A Reusable Scheduling Problem Decomposition Framework for Smart Factories In most scheduling problems, such as plant and supply chain problems, the linkages between resources, and operations are often clearly demarcated due to their lateral, sequential nature. In comparison, the combinatorial nature, arising from the inherent network structure of a flexible manufacturing system problem causes the associated jobs, automated guided vehicles and job shops to be intermeshed topologically and temporally. The temporal and spatial network structure impedes direct application of conventional decomposition approaches. In this study, a reusable scheduling problem decomposition framework for Industry 4.0 that facilitates agent embedded optimization is being proposed. Modularity is enabled when each agent and its respective governed resource or resource group is replaceable by one that is similar in functionality, but possibly of a different layout. This schema is attractive to potential schedulers as the plugandplay nature of resources, via modularity, optimizes user friendliness via minimal customizations.

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.