Metaheuristics for the online printing shop scheduling problem

Lunardi, Willian T.; Birgin, Ernesto G.; Ronconi, Débora P.; Voos, Holger

doi:10.1016/j.ejor.2020.12.021

Cited by 20 publications

(12 citation statements)

References 35 publications

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“…In order to compare SCHED-T with other heuristics, we consider the set of publicly available instances described in [12,16]. These instances have been created using the instance generator available at [31].…”

Section: Experimental Methodology and Settingsmentioning

confidence: 99%

“…If the next machine is occupied, the job remains on the belt until the destination machine becomes available. The JSS problem is well-known to be NP-hard [11], and the variant considered here is at least as challenging [12]. In order to address this complexity, we propose a heuristic named SCHED-T, which falls under the stochastic local search (SLS) approach [13].…”

Section: Introductionmentioning

confidence: 99%

“…The aim of the different heuristics proposed in the literature is to find the best schedule that optimizes an objective function [12,20,24]. These state-of-the-art solutions are based on graph-based representations of the execution sequence.…”

mentioning

confidence: 99%

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Dynamic Job and Conveyor-Based Transport Joint Scheduling in Flexible Manufacturing Systems

Gaiardelli,

Carra,

Spellini

et al. 2024

Applied Sciences

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Efficiently managing resource utilization is critical in manufacturing systems to optimize production efficiency, especially in dynamic environments where jobs continually enter the system and machine breakdowns are potential occurrences. In fully automated environments, co-ordinating the transport system with other resources is paramount for smooth operations. Despite extensive research exploring the impact of job characteristics, such as fixed or variable task-processing times and job arrival rates, the role of the transport system has been relatively underexplored. This paper specifically addresses the utilization of a conveyor belt as the primary mode of transportation among a set of production machines. In this configuration, no input or output buffers exist at the machines, and the transport times are contingent on machine availability. In order to tackle this challenge, we introduce a randomized heuristic approach designed to swiftly identify a near-optimal joint schedule for job processing and transfer. Our solution has undergone testing on both state-of-the-art benchmarks and real-world instances, showcasing its ability to accurately predict the overall processing time of a production line. With respect to our previous work, we specifically consider the case of the arrival of a dynamic job, which requires a different design approach since there is a need to keep track of partially processed jobs, jobs that are waiting, and newly arrived jobs. We adopt a total rescheduling strategy and, in order to show its performance, we consider a clairvoyant scheduling approach, in which job arrivals are known in advance. We show that the total rescheduling strategy yields a scheduling solution that is close to optimal.

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Section: Experimental Methodology and Settingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dynamic Job and Conveyor-Based Transport Joint Scheduling in Flexible Manufacturing Systems

Gaiardelli,

Carra,

Spellini

et al. 2024

Applied Sciences

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“…On the other hand, in the last decade DE has been successfully applied to a wide range of complex real-world problems (see for example Damak et al (2009); Wang et al (2010); Ali et al ( 2012); Tsai et al (2013); Yuan and Xu (2013)), but its performance in the FJS scheduling problem with sequencing flexibility was not tested yet. Another reason that reinforces the choice of DE is that preliminary experiments involving other well-known metaheuristics such as artificial bee colony, particle swarm optimization, and grey wolf optimizer, showed that DE achieves outstanding results among the considered methods (Lunardi, 2020). Finally, ILS is considered due to its simplicity of implementation and usage.…”

Section: Metaheuristicsmentioning

confidence: 99%

Metaheuristics for the Online Printing Shop Scheduling Problem

Lunardi,

Birgin,

Ronconi

et al. 2020

Preprint

Self Cite

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In this work, the online printing shop scheduling problem introduced in (Lunardi et al., Mixed Integer Linear Programming and Constraint Programming Models for the Online Printing Shop Scheduling Problem, Computers & Operations Research, to appear) is considered. This challenging real scheduling problem, that emerged in the nowadays printing industry, corresponds to a flexible job shop scheduling problem with sequencing flexibility; and it presents several complicating specificities such as resumable operations, periods of unavailability of the machines, sequence-dependent setup times, partial overlapping between operations with precedence constraints, and fixed operations, among others. A local search strategy and metaheuristic approaches for the problem are proposed and evaluated. Based on a common representation scheme, trajectory and populational metaheuristics are considered. Extensive numerical experiments with large-sized instances show that the proposed methods are suitable for solving practical instances of the problem; and that they outperform a half-heuristic-half-exact off-the-shelf solver by a large extent. Numerical experiments with classical instances of the flexible job shop scheduling problem show that the introduced methods are also competitive when applied to this particular case.

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“…Although the earth is rich in resources and the total amount of resources is relatively large, due to the rough development since the industrial revolution, resources such as land, energy, and water have formed a hard constraint and tight balance for development, and resource consumption has lit up the red light. In recent years, experts and scholars have devoted a lot of attention to the issue of resource deployment, and the areas involved include aviation [1] , railroads [2] , rail transportation [3] , industrial workshops [4], and many other aspects. Resource deployment is the rationalization of resource utilization, and the key to achieving optimization is to improve the influencing factors closely related to the desired project goals, such as project time management to make the project more efficient by optimizing the project schedule, cost management is to control the project budget by optimizing the consumption of resources required for the project, and quality management is to ensure the production quality by optimizing the production activities.…”

Section: Introductionmentioning

confidence: 99%

An improved multi-objective gray target model for project resource allocation selection Research

2023

E3S Web Conf.

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In response to the traditional method of using a single subjective or objective assignment, AHP (hierarchical analysis) subjective weights and CRITIC (hierarchical relevance of importance criteria method) objective weights are coupled with the Lagrange multiplier method to obtain combined weights, and the multiobjective gray target decision model is used to establish event set, countermeasure set, and situation set to obtain the resource ranking that affects the project resource deployment problem, which provides a basis for the project participants to make decisions and plan for the project. Taking the municipal road project as an example, a judgment matrix is established. The results show that the model constructed in this paper is relatively consistent with the actual situation.

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Metaheuristics for the online printing shop scheduling problem

Cited by 20 publications

References 35 publications

Dynamic Job and Conveyor-Based Transport Joint Scheduling in Flexible Manufacturing Systems

Dynamic Job and Conveyor-Based Transport Joint Scheduling in Flexible Manufacturing Systems

Metaheuristics for the Online Printing Shop Scheduling Problem

An improved multi-objective gray target model for project resource allocation selection Research

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