Results 51 to 60 of about 1,352 (208)
Efficient Greedy Algorithm for Multi-Objective Sensor Placement Optimization
Engineered ScienceThe sensor placement problem is a complex combinatorial optimization challenge, where greedy selection provides performance guarantees for submodular objective functions, yielding suboptimal yet efficient solutions. Multi-objective optimization addresses
Wangchun Zhang +4 more
doaj +1 more source
Resource redeployment as an entry advantage in resource‐poor settings
Strategic Management Journal, EarlyView.Abstract Research Summary Scarcity of productive factors poses a challenge for firms entering underdeveloped regions. We theorize that incumbent firms can overcome scarcity of skilled human capital in local labor markets by redeploying workers from existing units.
Jasmina Chauvin +2 more
wiley +1 more source
Submodular partition functions
Discrete Mathematics, 2009 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Amini, Omid +3 more
openaire +3 more sources
Effective submodularity of influence maximization on temporal networks
, 2022 We study influence maximization on temporal networks. This is a special setting where the influence function is not submodular, and there is no optimality guarantee for solutions achieved via greedy optimization. We perform an exhaustive analysis on both
Erkol, Sirag +2 more
core +1 more source
Assessing second‐price auctions for parcel exchanges in last‐mile logistics
International Transactions in Operational Research, Volume 34, Issue 2, Page 1040-1071, March 2027.Abstract The rapid growth of e‐commerce has led to multiple carriers operating in the same regions, creating opportunities for collaboration. However, logistics companies typically operate independently, leading to inefficiencies. Horizontal cooperation, where carriers share resources and infrastructure, can improve efficiency and reduce costs.
Christian Truden, Margaretha Gansterer
wiley +1 more source
Distributed Submodular Maximization
CoRR, 2014 Many large-scale machine learning problems--clustering, non-parametric learning, kernel machines, etc.--require selecting a small yet representative subset from a large dataset. Such problems can often be reduced to maximizing a submodular set function subject to various constraints.
Mirzasoleiman, Baharan +3 more
openaire +6 more sources
Submodular Reinforcement Learning
CoRR, 2023 Spotlight paper at ICLR ...
Prajapat, Manish; id_orcid0000-0002-3867-4575 +3 more
openaire +4 more sources
Optimizing Household Waste Recycling Centre Network Reorganization in Hampshire
Networks, Volume 88, Issue 3, Page 313-330, October 2026.ABSTRACT Local councils across the UK are facing sustained financial pressures, and Household Waste Recycling Centres (HWRCs) are being increasingly considered for closure to reduce expenditure. In 2024, Hampshire County Council, which operates the largest HWRC network in the UK, proposed closing either five or twelve existing sites.
Montree Jaidee +4 more
wiley +1 more source
CPT: Pharmacometrics &Systems Pharmacology, Volume 15, Issue 10, October 2026.ABSTRACT Tuberculosis (TB) treatment requires long‐term multidrug regimens where dosing, efficacy, and safety optimization remain challenging. This tutorial establishes an integrated, open‐source, modular framework connecting high‐throughput PBPK (HT‐PBPK) simulations, mechanistic PBPK models, pharmacodynamic endpoints, and safety predictions for anti ...
Marco Siccardi +9 more
wiley +1 more source
Scaling Submodular Maximization via Pruned Submodularity Graphs
CoRR, 2016 We propose a new random pruning method (called "submodular sparsification (SS)") to reduce the cost of submodular maximization. The pruning is applied via a "submodularity graph" over the $n$ ground elements, where each directed edge is associated with a pairwise dependency defined by the submodular function.
Tianyi Zhou 0001 +4 more
openaire +3 more sources