Heuristic and metaheuristic methods for the multi‐skill project scheduling problem with partial preemption

The multi‐skill project scheduling problem (MSPSP) has been first addressed in the scheduling community for more than 15 years. This article deals with a new variant of this problem, the MSPSP with partial preemption, where only a subset of resources can be released during the preemption periods. Li...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:International transactions in operational research Ročník 30; číslo 2; s. 858 - 891
Hlavní autoři: Polo‐Mejía, Oliver, Artigues, Christian, Lopez, Pierre, Mönch, Lars, Basini, Virginie
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford Blackwell Publishing Ltd 01.03.2023
Wiley
Témata:
Adaptive search techniques
Algorithms
Computer Science
GRASP
Greedy algorithms
Heuristic
Heuristic methods
Industrial applications
LNS
local search
multi‐skill scheduling
Operations Research
partial preemption
Preemption
Project management
Resource allocation
Scheduling
Search algorithms
LNS
multi-skill scheduling
GRASP
local search
partial preemption
ISSN:0969-6016, 1475-3995
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:The multi‐skill project scheduling problem (MSPSP) has been first addressed in the scheduling community for more than 15 years. This article deals with a new variant of this problem, the MSPSP with partial preemption, where only a subset of resources can be released during the preemption periods. Like the standard problem, this variant is NP‐hard, because of which we propose in this article a series of heuristic algorithms to solve instances arising from an industrial application. First, we present a serial greedy algorithm, based on priority rules and a flow problem for resource allocation. To improve the solutions of the greedy algorithm, we then introduce a binary tree based search algorithm and a greedy randomized adaptive search procedure (GRASP). Finally, we propose a large neighborhood search (LNS) algorithm integrating exact and heuristic methods. The best results in terms of solution quality and execution time are obtained by combining the GRASP algorithm and LNS approach. Furthermore, the proposed GRASP algorithm is able to find new best results on 56 of 216 instances on a standard MSPSP instance set, which shows the quality of the approach even on special cases of the considered problem.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0969-6016
1475-3995
DOI:10.1111/itor.13063