Priority rules for job shops with weighted tardiness costs
Management Science
Computers and Operations Research
Scheduling jobs on parallel machines with setup times and ready times
Computers and Industrial Engineering
Scheduling: Theory, Algorithms, and Systems
Scheduling: Theory, Algorithms, and Systems
Robotics and Computer-Integrated Manufacturing
Engineering Applications of Artificial Intelligence
Multiobjective scheduling of jobs with incompatible families on parallel batch machines
EvoCOP'06 Proceedings of the 6th European conference on Evolutionary Computation in Combinatorial Optimization
Optimal scheduling for refueling multiple autonomous aerial vehicles
IEEE Transactions on Robotics
Solving the aerial fleet refueling problem using group theoretic tabu search
Mathematical and Computer Modelling: An International Journal
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The Aerial Refueling Scheduling Problem (ARSP) can be defined as determining the refueling completion times for fighter aircrafts (jobs) on multiple tankers (machines) to minimize the total weighted tardiness. ARSP can be modeled as a parallel machine scheduling with ready times and due date-to-deadline window to minimize total weighted tardiness. ARSP assumes that the jobs have different ready times and a due date-to-deadline window between refueling due date and a deadline to return without refueling. In this paper, we first formulate the ARSP as a mixed integer programming model. The objective function is a piece-wise tardiness cost that takes into account due date-to-deadline windows and job priorities. Since ARSP is NP-hard, two heuristics are proposed to obtain solutions in reasonable computation times, namely (1) modified ATC rule (MATC), (2) a simulated annealing method (SA). The proposed heuristic algorithms are tested in terms of solution quality and CPU time through computational experiments with data randomly generated to represent aerial refueling operations of an in-theater air operation. Solutions provided by both algorithms were compared to optimal solutions for problems with up to 12 jobs and to each other for larger problems with up to 60 jobs. The results show that, MATC is more likely to outperform SA especially when the problem size increases, although it has significantly worse performance than SA in terms of deviation from optimal solution for small size problems. Moreover CPU time performance of MATC is significantly better than SA in both cases.