A new and general stochastic parallel machine ScheLoc problem with limited location capacity and customer credit risk

Scheduling-Location (ScheLoc) problem considering machine location and job scheduling simultaneously is a relatively new hot topic. The existing works assume that only one can be placed at location, which may not suitable for some practical applications. Besides, the customer credit risk largely impacts manufacturer’s profit has been addressed in ScheLoc problem. Therefore, this work, we study general stochastic parallel with limited capacity risk. consists of determining machine-to-location assignment, acceptance, job-to-machine accepted jobs on each machine. objective to maximize worst-case probability being greater than or equal given (referred as likelihood). For problem, distributionally robust chance-constrained (DRCC) programming model proposed. Then, develop two model-based approaches: (1) sample average approximation (SAA) method; (2) constructive heuristic. Numerical results 300 instances adapted from literature show likelihood proposed by heuristic 9.43% higher provided SAA, while computation time 4.24% needed SAA.