International Journal of Information Technology & Decision Making

This paper deals with an agile manufacturing system with two stages where the first stage is the machining stage including a single flexible machine while the second stage is assembly stage including parallel identical assembly machines. Products have an assembly structure where their parts should be processed at the first stage and then proceed to the assembly stage. The goal is to find the parts sequence which minimizes makespan. Since the studied problem is NP-hard, enhanced intelligent water drops (EIWD) algorithm as a new swarm-based nature inspired optimization algorithm is proposed. Also, artificial immune system (AIS) algorithm has been proposed to tackle the addressed problem. Computational results based on randomly generated instances show the effectiveness of the proposed technique.

In multiple criteria evaluation, criteria weights are of great importance. In practice, subjective criteria weights determined by specialists/experts are commonly used. The types of elements of a decision matrix also play an important role in the evaluation of alternatives. The objective weights help to estimate the structure of data. The entropy method is widely used for determining the weights (significances) of criteria. A new method of the criterion impact loss, CILOS, is used for determining a relative impact loss experienced by the criterion of an alternative, when another criterion is chosen to be the best. The authors of the paper have combined the best features of the entropy method and the CILOS approach to obtain a new method – Integrated Determination of Objective CRIteria Weights, or (IDOCRIW).

In this paper, we propose a novel approach for the computation of the probability distribution of a counting variable linked to a multivariate hierarchical Archimedean copula function. The hierarchy has a twofold impact: it acts on the aggregation step but also it determines the arrival policy of the random event. The novelty of this work is to introduce this policy, formalized as an arrival matrix, i.e., a random matrix of dependent 0–1 random variables, into the model. This arrival matrix represents the set of distorted (by the policy itself) combinatorial distributions of the event, i.e., of the most probable scenarios. To this distorted version of the CHC approach [see Ref. 7 and Ref. 27], we are now able to apply a pure hierarchical Archimedean dependence structure among variables. As an empirical application, we study the problem of evaluating the probability distribution of losses related to the default of various type of counterparts in a structured portfolio exposed to the credit risk of a selected set among the major banks of European area and to the correlations among these risks.

Aggregation operators play a key role in multiple criteria decision-making (MCDM). Extensions of aggregation operators to intuitionistic fuzzy sets (IFSs) usually involve replacing the standard arithmetic operations with those defined over the membership and nonmembership of IFS, which is essentially a pair of special Archimedean triangular norm (t-norm) and triangular conorm (t-conorm), called probabilistic sum t-conorm and product t-norm, on the membership and nonmembership of IFS, respectively. In this paper, we first introduce some operations on IFSs by means of Archimedean t-norm and t-conorm. Then some generalized Archimedean intuitionistic fuzzy aggregation operators are proposed, such as generalized Archimedean intuitionistic fuzzy weighted averaging operator, generalized Archimedean intuitionistic fuzzy ordered weighted averaging (GAIFOWA) operator, and generalized Archimedean intuitionistic fuzzy hybird averaging operator. Some desirable properties of these operators are investigated. The relations between these operators and the existing intuitionistic fuzzy aggregation operators are discussed. Finally, applying these proposed operators, we develop an approach for multi-criteria decision-making with intuitionistic fuzzy information, an illustrative example is used to verify the developed approach and to demonstrate its practicality and effectiveness.

Performance evaluation is one of the most important problems for retail chains and may have effect on tactical and strategic decisions. This paper proposes a grey-based multi-criteria performance evaluation model for retail sector. This model integrates Decision-Making Trial and Evaluation Laboratory (DEMATEL) and modified Grey Relational Analysis (GRA) methods. First, the grey-based DEMATEL method is used for determining the importance of performance indicators to be used in GRA based on the experts’ assessments. Then, the proposed modified GRA method is used for the performance evaluation and ranking of retail stores with respect to the predetermined performance indicators. Finally, the effectiveness and the applicability of the developed approach are illustrated with a case study with the actual data taken from a retail chain in Turkey.

Expert knowledge has been proved by substantial studies to be contributory to higher forecasting performance; meanwhile, its application is criticized and opposed by some groups for biases and inconsistency inherent in experts’ subjective judgment. This paper proposes a new approach to improving forecasting performance, which takes advantage of expert knowledge by constructing a constraint equation rather than directly adjusting the predicted values by experts. For the comparison purpose, the proposed approach, together with several widely used models including ARIMA, BP-ANN and the judgment model (JM), is applied to forecasting the container throughput of Guangzhou Port, which is one of the most important ports of China. Forecasting performances of the above models are compared and the results clearly show superiority of the proposed approach over its rivals, which implies that expert knowledge will make positive contribution as long as it is used in a right way.

As a generalization of intuitionistic fuzzy set, the Pythagorean fuzzy set is interesting and very useful in modeling uncertain information in real-world decision-making problems. In this paper, we develop a new method for Pythagorean fuzzy multiple-criteria decision-making (MCDM) problems with aggregation operators and distance measures. First, we present the Pythagorean fuzzy ordered weighted averaging weighted average distance (PFOWAWAD) operator. The main advantage of the PFOWAWAD operator is that it uses distance measures in a unified framework between the ordered weighted averaging (OWA) operator and weighted average (WA) that considers the degree of importance of each concept in the aggregation. Some of its main properties and special cases are studied. Then, based on the proposed operator, a hybrid TOPSIS method, called PFOWAWAD-TOPSIS is introduced for Pythagorean fuzzy MCDM problem. Finally, a numerical example is provided to illustrate the practicality and feasibility of the developed method.

Due to the uncertainty in oil markets, this paper proposes a novel approach for oil purchasing and distribution optimization by incorporating price and demand prediction, i.e., the prediction-based oil purchasing-and-distribution optimization model. In particular, the proposed method bridges the latest information technology and decision-making technique by introducing the recently proposed information technology (i.e., extreme learning machine (ELM)) into the oil purchasing-and-distribution optimization model. Two main steps are involved: market prediction and planning optimization in the proposed model. In market prediction, the ELM technique is employed to provide fast training time and accurate forecasting results for oil prices and demands. In planning optimization, two objectives of general profit maximization and inventory risk minimization are considered; and the most popular multi-objective evolutionary algorithm (MOEA), nondominated sorting genetic algorithm II (NSGA-II), is implemented to search approximate Pareto optimal solutions. For illustration and verification, the motor gasoline market in the US is focused on as the study sample, and the experimental results demonstrate the superiority of the proposed prediction-based optimization approach over its benchmark models (without market prediction and/or planning optimization), in terms of the highest profit and the lowest risk.

This paper proposes a new decision support approach for applying stochastic simulation to the multiplicative analytic hierarchy process (AHP) in order to deal with issues concerning the scale parameter. The paper suggests a new approach that captures the influence from the scale parameter by making use of probability distributions. Herein, the uncertainty both with regard to the scale and the inherent randomness from the parameter is captured by probabilistic input and output distributions. Provided that each alternative and criteria under consideration are independent it is assumed that the embedded uncertainty from the progression factors remains the same. The result is then an interval estimate for each alternative’s final scores. This can lead to overlapping intervals of scores which may be interpreted as possible rank reversals. Thus, the decision support approach makes it possible to calculate the probability of overlapping for any given set of pairwise comparisons.