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In this paper, we study a two-echelon supply chain inventory system with a single manufacturer and a single buyer, considering price-dependent demand and variable lead time. Lead time can be reduced by added crashing cost. We first assume that the lead time demand follows a normal probability distribution and then consider the case where it is distribution free. We develop both the centralized and decentralized models and derive the optimal solutions by using profit maximization criterion. Feasibility and efficiency of the proposed models are justified through two numerical examples. We numerically investigate how the optimal decisions are affected by changes in various model-parameters. From numerical study, we observe that even though the lead time reduction diminishes the retailer's selling price, it enhances the system profit significantly. [ABSTRACT FROM AUTHOR]

Abstract: One of the interesting subjects in supply chain management is supply management, which generally relates to the activities regarding suppliers such as empowerment, evaluation, partnerships and so on. A major objective of supplier evaluation involves buyers determining the optimal quota allocated to each supplier when placing an order. In this paper, we propose a multi-objective model in which purchasing cost, rejected units, and late delivered units are minimized, while the obtained total score from the supplier evaluation process is maximized. We assume that the buyer obtains multiple products from a number of predetermined suppliers. The buyer faces a stochastic demand with a probability distribution of Poisson regarding each product type. A major assumption is that the supplier prices are linearly dependent on the order size of each product. Since demand is stochastic, the buyer may incur holding and stockout costs in addition to the regular purchasing cost. We use the well-known L-1 metric method to solve the supplier evaluation problem by utilizing two meta-heuristic algorithms to solve the corresponding mathematical problems. [Copyright &y& Elsevier]