A decentralized metaheuristic approach applied to the FMS scheduling problem

La programación de FMS ha sido uno de los temas más populares para los investigadores. Se han entregado varios enfoques para programar los FMS, incluidas las técnicas de simulación y los métodos analíticos. Las metaheurísticas descentralizadas pueden verse como una forma en que la población se divide en varias subpoblaciones, con el objetivo de reducir el tiempo de ejecución y el número de evaluaciones, debido a la separación del espacio de búsqueda. La descentralización es una ruta de investigación prominente en la programación, por lo que el costo de la computación se puede reducir y las soluciones se pueden encontrar más rápido, sin penalizar la función objetivo. En este proyecto, se propone una metaheurística descentralizada en el contexto de un problema de programación flexible del sistema de fabricación. La principal contribución de este proyecto es analizar otros tipos de división del espacio de búsqueda, particularmente aquellos asociados con el diseño físico del FMS. El desempeño del enfoque descentralizado se validará con los puntos de referencia de programación de FMS.FMS scheduling has been one of the most popular topics for researchers. A number of approaches have been delivered to schedule FMSs including simulation techniques and analytical methods. Decentralized metaheuristics can be seen as a way where the population is divided into several subpopulations, aiming to reduce the run time and the numbers of evaluation, due to the separation of the search space. Decentralization is a prominent research path in scheduling so the computing cost can be reduced and solutions can be found faster, without penalizing the objective function. In this project, a decentralized metaheuristic is proposed in the context of a flexible manufacturing system scheduling problem. The main contribution of this project is to analyze other types of search space division, particularly those associated with the physical layout of the FMS. The performance of the decentralized approach will be validated with FMS scheduling benchmarks.Ingeniero (a) IndustrialPregrad

Pollux: a dynamic hybrid control architecture for flexible job shop systems

Nowadays, manufacturing control systems can respond more effectively to exigent market requirements and real-time demands. Indeed, they take advantage of changing their structural and behavioural arrangements to tailor the control solution from a diverse set of feasible configurations. However, the challenge of this approach is to determine efficient mechanisms that dynamically optimise the configuration between different architectures. This paper presents a dynamic hybrid control architecture that integrates a switching mechanism to control changes at both structural and behavioural level. The switching mechanism is based on a genetic algorithm and strives to find the most suitable operating mode of the architecture with regard to optimality and reactivity. The proposed approach was tested in a real flexible job shop to demonstrate the applicability and efficiency of including an optimisation algorithm in the switching process of a dynamic hybrid control architecture.This work was supported by the Colombian scholarship programme of department of science – COLCIENCIAS under grant ‘Convocatoria 568 – Doctorados en el exterior’ and the Pontificia Universidad Javeriana under grant ‘Programa de Formacion de posgrados del Profesor Javeriano’.info:eu-repo/semantics/publishedVersio

A Memetic Algorithm with Reinforcement Learning for Sociotechnical Production Scheduling

The following interdisciplinary article presents a memetic algorithm with applying deep reinforcement learning (DRL) for solving practically oriented dual resource constrained flexible job shop scheduling problems (DRC-FJSSP). From research projects in industry, we recognize the need to consider flexible machines, flexible human workers, worker capabilities, setup and processing operations, material arrival times, complex job paths with parallel tasks for bill of material (BOM) manufacturing, sequence-dependent setup times and (partially) automated tasks in human-machine-collaboration. In recent years, there has been extensive research on metaheuristics and DRL techniques but focused on simple scheduling environments. However, there are few approaches combining metaheuristics and DRL to generate schedules more reliably and efficiently. In this paper, we first formulate a DRC-FJSSP to map complex industry requirements beyond traditional job shop models. Then we propose a scheduling framework integrating a discrete event simulation (DES) for schedule evaluation, considering parallel computing and multicriteria optimization. Here, a memetic algorithm is enriched with DRL to improve sequencing and assignment decisions. Through numerical experiments with real-world production data, we confirm that the framework generates feasible schedules efficiently and reliably for a balanced optimization of makespan (MS) and total tardiness (TT). Utilizing DRL instead of random metaheuristic operations leads to better results in fewer algorithm iterations and outperforms traditional approaches in such complex environments.Comment: This article has been accepted by IEEE Access on June 30, 202

Robotix-Academy Conference for Industrial Robotics (RACIR) 2019

Robotix-Academy Conference for Industrial Robotics (RACIR) is held in University of Liège, Belgium, during June 05, 2019. The topics concerned by RACIR are: robot design, robot kinematics/dynamics/control, system integration, sensor/ actuator networks, distributed and cloud robotics, bio-inspired systems, service robots, robotics in automation, biomedical applications, autonomous vehicles (land, sea and air), robot perception, manipulation with multi-finger hands, micro/nano systems, sensor information, robot vision, multimodal interface and human-robot interaction.

A switching mechanism framework for optimal coupling of predictive scheduling and reactive control in manufacturing hybrid control architectures

Nowadays, manufacturing systems are seeking control architectures that offer efficient production performance and reactivity to disruptive events. Dynamic hybrid control architectures are a promising approach as they are not only able to switch dynamically between hierarchical, heterarchical and semi-heterarchical structures, they can also switch the level of coupling between predictive scheduling and reactive control techniques. However, few approaches address an efficient switching process in terms of structure and coupling. This paper presents a switching mechanism framework in dynamic hybrid control architectures, which exploits the advantages of hierarchical manufacturing scheduling systems and heterarchical manufacturing execution systems, and also mitigates the respective reactivity and optimality drawbacks. The main feature in this framework is that it monitors the system dynamics online and shifts between different operating modes to attain the most suitable production control strategy. The experiments were carried out in an emulation of a real manufacturing system to illustrate the benefits of including a switching mechanism in simulated scenarios. The results show that the switching mechanism improves response to disruptions in a global performance indicator as it permits to select the best alternative from several operating modes.This article was supported by COLCIENCIAS Departamento Administrativo de Ciencia, Tecnología e Innovación 10.13039/100007637 [Grant Number Convocatoria 568 Doctorados en el exterior]; Pontificia Universidad Javeriana [Grant Number Programa de Formacion de posgrados].info:eu-repo/semantics/publishedVersio

A multi objective volleyball premier league algorithm for green scheduling identical parallel machines with splitting jobs

Parallel machine scheduling is one of the most common studied problems in recent years, however, this classic optimization problem has to achieve two conflicting objectives, i.e. minimizing the total tardiness and minimizing the total wastes, if the scheduling is done in the context of plastic injection industry where jobs are splitting and molds are important constraints. This paper proposes a mathematical model for scheduling parallel machines with splitting jobs and resource constraints. Two minimization objectives - the total tardiness and the number of waste - are considered, simultaneously. The obtained model is a bi-objective integer linear programming model that is shown to be of NP-hard class optimization problems. In this paper, a novel Multi-Objective Volleyball Premier League (MOVPL) algorithm is presented for solving the aforementioned problem. This algorithm uses the crowding distance concept used in NSGA-II as an extension of the Volleyball Premier League (VPL) that we recently introduced. Furthermore, the results are compared with six multi-objective metaheuristic algorithms of MOPSO, NSGA-II, MOGWO, MOALO, MOEA/D, and SPEA2. Using five standard metrics and ten test problems, the performance of the Pareto-based algorithms was investigated. The results demonstrate that in general, the proposed algorithm has supremacy than the other four algorithms

Improving just-in-time delivery performance of IoT-enabled flexible manufacturing systems with AGV based material transportation

Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly

Improving just-in-time delivery performance of IoT-enabled flexible manufacturing systems with AGV based material transportation

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly

Flexible Job Shop Scheduling with Sequence-dependent Setup and Transportation Times by Ant Colony with Reinforced Pheromone Relationships

This paper proposes a swarm intelligence approach based on a disjunctive graph model in order to schedule a manufacturing system with resource flexibility and separable setup times. Resource flexibility assigns each operation to one of the alternative resources (assigning sub-problem) and, consequently, arranges the operation in the right sequence of the assigned resource (sequencing sub-problem) in order to minimize the makespan. Resource flexibility is mandatory for rescheduling a manufacturing system after unforeseen events which modify resource availability. The proposed method considers parallel (related) machines and enforces in a single step both the assigning and sequencing sub-problems. A neighboring function on the disjunctive graph is enhanced by means of a reinforced relation-learning model of pheromone involving more effective machine-sequence constraints and a dynamic visibility function. It also considers the overlap between the jobs feeding and the machine (anticipatory) setup times. It involves separable sequence-independent and dependent setup phases. The algorithm performance is evaluated by modifying the well-known benchmark problems for JOB shop scheduling. Comparison with other systems and lower bounds of benchmark problems has been performed. Statistical tests highlight how the approach is very promising. The performance achieved when the system addresses the complete problem is quite close to that obtained in the case of the classical job-shop problem. This fact makes the system effective in coping with the exponential complexity especially for sequence dependent setup times