Quantum algorithms for process parallel flexible job shop scheduling

Denkena, Berend; Schinkel, Fritz; Pirnay, Jonathan; Wilmsmeier, Sören

doi:10.1016/j.cirpj.2021.03.006

Cited by 39 publications

(26 citation statements)

References 40 publications

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“…First, jobs have fixed number of operations, equal to the number of jobs, i.e., , where || represents the cardinality of a set. Furthermore, each operation can be executed only on a specific machine which is chosen when the problem instance is created (differently from the flexible JSSP 13 ). This version of the JSSP therefore has as only degree of freedom choosing when each operation will start, taking into account the ordering of operations within a job and that there cannot be more operation running simultaneously on a machine.…”

Section: Job Shop Schedulingmentioning

confidence: 99%

“…More recently, a heuristic procedure to split JSSP instances into smaller ones was proposed and tested on a D-Wave 2000Q quantum annealer 12 , which holds 2048 qubits. Lastly, several problems were successfully evaluated on a classical digital annealer that could handle up to 8192 variables, using the same JSSP formulation developed for quantum annealers, showing improved solution quality on some instances 13 .…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the job shop scheduling problem on a D-wave quantum annealer

Carugno

Dacrema

Cremonesi

2022

Sci Rep

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Job Shop Scheduling is a combinatorial optimization problem of particular importance for production environments where the goal is to complete a production task in the shortest possible time given limitations in the resources available. Due to its computational complexity it quickly becomes intractable for problems of interesting size. The emerging technology of Quantum Annealing provides an alternative computational architecture that promises improved scalability and solution quality. However, several limitations as well as open research questions exist in this relatively new and rapidly developing technology. This paper studies the application of quantum annealing to solve the job shop scheduling problem, describing each step required from the problem formulation to the fine-tuning of the quantum annealer and compares the solution quality with various classical solvers. Particular attention is devoted to aspects that are often overlooked, such as the computational cost of representing the problem in the formulation required by the quantum annealer, the relative qubits requirements and how to mitigate chain breaks. Furthermore, the impact of advanced tools such as reverse annealing is presented and its effectiveness discussed. The results indicate several challenges emerging at various stages of the experimental pipeline which bring forward important research questions and directions of improvement.

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Section: Job Shop Schedulingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluating the job shop scheduling problem on a D-wave quantum annealer

Carugno

Dacrema

Cremonesi

2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…[Ajagekar et al, 2020] A flexible job shop scheduling problem has been developed with a quantum-inspired quantum annealer and digital annealer algorithms. , Denkena et al, 2021 The quantum annealer algorithm was implemented as a time-indexed QUBO problem derived from a makespan-minimization problem formulation. The digital annealer implementation extends the before mentioned QUBO formulation with a penalty term to adjust for shorter makespans in the schedule.…”

Section: Solid Materials Applicationsmentioning

confidence: 99%

Quantum Computing: Fundamentals, Trends and Perspectives for Chemical and Biochemical Engineers

Nourbakhsh¹,

Jones²,

Kristjuhan³

et al. 2022

Preprint

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We use the benefits and components of classical computers every day. However, there are many types of problems which, as they grow in size, their computational complexity grows larger than classical computers will ever be able to solve. Quantum computing (QC) is a computation model that uses quantum physical properties to solve such problems. QC is at the early stage of large-scale adoption in various industry domains to take advantage of the algorithmic speed-ups it has to offer. It can be applied in a variety of areas, such as computer science, mathematics, chemical and biochemical engineering, and the financial industry. The main goal of this paper is to give an overview to chemical and biochemical researchers and engineers who may not be familiar with quantum computation. Thus, the paper begins by explaining the fundamental concepts of QC. The second contribution this publication tries to tackle is the fact that the chemical engineering literature still lacks a comprehensive review of the recent advances of QC. Therefore, this article reviews and summarizes the state of the art to gain insight into how quantum computation can benefit and optimize chemical engineering issues.A bibliography analysis covers the comprehensive literature in QC and analyzes quantum computing research in chemical engineering on various publication topics, using Clarivate analytics covering the years 1990 to 2020. After the bibliographic analysis, relevant applications of QC in chemical and biochemical engineering are highlighted and a conclusion offers an outlook of future directions within the field.

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“…Experiments are conducted with 50 particles and 100 iterations (5000 visited points). Table 15 presents the comparison against PSO-based metaheuristics: PSO [35], artificial bee colony (ABC) [36], quantum annealing based optimization (QAO) [37], genetic algorithm (GA) [38], human learning optimization algorithm and PSO (HLO-PSO) [39] and hybrid brain storm optimization algorithm and late acceptance hill climbing (hybrid PSO) [40] (-denotes the data's unavailability). A bold value indicates that the E2L-PSO result is either optimal or the best.…”

Section: Experiments #4 Robustness Analyses Of Adaptive E2l-psomentioning

confidence: 99%

Embedded PSO for Solving FJSP on Embedded Environment (Industry 4.0 Era)

et al. 2022

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Since of the advent of Industry 4.0, embedded systems have become an indispensable component of our life. However, one of the most significant disadvantages of these gadgets is their high power consumption. It was demonstrated that making efficient use of the device’s central processing unit (CPU) enhances its energy efficiency. The use of the particle swarm optimization (PSO) over an embedded environment achieves many resource problems. Difficulties of online implementation arise primarily from the unavoidable lengthy simulation time to evaluate a candidate solution. In this paper, an embedded two-level PSO (E2L-PSO) for intelligent real-time simulation is introduced. This algorithm is proposed to be executed online and adapted to embedded applications. An automatic adaptation of the asynchronous embedded two-level PSO algorithm to CPU is completed. The Flexible Job Shop Scheduling Problem (FJSP) is selected to solve, due to its importance in the Industry 4.0 era. An analysis of the run-time performance on handling E2L-PSO over an STM32F407VG-Discovery card and a Raspberry Pi B+ card is conducted. By the experimental study, such optimization decreases the CPU time consumption by 10% to 70%, according to the CPU reduction needed (soft, medium, or hard reduction).

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Quantum algorithms for process parallel flexible job shop scheduling

Cited by 39 publications

References 40 publications

Evaluating the job shop scheduling problem on a D-wave quantum annealer

Evaluating the job shop scheduling problem on a D-wave quantum annealer

Quantum Computing: Fundamentals, Trends and Perspectives for Chemical and Biochemical Engineers

Embedded PSO for Solving FJSP on Embedded Environment (Industry 4.0 Era)

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