Abstract
'Customer support' includes all value-added product services after the product sale, such as installation, maintenance and repairs, etc. A high-level of uncertainty and fluctuations accompanies the demand for customer support services. Hence, the system needs optimal field-staffing decisions with the least risk. The problem becomes all the more challenging under restricted working hour conditions. Daily working hour duration embedded with 'decision epochs' partly mitigates demand uncertainty. This research work uses this framework and assesses the volatile demand with a probability distribution. Further, this research develops a decision model using the Markov Decision Process (MDP) in a Dynamic Programmin (DP) framework to optimise the field-staffing decisions considering demand fluctuations and risk. Results of the numerical analysis show that more decision epochs reduces cost. Also, the neutral risk policies become optimal with the increase in the number of decision epochs.
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