In this study, we develop a three echelon supply chain model for items to determine the optimal reliability and production rate, which achieves the biggest total integrated profit for an imperfect manufacturing process. Here, we have taken a supplier, a manufacturer and a retailer in which supplier supplies raw materials to manufacturer, manufacturer produces perfect and imperfect quality items because practically it happens and manufacturer supplies perfect quality items to the retailers. In production system, production facility may shift from an in-control state to an out-of-control state at any random time. The basic assumption of classical economic manufacturing quantity model is that all manufacturing items are of perfect quality but the assumption is not true in practice. The proposed study is formulated assuming that a certain percent of total product is defective. This percentage also varies with production rate and production run time. The defective items are restored in original quality by reworked at some costs to maintain the quality of products in a competitive market. Finally, numerical example and its graphical representation are given to illustrate the proposed model. Sensitivity analysis is also provided to test feasibility of the model.

Marketing strategies and proper inventory replenishment policies are often incorporated by enterprises to stimulate demand and maximize profit. The aim of this paper is to represent an integrated model for dynamic pricing and inventory control of deteriorating items. To reflect the dynamic characteristic of the problem, the selling price is defined as a time-dependent function of the initial selling price and the discount rate. In this regard, the price is exponentially discounted to compensate negative impact of the deterioration. The planning horizon is assumed to be infinite and the deterioration rate is time-dependent. In addition to price, the demand rate is dependent on advertisement as a powerful marketing tool. Several theoretical results and an iterative solution algorithm are developed to provide the optimal solution. Finally, to show validity of the model and illustrate the solution procedure, numerical results are presented.

Selection of robots from the several proposed alternatives is a very important and tedious task. Decision makers are not limited to one method and several methods have been proposed for solving this problem. This study presents Polygons Area Method (PAM) as a multi attribute decision making method for robot selection problem. In this method, the maximum polygons area obtained from the attributes of an alternative robot on the radar chart is introduced as a decision-making criterion. The results of this method are compared with other typical multiple attribute decision-making methods (SAW, WPM, TOPSIS, and VIKOR) by giving two examples. To find similarity in ranking given by different methods, Spearman’s rank correlation coefficients are obtained for different pairs of MADM methods. It was observed that the introduced method is in good agreement with other well-known MADM methods in the robot selection problem.

Enhancing the overall machining performance implies optimization of machining processes, i.e. determination of optimal machining parameters combination. Optimization of machining processes is an active field of research where different optimization methods are being used to determine an optimal combination of different machining parameters. In this paper, multi-stage Monte Carlo (MC) method was employed to determine optimal combinations of machining parameters for six machining processes, i.e. drilling, turning, turn-milling, abrasive waterjet machining, electrochemical discharge machining and electrochemical micromachining. Optimization solutions obtained by using multi-stage MC method were compared with the optimization solutions of past researchers obtained by using meta-heuristic optimization methods, e.g. genetic algorithm, simulated annealing algorithm, artificial bee colony algorithm and teaching learning based optimization algorithm. The obtained results prove the applicability and suitability of the multi-stage MC method for solving machining optimization problems with up to four independent variables. Specific features, merits and drawbacks of the MC method were also discussed.

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.