A Non-Permutation Flow Shop Manufacturing Cell Scheduling Problem with Part's Sequence Dependent Family Setup Times

2014 ◽  
Vol 5 (4) ◽  
pp. 70-86 ◽  
Author(s):  
Behzad Nikjo ◽  
Yaser Zarook
Keyword(s):  
Flow Shop ◽  
Optimal Solution ◽  
Setup Times ◽  
Computational Time ◽  
Scheduling Problem ◽  
Dynamic Flow ◽  
Exact Methods ◽  
Release Date ◽  
Manufacturing Cell ◽  
Cell Scheduling

This article presents a new mathematical model for a dynamic flow shop manufacturing cell scheduling problem (DFMCSP) with agreeable job release date for each part family where family setup times are dependent on sequence of parts within families. It means this article considers non-permutation schedules for both sequence of families and sequence of parts within families. The objective is minimizing the Makespan (Cmax). Since, this problem belongs to NP-Hard class. Therefore, reaching an optimal solution in a reasonable computational time by using exact methods is extremely difficult. Thus, this article proposes meta-heuristic methods such as Genetic Algorithms (GA) and Tabu Search (TS). Finally, the computational results compare efficiency of the proposed algorithms under the performance measures.

scholarly journals The Discrete Particle Swarm Optimization Algorithms For Permutation Flowshop Scheduling Problem

2019 ◽  
Vol 20 (2) ◽  
pp. 105
Author(s):  
Ikhlasul Amallynda
Keyword(s):  
Particle Swarm Optimization ◽  
Flow Shop ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Flow Shop Scheduling ◽  
Computational Time ◽  
Scheduling Problem ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Discrete Particle

In this paper, two types of discrete particle swarm optimization (DPSO) algorithms are presented to solve the Permutation Flow Shop Scheduling Problem (PFSP). We used criteria to minimize total earliness and total tardiness. The main contribution of this study is a new position update method is developed based on the discrete domain because PFSP is represented as discrete job permutations. In addition, this article also comes with a simple case study to ensure that both proposed algorithm can solve the problem well in the short computational time. The result of Hybrid Discrete Particle Swarm Optimization (HDPSO) has a better performance than the Modified Particle Swarm Optimization (MPSO). The HDPSO produced the optimal solution. However, it has a slightly longer computation time. Besides the population size and maximum iteration have any impact on the quality of solutions produced by HDPSO and MPSO algorithms 

scholarly journals The Discrete Particle Swarm Optimization Algorithms For Permutation Flowshop Scheduling Problem

2019 ◽  
Vol 20 (2) ◽  
pp. 1
Author(s):  
Ikhlasul Amallynda
Keyword(s):  
Particle Swarm Optimization ◽  
Flow Shop ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Flow Shop Scheduling ◽  
Computational Time ◽  
Scheduling Problem ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Discrete Particle

In this paper, two types of discrete particle swarm optimization (DPSO) algorithms are presented to solve the Permutation Flow Shop Scheduling Problem (PFSP). We used criteria to minimize total earliness and total tardiness. The main contribution of this study is a new position update method is developed based on the discrete domain because PFSP is represented as discrete job permutations. In addition, this article also comes with a simple case study to ensure that both proposed algorithm can solve the problem well in the short computational time. The result of Hybrid Discrete Particle Swarm Optimization (HDPSO) has a better performance than the Modified Particle Swarm Optimization (MPSO). The HDPSO produced the optimal solution. However, it has a slightly longer computation time. Besides the population size and maximum iteration have any impact on the quality of solutions produced by HDPSO and MPSO algorithms 

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 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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