A hybrid system of heuristics and simulated annealing for large size scheduling problems of batch processes with pipeless intermediate storage

1997 ◽  
Vol 14 (4) ◽  
pp. 225-232 ◽  
Author(s):  
Hyung Joon Kim ◽  
Jae Hak Jung ◽  
Minseok Kim ◽  
In-Beum Lee
Keyword(s):  
Simulated Annealing ◽  
Hybrid System ◽  
Batch Processes ◽  
Scheduling Problems ◽  
Large Size ◽  
Intermediate Storage

scholarly journals A two-agent one-machine multitasking scheduling problem solving by exact and metaheuristics

2021 ◽  
Author(s):  
Chin-Chia Wu ◽  
Ameni Azzouz ◽  
Jia-Yang Chen ◽  
Jianyou Xu ◽  
Wei-Lun Shen ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Completion Time ◽  
Simulated Annealing Algorithm ◽  
Optimal Schedule ◽  
Computational Studies ◽  
Scheduling Problem ◽  
Optimal Solutions ◽  
Large Size ◽  
Annealing Algorithm ◽  
One Machine

AbstractThis paper studies a single-machine multitasking scheduling problem together with two-agent consideration. The objective is to look for an optimal schedule to minimize the total tardiness of one agent subject to the total completion time of another agent has an upper bound. For this problem, a branch-and-bound method equipped with several dominant properties and a lower bound is exploited to search optimal solutions for small size jobs. Three metaheuristics, cloud simulated annealing algorithm, genetic algorithm, and simulated annealing algorithm, each with three improvement ways, are proposed to find the near-optimal solutions for large size jobs. The computational studies, experiments, are provided to evaluate the capabilities for the proposed algorithms. Finally, statistical analysis methods are applied to compare the performances of these algorithms.

Memetic Algorithm Approach to Two-Stage Hybrid Flow Shop Scheduling Problem with Identical Parallel Machines

2013 ◽  
Vol 651 ◽  
pp. 548-552
Author(s):  
Parinya Kaweegitbundit
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Parallel Machines ◽  
Flow Shop ◽  
Memetic Algorithm ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Scheduling Problems ◽  
Two Stage ◽  
Hybrid Flow Shop Scheduling

This paper considers two stage hybrid flow shop (HFS) with identical parallel machine. The objectives is to determine makespan have been minimized. This paper presented memetic algorithm procedure to solve two stage HFS problems. To evaluated performance of propose method, the results have been compared with two meta-heuristic, genetic algorithm, simulated annealing. The experimental results show that propose method is more effective and efficient than genetic algorithm and simulated annealing to solve two stage HFS scheduling problems.

A Thermoeconomic Comparison Between SOFC Hybrid Systems and the Most Worldwide Used Technologies Towards Competitive Innovative Plants

2009 ◽  
Author(s):  
A. Franzoni ◽  
L. Magistri ◽  
O. Tarnowsky ◽  
A. F. Massardo
Keyword(s):  
Power Generation ◽  
Power Plant ◽  
Hybrid Systems ◽  
Hybrid System ◽  
Gas Turbines ◽  
Point Of View ◽  
Thermodynamic Efficiency ◽  
Economic Point ◽  
Capital Costs ◽  
Large Size

This paper investigates options for highly efficient SOFC hybrid systems of different sizes. For this purpose different models of pressurised SOFC hybrids systems have been developed in the framework of the European Project “LARGE SOFC - Towards a Large SOFC Power Plant”. This project, coordinated by VTT Finland, counts numerous industrial partners such as Wartsila, Topsoe and Rolls-Royce FCS ltd. Starting from the RRFCS Hybrid System [1], considered as the reference case, several plant modifications have been investigated in order to improve the thermodynamic efficiency. The main options considered are (i) the integration of a recuperated micro gas turbine and (ii) the replacement of the cathodic ejector with a blower. The plant layouts are analysed in order to define the optimum solution in terms of operating parameters and thermodynamic performances. The study of a large size power plant (around 110 MWe) fed by coal and incorporated with SOFC hybrid systems is also conducted. The aim of this study is to analyse the sustainability of an Integrated Gasification Hybrid System from the thermodynamic and economic point of view in the frame of future large sized power generation. A complete thermoeconomic analysis of the most promising plants is carried out, taking into account variable and capital costs of the systems. The designed systems are compared from the thermodynamic and the thermoeconomic point of view with some of the common technologies used for distributed generation (gas turbines and reciprocating engines) and large size power generation (combined cycles and IGCC). The tool used for this analysis is WTEMP software, developed by the University of Genoa (DIMSET-TPG) [2], able to carry out a detailed thermodynamic and thermoeconomic analysis of the whole plants.

scholarly journals Hybrid Genetic Simulated Annealing Algorithm for Improved Flow Shop Scheduling with Makespan Criterion

2018 ◽  
Vol 8 (12) ◽  
pp. 2621 ◽  
Author(s):  
Hongjing Wei ◽  
Shaobo Li ◽  
Houmin Jiang ◽  
Jie Hu ◽  
Jianjun Hu
Keyword(s):  
Simulated Annealing ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Regulation Mechanism ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
Wide Range ◽  
Random Crossover ◽  
Crossover And Mutation ◽  
Makespan Criterion

Flow shop scheduling problems have a wide range of real-world applications in intelligent manufacturing. Since they are known to be NP-hard for more than two machines, we propose a hybrid genetic simulated annealing (HGSA) algorithm for flow shop scheduling problems. In the HGSA algorithm, in order to obtain high-quality initial solutions, an MME algorithm, combined with the MinMax (MM) and Nawaz–Enscore–Ham (NEH) algorithms, was used to generate the initial population. Meanwhile, a hormone regulation mechanism for a simulated annealing (SA) schedule was introduced as a cooling scheme. Using MME initialization, random crossover and mutation, and the cooling scheme, we improved the algorithm’s quality and performance. Extensive experiments have been carried out to verify the effectiveness of the combination approach of MME initialization, random crossover and mutation, and the cooling scheme for SA. The result on the Taillard benchmark showed that our HGSA algorithm achieved better performance relative to the best-known upper bounds on the makespan compared with five state-of-the-art algorithms in the literature. Ultimately, 109 out of 120 problem instances were further improved on makespan criterion.

scholarly journals Job Scheduling in Cloud Computing Using a Modified Harris Hawks Optimization and Simulated Annealing Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Ibrahim Attiya ◽  
Mohamed Abd Elaziz ◽  
Shengwu Xiong
Keyword(s):  
Cloud Computing ◽  
Simulated Annealing ◽  
Large Scale ◽  
Simulated Annealing Algorithm ◽  
Job Scheduling ◽  
Search Algorithm ◽  
Scheduling Algorithms ◽  
Scheduling Problems ◽  
It Services ◽  
On Demand

In recent years, cloud computing technology has attracted extensive attention from both academia and industry. The popularity of cloud computing was originated from its ability to deliver global IT services such as core infrastructure, platforms, and applications to cloud customers over the web. Furthermore, it promises on-demand services with new forms of the pricing package. However, cloud job scheduling is still NP-complete and became more complicated due to some factors such as resource dynamicity and on-demand consumer application requirements. To fill this gap, this paper presents a modified Harris hawks optimization (HHO) algorithm based on the simulated annealing (SA) for scheduling jobs in the cloud environment. In the proposed HHOSA approach, SA is employed as a local search algorithm to improve the rate of convergence and quality of solution generated by the standard HHO algorithm. The performance of the HHOSA method is compared with that of state-of-the-art job scheduling algorithms, by having them all implemented on the CloudSim toolkit. Both standard and synthetic workloads are employed to analyze the performance of the proposed HHOSA algorithm. The obtained results demonstrate that HHOSA can achieve significant reductions in makespan of the job scheduling problem as compared to the standard HHO and other existing scheduling algorithms. Moreover, it converges faster when the search space becomes larger which makes it appropriate for large-scale scheduling problems.

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