Mathematical formulation and hybrid meta‐heuristic algorithms for multiproduct oil pipeline scheduling problem with tardiness penalties

2021 ◽  
Author(s):  
Farzaneh Khalili Goudarzi ◽  
Hamid Reza Maleki ◽  
Sadegh Niroomand
Keyword(s):  
Heuristic Algorithms ◽  
Mathematical Formulation ◽  
Scheduling Problem ◽  
Oil Pipeline ◽  
Pipeline Scheduling

scholarly journals A multi-objective flexible job-shop cell scheduling problem with sequence-dependent family setup times and intercellular transportation by improved NSGA-II

2021 ◽  
pp. 095440542110446
Author(s):  
Yaoyao Han ◽  
Xiaohui Chen ◽  
Minmin Xu ◽  
Youjun An ◽  
Fengshou Gu ◽  
...  
Keyword(s):  
Job Shop ◽  
Heuristic Algorithms ◽  
Setup Times ◽  
Flexible Job Shop ◽  
Scheduling Problem ◽  
Nsga Ii ◽  
Multi Objective ◽  
Sequence Dependent ◽  
Cell Scheduling ◽  
Dependent Setup Times

With the development of Industry 4.0 and requirement of smart factory, cellular manufacturing system (CMS) has been widely concerned in recent years, which may leads to reducing production cost and wip inventory due to its flexibility production with groups. Intercellular transportation consumption, sequence-dependent setup times, and batch issue in CMS are taken into consideration simultaneously in this paper. Afterwards, a multi-objective flexible job-shop cell scheduling problem (FJSCP) optimization model is established to minimize makespan, total energy consumption, and total costs. Additionally, an improved non-dominated sorting genetic algorithm is adopted to solve the problem. Meanwhile, for improving local search ability, hybrid variable neighborhood (HVNS) is adopted in selection, crossover, and mutation operations to further improve algorithm performance. Finally, the validity of proposed algorithm is demonstrated by datasets of benchmark scheduling instances from literature. The statistical result illustrates that improved method has a better or an equivalent performance when compared with some heuristic algorithms with similar types of instances. Besides, it is also compared with one type scalarization method, the proposed algorithm exhibits better performance based on hypervolume analysis under different instances.

scholarly journals Generalized swarm intelligence algorithms with domain-specific heuristics

2021 ◽  
Vol 10 (1) ◽  
pp. 157
Author(s):  
P. Matrenin ◽  
V. Myasnichenko ◽  
N. Sdobnyakov ◽  
D. Sokolov ◽  
S. Fidanova ◽  
...  
Keyword(s):  
Swarm Intelligence ◽  
Minimum Distance ◽  
Heuristic Algorithms ◽  
Population Based ◽  
Scheduling Problem ◽  
Domain Specific ◽  
Hybrid Approaches ◽  
Self Organized ◽  
Swarm Algorithms ◽  
Tasks Scheduling

<span lang="EN-US">In recent years, hybrid approaches on population-based algorithms are more often applied in industrial settings. In this paper, we present the approach of a combination of universal, problem-free Swarm Intelligence (SI) algorithms with simple deterministic domain-specific heuristic algorithms. The approach focuses on improving efficiency by sharing the advantages of domain-specific heuristic and swarm algorithms. A heuristic algorithm helps take into account the specifics of the problem and effectively translate the positions of agents (particle, ant, bee) into the problem's solution. And a Swarm algorithm provides an increase in the adaptability and efficiency of the approach due to stochastic and self-organized properties. We demonstrate this approach on two non-trivial optimization tasks: scheduling problem and finding the minimum distance between 3D isomers.</span>

scholarly journals Smart DAG Tasks Scheduling between Trusted and Untrusted Entities Using the MCTS Method

2019 ◽  
Vol 11 (7) ◽  
pp. 1826 ◽  
Author(s):  
Yuxia Cheng ◽  
Zhiwei Wu ◽  
Kui Liu ◽  
Qing Wu ◽  
Yu Wang
Keyword(s):  
Reinforcement Learning ◽  
Heterogeneous Computing ◽  
Heuristic Algorithms ◽  
Scheduling Algorithm ◽  
Optimal Schedule ◽  
Scheduling Problem ◽  
Scheduling Policies ◽  
Monte Carlo Tree Search ◽  
Reward Function ◽  
Tasks Scheduling

Task scheduling is critical for improving system performance in the distributed heterogeneous computing environment. The Directed Acyclic Graph (DAG) tasks scheduling problem is NP-complete and it is hard to find an optimal schedule. Due to its key importance, the DAG tasks scheduling problem has been extensively studied in the literature. However, many previously proposed traditional heuristic algorithms are usually based on greedy methods and also lack the consideration of scheduling tasks between trusted and untrusted entities, which makes the problem more complicated, but there still exists a large optimization space to be explored. In this paper, we propose a trust-aware adaptive DAG tasks scheduling algorithm using the reinforcement learning and Monte Carlo Tree Search (MCTS) methods. The scheduling problem is defined using the reinforcement learning model. Efficient scheduling state space, action space and reward function are designed to train the policy gradient-based REINFORCE agent. The MCTS method is proposed to determine actual scheduling policies when DAG tasks are simultaneously executed in trusted and untrusted entities. Leveraging the algorithm’s capability of exploring long term reward, the proposed algorithm could achieve good scheduling policies while guaranteeing trusted tasks scheduled within trusted entities. Experimental results showed the effectiveness of the proposed algorithm compared with the classic HEFT/CPOP algorithms.

scholarly journals Single-Machine Parallel-Batch Scheduling with Nonidentical Job Sizes and Rejection

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 258
Author(s):  
Miaomiao Jin ◽  
Xiaoxia Liu ◽  
Wenchang Luo
Keyword(s):  
Integer Programming ◽  
Single Machine ◽  
Heuristic Algorithms ◽  
Batch Processing ◽  
Batch Scheduling ◽  
Scheduling Problem ◽  
Programming Formulation ◽  
Processing Times ◽  
Batch Processing Machine ◽  
Rejection Penalty

We investigate the single-machine parallel-batch scheduling problem with nonidentical job sizes and rejection. In this problem, a set of jobs with different processing times and nonidentical sizes is given to be possibly processed on a parallel-batch processing machine. Each job is either accepted and then processed on the machine or rejected by paying its rejection penalty. Preemption is not allowed. Our task is to choose the accepted jobs and schedule them as batches on the machine to minimize the makespan of the accepted jobs plus the total rejection penalty of the rejected jobs. We provide an integer programming formulation to exactly solve our problem. Then, we propose three fast heuristic algorithms to solve the problem and evaluate their performances by using a small numerical example.

DECOMPOSITION ALGORITHMS FOR THE INTERVAL SCHEDULING PROBLEM

2010 ◽  
Vol 27 (04) ◽  
pp. 517-537 ◽  
Author(s):  
SHIDONG WANG ◽  
LI ZHENG ◽  
ZHIHAI ZHANG
Keyword(s):  
Large Scale ◽  
Heuristic Algorithms ◽  
Integer Program ◽  
Computational Effort ◽  
Small Scale ◽  
Test Problems ◽  
Decomposition Algorithms ◽  
Scheduling Problem ◽  
Interval Scheduling ◽  
Average Percentage

Scheduling track lines at a marshalling station where the objective is to determine the maximal weighted number of trains on the track lines can be modeled as an interval scheduling problem: each job has a fixed starting and finishing time and can only be carried out by an arbitrarily given subset of machines. This scheduling problem is formulated as an integer program, which is NP-Complete when the number of machines and jobs are unfixed and the computational effort to solve large scale test problems is prohibitively large. Heuristic algorithms (HAs) based on the decomposition of original problem have been developed and the benefits lie in both conceptual simplicity and computational efficiency. Genetic algorithm (GA) to address the scheduling problem is also proposed. Computational experiments on low and high utilization rates of machines are carried out to compare the performance of the proposed algorithms with Cplex. Computational results show that the HAs and GA perform well in most condition, especially HA2 with the maximum of average percentage deviation on average 3.5% less than the optimal solutions found by Cplex in small-scale problem. Our methodologies are capable of producing improved solutions to large-scale problems with reasonable computing resources, too.

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