We develop new algorithmic approaches to compute provably near-optimal policies for multiperiod stochastic lot-sizing inventory models with positive lead times, general demand distributions, and ...

The travelling salesman problem (TSP) remains one of the most challenging NP‐hard problems in combinatorial optimisation, with significant implications for logistics, network design and route planning ...

Abstract: The problem of finding a path that satisfies two additive constraints, such as delay and cost, has been proved to be NP-complete. Many heuristic and approximation algorithms have been ...

Metric spaces form the backbone of modern computational geometry by providing a rigorous framework for measuring distances within abstract sets, while geometric approximation algorithms yield ...

The ATA algorithm provides a novel approximation framework for analytic functions that cannot be expressed in closed-form via elementary or algebraic functions. It introduces a hybrid approximation ...

Abstract: Identifying positive influence dominating set (PIDS) with the smallest cardinality can produce positive effect with the minimal cost on a social network. The purpose of this article is to ...

The Tactical Fixed Interval Scheduling Problem (TFISP) is the problem of determining the minimum number of parallel nonidentical machines, such that a feasible schedule exists for a given set of jobs.

A python file containing an implemented approximation algorithm A python file containing an implemented simulated annealing implementation A python file used to compare the findings of the two ...

Stochastic approximation algorithms are used to approximate solutions to fixed point equations that involve expectations of functions with respect to possibly unknown distributions. Among many ...

Mark Jerrum, Alistair Sinclair (UC Berkeley) and Eric Vigoda (Georgia Tech) received the Association for Computing Machinery (ACM) Test of Time Award at a virtual ceremony on Wednesday 23 June at the ...