A Hybrid Estimation of Distribution Algorithm for Simulation-Based Scheduling in a Stochastic Permutation Flowshop

The permutation flowshop scheduling problem (PFSP) is NP-complete and tends to be more complicated when considering stochastic uncertainties in the real-world manufacturing environments. In this paper, a two-stage simulation-based hybrid estimation of distribution algorithm (TSSB-HEDA) is presented to schedule the permutation flowshop under stochastic processing times. To deal with processing time uncertainty, TSSB-HEDA evaluates candidate solutions using a novel two-stage simulation model (TSSM). This model first adopts the regression-based meta-modelling technique to determine a number of promising candidate solutions with less computation cost, and then uses a more accurate but time-consuming simulator to evaluate the performance of these selected ones. In addition, to avoid getting trapped into premature convergence, TSSB-HEDA employs both the probabilistic model of EDA and genetic operators of genetic algorithm (GA) to generate the offspring individuals. Enlightened by the weight training process of neural networks, a self-adaptive learning mechanism (SALM) is employed to dynamically adjust the ratio of offspring individuals generated by the probabilistic model. Computational experiments on Taillard’s benchmarks show that TSSB-HEDA is competitive in terms of both solution quality and computational performance

Hybrid Particle Swarm Optimization for Hybrid Flowshop Scheduling Problem with Maintenance Activities

A hybrid algorithm which combines particle swarm optimization (PSO) and iterated local search (ILS) is proposed for solving the hybrid flowshop scheduling (HFS) problem with preventive maintenance (PM) activities. In the proposed algorithm, different crossover operators and mutation operators are investigated. In addition, an efficient multiple insert mutation operator is developed for enhancing the searching ability of the algorithm. Furthermore, an ILS-based local search procedure is embedded in the algorithm to improve the exploitation ability of the proposed algorithm. The detailed experimental parameter for the canonical PSO is tuning. The proposed algorithm is tested on the variation of 77 Carlier and Néron’s benchmark problems. Detailed comparisons with the present efficient algorithms, including hGA, ILS, PSO, and IG, verify the efficiency and effectiveness of the proposed algorithm

Asset Optimization and Predictive Maintenance in Discrete Manufacturing Industry

Nowadays, the current challenging issue in production is to deliver products in a more efficient manner by controlling, monitoring and centralising all intra-logistical processes. With the growing focus on sustainability, complexity grows even further as productions managers have to manage energy and material consumption, carbon footprint, and waste output in addition to Key Performance Indicators like process efficiency, asset utilization, quality, scrap rate and costs. Efforts to find the optimum for yield, quality, and speed or energy consumption individually often result in local optima, far from the ideal solution. Optimization must start at global bottlenecks within the plant or supply network, which can only be identified if overall process transparency is given. This project is included in a much larger project called PLANTCockpit (Production Logistics and Sustainability Cockpit) which is a FP7 FoF ICT Collaborative Project. Here the aim of this project is to ensure the optimized use of available resource (personal, equipment, material and energy) for a scheduled product plan with continuous asset monitoring in discrete manufacturing industry. This issue is closed to real-world manufacturing problems and demands awareness from production managers on the holistic aspect of engineering assets availability. It includes the reliable detection and anticipation of performance deviations via monitoring the production and product related process, diagnostic of possible causes and predicting the time of occurrence. In such a context, PLANTCockpit project has been specially proposed to provide a decision support mechanism for an integrated maintenance and production management and consequently for asset optimization. In this project, a study of the existing methodologies for asset management and optimization will be performed. The outcome of this study will provide a novel approach for asset management and optimization. Furthermore, predictive maintenance and MIMOSA (Machinery Information Management Open System Alliance) standard will be one of the current issued to be tackled in order to prove improvement in the optimization for asset utilization. Keywords: Asset management, Asset optimization, Predictive Maintenance, Product Lifecycle Management (PLM), MIMOSA.Outgoin

Mejora de tiempos de entrega en un flow shop híbrido flexible usando técnicas inteligentes. Aplicación en la industria de tejidos técnicos

Se busca aportar herramientas útiles para la programación de producción en la industria de tejidos técnicos. Se parte de las condiciones actuales de la programación de producción en este tipo de industria y de los antecedentes en la literatura científica sobre modelos aplicables a estos entornos. Se propone un modelo de solución por técnicas inteligentes a la problemática de la secuenciación y asignación de tareas en los entornos flow shop híbrido flexible considerando situaciones como: paralelismo entre máquinas no relacionadas, tiempos de montaje dependientes de la secuencia, entrada dinámica de trabajos, restricción de elegibilidad, maleabilidad y lotes de transferencia variables entre etapas. De allí se construye la propuesta de solución que involucra simultáneamente todas las condiciones de entorno real mencionadas y aplica un algoritmo genético modificado de acuerdo a las características del problema. Se concluye que el modelado considerando condiciones realistas es posible, que los algoritmos genéticos son una opción práctica para entornos reales y que las empresas pueden obtener mejoras en su capacidad de respuesta con este tipo de solucionesAbstract : It seeks to provide useful tools for production scheduling in the technical textiles industry. It begins in the current conditions of production scheduling in this type of industry and the background in scientific literature, applicable to these environments models. The mathematical model to solve the problem of sequencing and assigning jobs in Flexible hybrid flow shop environments is developed considering: unrelated parallel machines, sequence dependent setup time, dynamic entry of jobs, availability constrain, malleability and variable transfer batches between stages. The solution proposal is build including all actual environment features considered together and applying a modified genetic algorithm modeled according to the problem. It is concluded that the model of scheduling problems considering realistic conditions is possible, that genetic algorithms are a practical option for real environments, and that companies can achieve improvements in their responsiveness with this kind of solutionsDoctorad