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Robust-stable scheduling in dynamic flow shops based on deep reinforcement learning

Grumbach, Felix; Müller, Anna; Reusch, Pascal; … - In: Journal of Intelligent Manufacturing 35 (2022) 2, pp. 667-686

This proof-of-concept study provides a novel method for robust-stable scheduling in dynamic flow shops based on deep reinforcement learning (DRL) implemented with OpenAI frameworks. In realistic manufacturing environments, dynamic events endanger baseline schedules, which can require a cost...

Persistent link: https://www.econbiz.de/10015166003

Heuristics for predictive hoist scheduling problems

Zhang, Qiao; Manier, Hervé; Manier, Marie-Ange; Bloch, … - In: European Journal of Industrial Engineering 8 (2014) 5, pp. 695-715

In this paper, we consider hoist scheduling problems in a job shop environment. For each job several operations can be operated on tanks, and their processing times are bounded. The objective is to assign resources to both processing and transport operations and then to schedule those tasks on...

Persistent link: https://www.econbiz.de/10011093676

A pure proactive scheduling algorithm for multiple earth observation satellites under uncertainties of clouds

Wang, Jianjiang; Demeulemeester, Erik; Qiu, Dishan - 2014

Persistent link: https://www.econbiz.de/10010424190

A pure proactive scheduling algorithm for multiple earth observation satellites under uncertainties of clouds

Wang, Jianjiang; Demeulemeester, Erik; Qiu, Dishan - In: Computers & operations research : and their … 74 (2016), pp. 1-13

Persistent link: https://www.econbiz.de/10011514128

Addressing healthcare operational deficiencies using stochastic and dynamic programming

Geng, Na; Xie, Xiaolan; Zhang, Zheng - In: International journal of production research 57 (2019) 14, pp. 4371-4390

Persistent link: https://www.econbiz.de/10012193445

Planning horizons based proactive rescheduling for stochastic resource-constrained project scheduling problems

Brčić, Mario; Katić, Marija; Hlupić, Nikica - In: European journal of operational research : EJOR 273 (2019) 1, pp. 58-66

Persistent link: https://www.econbiz.de/10011979411

Research on the scheduling method of ground resource under uncertain arrival time

Xu, Guoning; Lin, Yupeng; Wu, Zhiying; Chen, Qingxin; … - In: Operations research perspectives 11 (2023), pp. 1-11

We present a two-stage scheduling approach including proactive and reactive scheduling to solve the ground resource scheduling problem with uncertain arrival time. In the first stage, an integer programming model is constructed to minimize the delay and transfer costs. After solving this model,...

Persistent link: https://www.econbiz.de/10014448622

Optimizing transport logistics under uncertainty with simheuristics: Concepts, review and trends

Castaneda, Juliana; Ghorbani, Elnaz; Ammouriova, Majsa; … - In: Logistics 6 (2022) 3, pp. 1-15

of simheuristics in the logistics and transportation field. The paper also discusses open research lines in this … knowledge area. Results: The simheuristics approaches to solving NP-hard and large-scale combinatorial optimization problems … several lines of research that are still open in the field of simheuristics. …

Persistent link: https://www.econbiz.de/10015194790

A simheuristic approach for evolving agent behaviour in the exploration for novel combat tactics

Lam, Chiou-Peng; Masek, Martin; Kelly, Luke; … - In: Operations Research Perspectives 6 (2019), pp. 1-13

The automatic generation of behavioural models for intelligent agents in military simulation and experimentation remains a challenge. Genetic Algorithms are a global optimization approach which is suitable for addressing complex problems where locating the global optimum is a difficult task....

Persistent link: https://www.econbiz.de/10012662788

Decision making using simulation methods in sustainable transportation

Rabe, Markus; Goldsman, David Morris - In: Sustainable transportation and smart logistics : …, (pp. 305-333). 2019

Persistent link: https://www.econbiz.de/10013263041