A Survey of Solving Approaches for Multiple Objective Flexible Job Shop Scheduling Problems

Genova, Krassimira; Kirilov, Leoneed; Guliashki, Vassil

doi:10.1515/cait-2015-0025

Cited by 20 publications

(12 citation statements)

References 47 publications

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“…The restriction to a non-preemptive setup is the general case in literature and therefore adopted in the considered problem [19][20][21][22][23]. In the description of the dependencies of a task τ on other tasks (R1), only direct dependencies are provided in the sets T τ .…”

Section: Restrictions and Flexibility In The Scheduling Problemmentioning

confidence: 99%

“…For the jobs, we assume that they represent products that can only be at one place, and thus they can only be handled by a single machine at a time. Together with the limited capabilities of the machines, the following restrictions are present, which are all the general case in the literature on scheduling of FJSs, despite they are not always stated explicitly [19][20][21]23]:…”

Section: Restrictions and Flexibility In The Scheduling Problemmentioning

confidence: 99%

“…The benefit of the different types of flexibility is that they offer the potential to find an improved schedule with respect to a nominal one. On the other hand the complexity of the scheduling problem increases and it is harder to find an optimal solution [20,29]. In fact, the considered problem is NP-hard as it is a generalization of the flexible job shop (FJS), which we will discuss in Section 2.3, and since the FJS is NP-hard itself [19,20,25].…”

Section: Restrictions and Flexibility In The Scheduling Problemmentioning

confidence: 99%

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Model Predictive Control for Flexible Job Shop Scheduling in Industry 4.0

Wenzelburger

Allgöwer

2021

Applied Sciences

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In the context of Industry 4.0, flexible manufacturing systems play an important role. They are designed to provide the possibility to adapt the production process by reacting to changes and enabling customer specific products. The versatility of such manufacturing systems, however, also needs to be exploited by advanced control strategies. To this end, we present a novel scheduling scheme that is able to flexibly react to changes in the manufacturing system by means of Model Predictive Control (MPC). To introduce flexibility from the start, the initial scheduling problem, which is very general and covers a variety of special cases, is formulated in a modular way. This modularity is then preserved during an automatic transformation into a Petri Net formulation, which constitutes the basis for the two presented MPC schemes. We prove that both schemes are guaranteed to complete the production problem in closed loop when reasonable assumptions are fulfilled. The advantages of the presented control framework for flexible manufacturing systems are that it covers a wide variety of scheduling problems, that it is able to exploit the available flexibility of the manufacturing system, and that it allows to prove the completion of the production problem.

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Section: Restrictions and Flexibility In The Scheduling Problemmentioning

confidence: 99%

Section: Restrictions and Flexibility In The Scheduling Problemmentioning

confidence: 99%

Section: Restrictions and Flexibility In The Scheduling Problemmentioning

confidence: 99%

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Model Predictive Control for Flexible Job Shop Scheduling in Industry 4.0

Wenzelburger

Allgöwer

2021

Applied Sciences

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“…With the advent of intelligent manufacturing, the various sensors of job-shop data acquisition and the Internet of things (IoT) for system interconnection have been increasingly applied to job-shop control [1][2][3][4][5][6]. The difficulty of job-shop control lies in the many uncertain disturbances in the processing environment and the complex changes of working conditions [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Game-Based Hybrid Particle Swarm Optimization of Job-Shop Production Control

Wang¹

2021

Int. j. simul. model.

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The traditional multi-objective particle swarm optimization (PSO) cannot effectively handle the production control problem involving multiple types of production lines or production objectives. Therefore, this paper designs a game-based hybrid PSO (GBHPSO) for job-shop production control. Firstly, a job-shop model was established with parts processing line, parts assembly line, and product assembly line, and the production control ideas were designed to combine real-time monitoring of events and operation sequence adjustment. Then, the production control objectives were determined for the three production lines. After that, the GBHPSO was applied to solve the job-shop production control problem, the product utility function was constructed, and the execution low was detailed for the solving algorithm. Experiments demonstrate the effectiveness of our algorithm. The research provides a reference for applying our algorithm in resource allocation of other production fields.

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“…El algoritmo fue implementado en C++, en una computadora personal Pentium IV de 1.8 GHz. es probado con cuatro el problema 4x 5, problema 8x8, problema 10 x10, y problema 15x10 de Kacem et al (2002 Genova et al (2015) realizan una revisión de la literatura sobre las soluciones para el FJSSP multiobjetivo, se revisan trabajos con soluciones en donde se aplican modelos aproximados matemáticos y trabajos con soluciones heurísticas y metaheurísticas, algunos de los investigadores anteriores. De la investigación de Genova, se puede notar a investigadores persiguiendo la minimización de otros criterios distintos al Makespan o Workload, estos criterios son por ejemplo: El retardo medio de los trabajos (mean job tardiness), la suma de los tiempos de ejecución ( ), la suma promedio de los tiempos de ejecución ( ), tiempo promedio de las maquinas sin utilizar (mean machine idle time), entre otros.…”

Section: Introductionunclassified

Enrutamiento y secuenciación óptimos en un flexible job shop multiobjetivo mediante algoritmos genéticos

Muñoz

2016

idata

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RESUMENEl artículo propone, un algoritmo genético para solucionar óptimamente el problema de la programación de tareas en un sistema de producción Flexible Job Shop Scheduling (FJSS) multiobjetivo, actualmente de interés por muchos investigadores, porque es un problema de optimización combinatoria de complejidad NP-hard, y porque una solución óptima redunda en un aumento en la producción. Se divide el problema, en el subproblema de enrutamiento, en donde se asigna, a cada operación de los Jobs, una de las máquinas más óptima (desde un conjunto disponible) minimizando el Máximo Workload, y Total Workload, y el subproblema de secuenciación, en donde es encontrado el orden óptimo de ejecución de las operaciones (distribuidas en cada máquina) minimizando Matlab, su desempeño es puesto a prueba, solucionando complejos problemas, y los resultados se comparan con los obtenidos por otros investigadores.Palabras clave: Flexible Job Shop Scheduling Problem, algoritmos genéticos, makespan, máximo workload, total workload OPTIMUM ROUTING AND SEQUENCING IN A MULTIOBJECTIVE FLEXIBLE JOB SHOP USING GENETIC ALGORITHMS ABSTRACTThe paper proposes a genetic algorithm to solve optimally the problem of scheduling in a multi-objective production system Flexible Job Shop (FJS), currently of interest for many researchers, because it is a combinatorial optimization problem of complexity NP-hard, and because an optimal solution results in an increase in production. the problem is divided, in the subproblem routing, where it is assigned to each operation of Jobs, one of the most optimum machines (from a set available) minimizing Maximum Workload, and Total Workload and subproblem sequencing, where it is found the optimal order of execution of operations (distributed on each machine) minimizing the Makespan. The algorithm is coded in Matlab M language, their performance is tested, solving complex problems, and the results are compared with those obtained by other researchers. Keywords INTRODUCCIÓNUno de los problemas más difíciles en la programación de tareas se encuentra en la manufactura tipo taller (Job Shop Scheduling Problem -JSSP), donde un conjunto de jobs (trabajos) deben ser procesados en un conjunto de máquinas, cada job implica la ejecución de una secuencia de operaciones, las cuales requieren cada una exactamente una máquina para su ejecución. Las máquinas están siempre disponibles y pueden procesar una operación a la vez sin interrupción, el problema consiste en secuenciar el orden de ejecución de las operaciones en las máquinas, teniendo a su vez como objetivo el de optimizar algún indicador típico de rendimiento, como por ejemplo, el Makespan, es decir, el tiempo necesario para completar todos los trabajos (Pezzella et al., 2008).El Flexible Job-shop Scheduling Problem (FJSSP) es una generalización del clásico JSSP, ya que a diferencia del Job Shop, en donde cada una de las operaciones, perteneciente operaciones pueden ser ejecutadas en cualesquiera de un conjunto de máquinas disponibles. Entonces, solucionar el problema FJ...

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A Survey of Solving Approaches for Multiple Objective Flexible Job Shop Scheduling Problems

Cited by 20 publications

References 47 publications

Model Predictive Control for Flexible Job Shop Scheduling in Industry 4.0

Model Predictive Control for Flexible Job Shop Scheduling in Industry 4.0

Game-Based Hybrid Particle Swarm Optimization of Job-Shop Production Control

Enrutamiento y secuenciación óptimos en un flexible job shop multiobjetivo mediante algoritmos genéticos

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