scholarly journals Solving Non-Permutation Flow Shop Scheduling Problem with Time Couplings

2021 ◽  
Vol 11 (10) ◽  
pp. 4425
Author(s):  
Radosław Idzikowski ◽  
Jarosław Rudy ◽  
Andrzej Gnatowski
Keyword(s):  
Genetic Algorithm ◽  
Tabu Search ◽  
Flow Shop ◽  
Optimal Solution ◽  
Search Method ◽  
Graph Representation ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Shop Scheduling ◽  
Tabu Search Method

In this paper, a non-permutation variant of the Flow Shop Scheduling Problem with Time Couplings and makespan minimization is considered. Time couplings are defined as machine minimum and maximum idle time allowed. The problem is inspired by the concreting process encountered in industry. The mathematical model of the problem and solution graph representation are presented. Several problem properties are formulated, including the time complexity of the goal function computation and block elimination property. Three solving methods, an exact Branch and Bound algorithm, the Tabu Search metaheuristic, and a baseline Genetic Algorithm metaheuristic, are proposed. Experiments using Taillard-based problem instances are performed. Results show that, for the Tabu Search method, the neighborhood based on the proposed block property outperforms other neighborhoods and the Genetic Algorithm under the same time limit. Moreover, the Tabu Search method provided high quality solutions, with the gap to the optimal solution for the smaller instances not exceeding 2.3%.

scholarly journals A Reinforcement Learning Method for a Hybrid Flow-Shop Scheduling Problem

Algorithms ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 222 ◽  
Author(s):  
Han ◽  
Guo ◽  
Su
Keyword(s):  
Reinforcement Learning ◽  
Flow Shop ◽  
Optimal Solution ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
First Choice ◽  
Hybrid Flow Shop Scheduling

The scheduling problems in mass production, manufacturing, assembly, synthesis, and transportation, as well as internet services, can partly be attributed to a hybrid flow-shop scheduling problem (HFSP). To solve the problem, a reinforcement learning (RL) method for HFSP is studied for the first time in this paper. HFSP is described and attributed to the Markov Decision Processes (MDP), for which the special states, actions, and reward function are designed. On this basis, the MDP framework is established. The Boltzmann exploration policy is adopted to trade-off the exploration and exploitation during choosing action in RL. Compared with the first-come-first-serve strategy that is frequently adopted when coding in most of the traditional intelligent algorithms, the rule in the RL method is first-come-first-choice, which is more conducive to achieving the global optimal solution. For validation, the RL method is utilized for scheduling in a metal processing workshop of an automobile engine factory. Then, the method is applied to the sortie scheduling of carrier aircraft in continuous dispatch. The results demonstrate that the machining and support scheduling obtained by this RL method are reasonable in result quality, real-time performance and complexity, indicating that this RL method is practical for HFSP.

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