. Economic dispatch is a power system management tool; it is used to allocate the total power generation to the generating units to meet the active load demand. The power systems are highly nonlinear due to the physical and operational constraints. The complexity of the resulting non-convex objective cost function led to inabilities to solve the problem by using analytical approaches, especially in the case of large-scale problems. Optimization techniques based on heuristics are used to overcome these difficulties. The Non-Linear Threshold Accepting Algorithm has demonstrated efficiency in solving various instances of static and dynamic allocation and scheduling problems but has never been applied to solve the economic dispatch problem. Existing benchmark systems are used to evaluate the performance of the proposed heuristic. Additional random instances with different sizes are generated to compare the adopted heuristic to the Harmony Search and the Whale Optimization Algorithms. The obtained results showed the superiority of the proposed algorithm in finding, for all considered instances, a high-quality solution in minimum computational time.
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Paek, Sung, Sangtae Kim, and Olivier de Weck. "Optimization of Reconfigurable Satellite Constellations Using Simulated Annealing and Genetic Algorithm." Sensors 19, no. 4 (February 13, 2019): 765. http://dx.doi.org/10.3390/s19040765.
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Agile Earth observation can be achieved with responsiveness in satellite launches, sensor pointing, or orbit reconfiguration. This study presents a framework for designing reconfigurable satellite constellations capable of both regular Earth observation and disaster monitoring. These observation modes are termed global observation mode and regional observation mode, constituting a reconfigurable satellite constellation (ReCon). Systems engineering approaches are employed to formulate this multidisciplinary problem of co-optimizing satellite design and orbits. Two heuristic methods, simulated annealing (SA) and genetic algorithm (GA), are widely used for discrete combinatorial problems and therefore used in this study to benchmark against a gradient-based method. Point-based SA performed similar or slightly better than the gradient-based method, whereas population-based GA outperformed the other two. The resultant ReCon satellite design is physically feasible and offers performance-to-cost(mass) superior to static constellations. Ongoing research on observation scheduling and constellation management will extend the ReCon applications to radar imaging and radio occultation beyond visible wavelengths and nearby spectrums.
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Wang, Ping, Liang Ma, and Kai Xue. "Multitarget tracking in sensor networks via efficient information-theoretic sensor selection." International Journal of Advanced Robotic Systems 14, no. 5 (September 1, 2017): 172988141772846. http://dx.doi.org/10.1177/1729881417728466.
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In networks composed of moving robots or static sensing nodes, multitarget tracking is critical and fundamental for high-level applications, such as scene analysis or event detection. However, tracking multiple targets in the sensor network is challenging for two reasons: multisensor multitarget fusion itself is difficult and dynamic sensor scheduling is necessary to balance the tracking accuracy and energy consumption of the sensor network. In this article, we present a novel information-theoretic sensor selection method for multitarget tracking via the multi-Bernoulli filter. The sensor selection is based on the multi-Bernoulli filtering and a collection of subselection problems for individual target to avoid the combinatorial optimization. A subselection problem for each target is investigated under the framework of partially observed Markov decision process, and we propose to solve it by maximizing the information gain of the probability hypothesis density. Simulation results validate the effectiveness and efficiency of our method for multitarget tracking in sensor networks.
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Inzillo, Vincenzo, Floriano Rango, Alfonso Quintana, and Amilcare Santamaria. "An Adaptive Beamforming Time with Round-Robin MAC Algorithm for Reducing Energy Consumption in MANET." Journal of Sensor and Actuator Networks 7, no. 4 (November 23, 2018): 50. http://dx.doi.org/10.3390/jsan7040050.
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The use of smart antenna systems (SASs) in mobile ad hoc networks (MANETs) has been promoted as the best choice to improve spatial division multiple access (SDMA) and throughput. Although directional communications are expected to provide great advantages in terms of network performance, directional MAC (medium access control) protocols introduce several issues. One of the most known problems in this context is represented by the fact that, when attempting to solve or at least mitigate the problems introduced by these kinds of antennas especially at MAC layer, a large amount of energy consumption is achieved; for example, due to excessive retransmissions introduced by very frequently issue such as deafness and handoff. The expedients proposed in order to reduce these drawbacks attempting to limit beamforming time of nodes in cooperation with a round-robin scheduling can grant high performance in terms of fairness and throughput. However, the overall energy distribution in the network is not efficient due to static approach. In view of this, we propose adaptive beamforming time with round-robin MAC providing a dynamic assignment of the beamforming time with the aim to limit the waste of energy of nodes. The proposed approach provides benefits in terms of energy consumption distribution among nodes in sectorized antennas environments and, simultaneously, improves MAC packet performance.