On optimal dynamic pegging in rescheduling for new order arrival

Moghaddam, Shokraneh K.

doi:10.1016/j.cie.2019.07.012

Cited by 12 publications

(5 citation statements)

References 33 publications

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“…The problem is treated as a three bi-criteria scheduling problem, using lexicographical and simultaneous optimisation approaches. Moghaddam and Saitou propose a rescheduling model for unplanned order arrival (Moghaddam and Saitou, 2019). The tool developed is based on the concept of dynamic pegging in multi-level production and on a mixed integer programming (MIP) model that links dynamic pegging with rescheduling.…”

Section: New Order-oriented Reschedulingmentioning

confidence: 99%

A Generic Methodology for Calculating Rescheduling Time for Multiple Unexpected Events in the Era of Zero Defect Manufacturing

et al. 2021

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Nowadays, the manufacturing industry is constantly changing. Production systems must operate in a highly dynamic environment where unexpected events could occur and create disruption, making rescheduling inevitable for manufacturing companies. Rescheduling models are fundamental to the robustness of production processes. This paper proposes a model to address rescheduling caused by unexpected events, aiming to achieve the zero-defect manufacturing (ZDM) concept. The goal of the model is to incorporate traditional and ZDM–oriented events into one methodology to calculate when the next rescheduling will be performed to effectively react to unexpected events. The methodology relies on the definition of two key time parameters for each event type: event response time (RT) and event delay response time (DRT). Based on these parameters, an event management algorithm is designed to identify the optimal rescheduling solution. The DRT parameter is calculated based on a multi-parametric dynamic formula to capture the dynamics of production. Moreover, ANOM, and ANOVA methods are used to analyse the behaviour of the developed method and to assess the level of robustness of the proposed approach. Finally, a case study based on real production scenarios is conducted, a series of simulation experiments are performed, and comparisons with other rescheduling policies are presented. The results demonstrate the effectiveness of the proposed event management algorithm for managing rescheduling.

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Section: New Order-oriented Reschedulingmentioning

confidence: 99%

A Generic Methodology for Calculating Rescheduling Time for Multiple Unexpected Events in the Era of Zero Defect Manufacturing

et al. 2021

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“…First, the breakdown problem is considered [12][13][14], and the original plan is reprogrammed [15]. Second, the existing rescheduling model is employed to construct a new rescheduling model [16], or corresponding rescheduling strategies [17][18][19][20][21] and frameworks [22] are proposed according to different breakdown problems. When proposing the rescheduling strategy, some researchers proposed that a method can be used to determine whether rescheduling is required [23].…”

Section: Introductionmentioning

confidence: 99%

Rescheduling Plan Optimization of Underground Mine Haulage Equipment Based on Random Breakdown Simulation

Feng

Lei

et al. 2022

Sustainability

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Due to production space and operating environment requirements, mine production equipment often breaks down, seriously affecting the mine’s production schedule. To ensure the smooth completion of the haulage operation plan under abnormal conditions, a model of the haulage equipment rescheduling plan based on the random simulation of equipment breakdowns is established in this paper. The model aims to accomplish both the maximum completion rate of the original mining plan and the minimum fluctuation of the ore grade during the rescheduling period. This model is optimized by improving the wolf colony algorithm and changing the location update formula of the individuals in the wolf colony. Then, the optimal model solution can be used to optimize the rescheduling of the haulage plan by considering equipment breakdowns. The application of the proposed method in an underground mine revealed that the completion rate of the mine’s daily mining plan reached 83.40% without increasing the amount of equipment, while the ore quality remained stable. Moreover, the improved optimization algorithm converged quickly and was characterized by high robustness.

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“…Thus, dynamic scheduling has more practical implications for a real workshop. Several dynamic characteristics have been considered, such as dynamic Sensors 2021, 21, 1019 2 of 20 job/order arrival [28][29][30][31], stochastic processing time [32][33][34], machine breakdown [35], process interruptions [36,37], etc. Among these dynamic characteristics, new job arrival has been recently receiving arising attention.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Decision-Making of Scheduling for Dynamic Permutation Flowshop via Deep Reinforcement Learning

Yang

Wang

2021

Sensors

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Dynamic scheduling problems have been receiving increasing attention in recent years due to their practical implications. To realize real-time and the intelligent decision-making of dynamic scheduling, we studied dynamic permutation flowshop scheduling problem (PFSP) with new job arrival using deep reinforcement learning (DRL). A system architecture for solving dynamic PFSP using DRL is proposed, and the mathematical model to minimize total tardiness cost is established. Additionally, the intelligent scheduling system based on DRL is modeled, with state features, actions, and reward designed. Moreover, the advantage actor-critic (A2C) algorithm is adapted to train the scheduling agent. The learning curve indicates that the scheduling agent learned to generate better solutions efficiently during training. Extensive experiments are carried out to compare the A2C-based scheduling agent with every single action, other DRL algorithms, and meta-heuristics. The results show the well performance of the A2C-based scheduling agent considering solution quality, CPU times, and generalization. Notably, the trained agent generates a scheduling action only in 2.16 ms on average, which is almost instantaneous and can be used for real-time scheduling. Our work can help to build a self-learning, real-time optimizing, and intelligent decision-making scheduling system.

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On optimal dynamic pegging in rescheduling for new order arrival

Cited by 12 publications

References 33 publications

A Generic Methodology for Calculating Rescheduling Time for Multiple Unexpected Events in the Era of Zero Defect Manufacturing

A Generic Methodology for Calculating Rescheduling Time for Multiple Unexpected Events in the Era of Zero Defect Manufacturing

Rescheduling Plan Optimization of Underground Mine Haulage Equipment Based on Random Breakdown Simulation

Intelligent Decision-Making of Scheduling for Dynamic Permutation Flowshop via Deep Reinforcement Learning

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