CP and Hybrid Models for Two-Stage Batching and Scheduling

Tang, T.; Beck, J. Christopher

doi:10.1007/978-3-030-58942-4_28

Cited by 8 publications

(3 citation statements)

References 12 publications

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“…Then we randomly selected 5 instances from every set of 1024 instances, creating a set of 80 instances which we used throughout our experiments. This set thus consists of 20 instances each with 10 (instances 1-20), 20 (21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40), 50 (41-60) and 100 jobs (61-80). All benchmark instances turn out to be satisfiable and solvable by the construction heuristic described in Section 8.2.…”

Section: Experimental Evaluation 91 Benchmark Instancesmentioning

confidence: 99%

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Exact methods and lower bounds for the Oven Scheduling Problem

Lackner¹,

Mrkvicka²,

Musliu³

et al. 2022

Preprint

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The Oven Scheduling Problem (OSP) is a new parallel batch scheduling problem that arises in the area of electronic component manufacturing. Jobs need to be scheduled to one of several ovens and may be processed simultaneously in one batch if they have compatible requirements. The scheduling of jobs must respect several constraints concerning eligibility and availability of ovens, release dates of jobs, setup times between batches as well as oven capacities. Running the ovens is highly energy-intensive and thus the main objective, besides finishing jobs on time, is to minimize the cumulative batch processing time across all ovens. This objective distinguishes the OSP from other batch processing problems which typically minimize objectives related to makespan, tardiness or lateness.We propose to solve this NP-hard scheduling problem via constraint programming (CP) and integer linear programming (ILP) and present corresponding models. For an experimental evaluation, we introduce a multi-parameter random instance generator to provide a diverse set of problem instances. Using state-of-theart solvers, we evaluate the quality and compare the performance of our CP-and ILP-models. We show that our models can find feasible solutions for instances of realistic size, many of those being provably optimal or nearly optimal solutions. Finally, we derive theoretical lower bounds on the solution cost of feasible solutions to the OSP; these can be computed within a few seconds. We show that these lower bounds are competitive with those derived by state-of-the-art solvers.

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Section: Experimental Evaluation 91 Benchmark Instancesmentioning

confidence: 99%

“…Another recent study [31] introduced a batch scheduling problem from the steel industry that includes setup times, release times, as well as due date constraints. Furthermore, a complex two-phase batch scheduling problem from the composites manufacturing industry has been solved with the use of CP and hybrid techniques [26].…”

Section: Introductionmentioning

confidence: 99%

Exact methods and lower bounds for the Oven Scheduling Problem

Lackner¹,

Mrkvicka²,

Musliu³

et al. 2022

Preprint

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“…In their paper, (Zhao et al 2020) propose a MIP model and a two-stage decomposition approach as solution methods. Recently, (Tang and Beck 2020) further investigated a complex two-phase batch scheduling problem from the composites manufacturing industry which they approach using CP, heuristics, and hybrid techniques. Further NP-hard single machine scheduling problem variants that do not include batching decisions but aim to minimize tardiness and setup time similarly as it is done in artificial teeth manufacturing, have been investigated for example by (Niu et al 2019) and by (de Weerdt, Baart, and He 2020).…”

Section: Introductionmentioning

confidence: 99%

Automated Production Scheduling for Artificial Teeth Manufacturing

Winter

Mrkvicka

Musliu

et al. 2021

ICAPS

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In industrial artificial teeth manufacturing, nowadays a high level of automation is utilized to produce a large quantity of teeth in short production cycles. As a large variety of different product shapes and colors have to be processed on a single machine, the creation of efficient production schedules becomes a very challenging task. Due to the complexity of the problem and several cost minimization objectives that need to be considered, there usually is a large potential to improve the currently manually created schedules with automated solution methods. In this paper, we formally specify and solve a novel challenging real-life machine batch scheduling problem from the area of artificial teeth manufacturing. Additionally, we provide a collection of real-life benchmark instances that can be used to evaluate solution methods for the problem. To efficiently solve the problem, we propose an innovative construction heuristic and metaheuristic approach as well as an exact method using constraint programming. An extensive experimental evaluation shows that exact techniques can efficiently solve small scheduling scenarios and can provide optimal solutions for four instances. Furthermore, we show that the proposed metaheuristic approach is able to reach optimal results for small instances and can find high quality solutions also for large real-life benchmark instances.

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