Results 171 to 180 of about 3,935 (225)

Bilevel optimization of mixed-integer nonlinear integrated planning and scheduling problems using the DOMINO framework. [PDF]

ESCAPE
Nikkhah H, Charitopoulos VM, Avraamidou S, Beykal B.   +3 more
europepmc   +1 more source

Private measures, random walks, and synthetic data. [PDF]

Probab Theory Relat Fields
Boedihardjo M, Strohmer T, Vershynin R.
europepmc   +1 more source

Bi-level Data-driven Enterprise-wide Optimization with Mixed-integer Nonlinear Scheduling Problems. [PDF]

Digit Chem Eng
Nikkhah H, Aghayev Z, Shahbazi A, Charitopoulos VM, Avraamidou S, Beykal B.   +5 more
europepmc   +1 more source

Connecting the dots: deep learning-based automated model building methods in cryo-EM. [PDF]

Front Mol Biosci
Bansia H, des Georges A.
europepmc   +1 more source

How to Whack Moles.

Krumke, Sven O., Megow, Nicole, Vredeveld, Tjark   +2 more
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Small and large TSP: Two polynomially solvable cases of the traveling salesman problem

European Journal of Operational Research, 1993
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Jack A A Van Der Veen, Gérard Sierksma
exaly   +3 more sources

Heuristic Methods for Solving the Traveling Salesman Problem (TSP): A Comparative Study

2023 IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 2023
Chenglin Zhang, Peng Sun 0007
exaly   +2 more sources

A Travelling Salesman Problem (TSP) with Multiple Job Facilties

OPSEARCH, 2001
In this paper we have considered a variation of usual travelling salesman problem introducing a more realistic situation.
Das, Shila, Ahmed, Nazimuddin
openaire   +1 more source

Multiagent Optimization System for Solving the Traveling Salesman Problem (TSP)

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2009
The multiagent optimization system (MAOS) is a nature-inspired method, which supports cooperative search by the self-organization of a group of compact agents situated in an environment with certain sharing public knowledge. Moreover, each agent in MAOS is an autonomous entity with personal declarative memory and behavioral components.
Xiao-Feng Xie 0001, Jiming Liu 0001
openaire   +2 more sources