Iranian Journal Of Operations Research
Volume:4 Issue: 2, Summer and Autumn 2013

A number of problems in several areas such as power transmission and distribution, communication and transportation can be formulated as a stochastic-flow network (SFN). The system reliability of an SFN can be computed in terms of all the upper boundary points, called d-MinCuts (d-MCs). Several algorithms have been proposed to find all the d-MCs in an SFN. Here, some recent studies in the literature on search for all d-MCs are investigated. We show that some existing results and the corresponding algorithms are incorrect. Then, correct versions of the results are established. By modifying an incorrect algorithm, we also propose an improved algorithm. In addition, complexity results on a number of studies are shown to be erroneous and correct counts are provided. Finally, we present comparative numerical results in the sense of performance profile of Dolan and Moré showing the proposed algorithm to be more efficient than some existing algorithms.

A high performance numerical technique in the study of aorto-coronaric bypass anastomoses configurations using steady Stokes equations is presented. The problem is first expressed as an optimal control problem. Then, by using an embedding method, the class of admissible shapes is replaced by a class of positive Borel measures. The optimization problem in measure space is then approximated by a linear programming problem. The optimal measure representing optimal shape is approximated by solving this finite-dimensional linear programming problem. An illustrative example demonstrates the effectiveness of the method.

Our aim is to maximize expected profit per item of a multi-stage production system by determining best adjustment points of the equipments used based on technical product specifications defined by designer. In this system, the quality characteristics of items produced should be within lower and higher tolerance limits. When a quality characteristic of an item either falls beneath the lower limit or lies above the upper limit, it is reworked or classified as scrap, each with its own cost. A function of the expected profit per item is first presented based on equipment adjustment points. Then, the problem is modeled by a Markovian approach. Finally, numerical examples are solved in order to illustrate the proposed model.

We develop a price competition model for a new supply chain that competes in a market comprised of some rival supply chains. The new supply chain has one risk-neutral manufacturer and one risk-averse retailer in which the manufacturer is a leader and retailer is a follower. The manufacturer pays a fraction of the risk cost (caused by demand uncertainty) to the retailer. We apply this competitive model to a real-world case in a supply chain under uncertain environment and obtain the optimal wholesale and retail prices. We show that our obtained prices are better than the existing wholesale and retail prices and admit more profits for both manufacturer and retailer and generally for the entire supply chain. Also, using this case, the effects of risk sensitivity of retailer and fraction of risk cost shared by manufacturer in the total risk cost on the new supply chain’s optimal wholesale and retail prices and profits are illustrated.

We focus on a three-stage supply chain problem for fast moving consumer goods including a supplier, a manufacturer and customers. There are different orders over identical cycles, to be processed in production site. The problem is to find a joint cyclic schedule of raw material procurement and job scheduling minimized the total cost comprised of raw material ordering cost and holding cost, production cost, holding cost of finished products, tardiness cost and rejection cost. An integrated mixed integer programing model is proposed and optimal solution of some instances are provided by solving the model.

Quality function deployment is a well-known customer-oriented design procedure for translating the voice of customers into a final production. This is a way that higher customer satisfaction is achieved while the other goals of company may also be met. This method, at the first stage, attempts to determine the best fulfillment levels of design requirements which are emanated by customer needs. In real-world applications, product design processes are performed in an uncertain and imprecise environment, more than one objective should be considered to identify the target levels of design requirements, and the values of design requirements are often discrete. Regarding these issues, a fuzzy mixed-integer linear goal programming model with a flexible goal hierarchy is proposed to achieve the optimized compromise solution from a given number of design requirement alternatives. To determine relative importance of customer needs, as an important input data, we apply the well-known fuzzy AHP method. Inspired by a numerical problem, the efficiency of our proposed approach is demonstrated by several experiments. Notably, the approach can easily and efficiently be matched with QFD problems.

We develop an inventory model to determine optimal ordering policy under permissible delay in payment by considering demand rate to be stock dependent. Mathematical models are derived under two different cases: credit period being greater than or equal to cycle time for settling the account, and credit period being less than or equal to cycle time for settling the account. The results are illustrated with numerical examples. Sensitivity analysis is given for the proposed model.