Enhancing local search algorithms for job shops with min-sum objectives by approximate move evaluation

Braune, Roland; Zäpfel, Günther; Affenzeller, Michael

doi:10.1007/s10951-012-0305-x

Cited by 9 publications

(12 citation statements)

References 27 publications

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“…As discussed in [26], no single priority rule performs well across all relevant measures of effectiveness and efficiency. Meta-heuristics, e.g., local search algorithms [27], [28], were developed to solve job-shop scheduling. In [27], a local search algorithm was developed.…”

Section: B Solution Methodologies For Job-shop Schedulingmentioning

confidence: 99%

“…Candidate solutions of each iteration are generated by swapping critical arcs, and a method is developed to efficiently evaluate the candidates. In [28], an approximation function is developed to provide quick estimates for the problem cost, and two methods for sorting candidate solutions are developed, i.e., First Improvement (FI) and Best Improvement (BI). Based on these, two tabu search (TS) algorithms and two iterated local search (ILS) algorithms are developed.…”

Section: B Solution Methodologies For Job-shop Schedulingmentioning

confidence: 99%

“…Example 3: In this example, Morton and Pentico dataset is considered, which is a public benchmark dataset for job-shop scheduling with a tardiness-related objective. Six instances (LJB1, LJB2, LJB7, LJB9, LJB10, and LJB12) of the dataset with sizes ranging from 30 parts and 3 machines to 50 parts and 8 machines are considered in many papers to test the performance of their local search algorithms [27], [28]. To compare with these methods, SLR+DNN is tested on these instances.…”

Section: Datasetmentioning

confidence: 99%

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Integrating Machine Learning and Mathematical Optimization for Job Shop Scheduling

Liu¹,

Luh²,

Sun³

et al. 2023

Preprint

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<p>Job-shop scheduling is an important but difficult combinatorial optimization problem for low-volume and high-variety manufacturing, with solutions required to be obtained quickly at the beginning of each shift. In view of the increasing demand for customized products, problem sizes are growing. A promising direction is to take advantage of Machine Learning (ML). Direct learning to predict solutions for job-shop scheduling, however, suffers from major difficulties when problem scales are large. In this paper, a Deep Neural Network (DNN) is synergistically integrated within the decomposition and coordination framework of Surrogate Lagrangian Relaxation (SLR) to predict good-enough solutions for subproblems. Since a subproblem is associated with a single part, learning difficulties caused by large scales are overcome. Nevertheless, the learning still presents challenges. Because of the high-variety nature of parts, the DNN is desired to be able to generalize to solve all possible parts. To this end, our idea is to establish “surrogate” part subproblems that are easier to learn, develop a DNN based on Pointer Network to learn to predict their solutions and calculate the solutions of the original part subproblems based on these predictions. Moreover, a masking mechanism is developed such that all the predictions are feasible. Numerical results demonstrate that good-enough subproblem solutions are predicted in many iterations, and high-quality solutions of the overall problem are obtained in a computationally efficient manner. The performance of the method is further improved through continuous learning. </p>

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Section: B Solution Methodologies For Job-shop Schedulingmentioning

confidence: 99%

Section: B Solution Methodologies For Job-shop Schedulingmentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

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Integrating Machine Learning and Mathematical Optimization for Job Shop Scheduling

Liu¹,

Luh²,

Sun³

et al. 2023

Preprint

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“…It identifies jobs or operations that cannot be delayed without immediately deteriorating the objective function value. This principle is most easily applied in the makespan context but also can be extended to sum-based objectives like total tardiness (Braune et al 2013). We use this concept to filter training examples as follows: add only those pairwise comparisons that involve at least one job that is critical in the CP schedule.…”

Section: Example Selection Based On Critical Pathsmentioning

confidence: 99%

“…The due date for an order was set deterministically by adding up the processing times on each processing stage for each job, plus its transport time. The resulting value was then multiplied by a factor of 1.3, which is a common procedure for tardiness job shop scheduling problems (Braune et al 2013). Furthermore, jobs of the same order are not necessarily processed in a row but probably simultaneously on parallel machines.…”

Section: Configuration Of the Instance Scenarios In The Total Tardinementioning

confidence: 99%

A machine learning approach for flow shop scheduling problems with alternative resources, sequence-dependent setup times, and blocking

et al. 2019

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In proposing a machine learning approach for a flow shop scheduling problem with alternative resources, sequence-dependent setup times, and blocking, this paper seeks to generate a tree-based priority rule in terms of a well-performing decision tree (DT) for dispatching jobs. Furthermore, generating a generic DT and RF that yields competitive results for instance scenarios that structurally differ from the training instances was another goal of our research. The proposed DT relies on high quality solutions, obtained using a constraint programming (CP) formulation. Novel aspects include a unified representation of job sequencing and machine assignment decisions, as well as the generation of random forests (RF) to counteract overfitting behaviour. To show the performance of the proposed approaches, different instance scenarios for two objectives (makespan and total tardiness minimisation) were implemented, based on randomised problem data. The background of this approach is a real-world physical system of an industrial partner that represents a typical shop floor for many production processes, such as furniture and window construction. The results of a comparison of the DT and RF approach with two priority dispatching rules, the original CP solutions and tight lower bounds retrieved from a strengthened mixed-integer programming (MIP) formulation show that the proposed machine learning approach performs well in most instance sets for the makespan objective and in all sets for the total tardiness objective.

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A study on local search neighborhoods for the job shop scheduling problem with total weighted tardiness objective

Kuhpfahl

Bierwirth

2016

Computers & Operations Research

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Enhancing local search algorithms for job shops with min-sum objectives by approximate move evaluation

Cited by 9 publications

References 27 publications

Integrating Machine Learning and Mathematical Optimization for Job Shop Scheduling

Integrating Machine Learning and Mathematical Optimization for Job Shop Scheduling

A machine learning approach for flow shop scheduling problems with alternative resources, sequence-dependent setup times, and blocking

A study on local search neighborhoods for the job shop scheduling problem with total weighted tardiness objective

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