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Fast Filter Pruning Method for Neural Network Compression
- Dongqi Wang
  Dongqi Wang
  [email protected]
  https://orcid.org/0009-0008-0016-9160
  School of Management, Zhejiang University, Hangzhou, Zhejiang 310058, China; and School of Management, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, China; and Zhejiang Key Laboratory of Decision Intelligence, Zhejiang University, Hangzhou, Zhejiang 310058, China
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- Weiwei Chen
  Weiwei Chen
  [email protected]
  https://orcid.org/0000-0002-7736-3411
  Department of Supply Chain Management, Rutgers University, Piscataway, New Jersey 08854
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- Cheng Hua
  Cheng Hua
  [email protected]
  https://orcid.org/0000-0002-1662-2424
  Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200030, China; and Data-Driven Management Decision Making Lab, Shanghai Jiao Tong University, Shanghai 200030, China
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- Weihua Zhou
  Corresponding Author
  Weihua Zhou
  [email protected]
  https://orcid.org/0000-0002-1056-7612
  School of Management, Zhejiang University, Hangzhou, Zhejiang 310058, China; and Zhejiang Key Laboratory of Decision Intelligence, Zhejiang University, Hangzhou, Zhejiang 310058, China
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- Xin Lin
  Xin Lin
  [email protected]
  School of Management, Zhejiang University, Hangzhou, Zhejiang 310058, China
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Published Online:June 24, 2026
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Abstract
The rapid adoption of artificial neural networks has led to the development of increasingly complex and large models. Although these models have achieved outstanding performance across various applications, their deployment and inference speed on devices ...
Solution Path of Time-Varying Markov Random Fields with Discrete Regularization
- Salar Fattahi
  Corresponding Author
  Salar Fattahi
  [email protected]
  https://orcid.org/0000-0001-7986-3148
  Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, Michigan 48109
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- Andrés Gómez
  Andrés Gómez
  [email protected]
  https://orcid.org/0000-0003-3668-0653
  Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, California 90089
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Published Online:June 23, 2026
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Abstract
We study the problem of inferring sparse time-varying Markov random fields (MRFs) with discrete regularization on the parameters. Because of the intractability of discrete regularization, most approaches for solving this problem rely on maximum-likelihood ...
Multiagent Environments for Vehicle Routing Problems
- Ricardo Gama
  Corresponding Author
  Ricardo Gama
  [email protected]
  https://orcid.org/0000-0002-7051-8310
  Escola Superior de Tecnologia e Gestão de Lamego, Instituto Politécnico de Viseu, 5100-074 Lamego, Portugal
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- Ricardo Cunha
  Ricardo Cunha
  [email protected]
  Escola Superior de Tecnologia e Gestão de Lamego, Instituto Politécnico de Viseu, 5100-074 Lamego, Portugal
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- Daniel Fuertes
  Daniel Fuertes
  [email protected]
  https://orcid.org/0000-0002-5746-2199
  Grupo de Tratamiento de Imágenes (GTI), Information Processing and Telecommunications Center, ETSI, Telecomunicación, Universidad Politécnica de Madrid, 28040 Madrid, Spain
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- Carlos R. del-Blanco
  Carlos R. del-Blanco
  [email protected]
  https://orcid.org/0000-0003-0618-3488
  Grupo de Tratamiento de Imágenes (GTI), Information Processing and Telecommunications Center, ETSI, Telecomunicación, Universidad Politécnica de Madrid, 28040 Madrid, Spain
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- Hugo L. Fernandes
  Hugo L. Fernandes
  [email protected]
  Singuli, Brooklyn, New York 11217
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Published Online:June 23, 2026
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Abstract
Research on reinforcement learning (RL) approaches for discrete optimization problems has increased considerably, extending RL to areas classically dominated by operations research (OR). Vehicle routing problems are a good example of discrete optimization ...
Entropic-Based Robust Vehicle Rental Revenue Management with Substitution and Repositioning
- Zhu Meng
  Zhu Meng
  [email protected]
  https://orcid.org/0009-0007-0474-1550
  College of Management and Economics, Tianjin University, Tianjin 300072, China
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- Chenyi Fu
  Corresponding Author
  Chenyi Fu
  [email protected]
  https://orcid.org/0009-0000-5300-5188
  School of Management, Northwestern Polytechnical University, Xi’an 710129, China
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- Ning Zhu
  Corresponding Author
  Ning Zhu
  [email protected]
  https://orcid.org/0000-0001-8560-5946
  International Institute of Finance, School of Management, University of Science and Technology of China, Hefei 230026, China
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Published Online:June 22, 2026
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Abstract
This paper investigates the practical inventory management challenges faced by the vehicle rental industry, which arise from the random arrival of orders and lead times before demand occurs, as well as the uncertainty and dynamic nature of cancellations ...
Contextual Stochastic Vehicle Routing with Time Windows
- Breno Serrano
  Corresponding Author
  Breno Serrano
  [email protected]
  https://orcid.org/0000-0002-8725-0218
  MIT Center for Transportation & Logistics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139; and School of Management, Technical University of Munich, Munich, 80333 Bayern, Germany
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- Alexandre M. Florio
  Alexandre M. Florio
  [email protected]
  https://orcid.org/0000-0002-7593-919X
  Last Mile Planning, Amazon, Bellevue, Washington 98004
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- Stefan Minner
  Stefan Minner
  [email protected]
  https://orcid.org/0000-0001-6127-8223
  School of Management, Technical University of Munich, Munich, 80333 Bayern, Germany; and Munich Data Science Institute, Technical University of Munich, Munich, 80333 Bayern, Germany
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- Maximilian Schiffer
  Maximilian Schiffer
  [email protected]
  https://orcid.org/0000-0003-2682-4975
  School of Management, Technical University of Munich, Munich, 80333 Bayern, Germany; and Munich Data Science Institute, Technical University of Munich, Munich, 80333 Bayern, Germany
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- Thibaut Vidal
  Thibaut Vidal
  [email protected]
  SCALE-AI Chair in Data-Driven Supply Chains, MAGI, Polytechnique Montreal, Montréal, Québec H3T 1J4, Canada
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Published Online:June 22, 2026
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Abstract
We study the vehicle-routing problem with time windows (VRPTW) and stochastic travel times, in which the decision-maker observes related contextual information, represented as feature variables, before making routing decisions. Despite the extensive ...

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