Iterative beam search algorithms for the permutation flowshop

09/12/2020
by   Luc Libralesso, et al.
0

We study an iterative beam search algorithm for the permutation flowshop (makespan and flowtime minimization). This algorithm combines branching strategies inspired by recent branch-and-bounds and a guidance strategy inspired by the LR heuristic. It obtains competitive results, reports many new-best-so-far solutions on the VFR benchmark (makespan minimization) and the Taillard benchmark (flowtime minimization) without using any NEH-based branching or iterative-greedy strategy. The source code is available at: https://gitlab.com/librallu/cats-pfsp.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/02/2018

Importance of a Search Strategy in Neural Dialogue Modelling

Search strategies for generating a response from a neural dialogue model...
research
11/25/2019

Tree search algorithms for the Sequential Ordering Problem

We present a study of several generic tree search techniques applied to ...
research
04/23/2020

gBeam-ACO: a greedy and faster variant of Beam-ACO

Beam-ACO, a modification of the traditional Ant Colony Optimization (ACO...
research
06/22/2022

The Bounded Beam Search algorithm for the Block Relocation Problem

In this paper we deal with the restricted Block Relocation Problem. We p...
research
09/03/1999

Iterative Deepening Branch and Bound

In tree search problem the best-first search algorithm needs too much of...
research
06/14/2021

Determinantal Beam Search

Beam search is a go-to strategy for decoding neural sequence models. The...

Please sign up or login with your details

Forgot password? Click here to reset