In response to increasing inflexible customer demands and to improve the competitive advantage, industrial organizations have to adopt strategies to achieve cost reduction, continual quality improvement, increased customer service and on-time delivery performance. Selection of the most suitable plant or facility layout design for an organization is one among the most important strategic issues to fulfill all these above-mentioned objectives. Nowadays, many industrial organizations have come to realize the importance of proper selection of the plant or facility layout design to survive in the global competitive market. Selecting the proper layout design from a given set of candidate alternatives is a difficult task, as many potential qualitative and quantitative criteria need to be considered. This paper proposes a Euclidean distance based approach (WEDBA) as a multiple attribute decision making method to deal with the complex plant or facility layout design problems of the industrial environment. Three examples are included to illustrate the approach.

Supply chain coordination as an effective tool plays an important role in improving supply chain performance. In this article, a two-level supply chain with one manufacturer and two retailers is considered. The order quantity that retailers are faced with depends on the amount of advertisements and both retailers compete with each other on advertising. The Stackelberg game is established between manufacturer and retailers such that the manufacturer and the retailers play the leader and the follower roles, respectively. First, the manufacturer determines the wholesale prices for retailers and instead, the retailers determine the order quantity and advertising level, simultaneously. The manufacturer produces one kind of product and delivers it to retailers before the beginning of selling season. Retailers can affect the order quantity regarding the demand dependency on advertising level through the incurred costs from the advertising. In this paper, we show that we can achieve the desirable supply chain coordination through using combined quantity discount and advertising cost sharing contracts. We also consider the win-win situation for all the members of the supply chain.

Evaluation of proper supplier for manufacturing organizations is one of the most challenging problems in real time manufacturing environment due to a wide variety of customer demands. It has become more and more complicated to meet the challenges of international competitiveness and as the decision makers need to assess a wide range of alternative suppliers based on a set of conflicting criteria. Thus, the main objective of supplier selection is to select highly potential supplier through which all the set goals regarding the purchasing and manufacturing activity can be achieved. Because of these reasons, supplier selection has got considerable attention by the academicians and researchers. This paper presents a combined multi-criteria decision making methodology for supplier evaluation for given industrial applications. The proposed methodology is based on a compromise ranking method combined with Grey Interval Numbers considering different cardinal and ordinal criteria and their relative importance. A ‘supplier selection index’ is also proposed to help evaluation and ranking the alternative suppliers. Two examples are illustrated to demonstrate the potentiality and applicability of the proposed method.

In this paper, we study a supply chain problem where a whole seller/producer distributes goods among different retailers. Such problems are always faces with uncertainty with input data and we have to use various techniques to handle the uncertainty. The proposed model of this paper considers different input parameters such as demand, capacity and cost in trapezoid fuzzy forms and using two ranking methods, we handle the uncertainty. The results of the proposed model of this paper have been compared with the crisp and other existing fuzzy techniques using some randomly generated data. The preliminary results indicate that the proposed models of this paper provides better values for the objective function and do not increase the complexity of the resulted problem.

Minimizing the total cost is absolutely the goal of each supply chain, which is most of the time pursued. In this regards, quality related costs that have significant roles are sometimes neglected. Selecting suppliers, which supply relatively high quality raw materials in a lower cost is considered as a strategic decision. Make or Buy decision can be also noticed in supplier selection process. In this paper, the supply strategy: Make or Buy decision (SS: MOB) is studied in order to find which strategy (Make or Buy) should be chosen to minimize the total costs of supply chain. Therefore, two separate models are generated for each strategy and several examples are solved for the respective models. Computational experiments show the efficiency of the proposed models for making decision about selecting the best strategy.

Continuous generation of electricity of a power plant depends on the higher availability of its components/equipments. Higher availability of the components/equipments is inherently associated with their higher reliability and maintainability. This paper investigates the reliability, availability and maintainability (RAM) characteristics of a 210 MW coal-fired thermal power plant (Unit-2) from a thermal power station in eastern region of India. Critical mechanical subsystems with respect to failure frequency, reliability and maintainability are identified for taking necessary measures for enhancing availability of the power plant and the results are compared with Unit-1 of the same Power Station. Reliability-based preventive maintenance intervals (PMIs) at various reliability levels of the subsystems are estimated also for performing their preventive maintenance (PM). The present paper highlights that in the Unit-2, Economizer (ECO) & Furnace Wall Tube (FWT) exhibits lower reliability as compared to the other subsystems and Economizer (ECO) & Baffle Wall Tube (BWT) demands more improvement in maintainability. Further, it has been observed that FSH followed Decreasing Failure Rate (DFR) and Economizer (ECO) is the most critical subsystem for both the plants. RAM analysis is very much effective in finding critical subsystems and deciding their preventive maintenance program for improving availability of the power plant as well as the power supply.

Clustering is a critical data analysis and it is a popular data mining technique. This paper presents a rapid Ant based clustering-genetic algorithm (RAC-GA) with local search to solve clustering problem. GA and local search are used as a global and local search to obtain better results. The proposed algorithm is evaluated by testing on some of the well-known real-world datasets, and the results are compared with other popular heuristics in clustering, such as GA, SA, TS, ACO and RAC. The results show strong improvement both in quality solution and process time area, especially in process time which is much less than previous algorithms

In this article, a production inventory model is developed to determine the retailer’s optimal replenishment decisions under two levels of trade credit and limited storage space. Two levels of trade credit refers that the supplier provides to his/her retailer a permissible delay period to settle the account and the retailer also in turn provides a delay period to his/her customer for paying of purchasing goods. The retailer’s credit period M offered by supplier is greater than customers credit period N provides by the retailer. This paper investigates retailer optimal replenishment policy under finite replenishment rate by minimizing the total annual inventory cost. Three theorems are developed to determine optimal cycle time, optimal relevant cost and optimal order quantity for retailers accurate and rapidly. A numerical example is used to analyze the validity of propose model, Sensitive analysis shows managerial decisions for retailer.

The complexity of large-scale projects has led to numerous risks in their life cycle. This paper presents a new risk evaluation approach in order to rank the high risks in large-scale projects and improve the performance of these projects. It is based on the fuzzy set theory that is an effective tool to handle uncertainty. It is also based on an extended VIKOR method that is one of the well-known multiple criteria decision-making (MCDM) methods. The proposed decision-making approach integrates knowledge and experience acquired from professional experts, since they perform the risk identification and also the subjective judgments of the performance rating for high risks in terms of conflicting criteria, including probability, impact, quickness of reaction toward risk, event measure quantity and event capability criteria. The most notable difference of the proposed VIKOR method with its traditional version is just the use of fuzzy decision-matrix data to calculate the ranking index without the need to ask the experts. Finally, the proposed approach is illustrated with a real-case study in an Iranian power plant project, and the associated results are compared with two well-known decision-making methods under a fuzzy environment.

In construction cost management system, normally earned value management (EVM) is applied as an efficient control approach in both status detection and estimation at completion (EAC) cost forecasting. The traditional approaches in EAC predictions normally extend the current situation of a project to the future by employing pervious performance factor. The proposed approach of this paper considers both qualitative and quantitative factors affecting the EAC prediction. The proposed approach of this research not only estimates the completion of the project, but also it can generate accurate forecast for the entire future periods using a fuzzy neural network model. The model is also implemented for a real-world case study and yields encouraging preliminary results.