The purpose of this paper is to discuss the state of the art on models and methods for reliability optimization problems (ROPs) including reliability allocation, redundancy allocation and reliability-redundancy allocation. There are literally few surveys for reviewing the literature of the ROPs. Tillman et al. (1980) classified the related papers by system structure, problem type, and solution methods, separately. In another work, Tzafestas (1980) reviewed system reliability optimization models and the optimization techniques. Yearout (1986) reviewed the literature related to standby redundancy. Kuo (2000) studied the system reliability optimization based on system structure and solution methods. Kuo and Prasad (2004) overviewed system reliability optimization methods. Later, Kuo (2007) reviewed recent advances in optimal reliability allocation problems. The present study adds to the previous literature surveys and focuses mainly on papers after year 2000 but with a quick review on the previous works so that the readers become familiar with the existing approaches. This research investigates the literature from system structure, system performance, uncertainty state and solution approach standpoints, simultaneously.

Inflation is an important factor influencing traditional economic order quality models. Marketing strategy depends on inflation due to public demand and availability of the materials. This paper presents an optimal inventory policy for deteriorating items using exponential demand rate under permissible delay in payments. Mathematical model has been derived under two cases: case I: cycle time is greater than or equal to permissible delay period, case II: cycle time is less than permissible delay period by considering holding cost as a function of time. Numerical examples and sensitivity analysis are given to reflect the numerical results. Mathematica software is used for finding optimal solutions.

This paper introduces a new comprehensive four-parameter distribution called the modified generalized linear failure rate (MGLFR) distribution. The method generalizes some well-known and most commonly used distributions in reliability such as exponential, Rayleigh, linear failure rate, generalized linear failure rate and modified Weibull distribution. The study also investigates some essential properties of this new distribution and considers the problem of the evaluation of system reliability by describing the lifetimes of components based on a fuzzy MGLFR distribution and by developing fuzzy reliability characteristics. The results can be applied to determine the reliability of real objects where parameters of lifetime variable are subject to uncertainty.

With the intensification of market competition, the competition form of firms is evolving from the competition among firms to the competition among supply chains. This paper considers a market with two competing supply chains consisting of one supplier and one manufacturer. The two supply chains compete on products’ quantities and research and development (R & D) level when the two manufacturers conduct technological innovation. This paper analyses the supply chain competition in three scenarios: two decentralized supply chains (DD), one decentralized supply chain and one centralized supply chain (DC) and two centralized supply chains (CC). The results indicate that the production quantity, the R & D level and the total profit of the integrated supply chain in DC scenario are the largest, CC scenario comes second, those of the DD scenario come third and those of the decentralized supply chain in DC scenario are the smallest. CC strategy is the supply chain system’s Nash equilibrium, which is good for the both supply chains, and there is no prisoner & apos; s dilemma.

Supply chain members cannot act independently and they need to act as a part of a unified system and coordinated with other members. Therefore, a coordination mechanism may be necessary to motivate members to achieve coordination. In this paper, the coordination problem is studied in a two-level supply chain consisting of a supplier and a retailer where demand is a function of price and advertising expenditures in two scenarios. The first scenario is “No coordination”, and the other scenario is “coordination with Revenue sharing contract”. The models are solved using game theory, Cooperative and Nash equilibrium. Finally, numerical examples are presented indicating that the average expected profit in the second scenario, coordination with revenue sharing, is higher than the first scenario. In addition numerical examples indicate that as price and advertising elasticity to demand increase, profitability of supply chain decreases.

This paper presents an experimental investigation on cutting temperature during hard turning of EN 24 steel (50 HRC) using TiN coated carbide insert under dry environment. The prediction model is developed using response surface methodology and optimization of process parameter is performed by desirability approach. A stiff rise in cutting temperature is noticed when feed and cutting speed are elevated. The effect of depth of cut on cutting temperature is not that much significant compared with cutting speed and feed as observed from main effects plot. The response surface second order model presented high correlation coefficient (R2 = 0.992) explaining 99.2 % of the variability in the cutting temperature which indicates the goodness of fit for the model to the actual data and high statistical significance of the model. The experimental and predicted values are very close to each other. The calculated error for cutting temperature lies between 1.88-3.19 % during confirmation trial. Therefore, the developed second order model correlates the relationship of the cutting temperature with the process parameters with good degree of approximation. The optimal combination for process parameter is depth of cut at 0.2mm, feed of 0.1597 mm/rev and cutting speed of 70m/min. Based on these combination, the value of cutting temperature is 302.950C whose desirability is one.

The objective of this work is to generalize the three echelon supply chain model proposed by Jaber and Goyal (2008) [Jaber, M. Y., & Goyal, S. K. (2008). Coordinating a three-level supply chain with multiple suppliers, a vendor and multiple buyers. International Journal Production Economics, 116, 95-103.] for multi-items where single item was considered in production and distribution. This paper develops the coordination amongst different parties in a three-echelon supply chain with a centralized decision process. Producer, suppliers and retailers are the parties of the supply chain where multiple suppliers deliver various types of raw materials to a producer; producer produces different types of items, multi-items, in different units of the factory and supplies the items to multiple retailers. Different deterioration rates for finished items and raw materials are also considered. The model developed of this paper guarantees that the local costs for the members either remain the same as before coordination, or decrease as a result of coordination. A numerical example along with graphical illustrations is considered and the sensitivity analysis is provided to test the feasibility of the proposed model.

In this paper, computational intelligence technique are presented for solving multi-point nonlinear boundary value problems based on artificial neural networks, evolutionary computing approach, and active-set technique. The neural network is to provide convenient methods for obtaining useful model based on unsupervised error for the differential equations. The motivation for presenting this work comes actually from the aim of introducing a reliable framework that combines the powerful features of ANN optimized with soft computing frameworks to cope with such challenging system. The applicability and reliability of such methods have been monitored thoroughly for various boundary value problems arises in science, engineering and biotechnology as well. Comprehensive numerical experimentations have been performed to validate the accuracy, convergence, and robustness of the designed scheme. Comparative studies have also been made with available standard solution to analyze the correctness of the proposed scheme.

Supplier selection management has been considered as an important subject for industrial organizations. In order to remain on the market, to gain profitability and to retain competitive advantage, business units need to establish an integrated and structured supplier selection system. In addition, environmental protection problems have been big solicitudes for organizations to consider green approach in supplier selection problem. However, finding proper suppliers involves several variables and it is critically a complex process. In this paper, the main attention is focused on finding the right supplier based on fuzzy multi criteria decision making (MCDM) process. The weights of criteria are calculated by analytical hierarchical process (AHP) and the final ranking is achieved by fuzzy technique for order preference by similarity to an ideal solution (TOPSIS). TOPSIS advantage among the other similar methods is to obtain the best solution close to ideal solution. The paper attempts to express better understanding by an example of an automobile manufacturing supply chain.

In this paper, an attempt is made to characterize the preservation technology for deteriorating items to reduce the deterioration rate. This model assumes a single producer and single supplier and formulates a production model with a time varying rate of deterioration rate. Here production and demand are treated as a fuzzy variables and total cost is minimized for both the crisp and fuzzy model. Shortage is allowed on the supplier’s part, which is partially backlogged. A solution procedure is presented to determine an optimal replenishment cycle and total cost per unit time, which is a convex function of preservation technology cost. Results have been validated with relevant example. In a way, the proposed model provides a unique theory to reduce the deterioration rate for the production model.