Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
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Items made of glass, ceramic, etc. are normally stored in stacks and get damaged during the storage due to the accumulated stress of heaped stock. The researchers have overlooked the inventory problems for this type of items. Again the classical iterative optimization techniques very often stuck to the local optimum present in the search space. This is one of the hindrances in optimizing the non-linear problems.Annealing is the physical process of heating up a solid until it melts followed by cooling it down until it crystallizes into a state with perfect lattice. Following this physical phenomenon, recently a soft computing method, Simulated Annealing (SA), has been developed to find the global optimum for a complex cost surface through stochastic search process.In this paper, a deterministic inventory model of a damageable item is developed with variable replenishment rate and unit production cost. Here replenishment rate and unit production cost are dependent on demand. Demand and damageability are stock dependent. This dependency may be linear or non-linear. The optimum inventory level is evaluated by the profit maximization principle through SA algorithm. The model is illustrated numerically with different forms of demand and damage functions.