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Manuel Mateo

dblp:13/3993 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2000
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Systems, architecture and hardware · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Mathematical optimization · 100%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Mathematical optimization › scheduling › production scheduling
assembly line balancing		 0.012000
Local Search Heuristics for the Assembly Line Balancing Problem with Incompatibilities Between Tasks · ICRA 2000
Mathematical optimization
combinatorial optimization		 0.012000
Local Search Heuristics for the Assembly Line Balancing Problem with Incompatibilities Between Tasks · ICRA 2000
Mathematical optimization › evolutionary computation
genetic algorithm		 0.012000
Local Search Heuristics for the Assembly Line Balancing Problem with Incompatibilities Between Tasks · ICRA 2000
Mathematical optimization
metaheuristic optimization		 0.012000
Local Search Heuristics for the Assembly Line Balancing Problem with Incompatibilities Between Tasks · ICRA 2000
Mathematical optimization › combinatorial optimization
local search		 0.012000
Local Search Heuristics for the Assembly Line Balancing Problem with Incompatibilities Between Tasks · ICRA 2000

Methods — techniques the papers use, named apart from their topics

priority rule heuristics · 0.0greedy randomized adaptive search · 0.0genetic algorithm · 0.0
YearPublicationVenuePosition
2000 Local Search Heuristics for the Assembly Line Balancing Problem with Incompatibilities Between Tasks
abstract
This paper deals with the assembly line balancing problem considering incompatibilities between the tasks with the aim of: first minimizing the number of workstations, and then minimizing the cycle time for the minimum number of workstations. In order to solve the problem we propose the use of a greedy randomized adaptive search procedure obtained from the application of some classic heuristics based on priority rules, and a genetic algorithm that searches for the solution in the heuristic space. A computational experience is included to illustrate the performance of the proposed approach.
Joaquín Bautista, Raúl Suárez, Manuel Mateo, Ramón Companys
 ICRA3