A Hybrid Method of Heuristic Algorithm and Constraint Programming for No-wait Integrated Scheduling Problem

2021 ◽  
Vol 22 (5) ◽  
pp. 1083-1090
Author(s):  
Zhiqiang Xie Zhiqiang Xie ◽  
Xiaowei Zhang Zhiqiang Xie ◽  
Yingchun Xia Xiaowei Zhang ◽  
Jing Yang Yingchun Xia ◽  
Yu Xin Jing Yang
Keyword(s):  
Heuristic Algorithm ◽  
Hybrid Method ◽  
Constraint Programming ◽  
Scheduling Problem ◽  
Integrated Scheduling ◽  
No Wait

A multi-shop integrated scheduling algorithm with fixed output constraint

2021 ◽  
pp. 1-9
Author(s):  
Yingchun Xia ◽  
Zhiqiang Xie ◽  
Yu Xin ◽  
Xiaowei Zhang
Keyword(s):  
Constraint Programming ◽  
Programming Model ◽  
Scheduling Algorithm ◽  
Processing Parameters ◽  
Scheduling Problem ◽  
Integrated Scheduling ◽  
Scheduling Scheme ◽  
Wide Range ◽  
Constraint Programming Model ◽  
Output Constraint

The customized products such as electromechanical prototype products are a type of product with research and trial manufacturing characteristics. The BOM structures and processing parameters of the products vary greatly, making it difficult for a single shop to meet such a wide range of processing parameters. For the dynamic and fuzzy manufacturing characteristics of the products, not only the coordinated transport time of multiple shops but also the fact that the product has a designated output shop should be considered. In order to solve such Multi-shop Integrated Scheduling Problem with Fixed Output Constraint (MISP-FOC), a constraint programming model is developed to minimize the total tardiness, and then a Multi-shop Integrated Scheduling Algorithm (MISA) based on EGA (Enhanced Genetic Algorithm) and B&B (Branch and Bound) is proposed. MISA is a hybrid optimization method and consists of four parts. Firstly, to deal with the dynamic and fuzzy manufacturing characteristics, the dynamic production process is transformed into a series of time-continuous static scheduling problem according to the proposed dynamic rescheduling mechanism. Secondly, the pre-scheduling scheme is generated by the EGA at each event moment. Thirdly, the jobs in the pre-scheduling scheme are divided into three parts, namely, dispatched jobs, jobs to be dispatched, and jobs available for rescheduling, and at last, the B&B method is used to optimize the jobs available for rescheduling by utilizing the period when the dispatched jobs are in execution. Google OR-Tools is used to verify the proposed constraint programming model, and the experiment results show that the proposed algorithm is effective and feasible.

Modeling and Optimization of Integrated Scheduling of Automated Warehouse System

2011 ◽  
Vol 230-232 ◽  
pp. 35-39 ◽  
Author(s):  
Sai Nan Liu
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Heuristic Algorithm ◽  
Control Policy ◽  
Automated Guided Vehicle ◽  
Scheduling Problem ◽  
Modeling And Optimization ◽  
Multi Objective ◽  
Integrated Scheduling ◽  
Crossover And Mutation

In order to improve the efficiency of automated warehouse system, the integrated scheduling problem was studied on the basis of a typical warehouse layout. A new multi-objective mathematical model was built with constraints. The reason that leads to automated guided vehicle (AGV) deadlock was analyzed on basis of bidirectional route of AGV. And a deadlock-free control policy named alternative path was proposed. A heuristic algorithm based on genetic algorithm was proposed to solve the problem. The rule of coding, selection, crossover and mutation was described in detail. The simulation result shows that the proposed algorithm is effective and can be used in practical.

scholarly journals Improved Hybrid Heuristic Algorithm Inspired by Tissue-Like Membrane System to Solve Job Shop Scheduling Problem

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 219
Author(s):  
Xiang Tian ◽  
Xiyu Liu
Keyword(s):  
Local Search ◽  
Heuristic Algorithm ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Membrane System ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Hybrid Heuristic ◽  
Shop Scheduling ◽  
Hybrid Heuristic Algorithm

In real industrial engineering, job shop scheduling problem (JSSP) is considered to be one of the most difficult and tricky non-deterministic polynomial-time (NP)-hard problems. This study proposes a new hybrid heuristic algorithm for solving JSSP inspired by the tissue-like membrane system. The framework of the proposed algorithm incorporates improved genetic algorithms (GA), modified rumor particle swarm optimization (PSO), and fine-grained local search methods (LSM). To effectively alleviate the premature convergence of GA, the improved GA uses adaptive crossover and mutation probabilities. Taking into account the improvement of the diversity of the population, the rumor PSO is discretized to interactively optimize the population. In addition, a local search operator incorporating critical path recognition is designed to enhance the local search ability of the population. Experiment with 24 benchmark instances show that the proposed algorithm outperforms other latest comparative algorithms, and hybrid optimization strategies that complement each other in performance can better break through the original limitations of the single meta-heuristic method.

scholarly journals Constraint programming approach to a bilevel scheduling problem

Constraints ◽  
2010 ◽  
Vol 16 (3) ◽  
pp. 317-340 ◽  
Author(s):  
András Kovács ◽  
Tamás Kis
Keyword(s):  
Constraint Programming ◽  
Programming Approach ◽  
Scheduling Problem
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