Sessions list

Vehicle routing problems (TSP, VRP, DARP, etc.) are a family of emblematic OR problems. In this session, we would like to focus on works dealing with these problems, and proposing approaches based on metaheuristics to solve them.

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The increase in the amount of available data has allowed a significant improvement in the performance of methods. Interest in using tools from data sciences and artificial intelligence has led to the appearance of hybrid processes. This session focus on hybrid metaheuristics for routing problems, involving  combining optimization and artificial learning.

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Session: GRASP with PATH RELINKING

Organizers: Rafael MARTÍ and Anna MARTINEZ-GAVARA

This session is devoted to the Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic and its hybridization with Path Relinking (PR). GRASP with PR has become a widely adopted approach for solving various optimization problems due to its effectiveness and versatility. We consider its different designs, such as: Greedy randomized PR, Evolutionary PR, Dynamic and Static implementations, and interior versus exterior PR. We accept both theoretical and applied submission

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Session: Quantum metaheuristic for Operations Research

Organizers: Riad AGGOUNE and Caroline PRODHON (GT ROQ x EWG QOR)

Quantum optimization is an emerging field that opens a new computing paradigm allowing the resolution of previously inaccessible problems. This session focuses on quantum metaheuristic approaches for Operations Research problems.

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Session: Optimization for forecasting

Organizer: Vittorio MANIEZZO

Optimization and forecasting are obviously related, since optimized results are intended to be used in future settings. This has led to studies on the uncertainty of data, but until recently there has been little work on optimization methods for forecasting. Forecasting algorithms were mainly based on statistical considerations, only the recent growth of AI introduced different effective approaches to forecasting. Mutual interaction between optimization and AI is a hot topic for meta- and matheuristics, this session wants to seed a focused interest on specific issues and results on the contribution of optimization to forecasting methods.

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Session: Metaheuristics for preference learning

Organizers: Patrick MEYER and Alexandru OLTEANU

Preference learning, a subfield of machine learning, focuses on learning from data that represents user preferences. By incorporating metaheuristics into preference learning frameworks, we can effectively address the challenges associated with traditional optimization techniques, such as scalability issues. This session will delve into the exciting intersection of metaheuristics and preference learning, exploring the diverse applications, theoretical underpinnings, and emerging trends in this domain. We will showcase the latest research advancements in utilizing metaheuristics to enhance preference learning algorithms, leading to improved performance, robustness, and applicability in various real-world scenarios.

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Recent events, notably the COVID-19 pandemic, have underscored significant logistical challenges. Consequently, healthcare centers are prompted to consolidate functions to enhance overall performance, particularly in the management of material flows within services. The goal of this session is to assemble the latest contributions and innovative methods for tackling a range of operations research challenges in healthcare, spanning hospital management, logistics, personnel, home health care, health networks, etc. Participants are encouraged to share their most recent and innovative research findings and experiences. The emphasis of the session lies in modeling and the resolution of deterministic and stochastic healthcare problems, employing metaheuristics and efficient algorithms to improve the intricate management of material and human flows in healthcare.

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Session: Hybrid metaheuristics

Organizer: Laurent DEROUSSI and Nicolas MONMARCHE (GT META)

For many years, hybrid methods based on metaheuristics have been widely used. They have demonstrated their effectiveness in addressing complex optimization problems. The aim of this session is to highlight these methods, whether through practical or theoretical contributions.

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The main goal of the International Conference on Variable Neighborhood Search (ICVNS) is to provide a stimulating environment in which researchers from various scientific fields can share and discuss their knowledge, expertise, and ideas related to the VNS Metaheuristic and its applications. The 10th International Conference on VNS is celebrated together with MIC with the aim of allowing specialists and practitioners on Variable Neighborhood Search to effectively screen papers and participate in lively debates.

Topics of interest include, but are not limited to: Theoretical or empirical analysis of VNS, New variants of deterministic and stochastic VNS, Continuous global VNS, Convergent VNS methods, Simulation-based VNS, Formulation space VNS, Multi-objective VNS, Parallel VNS, VNS hybrids and applications of VNS to real or theoretical problems.

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