Results 101 to 110 of about 12,030 (235)

AI-driven hybrid metaheuristic neural network approaches for medical diagnostics

Systems and Soft Computing
In recent years, metaheuristic algorithms have gained prominence as powerful computational techniques for solving complex optimization problems across various domains, including healthcare.
Abdullah Khan, Maria Ali, Dzati Athiar Ramli   +2 more
doaj   +1 more source

A unified formulation for home healthcare routing and scheduling problems

International Transactions in Operational Research, EarlyView.
Abstract Home Healthcare is an essential component of healthcare systems, where caregivers visit patients' homes to deliver services. While presenting advantages with respect to institutional care, such as being cost‐effective and alleviating family burdens, it presents challenges in scheduling and routing caregivers efficiently.
Sara Ceschia, Francesca Da Ros, Luca Di Gaspero, Simona Mancini, Vittorio Maniezzo, Roberto Montemanni, Roberto Maria Rosati, Andrea Schaerf   +7 more
wiley   +1 more source

Freight distribution optimization by implementing the team orienteering problem with occasional drivers

International Transactions in Operational Research, EarlyView.
Abstract This research evaluates the effectiveness of a hybrid crowdshipping system that integrates occasional drivers with traditional delivery services (OD+TD) to improve the efficiency of freight distribution. By implementing the team orienteering problem with occasional drivers model, we develop a mathematical model and an agile optimization ...
Alisson Garcia‐Herrera, Xabier A. Martin, Adrian Serrano‐Hernandez, Angel A. Juan, Javier Faulin   +4 more
wiley   +1 more source

Exact and metaheuristic approaches to minimizing makespan in parallel machine scheduling with conflicting jobs

International Transactions in Operational Research, EarlyView.
Abstract We address the scheduling conflicting jobs on parallel identical machines problem with makespan minimization, a classical and computationally challenging variant of parallel machine scheduling. We develop and evaluate three distinct solution methodologies: a novel constraint programming (CP) formulation, and two metaheuristics: a multi ...
Roberto Maria Rosati, Dinh Quy Ta, Minh Hoàng Hà, Andrea Schaerf   +3 more
wiley   +1 more source

Straddle carrier routing optimization at container terminals utilizing telemetry‐based prediction

International Transactions in Operational Research, EarlyView.
Abstract Efficient vehicle routing and scheduling for horizontal transport means on container terminals can reduce lead times and travel distances, resulting in fuel savings and productivity gains. We apply machine learning to emulate operational processes, bridging the gap between theoretical optimization models and real‐world practices at container ...
Julian Neugebauer, Ahmet Cürebal, Leonard Heilig, Stefan Voß   +3 more
wiley   +1 more source

Park‐and‐multi‐loop with autonomous delivery robots in last‐mile logistics

International Transactions in Operational Research, EarlyView.
Abstract This paper deals with the park‐and‐multi‐loop routing problem, in the context of last‐mile logistics, in which a fleet of traditional vehicles equipped with several autonomous delivery robots leaves from a depot to service a set of customer requests.
Tommaso Adamo, Gianpaolo Ghiani, Emanuela Guerriero, Emanuele Manni, Gianluca Solazzo   +4 more
wiley   +1 more source

Optimization of structural assembly scheduling in the aeronautics industry

International Transactions in Operational Research, EarlyView.
Abstract In the aeronautical industry, structural assembly refers to the stage in which parts, subassemblies, and main aircraft structures are fitted together, forming aerostructures. Aircraft structural assembly requires many complex activities to be performed over an intricate precedence network and is subject to interactions among the various ...
Bruno Jensen Virginio da Silva, Mateus Martin, Reinaldo Morabito, Vitoria Pureza   +3 more
wiley   +1 more source

Deep reinforcement learning for multi‐restart metaheuristics: an environment design for a hybrid of unbiased exploratory search and covariance matrix adaptation evolution strategy

International Transactions in Operational Research, EarlyView.
Abstract Multi‐restart metaheuristics can be highly effective for complex optimization problems, yet their performance depends critically on how restarts and algorithmic parameters are selected. This paper introduces a reinforcement learning approach for managing restart‐level decisions and parameter configurations in the UES–CMA‐ES hybrid ...
Antonio Bolufé‐Röhler, Bowen Xu
wiley   +1 more source

An enhanced adaptive large neighborhood search algorithm for the multiple allocation p‐hub location problem with a mesh backbone network in video‐on‐demand services

International Transactions in Operational Research, EarlyView.
Abstract This study tackles the multiple allocation p$p$‐hub location problem (MApHLP) in the context of video‐on‐demand services, where digital content is partitioned into segments and stored exclusively at selected hub locations. Users, distributed across a wide geographical area, can connect to multiple hubs based on demand patterns, with all hubs ...
Soumen Atta
wiley   +1 more source

A genetic algorithm simheuristic for solving the stochastic project portfolio selection problem with portfolio reliability constraints

International Transactions in Operational Research, Volume 33, Issue 6, Page 4039-4071, November 2026.
Abstract In response to the increasing complexity of modern products, dynamic markets, and intensified competition, project‐based organizations are actively seeking methodologies to efficiently manage their expanding project portfolios. This paper analyzes the project portfolio selection problem in uncertain environments. Despite recent advances in the
Miguel Saiz, David Lopez‐Lopez, Laura Calvet, Angel A. Juan   +3 more
wiley   +1 more source