An Efficient 2D Genetic Algorithm for Optimal Shift Planning Considering DailyWise Shift Formats: A Case of Airport Ground Staff Scheduling Owing to the computational efficiency in dealing with combinatorial optimization problems, the genetic algorithms (GAs) have been widely applied to human resource planning and scheduling. Shift planning is of particular importance for personnel scheduling when practical concerns must be taken into account. Dailywise shift formats are often introduced in practical operations in order to facilitate execution of the planned tasks and accommodate certain managerial convenience. However, the highly repetitive nature of running dailywise shift formats entails an extreme imbalance of set covering between the tasks and staff availability, which leads to tremendous computational challenges in solving the combinatorial optimization problem that is subject to large redundancy of zero elements. In line with the inherent twodimensions of shift planning in terms of shift formats and days, this paper proposes a twodimensional (2D) encoding scheme to implement the GA for efficient shift planning. An application to a reallife airport Ground Staff Scheduling (GSS) problem is presented to illustrate the feasibility and potential of the proposed 2D GA for efficient handling of dailywise shift formats.

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.