An Improved Tabu Search Meta-heuristic Approach for Solving Scheduling Problem with Non-availability Constraints

Abdellafou, Khaoula Ben; Hadda, Hatem; Korbaa, Ouajdi

doi:10.1007/s13369-018-3525-3

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…However, the idea of TS is to explore the neighborhood from a given position and to choose the position in this neighborhood which minimizes the objective function [38].…”

Section: Optimized Kpls Based On Tabu Search Methodsmentioning

confidence: 99%

Improved Dynamic Optimized Kernel Partial Least Squares for Nonlinear Process Fault Detection

Saïd

Taouali

2021

Mathematical Problems in Engineering

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We suggest in this article a dynamic reduced algorithm in order to enhance the monitoring abilities of nonlinear processes. Dynamic fault detection using data-driven methods is among the key technologies, which shows its ability to improve the performance of dynamic systems. Among the data-driven techniques, we find the kernel partial least squares (KPLS) which is presented as an interesting method for fault detection and monitoring in industrial systems. The dynamic reduced KPLS method is proposed for the fault detection procedure in order to use the advantages of the reduced KPLS models in online mode. Furthermore, the suggested method is developed to monitor the time-varying dynamic system and also update the model of reduced reference. The reduced model is used to minimize the computational cost and time and also to choose a reduced set of kernel functions. Indeed, the dynamic reduced KPLS allows adaptation of the reduced model, observation by observation, without the risk of losing or deleting important information. For each observation, the update of the model is available if and only if a further normal observation that contains new pertinent information is present. The general principle is to take only the normal and the important new observation in the feature space. Then the reduced set is built for the fault detection in the online phase based on a quadratic prediction error chart. Thereafter, the Tennessee Eastman process and air quality are used to precise the performances of the suggested methods. The simulation results of the dynamic reduced KPLS method are compared with the standard one.

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“…However, the idea of TS is to explore the neighborhood from a given position and to choose the position in this neighborhood which minimizes the objective function [38].…”

Section: Optimized Kpls Based On Tabu Search Methodsmentioning

confidence: 99%

Improved Dynamic Optimized Kernel Partial Least Squares for Nonlinear Process Fault Detection

Saïd

Taouali

2021

Mathematical Problems in Engineering

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“…shows a factorial increase, the exact solution cannot be obtained through the exhaustive method [23], dynamic programming [24,25], linear programming [26], branch and bound [27,28], and other precise algorithms for large-scale problems. With the development of computers, many intelligent optimization algorithms have emerged in the field of artificial intelligence, such as the genetic algorithm [29][30][31], tabu search algorithm [32], ant colony algorithm [33], particle swarm optimization algorithm [34], simulated annealing algorithm [35,36], online-learning algorithm [37], adaptive polyploid memetic algorithm [38], many-objective evolutionary algorithm [39], and salp swarm algorithm [40]. Among them, the idea of simulated annealing algorithm (SA) is derived from simulating the cooling process of solid annealing, that is, heating the solid to a sufficiently high temperature, then allowing it to cool down slowly to balance the internal energy, avoid falling into local minimum effectively, and eventually tend to a global optimum.…”

Section: Design Of Scheduling Algorithm Based On Simulated Annealing (Sa)mentioning

confidence: 99%

Research on the Scheduling of Tractors in the Major Epidemic to Ensure Spring Ploughing

Chen

Zhang

et al. 2021

Mathematical Problems in Engineering

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When the outbreak of COVID-19 began, people could not go out. It was not allowed to provide agricultural machinery services in different places across regions to reduce the flow and gathering of people. Improvement of utilization efficiency of agricultural machinery resources is required through scientific scheduling of agricultural machinery. With seizing the farming season and stabilizing production as the goal, this paper studied the scientific scheduling of tractors within the scope of town and established agricultural machinery operation scheduling model with the minimization of total scheduling cost as the optimization objective. Factors such as farmland area, agricultural machinery, and farmland location information and operating time window are considered in this model to improve the accuracy of the agricultural machinery operation scheduling model. The characteristics of multiple scheduling algorithms are analyzed comprehensively. The scheduling requirements of agricultural machinery operation to ensure spring ploughing are combined to design the agricultural machinery scheduling algorithm based on the SA algorithm. With Hushu Street, Jiangning District, Nanjing City, as an example, a comparative experiment is conducted on the simulated annealing algorithm (SA) designed in this paper and the empirical algorithm and genetic algorithm (GA). The results suggest that the total cost of the scheduling scheme generated by the SA algorithm is 19,042.07 yuan lower than that by the empirical scheduling algorithm and 779.19 yuan lower than that by the genetic algorithm on average. Compared with the GA algorithm, the transfer distance, waiting cost, and delay cost of the SA algorithm are reduced by 11.6%, 100%, and 98.1% on average, indicating that the transfer distance of agricultural machinery in the scheduling scheme generated by the SA algorithm is shorter, so is the waiting and delay time. Meanwhile, it can effectively obtain the near-optimal solution that meets the time window constraint, with good convergence, stability, and adaptability.

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“…Various specific implementation strategies of modern heuristic algorithms have been proposed independently, and there are significant differences between them. The modern heuristic algorithms include simulated annealing (SA) algorithm (Duan, 2012;Leite, Melício, and Rosa, 2019;Nino-Ruiz and Yang, 2019), genetic algorithm (GA) (Chekanin and Kulikova, 2017;Kim et al, 2020;Pandey, 2020), Tabu search (TS) algorithm (Ben Abdellafou, Hadda, and Korbaa, 2019;Mohammed and Duffuaa, 2020;Zhang et al, 2020), ant colony algorithm (ACO) (Jalali et al, 2020;Martin et al, 2020;Niu et al, 2007;Srichandum and Pothiya, 2020) and artificial neural network (ANN) (Amari, 1971;Leong et al, 2020). In recent years, an important trend related to computational intelligence has been to establish a new search mechanism based on sports competitions (Bouchekara et al, 2018;Chagwiza et al, 2016;Jaramillo et al, 2016a;Kashan, 2014a;Khattab, Sharieh, and Mahafzah, 2019;Moghdani and Salimifard, 2018;Purnomo and Wee, 2015), through which effective optimization algorithms can be designed.…”

Section: Introductionmentioning

confidence: 99%

A Competitive Volleyball Algorithm to Solve Global Optimization with the Engineering Problems

Sun¹,

Ma²,

Liu³

2021

Preprint

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In real life, many engineering problems are nonlineble NP problems, in order to solve some of these problems, we put forward a competitive volleyball algorithm.The algorithm proposed in this paper is a meta-heuristic technique based on swarm optimization. It is inspired by the competition between volleyball teams in a league and the improvement in players’ overall abilities in order to win the Most Valuable Player award. Several specific terms relating to competition, such as pre-match reinforcement, single round robin mechanism, optimal strategy constitute the structure of the algorithm. The sensitivity of several parameters of the algorithm is analyzed and tested for three types of benchmark functions: unimodal, high-dimensional multimodal and low-dimensional multimodal functions. Through the use of these three types of test functions, the performance of this algorithm is compared with nine classical metaheuristic algorithms: Genetic Algorithm (GA), Differential Evolution (DE), Harmony Search (HS), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), Sine Cosine Algorithm (SCA), Soccer League Competition (SLC), League Championship Algorithm (LCA) and Volleyball Premier League (VPL). CVA has been used to solve three real-world engineering problems. The results show that the performance of the CVA is behaviorally promising and better than the other classical metaheuristic algorithms.

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An Improved Tabu Search Meta-heuristic Approach for Solving Scheduling Problem with Non-availability Constraints

Cited by 7 publications

References 25 publications

Improved Dynamic Optimized Kernel Partial Least Squares for Nonlinear Process Fault Detection

Improved Dynamic Optimized Kernel Partial Least Squares for Nonlinear Process Fault Detection

Research on the Scheduling of Tractors in the Major Epidemic to Ensure Spring Ploughing

A Competitive Volleyball Algorithm to Solve Global Optimization with the Engineering Problems

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