Currently many companies are interested in reduction of the carbon emissions associated with their supply chain activities such as transportation and operations. Operational decisions, such as modifications in order quantities could an effective way in reducing carbon emissions in the supply chain. Cap-and-trade regulation, sometimes called emissions trading, is a market-based tool to limit greenhouse gas emissions. Under cap-and-trade regulation, emission credits are allocated to the firms and the firms trades emissions under cap-and-trade schemes. In this paper, we propose a single-manufacturer single-buyer two-echelon supply chain problem under the cap-and-trade mechanism incorporating the carbon emissions caused by transportation and warehousing activities where a single manufacturer produces 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 (JIT) Purchasing. An integrated multi-product lot-splitting model of facilitating multiple shipments in small lots between buyer and manufacturer is developed in a JIT Purchasing environment. Also, an iterative heuristic algorithm is developed to derive the common order interval, the number of intervals for each product and the number of shipments between the buyer and the manufacturer during the common interval. A numerical example is given to illustrate the savings in reduction of total cost and carbon emissions by the inventory model incorporating cap-and-trade mechanism compared to the classical inventory model. The proposed inventory model could be useful for the practical solution of two-echelon supply chain inventory problem under cap-and-trade mechanism.

Various inventory control theories have tried to modelling and analyzing supply chains by using quantitative methods and characterization of optimal control policies. However, despite of various efforts in this research filed, the existing models cannot afford to be applied to the realistic problems. The most unrealistic assumption for these models is customer demand. Most of previous researches assume that the customer demand is stationary with a known distribution, whereas, in reality, the customer demand is not known a priori and changes over time. In this paper, we propose a reinforcement learning based adaptive echelon base-stock inventory control policy for a multi-stage, serial supply chain with non-stationary customer demand under the service level constraint. Using various simulation experiments, we prove that the proposed inventory control policy can meet the target service level quite well under various experimental environments.

수리가능 제품은 가격 비싸고 중요성이 크면서 새 제품을 구매하기 어려운 부품을 의미하며, 항공기 또는 선박의 엔진 등을 들 수 있다. 수리가능 제품에 대하여 고장이 발생할 경우 가용성을 유지하기 위하여 즉지 교체하여야 하고, 교체된 부품은 수리에 들어가야 한다. 이러한 문제에 대한 통제 시스템은 시스템의 효율성을 결정하는 중요한 요소이기 때문에, 다양한 시스템 구성에 대하여 많은 연구가 이루어져 왔다. 본 연구에서는 우선 중앙 수리기지와 여러 지역 수리

In a real-life supply chain environment, demand forecasting is usually represented by probabilistic distributions due to the uncertainty inherent in customer demands. However, the customer demand used for an actual supply chain planning is a single deterministic value for each of periods. In this paper we study the choice of single demand value among of the given customer demand distribution for a period to be used in the supply chain planning. This paper considers distributed multi-echelon supply chain and the objective function of this paper is to minimize the total costs, that is the sum of holding and backorder costs over the distribution network under the service level constraint, by using demand selection scheme. Some useful findings are derived from various simulation-based experiments.

The main focus of this study is to investigate the performance of a clark-scarf type multi-echelon serial supply chain operating with a base-stock policy and to optimize the inventory levels in the supply chains so as to minimize the systemwide total inventory cost, comprising holding and backorder costs as all the nodes in the supply chain. The source of supply of raw materials to the most upstream node, namely supplier, is assumed to have an infinite raw material availability. Retailer faces random customer demand, which is assumed to be stationary and normally distributed. If the demand exceeds on-hand inventory, the excess demand is backlogged. Using the echelon stock and demand quantile concepts and an efficient simulation technique, we derive near optimal inventory policy. Additionally we discuss the derived results through the extensive experiments for different supply chain settings.