Optimization of schedule stability and efficiency under processing time variability and random machine breakdowns in a job shop environment

Goren, Selcuk; Sabuncuoğlu, İhsan; Koc, Utku

doi:10.1002/nav.20488

Cited by 25 publications

(6 citation statements)

References 34 publications

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“…Proactive, reactive, and predictive-reactive scheduling approaches are the three main methodologies used to overcome stochastic disruptions. The proactive approach predicts the occurrence of unexpected events, where the initial schedules are generated in anticipation of unexpected events [12]. In the reactive approach, no schedule is generated in advance, but the schedule is modified providing the necessary reactions by priority dispatching rules in real time [13].…”

Section: B Dynamic Scheduling Methodologiesmentioning

confidence: 99%

The Internet of Things Enabled Shop Floor Scheduling and Process Control Method Based on Petri Nets

Tian

Zhang

2019

IEEE Access

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Shop floor scheduling requires consideration of the dynamic, time-varying, and unpredictable natures of the manufacturing environment. A shop floor scheduling/rescheduling method based on Petri net and ant colony optimization (PN-ACO) is proposed given an abnormal event represented by machine breakdown. Because of the difficulty to schedule in the complex and changeable internal and external environment of the shop floor, an Internet-of-Things (IoT)-enabled process control method is proposed, using sensors, RFID, industrial wireless communication, automatic identification, and other technologies to perceive the shop floor field. The value of the mean relative error is 1.77, which illustrates the feasibility and efficiency of the proposed PN-ACO algorithm to solve flexible job shop scheduling problems. To represent operation sequencing information, a schedule timed transition Petri net (TTPN) is proposed which is evolved from the TTPN. On the basis of the mapping mechanism between the Petri net model and the XML, manufacturing resources become autonomous and interactive distributed intelligent manufacturing resources. The experimental results confirm that the proposed method is effective for scheduling and process control of the IoT-enabled shop floor. INDEX TERMS Internet of Things enabled shop floor, dynamic scheduling, ant colony optimization, Petri nets, XML, RFID.

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Section: B Dynamic Scheduling Methodologiesmentioning

confidence: 99%

The Internet of Things Enabled Shop Floor Scheduling and Process Control Method Based on Petri Nets

Tian

Zhang

2019

IEEE Access

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“…Goren et al [21] developed a surrogate stability measure to generate efficient and stable schedules for a job shop subject to processing time variability and random machine breakdowns. The variance of the realized completion time is estimated as the sum of the variances of the arc lengths that lie on the mean-critical path.…”

Section: Definition 2 Non-critical Path (Ncp)mentioning

confidence: 99%

“…However, the SMs, which consider only the total slacks [6], potential critical operations [20] or the variance on the critical path [21], are insufficient to estimate the robustness accurately. To overcome the weakness of SMs, we propose a quantitative evaluation method based on the probability distribution information of stochastic processing times and the slack times in the schedule.…”

Section: Definition 2 Non-critical Path (Ncp)mentioning

confidence: 99%

“…Therefore, to absorb as much disturbance as possible, the disturbances allocated to the critical path should be less. However, inspired by the surrogate measure proposed by Goren et al [21], when it comes to design the SMs for SJSSP, it is not reasonable to simply consider the disturbance on the critical path. If non-critical operations are allocated with so much disturbance that cannot be totally absorbed by the slacks, the non-critical operation would possibly become a critical operation and affect the predictive makespan of the schedule.…”

Section: Disturbance Absorption Capabilitymentioning

confidence: 99%

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Surrogate Measures for the Robust Scheduling of Stochastic Job Shop Scheduling Problems

2017

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This study focuses on surrogate measures (SMs) of robustness for the stochastic job shop scheduling problems (SJSSP) with uncertain processing times. The objective is to provide the robust predictive schedule to the decision makers. The mathematical model of SJSSP is formulated by considering the railway execution strategy, which defined that the starting time of each operation cannot be earlier than its predictive starting time. Robustness is defined as the expected relative deviation between the realized makespan and the predictive makespan. In view of the time-consuming characteristic of simulation-based robustness measure (RMsim), this paper puts forward new SMs and investigates their performance through simulations. By utilizing the structure of schedule and the available information of stochastic processing times, two SMs on the basis of minimizing the robustness degradation on the critical path and the non-critical path are suggested. For this purpose, a hybrid estimation of distribution algorithm (HEDA) is adopted to conduct the simulations. To analyze the performance of the presented SMs, two computational experiments are carried out. Specifically, the correlation analysis is firstly conducted by comparing the coefficient of determination between the presented SMs and the corresponding simulation-based robustness values with those of the existing SMs. Secondly, the effectiveness and the performance of the presented SMs are further validated by comparing with the simulation-based robustness measure under different uncertainty levels. The experimental results demonstrate that the presented SMs are not only effective for assessing the robustness of SJSSP no matter the uncertainty levels, but also require a tremendously lower computational burden than the simulation-based robustness measure.

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“…An anytime/anywhere comparison of the as-planned and as-built progress in BIM might keep the construction executors and supervisors (and even other participants with non-engineering backgrounds) very well informed of a variety of latent issues, such as constructability reviews, safety checks and proactive schedule optimizations, etc. [43][44][45][46].…”

Section: Design Review and Construction Simulation Using The 3d And mentioning

confidence: 99%

A Project-Based Quantification of BIM Benefits

Hou

Wang

et al. 2014

International Journal of Advanced Robotic Systems

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In the construction industry, research is being carried out to look for feasible methods and technologies to cut down project costs and waste. Building Information Modelling (BIM) is certainly currently a promising technology/method that can achieve this. The output of the construction industry has a considerable scale; however, the concentration of the industry and the level of informatization are still not high. There is still a large gap in terms of productivity between the construction industry and other industries. Due to the lack of first-hand data regarding how much of an effect can be genuinely had by BIM in real cases, it is unrealistic for construction stakeholders to take the risk of widely adopting BIM. This paper focuses on the methodological quantification (through a case study approach) of BIM's benefits in building construction resource management and real-time costs control, in contrast to traditional non-BIM technologies. Through the use of BIM technology for the dynamic querying and statistical analysis of construction schedules, engineering, resources and costs, the three implementations considered demonstrate how BIM can facilitate the comprehensive grasp of a project's implementation and progress, identify and solve the contradictions and conflicts between construction resources and costs controls, reduce project over-spends and protect the supply of resources.

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Optimization of schedule stability and efficiency under processing time variability and random machine breakdowns in a job shop environment

Cited by 25 publications

References 34 publications

The Internet of Things Enabled Shop Floor Scheduling and Process Control Method Based on Petri Nets

The Internet of Things Enabled Shop Floor Scheduling and Process Control Method Based on Petri Nets

Surrogate Measures for the Robust Scheduling of Stochastic Job Shop Scheduling Problems

A Project-Based Quantification of BIM Benefits

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