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The Organization for Economic Cooperation and Development (OECD) provides a forum where governments can work together to increase the global welfare and to seek solutions to common problems through economic growth, where logistics plays an important role and contributes to financial stability. Evaluation of the logistics competitiveness of countries is a technical decision-making issue involving a variety of criteria. Most importantly, these criteria usually conflict with each other and they often act and react upon one another. As in logistics competitiveness as well as in many decision-making problems, the relationships among criteria are interdependent. Moreover, different dimensions and criteria weights also affect the evaluation results. By considering these situations, in order to handle these criteria interactions, Mahalanobis distance (MD) based TODIM (an acronym in Portuguese for Interactive and Multicriteria Decision Making) method has been developed and it has been applied to evaluate the logistics competitiveness of the OECD countries. Evaluation of the correlation between criteria develops the consideration outcomes (regarding sorting) to a certain degree with the traditional TODIM method.

Players in the telecommunications sector struggle against the competition to keep customers, and therefore they need effective churn management. Most classification algorithms either ignore misclassification cost or assume that the costs of all incorrect classification errors are equal. But as in real life, many classification problems have different misclassification costs and this difference cannot be ignored. For this reason, studies on cost‐sensitive classification approaches have gained importance in recent years. The characteristics of telecommunications datasets such as high dimensionality and imbalance are making it difficult to achieve the desired performance for churn prediction. By taking this into consideration, we propose a multiobjective–cost‐sensitive ant colony optimization (MOC‐ACO‐Miner) approach which integrates the cost‐based nondominated sorted genetic algorithm feature selection and multiobjective ACO based cost‐sensitive learning. MOC‐ACO‐Miner is applied to one of Turkey's top 100 information technology companies for customer churn‐prediction. Finally, experiments find out that the model performs quite well with the area under receiver operating characteristic curve values of 0.9998 for predicting churners and therefore it can be beneficial for the highly competitive telecommunications sector. This article is categorized under: Algorithmic Development > Association Rules Application Areas > Industry Specific Applications Technologies > Prediction Technologies > Data Preprocessing

In this paper a new method, SMAA-VIKOR, was proposed for stochastic multi-criteria decision making (MCDM) problems and the effectiveness of the method was shown by comparing literature data and a case study. In some decision making situations, decision makers (DMs) can't or don't express their preferences openly. In such cases stochastic multi-criteria acceptability analysis (SMAA-2) is applicable. The proposed method, SMAA-VIKOR, is the combination of the SMAA-2 and VIKOR methods. Our aim was to see if we can employ VIKOR in handling imprecise, uncertain data, in a word to compose stochastic VIKOR. The SMAA-VIKOR method was applied to the drug benefit-risk analysis problem in the literature. In addition, a case study evaluating the reverse logistic option selection problem is used to illustrate the proposed method. This study indicated that VIKOR could be used with uncertain and arbitrarily distributed values for weights and criteria measurements by using SMAA-VIKOR. The results show that SMAA-VIKOR gives more significant and consistent SMAA outputs and it can also be effective in helping logistic managers in decision making Mathematics Subject Classification. 90B50.

Companies need to develop new products towards customer's satisfaction in order to survive in the boom and bust cycle in todays’ economy. The capturing of customer satisfaction depends on customer needs, and generally, understanding emotions has a challenge for designers. Kansei engineering is a type of methodology to help customers and designers analyze needs and emotion for the new product development. Producing new product design with Kansei data increases customer satisfaction and helps to reach market goals. In this study, a market-oriented baby cradle design methodology is proposed, and we obtain the new product strategies with association rule extraction by using rough set theory. To obtain efficient rules, beforehand we selected sales knowledge-related Kansei words with our proposed approach: cost-based and multiclass decision-theoretic rough set (DTRS) attribute reduction. The new product design strategies which are obtained with proposed design methodology are consistent with customer expectations (mood space) and expert opinions (design team).