A Hybrid Quantum Evolutionary Algorithm with Improved Decoding Scheme for a Robotic Flow Shop Scheduling Problem

Lei, Weidong; Manier, Hervé; Manier, Marié-Ange; Wang, Xinping

doi:10.1155/2017/3064724

Cited by 6 publications

(6 citation statements)

References 38 publications

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“…, 2006) and (3) process speed, which enables continuous workflows and a reduction in downtime (Lee et al. , 2020; Lei et al. , 2017).…”

Section: Framework Of Operational Capabilities That Enable Flexibilit...mentioning

confidence: 99%

Quantum-inspired computing technology in operations and logistics management

Núñez-Merino,

Maqueira-Marín,

Moyano-Fuentes

et al. 2024

IJPDLM

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PurposeThe purpose of this paper is to explore and disseminate knowledge about quantum-inspired computing technology's potential to solve complex challenges faced by the operational agility capability in Industry 4.0 manufacturing and logistics operations.Design/methodology/approachA multi-case study approach is used to determine the impact of quantum-inspired computing technology in manufacturing and logistics processes from the supplier perspective. A literature review provides the basis for a framework to identify a set of flexibility and agility operational capabilities enabled by Industry 4.0 Information and Digital Technologies. The use cases are analyzed in depth, first individually and then jointly.FindingsStudy results suggest that quantum-inspired computing technology has the potential to harness and boost companies' operational flexibility to enhance operational agility in manufacturing and logistics operations management, particularly in the Industry 4.0 context. An exploratory model is proposed to explain the relationships between quantum-inspired computing technology and the deployment of operational agility capabilities.Originality/valueThis is study explores the use of quantum-inspired computing technology in Industry 4.0 operations management and contributes to understanding its potential to enable operational agility capability in manufacturing and logistics operations.

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“…, 2006) and (3) process speed, which enables continuous workflows and a reduction in downtime (Lee et al. , 2020; Lei et al. , 2017).…”

Section: Framework Of Operational Capabilities That Enable Flexibilit...mentioning

confidence: 99%

Quantum-inspired computing technology in operations and logistics management

Núñez-Merino,

Maqueira-Marín,

Moyano-Fuentes

et al. 2024

IJPDLM

View full text Add to dashboard Cite

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“…For a single robot and flexible processing times in a robotic flow shop, Lei et al [33] aim to increase the throughput rate. A hybrid algorithm based on the quantuminspired evolutionary algorithm and genetic operators is presented for solving the cyclic scheduling problem.…”

Section: Metaheuristicsmentioning

confidence: 99%

Simulation Optimization for the Multihoist Scheduling Problem

Pérez-Rodríguez

2021

Applied Computational Intelligence and Soft Computing

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Although the Multihoist Scheduling Problem (MHSP) can be detailed as a job-shop configuration, the MHSP has additional constraints. Such constraints increase the difficulty and complexity of the schedule. Operation conditions in chemical processes are certainly different from other types of processes. Therefore, in order to model the real-world environment on a chemical production process, a simulation model is built and it emulates the feasibility requirements of such a production system. The results of the model, i.e., the makespan and the workload of the most loaded tank, are necessary for providing insights about which schedule on the shop floor should be implemented. A new biobjective optimization method is proposed, and it uses the results mentioned above in order to build new scenarios for the MHSP and to solve the aforementioned conflicting objectives. Various numerical experiments are shown to illustrate the performance of this new experimental technique, i.e., the simulation optimization approach. Based on the results, the proposed scheme tackles the inconvenience of the metaheuristics, i.e., lack of diversity of the solutions and poor ability of exploitation. In addition, the optimization approach is able to identify the best solutions by a distance-based ranking model and the solutions located in the first Pareto-front layer contributes to improve the search process of the aforementioned scheme, against other algorithms used in the comparison.

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“…Their proposed strategy is capable of generating near-optimal schedules to achieve their aim of reducing the makespan and cost for many scenarios in a short time period. A Hybrid Quantum Evolutionary Algorithm (QEA) is developed by Lei, Manier, Manier, and Wang (2017) that combines two types of decoding schemes (binary-decimal decoding and shifting decoding) to write their genetic algorithm for their single-hoist CHSP. Lei's algorithm shows the same cycle times obtained by the CPLEX with faster computational time.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Another way to deal with such complex problems is to solve them by graphical simulation program rather than analytically (Bas an et al, 2017). Moreover, the algorithms in the field of HSP are exhibiting a huge improvements like the study by Lei et al (2017), and dealing with multi-objectives like the study by Liu et al (2012) and Feng et al (2015). However, the simulated plants in the recent studies are still lacking additional characteristics that make their plants mimic real complex plants, such as multi-hoist, multi-capacity tanks, loading stations, unloading stations and buffers.…”

Section: Literature Reviewmentioning

confidence: 99%

Hoist scheduling for an anodizing plant at an extrusion company

Alsaffar

Suliman

2020

Arab Journal of Basic and Applied Sciences

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The field of scheduling metal surface treatment plants is extensively studied in the literature and it is referred to by the name of Hoist Scheduling Problem (HSP). HSP is classified as a flow-shop scheduling problem, but differs because of the transportation constraints and the no-wait restriction. This research deals with a real anodizing plant in an extrusion company with multiple-hoist. The objective of this study is to minimize the makespan of a demand of selected days and to improve production of the plant. A heuristic algorithm was developed for scheduling the anodizing demand of the extrusion company. The algorithm was coded in Matlab (R2015beditor) to simulate the production of certain days. The runs of the simulation were divided into three phases: validation phase; experimentation phase; and productivity enhancement phase. The results showed a reduction in the makespan with an approximated average of 18%. Furthermore, it revealed that the algorithm was able to utilize the time by an approximated average of 98% and to increase the number of jobs by an approximated average of 20%.

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A Hybrid Quantum Evolutionary Algorithm with Improved Decoding Scheme for a Robotic Flow Shop Scheduling Problem

Cited by 6 publications

References 38 publications

Quantum-inspired computing technology in operations and logistics management

Quantum-inspired computing technology in operations and logistics management

Simulation Optimization for the Multihoist Scheduling Problem

Hoist scheduling for an anodizing plant at an extrusion company

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