In today & apos; s world, many planning problems include a hierarchical decision structure with independent and often conflicting objectives. Therefore, the optimization of supply chains with hierarchical structure is essential. In this paper, we investigate a fuzzy multi-objective multi-products supply chain optimization problem in a bi-level structure with one level corresponding to a manufacturer planning problem, while the other to K distribution centers problem. In our model, customer demand and supply chain costs are considered uncertain and will be modeled with use of fuzzy sets. We first describe how different kinds of problems can be modeled as bi-level programming problems. Then, this fuzzy model is first converted into an equivalent crisp model by using ?-cut method in each level, and then by applying extended Kuhn–Tucker approach, we have a linear multi-objective programming problem. Fuzzy goal programming technique is applied to solve the multi-objective linear programming problem to obtain a set of Pareto-optimal solutions. Finally, a numerical example is illustrated to demonstrate the feasibility of the proposed approach.

This paper studies the issue of channel coordination for a decentralized supply chain consisting of one vendor and one buyer in multi-period setting. Considering the allowance of the backorder at the buyer side distinguishes this work in the literature. The credit option contract as an incentive scheme is pursued to encourage the buyer to participate in the coordination model. Order quantity, credit time and backordered demand in each period as decision variables are determined jointly in order to encourage the chain’s member to participate in the coordinating contract. The proposed model shows that the credit option contract for the case of backorder allowance is able to realize the channel coordination in the studied supply chain. A wide range of numerical examples are studied to analyze the sensitivity of the contract efficiency to the inventory system’s parameters such as order cost, holding cost and shortage cost.

Cultural heritage and properties in collections are the most important organizational resources of museums, which can be deteriorated under improper environmental conditions. Identifying the climate deteriorating agents, and assessing and prioritizing their risk levels are among the most recent issues studied by museum conservators and researchers. Creating risk management programs leads to enhancing service life of cultural properties. Temperature and relative humidity are two important factors related directly to objects permanence; Thus, using methods which combine and quantify the conservation effects of temperature and relative humidity, could yield to useful and reliable insights for proposition of effective preventive conservation solutions. This research tries to explain the role and importance of risk management programs in the museums -as one of the main pillars of the country & apos; s cultural heritage; therefore, by using a multidisciplinary approach and existing experiments, proposes a method to examine and assess risks of temperature and relative humidity on museum objects. Such quantitative analysis could help estimate objects & apos; service life and provide solutions in order to enhance it. In order to examine the proposed research method, temperature and relative humidity data were gathered from textile storage room of the Cultural Institute of Bonyad Museums in a period of 18 months.

During the past few years, new methods of advertisements such as internet, email services, etc. have created new opportunities to promote products and services. The cost of an email message is normally negligible but the outcome could be significant. This paper presents an empirical investigation to find the effect of new advertisement techniques on export based product development in a case study of mobile devices. The proposed study designs a questionnaire in Likert scale consists of 29 questions, distributes it among 150 exporters of mobile devices in Iran. Cronbach alpha is calculated as 0.826. In addition, Kaiser-Meyer-Olkin Measure of Sampling Adequacy and Approx. Chi-Square are 0.790 and 1023.3, respectively. Based on the results of our survey, we have derived five factors including emotional effects, brand promises, pricing strategy, modern advertisements and detected promotions.

Reverse Logistics has become buzzword in today’s industrial word and big differentiating factor in quest of success for any organization word wide. Whole world is suffering from scarcity of resources and we have finite area available for the waste disposal. One of the sustainable solutions is to design product in such a way so that we can maximize its reuse through reverse logistics. Reverse logistics is fast becoming industrial design standard for majority of organizations worldwide. Reverse logistics has become a need of an hour. Some of the organization do it voluntarily and some because of the environmental rules and legislations. In order to achieve total customer satisfaction, one has to take care of product for its entire life cycle through activities of reverse logistics. In this research I tried to investigate the factors affecting performance of reverse logistics in relation to customer satisfaction. I have used interpretive structural modelling technique to understand the complex pattern of contextual relationship amongst the most important factors through diagraph, which explain performance of reverse logistics in relation to customer satisfaction. I have also used MICMAC analysis to classify these factors according to their driving power and dependency power. MICMAC analysis facilitate in identification of most important factors in explaining performance of reverse logistics in relation to customer satisfaction. Further this research may helpful in reverse logistics design process planning, driven by customer requirement.

Marketing the network of organizations, including manufacturers, wholesalers, and retailers, that distributes goods or services to consumers is one of the most important decisions for marketing managers and producers. In this study, we identify and prioritize the factors, which affect marketing strategy in selecting distribution channels by using fuzzy multiple criteria decision-making (FMCDM). The proposed study uses Fuzzy Delphi Analytic Hierarchy Process to determine the weights of the criteria by decision makers and marketing strategies is ranked by Fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Finally, the case study within a Kaleh Company (Dairy products) is performed and the results indicate that the “diversifying product” is the most important marketing strategies considered by experts.

Retail atmospherics have been well studied to have both functional and emotional impact on the behavioural intent of shoppers. The usage of well-orchestrated retail servicescape with its expanding canvas, both inside and outside the retail premise, has been targeted to induce subliminal perception of comfort and convenience in the mind of shoppers. Modern retail practice has incorporated customer relationship management (CRM), a pivotal business analytical process, to strengthen their interaction with shoppers. This paper attempts to gather empirical evidence on the possible mediating effects of CRM dimensional performance on the emotional orientation of shoppers, apprehended to be antecedent to favourable shopping behaviour. Appropriate statistical applications, following adequate literature survey, were deployed to test the hypotheses and the robustness of the default model. The results were indicative of strong moderating impact of CRM dimensional performance in augmenting emotional behaviour of customers to induce a fovourable behavioural intent. The default model also holds good confirming the causal convergence of constructs.

The purpose of in this paper is to study demand fluctuation of dairy product and the forecasting practices that have been used by Raipur Dugdh Sangh (Devbhog dairy industry) in Chhattisgarh. The objective is to detect how these industries have used forecasting method, what are the main factors that influence their choice and what are the main difficulties in the use of forecasting methods. Based on literature survey, weekly data collected over October 2011 to October 2012 from Raipur Dugdh Sangh (Devbhog dairy industry) in Chhattisgarh. Data were analyzed by statistics technique using the Microsoft excel software. The result shows that the demand of milk product fluctuated over the period of time. The factors that influence the choice of forecasting model are the type of product, time spent in forecasting and main difficulties are the availability of software.

Crude oil price volatility dynamics are governed by nonlinear and chaotic behaviour. This paper presents and compares the performance of four hybrid systems used to estimate and predict crude oil price volatility data. A GARCH family model is employed to estimate oil price volatility data and the Elman artificial neural network (ENN) system is used to model and predict the obtained data. Indeed, unlike previous studies found in the literature, recurrent artificial neural networks are chosen in this paper to model and predict future crude oil price volatility data estimated by GARCH family models since they are nonlinear systems capable of learning noisy and nonstationary data. In particular, four hybrid systems are tested and compared; including the GARCH-ENN, EGARCH-ENN, APARCH-ENN, and TARCH-ENN system. Using Brent crude oil price data, the obtained out-of-sample simulation results indicate that all hybrid systems provide very accurate forecasts of Brent future volatility. In addition, they show evidence of the superiority of the GARCH-ENN system over the EGARCH-ENN, TARCH-ENN, and APARCH-ENN systems. The presented four hybrid systems achieved very low forecasting errors. Thus, they could be effective in oil industry management and applications.

Hub covering problem is one of the most popular areas of research due to wide ranges of applications in different service or manufacturing industries. This paper considers a multi-objective hub covering location problem under congestion. The proposed study of this paper considers two objectives where the first one minimizes total transportation cost and the second one minimizes total waiting time for all hobs. The resulted multi-objective decision making problem is formulated as mixed integer programming. Simulated annealing is used to solve the resulted model and the performance of the proposed model is compared against two other alternative methods, particle sward optimization and NSGA-II. The results are compared in terms of four criteria including quality metric, mean ideal distance, diversification metric and spacing metric. The results indicate that the proposed model could perform better than the other two alternative methods in terms of quality metric but the results are somehow mix in terms of other three criteria.