Kemal Subulan

An analysis of fully fuzzy linear programming with fuzzy decision variables through logistics network design problem

Recently, there is a growing attention by the researchers to solve and interpret the analysis of fully fuzzy linear programming problems in which all of the parameters as well as the decision variables are considered as fuzzy numbers. Under a fully uncertain environment where all of the data are stated as fuzzy, presenting the reasonable range of values for the decision variables may be comparatively better than the currently available crisp solutions so as to provide ranges of flexibility to decision makers. However, there is still a scarcity of solution methodologies on fuzzy mathematical programs with fuzzy decision variables. Based on this motivation, a new parametric method which is mainly based on α-cut representation of fuzzy intervals is proposed in this paper by incorporating the decision makers attitude toward risk. In order to illustrate validity and practicality of the proposed method, it is applied to a generic reverse logistics network design model including fuzzy decision variables. To the best of our knowledge, this is the first study in the literature which presents fuzzy efficient solutions and analysis for a fully fuzzy reverse logistics network design problem with fuzzy decision variables. The provided solutions by the proposed method are also compared to the available solution methodologies from the literature in terms of computational efficiency, solution quality and ease of use. By using the proposed method, the decision makers can be supported by yielding fuzzy efficient solutions under different uncertainty levels and risk attitudes. The computational results have also shown that more reliable and necessarily precise solutions can be generated by the proposed method for a risk-averse decision maker.

Constrained fuzzy arithmetic approach to fuzzy transportation problems with fuzzy decision variables

A constrained fuzzy arithmetic approach for solving fuzzy transportation problems.Ability to solve various types of fuzzy transportation problems.Ability to take into account decision makers attitude toward risk.Analysis and comparison with existing state of the art approaches. Most of the existing methods for solving fully fuzzy mathematical programs are based on the standard fuzzy arithmetic operations and/or Zadehs extension principle. These methods may produce questionable results for many real-life applications. Due to this fact, this paper presents a novel method based on the constrained fuzzy arithmetic concept to solve fully fuzzy balanced/unbalanced transportation problems in which all of the parameters (source capacities, demands of destinations, transportation costs etc.) as well as the decision variables (transportation quantities) are considered as fuzzy numbers. In the proposed method, the requisite crisp and/or fuzzy constraints between the base variables of the fuzzy components are provided from the decision maker according to his/her exact or vague judgments. Thereafter, fuzzy arithmetic operations are performed under these requisite constraints by taking into account the additional information while transforming the fuzzy transportation model into crisp equivalent form. Therefore, various fuzzy efficient solutions can be generated by making use of the proposed method according to the decision makers risk attitude. In order to present the efficiency/applicability of the proposed method, different types of fully fuzzy transportation problems are generated and solved as illustrative examples. A detailed comparative study is also performed with other methods available in the literature. The computational analysis have shown that relatively more precise solutions are obtained from the proposed method for risk-averse and partially risk-averse decision makers. The proposed method also successfully provided fuzzy acceptable solutions for risk seekers with high degree of uncertainty similar to the other existing methods in the literature.

A new fuzzy linear assignment method for multi-attribute decision making with an application to spare parts inventory classification

A new fuzzy linear assignment model for fuzzy MADM problems is developed.The model can also be used in group decision making environments.The model is applied to a real life problem namely spare parts inventory classification.Detailed explanations and numerical examples are provided to enable a better understanding of the model. In this paper, a novel fuzzy linear assignment method is developed for multi-attribute group decision making problems. Since uncertain nature of many decision problems, the proposed method incorporates various concepts from fuzzy set theory such as fuzzy arithmetic and aggregation, fuzzy ranking and fuzzy mathematical programming into a fuzzy concordance based group decision making process. Fuzziness in the group hierarchy and quantitative type criteria are also taken into account. In order to present the validity and practicality of the proposed method, it is applied to a real life multi-criteria spare part inventory classification problem. The case study has demonstrated that the proposed method is easy to apply and able to provide effective spare parts inventory classes under uncertain environments. In addition to the practical verification by the company experts, the proposed method is also compared with some of the commonly used fuzzy multi-attribute decision making methods from the literature. According to the comparison of the results, there is an association between classes of spare parts obtained by the proposed method and the benchmarked methods.

An improved decoding procedure and seeker optimization algorithm for reverse logistics network design problem

Recently, Reverse Logistics (RL) and product recovery options such as recycling, remanufacturing and reusing have become important issues due to the environmental, economical issues and legal regulations. Due to this fact, companies should take into account the utilized recovery option while preparing their strategic planning activities (like network design) instead of using traditional production planning models. However, since RL network design problems are in the class of NP-hard, solving large scaled problems by exact algorithms is very difficult. Therefore, many meta-heuristics optimization algorithms have been proposed to provide near optimal solutions for supply chain, RL and closed-loop supply chain network design problems in the literature. In this paper, available decoding algorithms for solving generic RL design problems are revised so as to balance the problem without introducing any dummy node on the chromosome. Moreover, the proposed decoding procedure takes into account “equal transportation cost” situation. Then, a priority-based seeker optimization algorithm (SOA) which utilizes fuzzy reasoning procedure is developed for solution of the problem. In order to test performance of the algorithm, a numerical examined is provided and obtained results are compared with particle swarm optimization (PSO) algorithm which is another swarm intelligence technique. Computational results show that SOA is superior to PSO in terms of both solution quality and computational time for the example RL network design problem.

A review of fleet planning problems in single and multimodal transportation systems

ABSTRACT This paper mainly evaluates the previously published fleet planning researches which primarily utilize operations research and computational intelligence based techniques. An intermodal fleet planning framework with a classification scheme is also proposed by considering different problem/modeling characteristics, decision-making levels and multiple players or decision makers. Majority of the papers in this field generally focused on single mode fleet planning problems from the viewpoint of a single decision maker. However, there is a limited number of studies on intermodal fleet planning with multiple transport modes and resources. On the other hand, there is a strong interaction among the fleet planning components such as fleet sizing/composition, fleet deployment, fleet inventory control, empty vehicle repositioning problems etc. Nevertheless, these problems are mostly modeled and solved in a separated way in the available literature. Evaluation of the previous fleet planning researches enabled us to identify gaps in the literature and comment on the directions for future works which can lead to develop expert systems/decision support systems in the context of Logistics 4.0 for the integrated fleet planning problems with sustainability objectives under uncertain environments.

Process mining based approach to performance evaluation in computer-aided examinations

A considerable part of educational systems tends to be online and computer oriented. However, online examination may create some difficulties during the evaluation of student performance. Process mining which arises as a new concept presents various powerful techniques for processing and analyzing different types of data by making use of some advanced information technologies. This paper proposes a novel approach based on process mining for evaluating the performance of students that should follow certain tasks on the computer. The proposed approach is composed of two main phases which are process mining and similarity analysis. Automatic assessment is performed totally in six steps in order to obtain students’ final grades. In addition, cheat control is possible in the last step thanks to the similarity analysis. A real‐life application in an Enterprise Resource Planning (ERP) course is performed in order to present usefulness, validity and practicality of the proposed approach. Furthermore, to evaluate the performance of the assessment system, we compared the assessment mechanism against instructor. A total of 15 students’ answers belonging to computer‐aided exam are assessed by instructor and the results showed a very good agreement between the automatic assessment system and instructor.

A direct solution approach based on constrained fuzzy arithmetic and metaheuristic for fuzzy transportation problems

This paper presents a novel direct solution approach for fully fuzzy transportation problems in which all of the model parameters as well as decision variables are considered as fuzzy numbers. In detail, a fuzzy decoding procedure based on constrained fuzzy arithmetic operations and a fuzzy ranking technique is first introduced for solution of the problem directly without any fuzzy to crisp transformation process. Then, this decoding procedure is embedded into a metaheuristic, namely priority-based PSO algorithm for generating new solution vectors and seeking for better fuzzy acceptable solutions. By making use of the constrained fuzzy arithmetic concept, the proposed approach is also able to handle the decision maker’s attitude toward risk. In order to show validity and applicability of the proposed approach, numerical examples on both balanced and unbalanced fully fuzzy transportation cases are generated and solved. The computational results have shown that relatively more precise and information efficient solutions can be obtained from the proposed approach for “risk-averse” and “partially risk-averse” decision makers. Furthermore, the proposed approach is also able to produce fuzzy solutions for “risk seekers” with high degree of uncertainty similar to the other methods available in the literature.