This paper studies inventory and pricing policies in a non-cooperative supply chain with one supplier and several retailers who are involved in producing, delivering and selling a single product. We consider inventory policies in an information-asymmetric vendor managed inventory. The study consists of different scenarios where a supplier produces the product at the wholesale price to multiple retailers. The retailers also distribute the product in dispersed and independent markets at retail selling prices. The demand rate for each market is a non-decreasing concave function of the marketing expenditures of both local retailers and the manufacturer, but a non-increasing and convex function of the retail selling prices. The primary purpose is to determine wholesale price, marketing expenditure for supplier and retailers, replenishment cycles for the product, and backorder quantity to maximize the total profit for both groups of supplier and retailers. All scenarios are modeled as a Stackelberg game where the manufacturer is the leader and the retailers are the followers. A numerical study has been presented to demonstrate the influences of decision variables and/or parameters in various scenarios. Finally, sensitivity analysis is performed to study the impact of different parameters on total profitability.

Vendor Managed Inventory (VMI) is one of the effective techniques for managing the inventory in supply chain. VMI models have been proven to reduce the cost of inventory compared with traditional ones under some conditions such as constant demand and production expenditure. However, the modeling of the VMI problem has never been studied under some realistic assumptions such as price dependent demand. In this paper, three problem formulations are proposed. In the first problem formulation, we study a VMI problem with one buyer and one supplier when demand is considered to be a function of price and price elasticity to demand, and production cost is also a function of demand. The proposed model is formulated and solved in a form of geometric programming. For the second and the third models, we consider VMI problem with two buyers and two suppliers assuming that each buyer centre is relatively close to the other buyer centre. Each supplier has only one product which is different from the product of the other supplier. Two suppliers cooperate in customer relationship management and two buyers cooperate in supplier relationship management as well, so the suppliers send the orders of two buyers by one vehicle, simultaneously. For the third model, an additional assumption which is practically applicable and reasonable is considered. For all the proposed models, the optimal solution is compared with the traditional one. We demonstrate the implementation of our proposed models using some numerical examples.

Vendor Managed Inventory (VMI) is one of the effective techniques for managing the inventory in supply chain. VMI models have been proven to reduce the cost of inventory compared with traditional ones under some conditions such as constant demand and production expenditure. However, the modeling of the VMI problem has never been studied under some realistic assumptions such as price dependent demand. In this paper, three problem formulations are proposed. In the first problem formulation, we study a VMI problem with one buyer and one supplier when demand is considered to be a function of price and price elasticity to demand, and production cost is also a function of demand. The proposed model is formulated and solved in a form of geometric programming. For the second and the third models, we consider VMI problem with two buyers and two suppliers assuming that each buyer centre is relatively close to the other buyer centre. Each supplier has only one product which is different from the product of the other supplier. Two suppliers cooperate in customer relationship management and two buyers cooperate in supplier relationship management as well, so the suppliers send the orders of two buyers by one vehicle, simultaneously. For the third model, an additional assumption which is practically applicable and reasonable is considered. For all the proposed models, the optimal solution is compared with the traditional one. We demonstrate the implementation of our proposed models using some numerical examples.

Environmental legislation and customer expectations force manufactures to take back their products after use. In this paper a recovery system for a single item is studied, in which the random returns depend on the demand stream. The model is different from other works in this field by considering the effect of disposal cost at the end of the planning horizon, on the total inventory cost. The proposed model of this paper explains how to change the reorder point to maintain the optimal policy. The paper is also supported with some numerical example to demonstrate the implementation of the optimal reordering policy.

During the past few years, there have tremendous efforts on improving the cost of logistics using varieties of Vehicle Routing Problem (VRP) models. In fact, the recent rise on fuel prices has motivated many to reduce the cost of transportation associated with their business through an improved implementation of VRP systems. We study a specific form of VRP where demand is supposed to be uncertain with unknown distribution. A Particle Swarm Optimization (PSO) is proposed to solve the VRP and the results are compared with other existing methods. The proposed approach is also used for real world case study of drug distribution and the preliminary results indicate that the method could reduce the unmet demand significantly.

Data Envelopment Analysis (DEA) has been one of the most important tools on measuring the relative efficiency of different similar units such as transportation systems using terminals, airports, etc. In this study, we perform an empirical analysis on Iranian airports based on DEA methods to measure the efficiencies of various airports. One of the primary issues on many traditional DEA methods is that the data are almost always contaminated with noise. We use a DEA method which could handle the uncertainty associated with input and output data. The results of this comprehensive study show that most of the active airlines are practically inefficient and the government could significantly increase the efficiencies of the airports by setting new regulations and rules.

When a production facility is designed, there are various parameters affecting the number machines such as production capacity and reliability. It is often a tedious task to optimize different objectives, simultaneously. The other issue is the uncertainty in many design parameters which makes it difficult to reach a desirable solution. In this paper, we present a new mathematical model with two objectives. The primary objective function is considered to be the production capacity and the secondary objective function is total reliability. The proposed model is formulated on different units of production which are connected together in serial form and for each unit, we may have various machines. The resulted model is formulated using recent advances of robust optimization and solution procedure is analyzed with some numerical examples.

This article aims to review, identify and prioritize challenge factors of the implementation of knowledge management portals for Iranian organizations. The study determines several important weakness factors affecting the implications of the knowledge management such as the weakness in organizational strategy, information overcrowd, content management, portals project management, and etc. The study also indicates that the factors have different priorities where managerial factors are in the highest priority and financial factors are in the lowest priority. We also perform factor analysis to summarize seventeen factors into six issues: Financial and information security, Technology and management, Senior management support and strategy, Acceptance, User's motivation and culture, Project management, Change management and training.