An Advanced Tabu Search Algorithm for the Job Shop Problem

Nowicki, Eugeniusz; Smutnicki, Czesław

doi:10.1007/s10951-005-6364-5

Cited by 262 publications

(160 citation statements)

References 20 publications

Supporting

Mentioning

158

Contrasting

Unclassified

Order By: Relevance

“…Value Ordering Our value ordering is based on the solution guided method (SGMPCS) proposed for JSPs (Beck (2007)). This approach uses previous solutions as guidance for the current search, intensifying search around a previous solution in a similar manner to the tabu search algorithm of Nowicki and Smutnicki (2005). In SGMPCS, a set of elite solutions is initially generated.…”

Section: Light Weighted Approach (Lw)mentioning

confidence: 99%

“…• JSP : Metaheuristic approaches have proven to be the most successful on JSPs, in particular the well-known tabu search method i-TSAB of Nowicki and Smutnicki (2005). The current best metaheuristic method is a tabu search with path-relinking recently proposed by Peng et al (2014), which improved the best-known solutions on 49 of 205 well-known instances studied.…”

Section: State-of-the-art Systematic and Metaheuristic Comparison Metmentioning

confidence: 99%

See 1 more Smart Citation

Solving Variants of the Job Shop Scheduling Problem Through Conflict-Directed Search

Grimes

Hébrard

2015

INFORMS Journal on Computing

View full text Add to dashboard Cite

We introduce a simple technique for disjunctive machine scheduling problems and show that this method can match or even outperform state of the art algorithms on a number of problem types. Our approach combines a number of generic search techniques such as restarts, adaptive heuristics and solution guided branching on a simple model based on a decomposition of disjunctive constraints and on the reification of these disjuncts. This paper describes the method and its application to variants of the job shop scheduling problem (JSP ).We show that our method can easily be adapted to handle additional side constraints and different objective functions, often outperforming the state of the art and closing a number of open problems.* Moreover, we perform in-depth analysis of the various factors that makes this approach efficient. We show that, while most of the factors give moderate benefits, the variable and value ordering components are key.

show abstract

Section: Light Weighted Approach (Lw)mentioning

confidence: 99%

Section: State-of-the-art Systematic and Metaheuristic Comparison Metmentioning

confidence: 99%

Solving Variants of the Job Shop Scheduling Problem Through Conflict-Directed Search

Grimes

Hébrard

2015

INFORMS Journal on Computing

View full text Add to dashboard Cite

show abstract

“…One of the most famous algorithms is the tabu search (TS) algorithm TSAB proposed by Nowicki and Smutnicki (1996). Nowicki and Smutnicki (2005) later extend algorithm TSAB to algorithm i-TSAB, which Beck et al (2011) combine with a constraint programming based constructive search procedure to create algorithm CP/LS. Pardalos and Shylo (2006) propose algorithm GES, which is based on global equilibrium search techniques.…”

Section: Introductionmentioning

confidence: 99%

“…As one of the most popular local search algorithms, TS has been widely used by researchers to tackle JSP, e.g., Nowicki and Smutnicki (2005), Zhang et al (2007), Nasiri and Kianfar (2012a), Shen and Buscher (2012), and Gonçalves and Resende (2013), among others.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A tabu search/path relinking algorithm to solve the job shop scheduling problem

Peng

Cheng

2015

Computers & Operations Research

131

View full text Add to dashboard Cite

We present an algorithm that incorporates a tabu search procedure into the framework of path relinking to tackle the job shop scheduling problem (JSP).This tabu search/path relinking (TS/PR) algorithm comprises several distinguishing features, such as a specific relinking procedure and a reference solution determination method. To test the performance of TS/PR, we apply it to tackle almost all of the benchmark JSP instances available in the literature. The test results show that TS/PR obtains competitive results compared with state-of-the-art algorithms for JSP in the literature, demonstrating its efficacy in terms of both solution quality and computational efficiency. In particular, TS/PR is able to improve the upper bounds for 49 out of the 205 tested instances and it solves a challenging instance that has remained unsolved for over 20 years.

show abstract

Tabu Search

Smith

Cochran

Gendreau

et al. 2011

Wiley Encyclopedia of Operations Research and Management Science

View full text Add to dashboard Cite

Ant colony optimization (ACO) [1-3] is a metaheuristic for solving hard combinatorial optimization problems inspired by the indirect communication of real ants. In ACO algorithms, (artificial) ants construct candidate solutions to the problem being tackled, making decisions that are stochastically biased by numerical information based on (artificial) pheromone trails and available heuristic information. The pheromone trails are updated during algorithm execution to bias the ants search toward promising decisions previously found. The article titled Ant Colony Optimization gives a detailed overview of the main concepts of ACO.Despite being one of the youngest metaheuristics, the number of applications of ACO algorithms is very large. In principle, ACO can be applied to any combinatorial optimization problem for which some iterative solution construction mechanism can be conceived. Most applications of ACO deal with NP-hard combinatorial optimization problems, that is, with problems for which no polynomial time algorithms are known. ACO algorithms have also been extended to handle problems with multiple objectives, stochastic data, and dynamically changing problem information. There are extensions of the ACO metaheuristic for dealing with problems with continuous decision variables, as well.This article provides a concise overview of several noteworthy applications of ACO algorithms. This overview is necessarily incomplete because the number of currently available ACO applications goes into the hundreds. Our description of the applications

show abstract

An Advanced Tabu Search Algorithm for the Job Shop Problem

Cited by 262 publications

References 20 publications

Solving Variants of the Job Shop Scheduling Problem Through Conflict-Directed Search

Solving Variants of the Job Shop Scheduling Problem Through Conflict-Directed Search

A tabu search/path relinking algorithm to solve the job shop scheduling problem

Tabu Search

Contact Info

Product

Resources

About