Bi-objective optimization of a job shop with two types of failures for the operating machines that use automated guided vehicles

Karimi, Behzad; Niaki, Seyed Taghi Akhavan; Haleh, Hassan; Naderi, Bahman

doi:10.1016/j.ress.2018.01.018

Cited by 18 publications

(14 citation statements)

References 37 publications

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“…Moreover, there are three parameters in MOTLBO that should be tuned, which are the maximum number of iterations (Max it) the population size (N pop), and the mutation probability (Pm). Similar to the study conducted in Maghsoudlou et al 32 and Karimi et al, 8 these parameters and their levels used for the experiments are listed in Table 4. Moreover, the response obtained based on the Taguchi method for each experiment is defined as…”

Section: Methodsmentioning

confidence: 93%

“…Similar to the study conducted in Karimi et al, 8 three levels of the shape and the scale parameters of the Weibull distribution chosen for experiments are shown in Table 3 along with the number of iterations and machines for each combination. In addition, Figure 15 shows the signal-to-noise ratio plots obtained by performing the experiments using the Taguchi method.…”

Section: Methodsmentioning

confidence: 99%

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Multi-objective optimization of job shops with automated guided vehicles: A non-dominated sorting cuckoo search algorithm

Karimi

Niaki

Niknamfar

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part O:

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The reliability of machinery and automated guided vehicle has been one of the most important challenges to enhance production efficiency in several manufacturing systems. Reliability improvement would result in a simultaneous reduction of both production times and transportation costs of the materials, especially in automated guided vehicles. This article aims to conduct a practical multi-objective reliability optimization model for both automated guided vehicles and the machinery involved in a job-shop manufacturing system, where different machines and the storage area through some parallel automated guided vehicles handle materials, parts, and other production needs. While similar machines in each shop are limited to failures based on either an Exponential or a Weibull distribution via a constant rate, the machines in different shops fail based on different failure rates. Meanwhile, as the model does not contain any closed-form equation to measure the machine reliability in the case of Weibull failure, a simulation approach is employed to estimate the shop reliability to be further maximized using the proposed model. Besides, the automated guided vehicles are restricted to failures according to an Exponential distribution. Furthermore, choosing the best locations of the shops is proposed among some potential places. The proposed NP-Hard problem is then solved by designing a novel non-dominated sorting cuckoo search algorithm. Furthermore, a multi-objective teaching-learning-based optimization, as well as a multi-objective invasive weed optimization are designed to validate the results obtained. Ultimately, a novel AHP-TOPSIS method is carried out to rank the algorithms in terms of six performance metrics.

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Section: Methodsmentioning

confidence: 93%

Section: Methodsmentioning

confidence: 99%

Multi-objective optimization of job shops with automated guided vehicles: A non-dominated sorting cuckoo search algorithm

Karimi

Niaki

Niknamfar

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part O:

Self Cite

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“…Therefore, a good AGV scheduling scheme will enable consumers' orders to be sorted and delivered to customers earlier, which will significantly enhance consumer experience and improve consumer satisfaction. Although there have been many studies on AGV scheduling, they are mostly concentrated in the manufacturing field, especially in the FMS [2–15]. In the logistics industry, the application of AGVs is still a new trend.…”

Section: Introductionmentioning

confidence: 99%

“…In the earlier works, the scheduling objective was mostly based on minimizing the makespan to ensure the operating efficiency of the multi-AGV system [2–5,10,11,15–17]. However, these studies ignored the impact of the number of AGVs used in the AGV system, for AGV is a relatively expensive device.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective AGV scheduling in an automatic sorting system of an unmanned (intelligent) warehouse by using two adaptive genetic algorithms and a multi-adaptive genetic algorithm

Liu

Su³

et al. 2019

PLoS ONE

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Automated guided vehicle (AGV) is a logistics transport vehicle with high safety performance and excellent availability, which can genuinely achieve unmanned operation. The use of AGV in intelligent warehouses or unmanned warehouses for sorting can improve the efficiency of warehouses and enhance the competitiveness of enterprises. In this paper, a multi-objective mathematical model was developed and integrated with two adaptive genetic algorithms (AGA) and a multi-adaptive genetic algorithm (MAGA) to optimize the task scheduling of AGVs by taking the charging task and the changeable speed of the AGV into consideration to minimize makespan, the number of AGVs used, and the amount of electricity consumption. The numerical experiments showed that MAGA is the best of the three algorithms. The value of objectives before and after optimization changed by about 30%, which proved the rationality and validity of the model and MAGA.

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“…For example, Tavana et al [14] considered reliability to be an essential property of the MHS, modeled it as a cost function, and applied both cost and time measures as bi-objectives in the stochastic programming problem. Karimi et al [15] analyzed and simulated the reliability of flexible manufacturing systems and used a bi-objective nonlinear optimization model to maximize shop reliability, as well as minimize production time. Beschorner and Glüer [16] further analyzed the availability of the MHS on the basis of the maximum flow that could be transported from the source to the destination in the system, i.e., they evaluated the percentage of the maximum flow that the MHS could provide with the consideration of component failures.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Disturbance Analysis Method for Resilience Evaluation: A Case Study in Material Handling Systems

Tian

et al. 2019

Sustainability

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With the development of intelligent manufacturing technology, the material handling system (MHS) faces larger resilience challenges that threaten the sustainability of the system. To evaluate system resilience, the disturbance that the system may experience and the system response need to be identified in advance. This paper proposes a systematic and innovative approach to performing resilience-related disturbance analysis, i.e., disturbance mode and effects analysis (DMEA). Using this method, the possible disturbance modes, their occurrence probabilities, and the quantitative effects on system performance can be collected in a bottom-up process, and the information can be applied to further resilience quantification. Moreover, a quantitative system resilience evaluation framework for the MHS based on DMEA and the Monte Carlo method is presented. Production is defined as the key performance index of the system and is monitored to reflect the resilience behavior of the system after the disturbance occurs. The resilience of a tire tread handing system is quantified in our case study, and the results show the effectiveness of our DMEA-based resilience evaluation method. We also find that a reasonable system configuration and maintenance strategy can effectively improve system resilience, and a trade-off can be made between resilience and cost.

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Bi-objective optimization of a job shop with two types of failures for the operating machines that use automated guided vehicles

Cited by 18 publications

References 37 publications

Multi-objective optimization of job shops with automated guided vehicles: A non-dominated sorting cuckoo search algorithm

Multi-objective optimization of job shops with automated guided vehicles: A non-dominated sorting cuckoo search algorithm

Multi-objective AGV scheduling in an automatic sorting system of an unmanned (intelligent) warehouse by using two adaptive genetic algorithms and a multi-adaptive genetic algorithm

A Systematic Disturbance Analysis Method for Resilience Evaluation: A Case Study in Material Handling Systems

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