Solving flexible job shop scheduling problems in manufacturing with Quantum Annealing

Schworm, Philipp; Wu, Xiangqian; Glatt, Moritz; Aurich, Jan C.

doi:10.1007/s11740-022-01145-8

Cited by 19 publications

(3 citation statements)

References 55 publications

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“…Although the methods for generating the Hamiltonian and the detailed usage of the hybrid heuristics for LHS are in Blackbox, it remains a unique solution for industrial-scale applications. Recent research by (Schworm et al, 2023) tested LHS solvers for solving FJSP, using various models and methods to demonstrate their problem-solving capacity. However, as discussed in Section 2, FJSST is significantly more complex than FJSP, making their results inapplicable to our case.…”

Section: Leap's Hybrid Solver Using Quantum Annealingmentioning

confidence: 99%

Practical approach to flexible job shop scheduling with tool switching constraints using quantum annealing

INOUE,

QIN,

FUJITA

et al. 2024

JAMDSM

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Behind production scheduling lies a trade-off between the comprehensive modeling of production system constraints and quick schedule provision. To realize mass customization, this trade-off must be addressed, requiring a model comprehensive enough for the automation of high-mix production yet quick in adapting to the dynamics of the production system. In this context, we propose a practical approach in which Flexible Job Shop Scheduling with Tool Switching Constraints, offering a model capable of representing a wide range of high-mix productions and Quantum Annealing are combined. The resulting schedules are demonstrated to be competitive, in terms of quality and computation time, are superior when compared with those obtained using Dispatching rules or Python MIP, respectively.

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Section: Leap's Hybrid Solver Using Quantum Annealingmentioning

confidence: 99%

Practical approach to flexible job shop scheduling with tool switching constraints using quantum annealing

INOUE,

QIN,

FUJITA

et al. 2024

JAMDSM

View full text Add to dashboard Cite

show abstract

“…10.12928/ijio.v5i1. 8743 𝑇 𝑗 = 𝑇 𝐸 − 𝑇 𝑆 (4) where, 𝑇 𝑗 denotes a predicted job completion time, 𝑇 𝐸 represents an ending time to complete the operation of the job and 𝑇 𝑆 represents a starting time to process the operation of the job. The optimal machine is selected through the fitness function based on the resources.…”

Section: Multi-objective Elitist Spotted Hyena Optimizationmentioning

confidence: 99%

Multi-objective elitist spotted hyena resource optimized flexible job shop scheduling

Senthilvel,

Hemamalini,

Geetha

2024

Int. J. Ind. Optim.

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The job shop scheduling problem (JSSP) has drained a lot of consideration since it is one of the most important optimization problems in the manufacturing domain. The scheduling method is crucial for optimizing the objective of minimizing makespan among thousands of jobs, but evaluating machine capacity for achieving this goal remains challenging despite the development of various population-based optimization algorithms for job shop scheduling problems. To improve the efficiency of Job shop scheduling, a novel Multi-objective Elitist Spotted Hyena Monotonic Scheduling (MESHS) technique is introduced. The proposed MESHS technique includes two major processes: machine selection and operation sequences. The number of jobs is considered for solving the scheduling problem. First, the machine selection is performed by applying the Multi-objective Elitist Spotted Hyena optimization technique. The optimization technique selects the optimal machines parallelly based on multiple objective functions such as energy consumption, CPU utilization, and job completion time. The fitness of every machine is calculated based on these multiple objective functions using Levenberg–Marquardt method. Then the Elitist strategy is applied to select the optimal machine based on fitness. After the machine selection, the rate-monotonic preemptive scheduling is modeled to provide a robust operation sequence by assigning high-priority jobs to the optimal machines. As a result, efficient job scheduling is achieved with minimum time. Finally, the experimental valuation is carried out using a benchmark OR-Library dataset with different factors such as job shop scheduling efficiency, job scheduling time, makespan, and memory consumption concerning a number of jobs.

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“…In fact, metaheuristics have proven to be powerful for solving hard scheduling problems. In this context, [6] proposed an effective Quantum Annealing (QA) metaheuristic for solving the FJSSP in a time-efficient manner. In [7], a discrete improved grey wolf optimization (DIGWO) algorithm proves effective in solving the FJSSP.…”

Section: Literature Reviewmentioning

confidence: 99%

A Constraint Programming Formulation of the Multi-Mode Resource-Constrained Project Scheduling Problem for the Flexible Job Shop Scheduling Problem

Yuraszeck,

Montero,

Canut-De-Bon

et al. 2023

IEEE Access

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In this work, a constraint programming (CP) formulation of the multi-mode resourceconstrained project scheduling problem (MMRCPSP) is proposed for solving the flexible job shop scheduling problem (FJSSP) under the makespan minimization criterion. The resulting CP model allows us to tackle the classical instances of the FJSSP (such as where the operations of a given job follow a linear order). It can also handle FJSSP instances where the precedence relationships between operations are defined by an arbitrary directed acyclic graph (sequencing flexibility). The performance of our approach was tested using 271 classical FJSSP instances and 50 FJSSP instances with sequencing flexibility. We establish the validity of our approach by achieving an average relative percentage deviation of 3.04% and 0.18% when compared to the best-known lower and upper bounds, respectively. Additionally, we were able to contribute to the literature with ten new lower bounds and two new upper bounds. Our CP approach is relatively simple yet competitive and can be quickly applied and adapted by new practitioners in the area.INDEX TERMS constraint programming, flexible job shop, fjssp, job shop, jssp, multi-mode resourceconstrained project scheduling problem, mmrcpsp, sequencing flexibility.

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Solving flexible job shop scheduling problems in manufacturing with Quantum Annealing

Cited by 19 publications

References 55 publications

Practical approach to flexible job shop scheduling with tool switching constraints using quantum annealing

Practical approach to flexible job shop scheduling with tool switching constraints using quantum annealing

Multi-objective elitist spotted hyena resource optimized flexible job shop scheduling

A Constraint Programming Formulation of the Multi-Mode Resource-Constrained Project Scheduling Problem for the Flexible Job Shop Scheduling Problem

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