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This research develops an optimization model for determining the order quantity for growing items by considering the imperfect quality and incremental discount by involving three supply chain members: farmers, processors, and retailers. The farmers are responsible for caring for the newborn items until they reach their ready-to-eat weight. The processors perform two roles, namely processing and screening. In the processing role, the processors process the grown items by a slaughtering and packaging process. Afterward, they inspected the processed items and categorized the items into good and poor quality. Finally, they shipped the end products to retailers. The retailers are responsible for selling good-quality items to the final consumers.  This research considers two kinds of poor quality. First is the poor quality of growing items in terms of mortality rate. The second is the poor quality of final products on the processor side. The processed items with poor quality are then sold to the secondary market at lower prices in one batch at the end of the period. This model also considers the incremental discounts offered by vendors to farmers and retailers to consumers for specific amounts of purchases. The model's objective function is to maximize the total supply chain profit, with the number of orders quantity, cycle time, and the number of batches delivery set as the decision variables. The sensitivity analysis results show that the most sensitive parameter in the model is the probability that the live items survive throughout the growth period.

In open pit mining industry, cut-off grade has an important roles in affecting the total profit that will be earned by the company. In this research, cut-off grade optimization model is developed for open pit mining industry to maximize the total profit. We consider the environmental aspect in this model which consists of reclamation cost and reclamation revenue. We also consider the revenue of sales and valuable wasted materials revenue, and also the cost of selling stage, processing cost, mining cost, waste removal/rehabilitation cost and fixed cost. The results show that the model is able to determine the optimal cut-off grade and total profit that will be earned by the company can be estimated. Besides that, we can also estimate the completion time of mining project and the value of Net Present Value (NPV) and Return on Investment (ROI). The application of the model can be illustrated using numerical example that given in this study.

In this research we developed an optimization model to determine truck allocation in open pit mining. The objective function is to minimize a total cost consists of investment and transportation costs. Investment cost is the purchase price of truck in early mining project according to the number of truck needed in each route. The transportation cost consists of fix cost for operators’ wages and variable cost in the form of fuel cost of trucks and shovels. The selection of truck types with different specifications is an important factor in the continuity of materials transportation by considering the costs incurred The model is solved using Oracle Crystal Ball Software for 5000 iterations. The result of optimization shows that Truck 75570 is selected to transport for route 1 and Route 4, Truck 75174 is selected to transport for Route 2, Truck 772 is selected to transport for Route 3, Truck 777F is selected to transport for Route 5, and Truck 7547 is selected to transport for Route 6. The total cost resulted from the optimization is IDR 295,783,073,068.69.

In the daily operation, there are frequently changes in customer order requirement which will induce instability of the MPS. Moreover, the frequently adjustment of MPS can induce fluctuation of production and increasing of inventory cost as well as decreasing service level of customer. Most of studies about instability of MPS use freezing method and rolling procedure to adjust MPS periodically. Freezing is the proportion of planning horizon being frozen, whereas rolling procedure is a method replanning periodically of MPS using newly updated demand data. This study is focused on interval freezing length as an issue of decision making. In supply chain, a manufacturer is supported by suppliers to supply material requirement. Since a manufacturer plan production schedule on MPS the freezing interval is determined that will be informed to suppliers which supply the material requirement. In previous research, the freezing interval is decided by manufacturer as necessary decision maker. This decision must be followed by suppliers though it is not beneficial for them. It can be concluded that this condition is no win-win situation. Hence, this research proposes that suppliers will be involved as decision maker besides a manufacturer so the interval freezing is decided by two-side decision maker.