نشریه پژوهشهای مهندسی صنایع در سیستمهای تولید
پیاپی 2 (پاییز و زمستان 1392)

Because of the finite sale period، the inventory and market management of the seasonal goods is important. Also; in the competitive market، the price differences among market segments lead customers to substitute the similar cheaper goods with the expensive ones. We called this substitution “customer-based price driven substitution”. In this paper the joint pricing and inventory control for the seasonal and substitute goods is developed. We assume that two substitute goods belong to two different rival firms. The objective is to determine the optimal price، order quantity and the number of periods for one product such that the total profit is maximized. We first prove that the objective function is concave and have an optimum solution. Next we present an exact algorithm to determine the optimal solutions. Finally، we solve a numerical example for determination the efficiency of the model and algorithm.

Logistics service providers (LSPs) are playing an increasing role in the management of supply chains. LSPs offer a wide range of logistics services، e. g.، warehousing and transportation services and hence play an important role in supporting production and service systems. We consider an LSP who is responsible for managing the distribution of goods from multiple origins to multiple destinations for its clients. It is assumed that the commodity flow between each origin and destination is of stochastic nature with a symmetric and bounded probability distribution. The objective is to determine the number، location، and capacity of the hubs and also to allocate the customers to these hubs in such a way that transferring all the commodities in the network is ensured without capacity constraints associated with the hubs are not violated. At the same time، total expected system-wide costs will be minimized. The problem is modeled using one of the most recent robust optimization approaches and a standard optimization package being used to solve it. Results obtained via numerical experiments show the capability of the presented robust model to immunize the system against violation of capacity constraints with a relatively small cost increase، known as the robustness cost.

Economic Production Quantity (EPQ) model is one of the most famous inventory management models. In the economic production models، there is the possibility of producing defective items that missing (lacking) of it leads a nunrealistic model. In addition، inflation and the time value of money in the economic model make a considerable difference in the cost calculations. In this paper، the production of defective items in multi-product state by considering inflation، the time value of money and sequence dependent setup times are studied. The findings of previous studies showed that inserting the issue of inflation، the time value of money and also sequence dependent setup times in inventory management model leads to change optimal batch. A heuristic algorithm was used for reaching optimal point. The results of sensitivity analysis based on the parameters of interest rates، inflation rates and combination rate showed that not considering inflation and interest rates and sequence dependent setup times will lead to errors in calculating costs.

Firms increasingly purchase goods and services they require while focusing on their core competencies. In case of supplier’s poor performance، the typically aware buying firms either run development programs to improve the supplier or switch to a new better supplier depending on certain conditions. The conditions seem to be more challenging if the buying firm purchases its supplies from a firm with whom he competes in the market، a practice nowadays with many instances from various industries. In order to examine the issue analytically، we propose a mathematical model to compare supplier development and switching. We consider three firms competing in the market of a final product where one of them purchases his product from one of the competitors. Being dissatisfied with his current supplier’s price، the buying firm may participate in a cooperative investment with his supplier to help him reduce costs. Second، he may switch to the other lower cost supplier and competitor in the market. Despite being counter-intuitive، the mathematical results show that cooperation with a competing supplier is preferable to supplier switching under certain conditions.

Sometimes due to the inefficient transportation and inspection methods or improper storage، some items sent to the buyer are damaged that they might be reused by some types of remanufacturing. However، the defective items are naturally purchased at a lower price than the perfect ones. In this paper، a mixed-integer linear programming model is proposed for the multi-period joint supplier selection and order lot-sizing problem concerning both the percentage of defective as well as the remanufacturable items in which besides the purchase price، the transportation costs are also discounted according to the number of departure vehicles. The order lot-size is optimized with respect to three factors including the purchase price discount، the capacity of supplier''s vehicles and the carrying costs. To assess the validity of the proposed model، a numerical example from the recent literature is solved through the GAMS modeling language and the sensitivity of the model parameters is analyzed. The results confirm that considering simultaneously the three proposed objectives، i. e.، - purchase costs، transportation and ordering costs، and carrying costs، could result in an decrement of almost 20% in the total costs. Furthermore، suppliers can achieve a more chance of selection by delivery of the smaller percentage of defective items.

In this paper، a design problem for a two-echelon multi-commodity supply chain network with stochastic demands is taken into account. This network consists of production plants، warehouses and retailer (or final customers) in a single period. The problem considers strategic decisions (including location and capacities of production plants and warehouses) and operational decisions (including the transportation of commodities from the production plant to the warehouses and from warehouses to customers). Additionally، in this study، the demand is assumed to be stochastic and the given problem is modeled using a scenario-based stochastic programming approach. Specifically a two-stage stochastic programming model is presented to solve this problem. Furthermore، a Benders decomposition method is proposed in order to efficiently solve this problem. Finally، the conclusion is presented.

In this paper، an integrated production and distribution problem with goal of minimizing total weighted number of tardy jobs and transportation costs is considered. There is k customer and a production facility in which jobs are processed and delivered to customers in batches with routing. Delivering the products in batches reduces the delivery cost but it may increase the number of tardy jobs. The mentioned problem which complexity''s is strongly NP-Hard is considered for the first time. In this paper، a mixed integer programing model and a genetic algorithm with a heuristic crossover is developed to solve problem. Full factorial computational test and analysis of variance is performed for evaluation of these two methods. The obtained results show that the genetic algorithm is efficient for the problem.