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Daniele Manerba

Associate Professor

University of Brescia

Italy

Research Interests

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Professors in Italy

About

Daniele Manerba is an Associate Professor at Università degli Studi di Brescia in Italy. His research focuses on logistics optimization, machine learning applications in Industry 4.0, and scheduling problems. Recent articles include studies on home delivery optimization, facility location models, and solutions for complex routing issues.

Articles (13)

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2026

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2026

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2026

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2026

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2025

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2025

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2024

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2023

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2023

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2021

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2021

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2020

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2020

Collaborators (5)

Roberto TADEI

Full Professor of Operations Research

Politecnico di Torino

ITALY

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Ivana Ljubic

Professor

WU Vienna University of Economics and Business

AUSTRIA

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Renata Mansini

Docenti di ruolo di Ia fascia

University of Brescia

ITALY

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Roberto Zanotti

University of Brescia

ITALY

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Edoardo Fadda

Personale esterno ed autonomi

Politecnico di Torino

ITALY

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Daniele Manerba

Research Interests

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Articles (13)

An exact scenario-independent deterministic equivalent form of stochastic programs embedding Multivariate Extreme Value discrete choice problems

A Tailored Branch-and-Benders-Cut Approach for Backup Covering Problems

Code and Data for A tailored Branch-and-Benders-Cut approach for Backup Covering Problems

Incorporating stochastic optional pickup demand in routing operations with divisible services for hub-and-spoke e-commerce returns management systems

Optimizing Attended Home Delivery: Multiple recovery options and customer availability profiles to face synchronization failures

The price of customer presence in Attended Home Delivery with Customer Availability Profiles

How to locate services optimizing redundancy: A comparative analysis of K-Covering Facility Location models

The synchronized multi-commodity multi-service Transshipment-Hub Location Problem with cyclic schedules

Hybridizing adaptive large neighborhood search with kernel search: a new solution approach for the nurse routing problem with incompatible services and minimum demand

Stochastic single machine scheduling problem as a multi-stage dynamic random decision process

Smart Steaming: A New Flexible Paradigm for Synchromodal Logistics

Machine learning and optimization for production rescheduling in Industry 4.0

The Stochastic Multipath Traveling Salesman Problem with Dependent Random Travel Costs

Collaborators (5)

Roberto TADEI

Ivana Ljubic

Renata Mansini

Roberto Zanotti

Edoardo Fadda

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