In this paper, we develop an efficient approach to solve a multiple-item budget-constraint newsboy problem with a reservation policy. A conventional approach for solving such problem utilizes an approximation for the evaluation of an inverse of a Gaussian cumulative density function when the argument of the function is small, and a heuristic method for finding an optimal Lagrangian multiplier. In contrast to the conventional approach, this paper proposes more accurate method of evaluating the function by using the normalization and an effective numerical integration method. We also propose an efficient way to find an optimal Lagrangian multiplier by proving that the equation for the budget-constraint is in fact a monotonically increasing function in the Lagrangian multiplier. Numerical examples are tested to show the performance of the proposed approach with emphases on the behaviors of the inverse of a Gaussian cumulative density function and the Lagrangian multiplier. By using sensitivity analysis of different budget constraints, we show that the reservation policy indeed provides greater expected profit than the classical model of not having the reservation policy.

This paper deals with a centralized warehouse problem with multi-item and capacity constraint. The objective of this paper is to decide the number and location of centralized warehouses and determineorder quantity (Q), reorder point (r) of each centralized warehouse to minimize holding, setup, penalty, and transportation costs. Each centralized warehouse uses continuous review in- ventory policy and its budget is limited. A SA (Simulated Annealing) approach is developed and its performance is tested by using some computational experiments.

In this paper, we consider a single-manufacturer single-buyer supply chain problem where a single manufacturer purchases and processes raw materials into a family of items in order to deliver a family of items to a single buyer at a fixed interval of time for effective implementation of Just-In-Time Purchasing. An integrated multi-item lot-splitting model of facilitating multiple shipments in small lots between buyer and manufacturer is developed in a JIT Purchasing environment. Previous research on the integrated model assumed that the manufacturer orders raw materials m (integer) times for every production run (lot multiplier policy for the raw material). In this paper, we consider a generalized policy in the replenishment of raw materials, allowing lot multiplier policy and lot splitting policy. An iterative solution procedure is developed to find the order interval for finished goods and raw materials, and number of shipments between buyer and manufacturer. We show by numerical example that when the integrated policy is adopted by both buyer and manufacturer in a cooperative manner, both parties can benefit.

Inventory centralization for a number of stores may reduce inventory costs by establishing and maintaining a central ordering and distribution point. In this study, a centralized warehouse problem with multi-item and capacity constraint is considered. The objective of this study is to develop a methodology to decide the number and location of centralized warehouses and determine order quantity(Q), reorder point(r) of each centralized warehouse to minimize holding, setup, penalty, and transportation costs. In this problem, each centralized warehouse uses continuous review inventory policy and its budget is limited. A SA(Simulated Annealing) approach for this problem is developed.

논문은 혼합정수계획법(MIP)을 통하여 다품종 제조 시스템을 위한 생산계획수립 방안에 대하 여 연구하였다. 특히 자동차 부품 제조 기업의 다양한 제조 품목에 대한 수요 변화에 따라 Rolling Horizon 방식으로 생산계획을 수립하도록 함으로써, 수요 변화에 능동적으로 대응하면서 도 한정된 기업의 생산 능력을 최대한 활용하고 생산 비용을 최소화하는 최적 생산계획을 수립하 여 실제로 기업에서 활용할 수 있도록 하는 사례를 연구하였다.

In this paper, we examine the effect of demand variability in multi-item inventory systems where supply lead-times are endogenously generated by a production system with finite capacity. In contrast to systems with exogenous lead times, we show that variability in lead-time demand actually decreases with demand variability. We also show that higher demand variability leads to a smaller fraction of total stock being devoted to safety stock. More significantly, we show that a sufficiently large increase in demand variability can lead to the elimination of safety stocks altogether. These results suggest that strategies used to reduce lead-time demand variability would be less valuable when demand variability is high.

ABC analysis is the process of dividing inventory items into three classes according to their monetary usage so that managers can focus on Items that have the highest monetary usage value, and has been widely used in practice since Its development in 1950