Resource-constrained assignment scheduling
Operations Research
Single facility multi-class job scheduling
Computers and Operations Research
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Designing optimal aviation baggage screening strategies using simulated annealing
Computers and Operations Research
Simulated annealing heuristics for the dynamic facility layout problem
Computers and Operations Research
A Stochastic Optimization Algorithm Minimizing Expected Flow Times on Uniforn Processors
IEEE Transactions on Computers
Minimizing the number of late jobs in a stochastic setting using a chance constraint
Journal of Scheduling
Minimizing the number of tardy jobs with stochastically-ordered processing times
Journal of Scheduling
Batch scheduling to minimize total completion time
Operations Research Letters
Minimizing maximal regret in the single machine sequencing problem with maximum lateness criterion
Operations Research Letters
Robust vertex p-center model for locating urgent relief distribution centers
Computers and Operations Research
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In a real-world manufacturing environment featuring a variety of uncertainties, production schedules for manufacturing systems often cannot be executed exactly as they are developed. In these environments, schedule robustness that guarantees the best worst-case performance is a more appropriate criterion in developing schedules, although most existing studies have developed optimal schedules with respect to a deterministic or stochastic scheduling model. This study concerns robust single machine scheduling with uncertain job processing times and sequence-dependent family setup times explicitly represented by interval data. The objective is to obtain robust sequences of job families and jobs within each family that minimize the absolute deviation of total flow time from the optimal solution under the worst-case scenario. We prove that the robust single machine scheduling problem of interest is NP-hard. This problem is reformulated as a robust constrained shortest path problem and solved by a simulated annealing-based algorithmic framework that embeds a generalized label correcting method. The results of numerical experiments demonstrate that the proposed heuristic is effective and efficient for determining robust schedules. In addition, we explore the impact of degree of uncertainty on the performance measures and examine the tradeoff between robustness and optimality.