Zobrazeno 1 - 10
of 82
pro vyhledávání: '"Letsios, Dimitrios"'
Time-dependent scheduling with linear deterioration involves determining when to execute jobs whose processing times degrade as their beginning is delayed. Each job i is associated with a release time r_i and a processing time function p_i(s_i)=alpha
Externí odkaz:
http://arxiv.org/abs/2307.00627
Motivated by distribution problems arising in the supply chain of Haleon, we investigate a discrete optimization problem that we call the "container delivery scheduling problem". The problem models a supplier dispatching ordered products with shippin
Externí odkaz:
http://arxiv.org/abs/2306.17789
Publikováno v:
In Theoretical Computer Science 27 July 2024 1006
Motivated by mail delivery scheduling problems arising in Royal Mail, we study a generalization of the fundamental makespan scheduling P||Cmax problem which we call the bounded job start scheduling problem. Given a set of jobs, each specified by an i
Externí odkaz:
http://arxiv.org/abs/1912.06862
Autor:
Letsios, Dimitrios, Baltean-Lugojan, Radu, Ceccon, Francesco, Mistry, Miten, Wiebe, Johannes, Misener, Ruth
Designing and analyzing algorithms with provable performance guarantees enables efficient optimization problem solving in different application domains, e.g.\ communication networks, transportation, economics, and manufacturing. Despite the significa
Externí odkaz:
http://arxiv.org/abs/1909.12328
Mathematical optimization offers highly-effective tools for finding solutions for problems with well-defined goals, notably scheduling. However, optimization solvers are often unexplainable black boxes whose solutions are inaccessible to users and wh
Externí odkaz:
http://arxiv.org/abs/1811.05437
In industrial resource allocation problems, an initial planning stage may solve a nominal problem instance and a subsequent recovery stage may intervene to repair inefficiencies and infeasibilities due to uncertainty, e.g.\ machine failures and job p
Externí odkaz:
http://arxiv.org/abs/1805.03437
Decision trees usefully represent sparse, high dimensional and noisy data. Having learned a function from this data, we may want to thereafter integrate the function into a larger decision-making problem, e.g., for picking the best chemical process c
Externí odkaz:
http://arxiv.org/abs/1803.00952