Integrated automation system with PSO based scheduling for PCB remanufacturing plants Flexibility and reconfigurability represent crucial challenges to be addressed to achieve sustainable remanufacturing processes, supporting efficient EndOfLife treatment of heterogeneous mixes of postuse products. In this context, Printed Circuit Boards (PCB) represent a key target to be addressed. In this work, we propose an integrated automation system fostering the implementation of flexible PCB remanufacturing plants. In particular, we extend previous works by introducing online scheduling facilities. To this end, we developed a predictivereactive algorithm by a Unified Particle Swarm Optimization form. The objective function combines the total weighted tardiness and the idle time of the rework machine to minimize energy consumption due to dissipations. We show the effectiveness of the proposed approach by application to the STIIMACNR DeReManufacturing pilot plant, reporting improved performances compared to previously implemented scheduling rules.

Minimizing total energy cost and tardiness penalty for a schedulinglayout problem in a flexible job shop system: A comparison of four metaheuristic algorithms Abstract   Job scheduling and machine layout are interrelated in improving energy consumption (EC) and productivity measures such as tardiness and represent two important decisions that must be made by manufacturers. This interdependency can be explained by transportation time, which connects scheduling and layout. Scheduling and layout, however, have not been thoroughly studied in conjunction using an integrated model in the context of sustainable manufacturing. Hence, we propose an energyaware optimization model in which scheduling is integrated with layout in a singlelevel framework. More specifically, a single objective function is defined to minimize the facility energy cost and the job tardiness penalty, which control EC and tardiness respectively in a flexible job shop system. In order to model machine EC more accurately, we also consider three different machine states: a processing state and two idle states. Our case studies show that the integrated model exhibits better performance in controlling manufacturing EC and job tardiness than a nonintegrated model in which machine locations are uncontrollable and transportation times between machines are unchangeable. To deal with largesized problems, we also introduce four new metaheuristics. The performances of these new metaheuristics are compared in terms of objective function values and CPU times using various case studies. The results indicate that a hybrid ant colony optimization and simulated annealing (ACOSA) algorithm provides better performance than the other algorithms. Specifically, our case studies show that the integrated model using ACOSA can improve the objective function value by around 5% when compared to the nonintegrated model.