The process of transition and growth in Central and Eastern Europe (CEE) provides interesting insights, considering the countries' historical relationship with the Soviet Union. The 20 years after the Eastern enlargement of the European Union show an integrative power within the Single Market....

Based on the job demands-resources (JD-R) model, this study aims to investigate how algorithmic behavioral constraint and algorithmic tracking evaluation affect gig workers' turnover intention by increasing relative deprivation and the extent to which this mediating role is moderated by...

The process of transition and growth in Central and Eastern Europe (CEE) provides interesting insights, considering the countries' historical relationship with the Soviet Union. The 20 years after the Eastern enlargement of the European Union show an integrative power within the Single Market....

Algorithms are increasingly used to aid with high-stakes decision making. Yet, their predictive ability frequently exhibits systematic variation across population subgroups. To assess the trade-off between fairness and accuracy using finite data, we propose a debiased machine learning estimator...

The inability of small companies to compete with large production systems has led to the sharing resources in cloud production systems among smaller companies to compensate for their production deficiencies. This study proposes a multi-objective, multi-level cloud production system by minimizing...

The Dwarf Mongoose Optimization (DMO) algorithm, inspired by the behaviors and foraging patterns of dwarf mongooses, is a recently formulated swarm-based metaheuristic method emulating the cooperative behavior of mongooses during food searches. The DMO algorithm effectively addresses various...

This study presents a comprehensive approach to managing pharmaceutical inventory during pandemics. The study focuses on optimizing investment strategies for promoting COVID-19 medicine across various price ranges while carefully preserving pharmaceutical products. We develop a customized...

The evolution of Industry 5.0, along with its predecessor Industry 4.0, has significantly boosted the adoption of predictive maintenance through integrating Internet of Things (IoT) sensors and real-time big data analysis, enabling the identification and prevention of equipment failures. This...

This study investigates an NP-hard parallel machine scheduling problem, a critical challenge in manufacturing, healthcare, and logistics industries where efficient resource allocation is essential. The issue involves scheduling operations where each task requires an additional resource, with...

Metaheuristics are widely used to address complex optimisation problems where traditional exact methods are computationally infeasible or insufficiently flexible. With the rapid advancement of artificial intelligence, large language models, such as ChatGPT, Claude, Gemini, and LLaMA, have...

Gaming the system - i.e., strategic attempts to manipulate the input(s) for, or one's interactions with, an algorithmic system to try to secure a better outcome than intended by the system's design - is commonly portrayed as a threat to online platforms and services. Tech companies often use...

Employers are adopting and refining artificial intelligence (AI) and algorithm-based tools in the workplace, with wide-ranging implications for work and employment. This working paper examines case studies of social dialogue on AI at national, regional, sectoral, company, and workplace levels in...

This study introduces an approach to improving the utilization of solar energy in facilities by integrating advanced machine learning (ML) techniques into solar power scheduling. Traditional methods, often constrained by static schedules, fail to adequately adapt to the inherently dynamic and...

This working paper provides an assessment of the impacts of algorithmic management (AM) tools on working conditions, job quality, and industrial relations in the logistics sector of France. It analyses the different ways algorithmic management technologies affect coordination, distribution and...

Background: Logistics and transport, core of many business processes, are continuously optimized to improve efficiency and market competitiveness. The paper describes a modular coordination of vehicle routing and bin packing problems that enables independent instances of the problems to be...

With the widespread adoption of automated guided vehicle (AGV) systems for material handling in manufacturing plants, it has become practical and crucial to delve into the layout problem associated with AGV systems. In this work, we focus on a unique layout problem encountered in a hybrid...

Efficient supply chain models are crucial for ensuring swift medical intervention and the timely delivery of essential supplies in disaster management. This study focuses on optimizing disaster relief efforts in meteorological disasters, specifically flash floods triggered by cloudburst events....

This study presents a supply chain model for optimizing milk collection routing in multi-depot networks. The problem consists of a fleet of vehicles that leaves their depots (i.e., typically the driver's houses), visits an assigned set of farms to collect the raw milk, and delivers it to the...

As the consumer market changes rapidly, retail networks require a system to optimize the quantity and assortment of goods. The authors develop and test theoretical and practical assortment optimization and distribution principles. The study aims to improve retail assortment management by...

This document describes the design process, definitions, and algorithmic implementation for a pan-European assessment of forest fragmentation. This study is a preceding presentation of the indicator 4.7 in the State of Europe's Forests 2025 report, published by FOREST EUROPE. Forest cover is...

Anomaly detection is an important research area in a diverse range of real-world applications. Although many algorithms have been proposed to address anomaly detection for numerical datasets, categorical and mixed datasets remain a significant challenge, primarily because a natural distance...