INVENTORY models in operation research, Lecture notes of Mathematical finance

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Invenrory/PropucTION MANAGEMENT-U (Stochastic Inventory Models & ABC Analysis) 21.1. INTRODUCTION e inventory models discussed in the previous chapter seems to be unrealistic because, in practical situations seldom happens that future demand is known exactly. But, the probability distribution of future demand can determined by using well-known statistical techniques. Since the probabilistic demand may also be tionary OT non-stationary, the probabilistic models can be further classified according to these aracteristics as follows : . Further Classification of Inventory Models Inventory Models r 1 1 Elementary Inventory Quantity Discount (Price Break) Models with Restrictions Models. and Others Maels Deterministic (Known Demand) Models [Note : These models (I to Vv) are already classified and discussed in Chapter 20] Probabilistic (Stochastic) Demand Models Stationary Demand Non-stationary Demand Models. Model Mobil Ix Multiperiod probabilistic model with variable lead time. (Fixed Order System) madel vl Mode! vil mode! vutt Uniform demand, no setup cost lead timo zero or constant except withdrawals from stock are uniform rather than instantaneous, (Vila) Discrete units (VII b) Continuous units (VIlc) Lead timo is significant Instantaneous demand, No set-up cost, lead time zero or constant. (Vla) Discrete units (Vib) Continuous units (Vic) Re-order lead time prescribed. Probabilistic order level system with lead time (Villa) Discrete units (VIIlb) Continuous units In probabilistic systems, we minimize the total expected costs rather than actual costs. Scanned with CamScanner