On multistage approach for flexible routing in flexible manufacturing systems

Qudeiri, Jaber Abu; Umer, Usama; Abidi, Mustufa Haider; Al-Ahmari, Abdulrahman; Khadra, Fayiz Abu

doi:10.1177/1687814016659012

Cited by 3 publications

(5 citation statements)

References 33 publications

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“…Many authors treated this topic such as Spinellis et.al. who used decomposition method with simulated annealing to optimize line's production rate [16], where others have used genetic algorithm to find the optimal buffer sizes [17]. Blumenfeld and Li developed a methodology for solving the BSP for multiple machines but always with identical speeds, number of failures per hour and repair rate [18].…”

Section: State Of the Artmentioning

confidence: 99%

A Computational Method for Identifying the Optimum Buffer Size in the Era of Zero Defect Manufacturing

Psarommatis

Boujemaoui

Kiritsis

2020

IFIP Advances in Information and Communication Technology

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Decreasing defects, waste time, meeting customer demand and being adaptable are the goals of a Zero Defect Manufacturing (ZDM) strategy. Scheduling is an important tool to perform that. It should take in account buffer size allocation. In this study, a method to solve the Buffer Sizing Problem (BSP), which is NP-hard problem. The current research work focuses on finding the optimal buffer allocation using Tabu-search (TS) algorithm. The goal is to minimize buffers' sizes while maintaining a certain productivity. The evaluation of the alternative buffer solutions were performed using the following performance indicators; Makespan, Tardiness and the Buffers Cost. In the developed method the following are considered: multitasking machines subjected to non-deterministic failure, non-homogeneous buffer sizing, and non-sequential production line. The propose approach was tested via a real life industrial use case from a leading Swiss company in high precision sensors. The simulation results showed that the proposed methodology can effectively design the buffer strategy for complex production lines.

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Section: State Of the Artmentioning

confidence: 99%

A Computational Method for Identifying the Optimum Buffer Size in the Era of Zero Defect Manufacturing

Psarommatis

Boujemaoui

Kiritsis

2020

IFIP Advances in Information and Communication Technology

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“…However, they regarded the routing problem as a mixed-integer linear program. Qudeiri et al 9 proposed an artificial neural network approach to predict the products' routes' group and then combined with genetic algorithms (GAs) to optimize proper routes for all product types in flexible manufacturing systems. Gultekin et al 10 developed a mathematical model for the scheduling problem of flexible robotic cell considering fixed process time and sequence for the CNC machines.…”

Section: Related Workmentioning

confidence: 99%

“…where HMCR is a constant between 0 and 1, and P 1 is a random number generated from the linear space spanning ½0, 1. Equation (9) indicates that x 0 i should be selected from the ith column of HM, that is, fx 1, i , x 2, i , . .…”

Section: Brief Introduction Of Hs Algorithmmentioning

confidence: 99%

A new AGV scheduling algorithm based on harmony search for material transfer in a real-world manufacturing system

Zeng

Liao

et al. 2018

Advances in Mechanical Engineering

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With the development of advanced manufacturing technology, more and more enterprises utilize automated guided vehicles to transfer materials for computer numerical control machines in the workshop. Because automated guided vehicle scheduling problem is non-deterministic polynomial-hard, highly efficient automated guided vehicle scheduling algorithms are necessary to improve productivity of the workshop. In order to solve this problem, a mathematical model with a new objective function is established firstly, which incorporates two indicators, that is, the standard deviation of the waiting time of computer numerical control material buffers and the total travel distance of automated guided vehicle, reflecting the overall capacity of computer numerical control machines and the energy efficiency of automated guided vehicles during the actual production, respectively. Then, an improved harmony search algorithm is proposed, which includes an effective discrete encoding scheme of harmony, a new initialization method for harmony memory based on opposition-based learning strategy, a dynamic harmony memory considering rate parameter and a local search strategy. The property of the proposed algorithm is evaluated through three cases from the real-world manufacturing system for producing back cover of smart phone. Compared with the competing algorithms, the results demonstrate the superiority of proposed algorithm for solving automated guided vehicle scheduling problem obviously.

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“…With the rapid development of information technology and computing science and the integration of advanced analytics into cyber manufacturing today, many production activities are facing new opportunities and challenges. [1][2][3] Proper maintenance strategies are drawing increasing attention and facing new challenges as important methods in improving the reliability and availability of manufacturing systems in order to ensure the timely delivery of high-quality products to customers. 4,5 In many cases, maintenance costs can reach 15%-70% of the total production cost or even exceed the annual net profit.…”

Section: Introductionmentioning

confidence: 99%

“…An increasing number of scholars have realized that manufacturing systems are multi-state 3 and proposed a new method called predictive maintenance strategy, also referred to as condition-based maintenance strategy. Zhou et al 17 considered the system as subject to degradation that required continuous and meticulous monitoring, and they proposed a reliability-oriented predictive maintenance method based on the continuous monitoring system.…”

Section: Introductionmentioning

confidence: 99%

Cost-oriented predictive maintenance based on mission reliability state for cyber manufacturing systems

Han

et al. 2018

Advances in Mechanical Engineering

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With the advent of Industry 4.0, maintenance strategy faces new demands to avoid the hysteresis of the conventional passive maintenance mode and the non-feasibility of the periodic preventive maintenance model. In view of the inherent polymorphism of manufacturing systems and with the objective of maximizing benefits, a novel cost-oriented predictive maintenance based on mission reliability state for manufacturing systems is proposed. First, the cyber-physical system is adopted to organize and analyze big data in the operational process of manufacturing systems in terms of predictive analytics in cyber manufacturing environment. Second, a new connotation of mission reliability is defined based on the big operational data to comprehensively characterize the dynamic state of the equipment health states and the qualified degree of the production task. Third, the predictive maintenance mode based on mission reliability state is quantified by the comprehensive cost, and the relationship between mission reliability and cost is established. Thereafter, costoriented dynamic predictive maintenance strategy is proposed. Finally, a case study on the maintenance decision-making problem of a cylinder head manufacturing system is presented. The final result shows that the comprehensive cost can be further reduced by the proposed method relative to the traditional periodic preventive maintenance strategy.

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On multistage approach for flexible routing in flexible manufacturing systems

Cited by 3 publications

References 33 publications

A Computational Method for Identifying the Optimum Buffer Size in the Era of Zero Defect Manufacturing

A Computational Method for Identifying the Optimum Buffer Size in the Era of Zero Defect Manufacturing

A new AGV scheduling algorithm based on harmony search for material transfer in a real-world manufacturing system

Cost-oriented predictive maintenance based on mission reliability state for cyber manufacturing systems

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