A cooperative co-evolutionary particle swarm optimiser based on a niche sharing scheme for the flow shop scheduling problem under uncertainty

2014 ◽  
Vol 24 (5) ◽  
Author(s):  
BIN JIAO ◽  
SHAOBIN YAN
Keyword(s):  
Flow Shop ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Rough Sets Theory ◽  
Shop Scheduling ◽  
Sharing Scheme ◽  
Scheduling Model ◽  
Particle Swarm Optimisation Algorithm

The flow shop scheduling problem based on ideal and precise conditions has been a focus of considerable research since the first easy scheduling problem was formulated. In reality, some uncertain factors always restrict the scheduling optimisation problem. In this paper, taking uncertain processing time as an example, we use generalised rough sets theory to transform the rough flow shop scheduling model into the precise scheduling model. We adopt a cooperative co-evolutionary particle swarm optimisation algorithm based on a niche sharing scheme (NCPSO) to minimise the makespan in comparison with the particle swarm optimiser (PSO) and co-evolution particle swarm optimiser (CPSO) algorithms. The new algorithm is characterised by a strengthening of the ability to reserve excellent particles and searching the optimal solution. Experimental results show that the new algorithm is more effective and efficient than the others.

Research on a Main-Branch Hybrid Flow Shop Scheduling Problem in Production Manufacturing System

2012 ◽  
Vol 252 ◽  
pp. 354-359
Author(s):  
Xin Min Zhang ◽  
Meng Yue Zhang
Keyword(s):  
Manufacturing System ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Scheduling Problem ◽  
Quantum Genetic Algorithm ◽  
Shop Scheduling ◽  
Scheduling Model ◽  
Hybrid Flow Shop Scheduling ◽  
Two Stages

A main-branch hybrid Flow shop scheduling problem in production manufacturing system is studied. Under the premise of JIT, targeting of smallest cost, a Flow-Shop production line scheduling model is built in cycle time of buffer. Two stages Quantum Genetic Algorithm (QGA) is proposed. By the results of numerical example, the effective and advantageous of QGA was shown.

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.

scholarly journals Minimization of Utilization Time for Specially Structured n × 3 Scheduling Model with Jobs in a String of Disjoint Job Blocks

2019 ◽  
Vol 8 (12) ◽  
pp. 3554-3558
Keyword(s):  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Scheduling Problem ◽  
Numerical Illustration ◽  
Optimal Sequence ◽  
Shop Scheduling ◽  
Elapsed Time ◽  
Processing Times ◽  
Scheduling Model ◽  
Utilization Time

The present paper investigates n×3 specially structured flow shop scheduling model with processing of jobs on given machines in a string of disjoint job blocks and with probabilities associated to the processing times of jobs. The objective is to minimize utilization time of second and third machine and also minimize the total elapsed time for processing the jobs for n×3 specially structured flow shop scheduling problem. The algorithm developed in this paper is quite straightforward and easy to understand and also present an essential way out to the decision maker for attaining an optimal sequence of jobs. The algorithm developed in this paper is validated by a numerical illustration.

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 

Surgical Scheduling Based on Hybrid Flow-Shop Scheduling

2012 ◽  
Vol 201-202 ◽  
pp. 1004-1007 ◽  
Author(s):  
Guo Xun Huang ◽  
Wei Xiang ◽  
Chong Li ◽  
Qian Zheng ◽  
Shan Zhou ◽  
...  
Keyword(s):  
Parallel Machines ◽  
Flow Shop ◽  
Elective Surgery ◽  
Optimal Solution ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Scheduling Problem ◽  
Shop Scheduling ◽  
Surgical Scheduling ◽  
Hybrid Flow Shop Scheduling

The efficient surgical scheduling of the operating theatre plays a significant role in hospital’s income and cost. Currently surgical scheduling only considered the surgery process in operating room and ignored other stages which should not be left out in real situations. The surgical scheduling problem is regarded as the hybrid flow-shop scheduling problem in this study. Each elective surgery which need local anesthesia has to go through a two-stage surgery procedure. Beds and operating rooms are represented as parallel machines. A mathematical model for such surgical scheduling problem is proposed and solved by LINGO. A case study with its optimal solution is also presented to verify the model.

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