A simple and effective hybrid genetic search for the job sequencing and tool switching problem

Mecler, Jordana; Subramanian, Anand; Vidal, Thibaut

doi:10.1016/j.cor.2020.105153

Cited by 15 publications

(7 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…M P CA − bitwise, KT N S − bitwise, M P CA are implemented in C + +. The implementation of KT N S was taken from the repository published by Mecler et al [7] Both MPCA and KTNS was compilated with g++ version 10.3.0 using −O3 flag. 10 5 random job sequences were generated for each Catanzaro et al [5] dataset, each dataset contains 10 instances, so each row in Table 1 shows the processing time of 10 6 problems by KTNS and proposed MPCA.…”

Section: Resultsmentioning

confidence: 99%

“…Note that most publications devoted to solving the SSP consider heuristics based on completely different classes of metaheuristics, e.g., tabu search, iterated local search, and genetic algorithms [7]. Regardless of the designed heuristic's nature they try to replace all permutations defined on the entire set of jobs and select the best values of the SSP objective function (OF), i.e.…”

Section: ]mentioning

confidence: 99%

“…So for example, π 1 5,7 saves tool 1 from time 5 to time 7 even though it is not used for job at time 6. Note that π 7 1,2 is also a pipe, although there are no intermediate times between the times 1, 2. The figure marked with a red pipe is not, since at the moment of time 1 the tool 2 is not used for job, i.e.…”

Section: Problem Formulationmentioning

confidence: 99%

See 2 more Smart Citations

An improved KTNS algorithm for the job sequencing and tool switching problem

Cherniavskii¹,

Goldengorin²

2022

Preprint

View full text Add to dashboard Cite

We outline a new Max Pipe Construction Algorithm (MPCA) with the purpose to reduce the CPU time for the classic Keep Tool Needed Soonest (KTNS) algorithm. The KTNS algorithm is applied to compute the objective function value for the given sequence of jobs in all exact and approximating algorithms for solving the Job Sequencing and Tool Switching Problem (SSP). Our MPCA outperforms the KTNS algorithm by at least an order of magnitude in terms of CPU times. Since all exact and heuristic algorithms for solving the SSP spend most of their CPU time on applying the KTNS algorithm we show that our MPCA solves the entire SSP on average 59 times faster for benchmark instances of D compared to current state of the art heuristics.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: ]mentioning

confidence: 99%

See 1 more Smart Citation

An improved KTNS algorithm for the job sequencing and tool switching problem

Cherniavskii¹,

Goldengorin²

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…These metaheuristics are often hybridized with local search to achieve state-of-the-art performance on challenging combinatorial optimization problems [see, e.g., 16,52,38,51]. In a sense, hybrid GAs with local search extends the capabilities of ILS approaches, as they generate new starting points for the local search by recombining existing solutions from a population of high-quality local minima found in previous iterations.…”

Section: Fundamental Notions and Related Studiesmentioning

confidence: 99%

Regularization and Global Optimization in Model-Based Clustering

Sampaio¹,

Garcia²,

Poggi³

et al. 2023

Preprint

View full text Add to dashboard Cite

Due to their conceptual simplicity, k-means algorithm variants have been extensively used for unsupervised cluster analysis. However, one main shortcoming of these algorithms is that they essentially fit a mixture of identical spherical Gaussians to data that vastly deviates from such a distribution. In comparison, general Gaussian Mixture Models (GMMs) can fit richer structures but require estimating a quadratic number of parameters per cluster to represent the covariance matrices. This poses two main issues: (i) the underlying optimization problems are challenging due to their larger number of local minima, and (ii) their solutions can overfit the data. In this work, we design search strategies that circumvent both issues. We develop efficient global optimization algorithms for general GMMs, and we combine these algorithms with regularization strategies that avoid overfitting. Through extensive computational analyses, we observe that global optimization or regularization in isolation does not substantially improve cluster recovery. However, combining these techniques permits a completely new level of performance previously unachieved by k-means algorithm variants, unraveling vastly different cluster structures. These results shed new light on the current status quo between GMM and k-means methods and suggest the more frequent use of general GMMs for data exploration. To facilitate such applications, we provide open-source code as well as Julia packages (UnsupervisedClustering.jl and RegularizedCovarianceMatrices.jl) implementing the proposed techniques.

show abstract

“…A multi-commodity flow mathematical model for SSP is presented (da Silva, Chaves, and Yanasse 2020). In the most recent study (Mecler, Subramanian, and Vidal 2021), SSP is solved by a hybrid genetic search based on a generic solution representation.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-objective optimisation of tool indexing problem: a mathematical model and a modified genetic algorithm

Amouzgar

Nourmohammadi

2021

International Journal of Production Research

View full text Add to dashboard Cite

Machining process efficiencies can be improved by minimising the non-machining time, thereby resulting in short operation cycles. In automatic-machining centres, this is realised via optimum cutting tool allocation on turret-magazine indices-the "tool-indexing problem". Extant literature simplifies TIP as a single-objective optimisation problem by considering minimisation of only the tool-indexing time. In contrast, this study aims to address the multi-objective optimisation toolindexing problem (MOOTIP) by identifying changes that must be made to current industrial settings as an additional objective. Furthermore, tool duplicates and lifespan have been considered. In addition, a novel mathematical model is proposed for solving MOOTIP. Given the complexity of the problem, the authors suggest the use of a modified strength Pareto evolutionary algorithm combined with a customised environment-selection mechanism. The proposed approach attained a uniform distribution of solutions to realise the above objectives. Additionally, a customised solution representation was developed along with corresponding genetic operators to ensure the feasibility of solutions obtained. Results obtained in this study demonstrate the realization of not only a significant (70%) reduction in non-machining time but also a set of tradeoff solutions for decision makers to manage their tools more efficiently compared to current practices.

show abstract

A simple and effective hybrid genetic search for the job sequencing and tool switching problem

Cited by 15 publications

References 62 publications

An improved KTNS algorithm for the job sequencing and tool switching problem

An improved KTNS algorithm for the job sequencing and tool switching problem

Regularization and Global Optimization in Model-Based Clustering

Multi-objective optimisation of tool indexing problem: a mathematical model and a modified genetic algorithm

Contact Info

Product

Resources

About