Customer service is increasingly being recognized as a source of competitive advantage. The keys to provide effective customer service are determining the customer needs, accurately, and meeting and exceeding the needs in a consistent manner. Companies should adapt a strategic, proactive focus on customer service based on understanding logistic processes and designing the logistics system to meet their needs. This paper proposes an approach based on the quality function deployment (QFD), for ranking strategic actions to improve logistics service. The paper addresses the issue of how to deploy the house of quality (HOQ) to effectively and efficiently improve logistics processes and thus customer satisfaction. For data collection, fuzzy logic is used to deal with the ill-defined nature of the qualitative linguistic judgments required in the proposed HOQ. The methodology has been tested by means of a real case application, which refers to an Iranian company operating in the manufacturing industry.

The way orders are accepted or rejected is the most important factor in customer satisfaction and success of make-to-order systems. The incoming orders to such organizations have certain delivery date in which the customer expects the order to be fulfilled and delivered. In some cases, unexpectedly increased orders exceed the existing capacity for on time fulfillment. In addition to rejection of order, as a typical choice, other options like outsourcing and capacity expansion are available to compensate for capacity shortage and deliver incoming orders according to schedule. However, each of the proposed options is superior in one or more criteria and so selecting the best one is not simply possible. The main goal of this study is to provide managers with a comprehensive, systematic, and applicable approach to evaluate and select the best of the existing options. For this purpose, a model comprised of some multi-criteria techniques is delivered. Our proposed model is a blend of FAHP and FTOPSIS methods. In this model, FAHP is first used to determine the weight of criteria and then Fuzzy-TOPSIS (FTOPSIS) is employed to rank the options. Finally, the proposed model is applied on an actual case to assess and examine its efficiency.

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.

Quadratic assignment problem (QAP) has been considered as one of the most complicated problems. The problem is NP-Hard and the optimal solutions are not available for large-scale problems. This paper presents a hybrid method using tabu search and simulated annealing technique to solve QAP called TABUSA. Using some well-known problems from QAPLIB generated by Burkard et al. (1997) [Burkard, R. E., Karisch, S. E., & Rendl, F. (1997). QAPLIB–a quadratic assignment problem library. Journal of Global Optimization, 10(4), 391-403.], two methods of TABUSA and TS are both coded on MATLAB and they are compared in terms of relative percentage deviation (RPD) for all instances. The performance of the proposed method is examined against Tabu search and the preliminary results indicate that the hybrid method is capable of solving real-world problems, efficiently.

Since independence, India has been one of the few developing countries to invest extensively in both science and technical education. In India, technical education plays a pivotal role in human resource development while creating skilled manpower, increasing industrial productivity and enhancing the quality of life. If a technical institute means to be effective in developing learned and qualified engineers, then it would be useful to know the performance of that technical institution. However, measuring the performance of a technical institution has received very little attention because it is very difficult to measure its output. Thus, this paper focuses on assessing the performance of eight Indian Institutes of Technology (IITs) using a combined approach of data envelopment analysis (DEA) and technique of order preference by similarity to ideal solution (TOPSIS). In the first phase, DEA is applied to shortlist the efficient IITs having the desired characteristics from the stakeholders’ point of view, and TOPSIS method is then employed to rank those efficient IITs while also identifying the best performing IIT. It is observed that IIT Kharagpur outperforms all the considered IITs which exactly corroborates with the findings of the recently published surveys/reports.

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.

The software architecture plays essential role for the development of the complicated software systems and it is important to evaluate the software architecture efficiency. One way to evaluate the software architecture is to create an executable model from the architecture. Unified Modeling Language (UML) diagrams are used to describe the software architecture. UML has made it easy to use and to evaluate the necessary requirements at the software architecture level. It creates an executable model from these diagrams; yet, since the UML is a standard semi-formal language for describing the software architecture, evaluating the software architecture is not directly possible through it. Furthermore, in order to evaluate the software architecture, one needs to turn the actual model into the formal model. In this study, first we describe the architecture using the UML. Then, some properties of the software architecture are mentioned using the UML sequence diagram, deployment diagram, use case diagram, and component diagram. The necessary information associated with the qualitative characteristic of efficiency will be margined as clichés and labels to these diagrams. The independent and dependent components will be extracted from the component diagram. Finally, the resulted semi-formal model will be mapped into a formal model based on the colored Petri net and finally the evaluation will take place.

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.

The purpose of this article is to consider system safety and reliability analysts to evaluate the risk associated with item failure modes. The factors considered in traditional failure mode and effect analysis (FMEA) for risk assessment are frequency of occurrence (O), severity (S) and detectability (D) of an item failure mode. Because of the subjective, qualitative and dynamic nature of the information and to make the analysis more consistent and logical, an approach using fuzzy logic and system dynamics methodology is proposed. In the proposed approach, severity is replaced by dependency parameter then, these parameters are represented as members of a fuzzy set fuzzified by using appropriate membership functions and they are evaluated in fuzzy inference engine, which makes use of well-defined rule base and fuzzy logic operations to determine the value of parameters related to system’s transfer functions. The fuzzy conclusion is then defuzzified to get transfer function for risk and failure rate. The applicability of the proposed approach is investigated with the help of an illustrative case study from the automotive industry.