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

Abstract

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.

The Set Covering and Other Problems: An Empiric Complexity Analysis Using the Minimum Ellipsoidal Width

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“…Literature [12] considers the problems of process connection and blocking of prefabricated parts in the process of workshop assembly line operation and establishes a scheduling model to minimize the total penalty cost of advance and delay, which improves the production efficiency of Prefabrication Yard. Literature [13] uses a machine learning method to assign jobs based on the priority rules of the decision tree as the scheduling method, which shows good performance in the case scenario with completion goal and total delay goal. Considering from workshop production collaborative job scheduling, Literature [14] estimates the process processing time in the production process through machine learning and uses the estimated processing time to schedule and optimize parallel machines, which reduces the maximum completion time by about 30% on average.…”

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“…With the rapid development in data science, machine learning has become a widely used technique in many areas [1][2][3][4], such as computer science, electrical engineering, manufacturing, and transportation [5][6][7][8]. As a classical combinatorial optimization problem, scheduling is known for its practical value and nondeterministic polynomial-time (NP) hardness.…”