Tootfinder

Optimizing Fairness in Production Planning: A Human-Centric Approach to Machine and Workforce Allocation
Alexander Nasuta, Alessandro Cisi, Sylwia Olbrych, Gustavo Vieira, Rui Fernandes, Lucas Paletta, Marlene Mayr, Rishyank Chevuri, Robert Woitsch, Hans Aoyang Zhou, Anas Abdelrazeq, Robert H. Schmitt
https://arxiv.org/abs/2510.01094

Optimizing Fairness in Production Planning: A Human-Centric Approach to Machine and Workforce Allocation
This work presents a two-layer, human-centric production planning framework designed to optimize both operational efficiency and workforce fairness in industrial manufacturing. The first layer formulates the Order-Line allocation as a Constraint Programming (CP) problem, generating high-utilization production schedules that respect machine capacities, processing times, and due dates. The second layer models Worker-Line allocation as a Markov Decision Process (MDP), integrating human factors suc…

Efficient Approximation Algorithms for Fair Influence Maximization under Maximin Constraint
Xiaobin Rui, Zhixiao Wang, Chen Peng, Qiangpeng Fang, Wei Chen
https://arxiv.org/abs/2509.26579

Efficient Approximation Algorithms for Fair Influence Maximization under Maximin Constraint
Aiming to reduce disparities of influence across different groups, Fair Influence Maximization (FIM) has recently garnered widespread attention. The maximin constraint, a common notion of fairness adopted in the FIM problem, imposes a direct and intuitive requirement that asks the utility (influenced ratio within a group) of the worst-off group should be maximized. Although the objective of FIM under maximin constraint is conceptually straightforward, the development of efficient algorithms wit…

Optimizing Fairness in Production Planning: A Human-Centric Approach to Machine and Workforce Allocation
Alexander Nasuta, Alessandro Cisi, Sylwia Olbrych, Gustavo Vieira, Rui Fernandes, Lucas Paletta, Marlene Mayr, Rishyank Chevuri, Robert Woitsch, Hans Aoyang Zhou, Anas Abdelrazeq, Robert H. Schmitt
https://arxiv.org/abs/2510.01094

Optimizing Fairness in Production Planning: A Human-Centric Approach to Machine and Workforce Allocation
This work presents a two-layer, human-centric production planning framework designed to optimize both operational efficiency and workforce fairness in industrial manufacturing. The first layer formulates the Order-Line allocation as a Constraint Programming (CP) problem, generating high-utilization production schedules that respect machine capacities, processing times, and due dates. The second layer models Worker-Line allocation as a Markov Decision Process (MDP), integrating human factors suc…

Information-Theoretic Fairness with A Bounded Statistical Parity Constraint
Amirreza Zamani, Abolfazl Changizi, Ragnar Thobaben, Mikael Skoglund
https://arxiv.org/abs/2508.12847

Information-Theoretic Fairness with A Bounded Statistical Parity Constraint
In this paper, we study an information-theoretic problem of designing a fair representation that attains bounded statistical (demographic) parity. More specifically, an agent uses some useful data $X$ to solve a task $T$. Since both $X$ and $T$ are correlated with some sensitive attribute or secret $S$, the agent designs a representation $Y$ that satisfies a bounded statistical parity and/or privacy leakage constraint, that is, such that $I(Y;S) \leq ε$. Here, we relax the perfect demographic …

Group Fair Matchings using Convex Cost Functions
Atasi Panda, Harsh Sharma, Anand Louis, Prajakta Nimbhorkar
https://arxiv.org/abs/2508.12549 https://arxiv…

Group Fair Matchings using Convex Cost Functions
We consider the problem of assigning items to platforms where each item has a utility associated with each of the platforms to which it can be assigned. Each platform has a soft constraint over the total number of items it serves, modeled via a convex cost function. Additionally, items are partitioned into groups, and each platform also incurs group-specific convex cost over the number of items from each group that can be assigned to the platform. These costs promote group fairness by penalizin…