A New Production Scheduling Module Using Priority-Rule Based Genetic Algorithm

Aydemir, Erdal; Koruca, Halil İbrahim

doi:10.2507/ijsimm14(3)7.299

Cited by 14 publications

(15 citation statements)

References 38 publications

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“…It has been proved that manufacturing scheduling problems, essentially belonging to Job shop scheduling problems (JSP), are a class of NP-hard problems [ 59 ]. Many models and algorithms have been proposed to obtain a suboptimal solution, such as PICRO (preference-inspired chemical reaction optimization algorithm) [ 60 ], HPGA (hybrid PSO-GA algorithm) [ 61 ], PRGA-Sched (priority rule-based genetic algorithm scheduling) [ 62 ], LCAFS (league championship algorithm with free search) [ 63 ], MOGA-TIG (multi-objective genetic algorithm with tabu-enhanced iterated greedy local search strategy) [ 64 ], and so on. Though an approximate optimal production plan can be effectively solved by these methods, the availability and reliability of sensor nodes is seldom followed closely.…”

Section: Related Workmentioning

confidence: 99%

A Framework of Joint Energy Provisioning and Manufacturing Scheduling in Smart Industrial Wireless Rechargeable Sensor Networks

Feng

Wang

Zheng

et al. 2018

Sensors

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Energy provisioning is always a crucial problem restricting the further development and application of smart industrial wireless sensor networks in smart factories. In this paper, we present that it is necessary to develop smart industrial wireless rechargeable sensor networks (SIWRSNs) in a smart factory environment. Based on the complexity and time-effectiveness of factory operations, we establish a joint optimization framework named J-EPMS to effectively coordinate the charging strategies of wireless sensors and the scheduling plans of machines running. Then, we propose a novel double chains quantum genetic algorithm with Taboo search (DCQGA-TS) for J-EPMS to obtain a suboptimal solution. The simulation results demonstrate that the DCQGA-TS algorithm can maximally ensure the continuous manufacturing and markedly shorten the total completion time of all production tasks.

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Section: Related Workmentioning

confidence: 99%

A Framework of Joint Energy Provisioning and Manufacturing Scheduling in Smart Industrial Wireless Rechargeable Sensor Networks

Feng

Wang

Zheng

et al. 2018

Sensors

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“…Increasing problem size for solving real-life instances increases the computational time exponentially [13]. Various mathematical formulations SRs, heuristics and artificial intelligence techniques such as neural networks, linear/non-linear searches and metaheuristics are usually designed to optimise the static scheduling problems [9], [16].…”

Section: Introductionmentioning

confidence: 99%

A simulated annealing approach based simulation-optimisation to the dynamic job-shop scheduling problem

Sel¹,

Hamzadayı²

2018

Pamukkale J Eng Sci

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ÖzIn this study, we address a production scheduling problem. The scheduling problem is encountered in a job-shop production type. The production system is discrete and dynamic system in which jobs arrive continually. We introduce a simulation model (SM) to identify several situations such as machine failures, changing due dates in which scheduling rules (SRs) should be selected independently. Three SRs, i.e. the earliest due date rule (EDD), the shortest processing time first rule (SPT) and the first in first out rule (FIFO), are incorporated in a SM. A simulated annealing heuristic (SA) based simulation-optimisation approach is proposed to identify the unknown schedules in the dynamical system. In the numerical analysis, the performance of SRs and SA are compared using the simulation experiments. The objective functions minimising the mean flowtime and the mean tardiness are examined with varying levels of shop utilization and due date tightness. As an overall result, we observe that the proposed SA heuristic outperforms EDD and FIFO, the well-known SPT rule provides the best results. However, SA heuristic achieves very close results to the SPT and offers a reasonable computational burden in time-critical applications.Bu çalışmada, bir üretim çizelgeleme problem ele alınmaktadır. Bu çizelgeleme problemine atölye tipi bir üretim tipinde karşılaşılmaktadır. Üretim sistemi sürekli iş gelişlerinin söz konusu olduğu kesikli dinamik sistemdir. Çizelgeleme kurallarının birbirinden bağımsız şekilde kullanılmasının gerektiği makine bozulmaları ve değişen teslim süreleri gibi bazı durumların değerlendirilmesi için bir simülasyon modeli sunulmaktadır. En erken teslim süresi, en kısa işlem süresi ve ilk giren ilk çıkar kuralı olmak üzere üç çizelgeleme kuralı bu simülasyon modeline dahil edilmiştir. Dinamik sistemdeki belirsiz çizelgeleri ortaya koymak için tavlama benzetimi sezgiseli tabanlı bir simülasyon optimizasyonu yöntemi önerilmektedir. Sayısal analizlerde çizelgeleme kurallarının ve önerilen tavlama benzetimi sezgiselinin performansları simülasyon deneyleri kullanılarak kıyaslanmıştır. Ortalama akış süresini ve ortalama gecikme süresini en küçükleyen amaç fonksiyonları farklı seviyelerdeki atölye kullanım oranı ve teslim süresi durumlarında incelenmiştir. Genel bir sonuç olarak, önerilen tavlama benzetimi sezgiselinin en erken teslim zamanı ve ilk giren ilk çıkar kurallarından daha iyi sonuç verdiği, en kısa işlem süresi kuralının en iyi sonuçları sağladığı gözlenmiştir. Fakat tavlama benzetimi sezgiseli en kısa işlem süresi kuralına çok yakın sonuçlara erişmektedir ve çözüm zamanının kritik olduğu uygulamalarda kabul edilebilir bir hesaplama yükü getirmektedir.

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“…Although AGVs scheduling problem has been dealt with before [5][6][7][8][9], it is still an open area of research to improve it for real environment results by considering number of AGVs and their battery charge while minimizing the makespan. Makespan minimization keeps the resources utilization rate at a balanced level and results in a better implementation of expensive FMSs [8,10].…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy Hybrid GA-PSO Algorithm for Multi-Objective AGV Scheduling in FMS

Mousavi¹,

Yap²,

Musa³

2017

Int. j. simul. model.

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An automated guided vehicle (AGV) is a mobile robot with remarkable industrial applicability for transporting materials within a manufacturing facility or a warehouse. AGV scheduling refers to the process of allocating AGVs to tasks, taking into account the cost and time of operations. Multiobjective scheduling is adopted in this study to acquire a more complex and combinatorial model in contrast with single objective practices. The model objectives are the makespan and number of AGVs minimization while considering the AGVs battery charge. A fuzzy hybrid GA-PSO (genetic algorithm -particle swarm optimization) algorithm was developed to optimize the model. Results have been compared with GA, PSO, and hybrid GA-PSO algorithms to explore the applicability of the algorithm developed. Model's feasibility and the algorithms' performance were investigated through a numerical example before and after the optimization. The model evaluation and validation was conducted through simulation via Flexsim software. The fuzzy hybrid GA-PSO surpassed the other methods, although obtaining less mean computational time was the only significant improvement over hybrid GA-PSO.

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A New Production Scheduling Module Using Priority-Rule Based Genetic Algorithm

Cited by 14 publications

References 38 publications

A Framework of Joint Energy Provisioning and Manufacturing Scheduling in Smart Industrial Wireless Rechargeable Sensor Networks

A Framework of Joint Energy Provisioning and Manufacturing Scheduling in Smart Industrial Wireless Rechargeable Sensor Networks

A simulated annealing approach based simulation-optimisation to the dynamic job-shop scheduling problem

A Fuzzy Hybrid GA-PSO Algorithm for Multi-Objective AGV Scheduling in FMS

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