On Pareto Optimal Solution for Production and Maintenance Jobs Scheduling Problem in a Job Shop and Flow Shop with an Immune Algorithm

2014 ◽  
Vol 1036 ◽  
pp. 875-880 ◽  
Author(s):  
Iwona Paprocka ◽  
Wojciech M. Kempa ◽  
Krzysztof Kalinowski ◽  
Cezary Grabowik
Keyword(s):  
Job Shop ◽  
Flow Shop ◽  
Optimal Solution ◽  
Pareto Optimal Solution ◽  
Immune Algorithm ◽  
Flow Shop Scheduling ◽  
Pareto Optimal ◽  
Scheduling Problem ◽  
Shop Scheduling ◽  
Mean Time

In the paper a job shop and flow shop scheduling problems with availability time constraint for maintenance are considered. Unavailability time due to maintenance is estimated basing on information about predicted Mean Time To Failure/To First Failure and Mean Time of Repair of a machine. Maintenance actions are introduced into a schedule to keep the machine available in a good operation condition. The efficiency of predictive schedules (PS) is evaluated using criteria: makespan, flow time, total tardiness, idle time. The efficiency of reactive schedules (RSs) is evaluated using criteria: solution and quality robustness. For basic schedule generation Multi Objective Immune Algorithm is applied. For predictive scheduling Minimal Impact of Disturbed Operation on the Schedule is applied. After doing computer simulations for the job shop scheduling problem following question arises: do dominated Pareto optimal basic schedules achieve better PSs? Although a single Pareto-optimal solution is achieved on Pareto-optimal frontier three different schedules have the same quality in the flow shop scheduling problem. The question is: which schedule is the most robust solution?

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.

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.

scholarly journals Grid Computing Model to Solve Job Shop Scheduling and Flow Shop Scheduling by Fuzzy C-Mean Algorithm

2019 ◽  
Vol 9 (1) ◽  
pp. 170-179
Keyword(s):  
Grid Computing ◽  
Clustering Algorithm ◽  
Job Shop ◽  
Flow Shop ◽  
Job Shop Scheduling ◽  
Flow Shop Scheduling ◽  
Experimental Result ◽  
Scheduling Problem ◽  
Shop Scheduling ◽  
Computing Model

This paper presents two computing model in grid environment to utilize the waiting time of a job on particular machines in Job Shop Scheduling and Flow Shop Scheduling for minimize the makespan or total elapsed time. To determine the sequencing of a job we have applied Fuzzy C-Mean (FCM) clustering algorithm in both Job Shop Scheduling problem and Flow Shop Scheduling problem. Flow Shop Scheduling is a classified case of Job Shop Scheduling in which a specific job sequence is pursued strictly. Two illustrative examples of scheduling problems have been solved by this method and compared our results to some other existing methods discussed in the literature. The experimental result shows that the scheduling system using grid computing can allocate the makespan of service jobs effectively and more efficiently.

scholarly journals Solving No-Wait Flow Shop Scheduling Problem Based on Discrete Wolf Pack Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Rongshen Lai ◽  
Bo Gao ◽  
Wenguang Lin
Keyword(s):  
Flow Shop ◽  
Optimal Solution ◽  
Solution Space ◽  
Flow Shop Scheduling ◽  
Searching Behavior ◽  
Scheduling Problem ◽  
Application Research ◽  
Shop Scheduling ◽  
No Wait ◽  
The Individual

Aiming at the no-wait flow shop scheduling problem with the goal of minimizing the maximum makespan, a discrete wolf pack algorithm has been proposed. First, the methods for solving the no-wait flow shop scheduling problem and the application research of the wolf pack algorithm were summarized, and it was pointed out that there was lack of research on the application of the wolf pack algorithm to solve the no-wait flow shop scheduling problem. According to the analysis of characteristics of the no-wait flow shop scheduling problem, the individual wolf was coded by a decimal integer; wolf searching behavior was realized through the exchange of different code bits in the individual wolf, and the continuous code segment of the head wolf was randomly selected to replace the corresponding code of the fierce wolf, by which the behaviors of wolves raiding and sieging were realized, and the population was updated according to the rule of “survival of the strong.” In particular, to fully explore the potential optimal solution in the solution space, loop operations were added to the wandering, summoning, and siege processes. Finally, based on a comparison with the leapfrog algorithm and the genetic algorithm, the effectiveness of the algorithm was verified.

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.

An Effective Immune Based Approach for the No-Wait Flow Shop Scheduling Problems with Multiple Machines

2010 ◽  
Vol 97-101 ◽  
pp. 2432-2435 ◽  
Author(s):  
Yi Chih Hsieh ◽  
Y.C. Lee ◽  
Peng Sheng You ◽  
Ta Cheng Chen
Keyword(s):  
Flow Shop ◽  
Immune Algorithm ◽  
Flow Shop Scheduling ◽  
Benchmark Problems ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Practical Applications ◽  
Shop Scheduling ◽  
No Wait ◽  
Better Than

For scheduling problems, no-wait constraint is an important requirement for many industries. As known, the no-wait scheduling problem is NP-hard and has several practical applications. This paper applies an immune algorithm to solve the multiple-machine no-wait flow shop scheduling problem with minimizing the makespan. Twenty-three benchmark problems on the OR-Library are solved by the immune algorithm. Limited numerical results show that the immune algorithm performs better than the other typical approaches in the literature for most of instances.

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