Serpil Erol

Bio: Serpil Erol is an academic researcher from Gazi University. The author has contributed to research in topics: Simulated annealing & Literature survey. The author has an hindex of 10, co-authored 37 publications receiving 407 citations.

Selecting the Best Project Using the Fuzzy ELECTRE Method

Abstract: Selecting projects is often a difficult task. It is complicated because there is usually more than one dimension for measuring the impact of each project, especially when there is more than one decision maker. This paper is aimed to present the fuzzy ELECTRE approach for prioritizing the most effective projects to improve decision making. To begin with, the ELECTRE is one of most extensively used methods to solve multicriteria decision making (MCDM) problems. The ELECTRE evaluation method is widely recognized for high-performance policy analysis involving both qualitative and quantitative criteria. In this paper, we consider a real application of project selection using the opinion of experts to be applied into a model by one of the group decision makers, called the fuzzy ELECTRE method. A numerical example for project selection is given to clarify the main developed result in this paper.

A neuro-fuzzy model for a new hybrid integrated Process Planning and Scheduling system

Abstract: In customized mass production, isolation of Process Planning (PP) and Scheduling stages has a critical effect on the efficiency of production. In this study, to overcome this isolation problem, we propose an integrated system that does PP and Scheduling in parallel and responds to fluctuations in job floor on time. One common problem observed in integration models is the increase in computational time in conjunction with the increase of problem size. Therefore in this study, we use a hybrid heuristic model combining both Genetic Algorithm (GA) and Fuzzy Neural Network (FNN). To improve GA performance and increase the efficiency of searching, we use a clustered chromosome structure and test the performance of GA with respect to different scenarios. Data provided by GA is used in constructing an FNN model that instantly provides new schedules as new constraints emerge in the production environment. Introduction of fuzzy membership functions in Artificial Neural Network (ANN) model allows us to generate fuzzy rules for production environment.

Fuzzy branch-and-bound algorithm for flow shop scheduling

Abstract: Scheduling is the allocation of resources over time to perform a collection of task. It is an important subject of production and operations management area. For most of scheduling problems made so far, the processing times of each job on each machine and due dates have been assigned as a real number. However in the real world, information is often ambiguous or imprecise. In this paper fuzzy concept are applied to the flow shop scheduling problems. The branch-and-bound algorithm of Ignall and Schrage was modified and rewritten for three-machine flow shop problems with fuzzy processing time. Fuzzy arithmetic on fuzzy numbers is used to determine the minimum completion time (C max). Proposed algorithm gets a scheduling result with a membership function for the final completion time. With this membership function determined, a wider point of view is provided for the manager about the optimal schedule.

Trends and applications of resilience analytics in supply chain modeling: systematic literature review in the context of the COVID-19 pandemic.

Abstract: The increasingly global context in which businesses operate supports innovation, but also increases uncertainty around supply chain disruptions. The COVID-19 pandemic clearly shows the lack of resilience in supply chains and the impact that disruptions may have on a global network scale as individual supply chain connections and nodes fail. This cascading failure underscores the need for the network analysis and advanced resilience analytics we find lacking in the existing supply chain literature. This paper reviews supply chain resilience literature that focuses on resilience modeling and quantification and connects the supply chain to other networks, including transportation and command and control. We observe a fast increase in the number of relevant papers (only 47 relevant papers were published in 2007-2016, while 94 were found in 2017-2019). We observe that specific disruption scenarios are used to develop and test supply chain resilience models, while uncertainty associated with threats including consideration of "unknown unknowns" remains rare. Publications that utilize more advanced models often focus just on supply chain networks and exclude associated system components such as transportation and command and control (C2) networks, which creates a gap in the research that needs to be bridged. The common goal of supply chain modeling is to optimize efficiency and reduce costs, but trade-offs of efficiency and leanness with flexibility and resilience may not be fully addressed. We conclude that a comprehensive approach to network resilience quantification encompassing the supply chain in the context of other social and physical networks is needed to address the emerging challenges in the field. The connection to systemic threats, such as disease pandemics, is specifically discussed.

(OMEGA- International Journal of Management Science, 35(3):247-257)A fuzzy set approach for R&D portfolio selection using a real options valuation model

Abstract: Making R&D portfolio decision is difficult, because long lead times of R&D and market and technology dynamics lead to unavailable and unreliable collected data for portfolio management. The objective of this research is to develop a fuzzy R&D portfolio selection model to hedge against the R&D uncertainty. Fuzzy set theory is applied to model uncertain and flexible project information. Since traditional project valuation methods often underestimate the risky project, a fuzzy compound-options model is used to evaluate the value of each R&D project. The R&D portfolio selection problem is formulated as a fuzzy zero-one integer programming model that can handle both uncertain and flexible parameters to determine the optimal project portfolio. A new transformation method based on qualitative possibility theory is developed to convert the fuzzy portfolio selection model into a crisp mathematical model from the risk-averse perspective. The transformed model can be solved by an optimization technique. An example is used to illustrate the proposed approach. We conclude that the proposed approach can assist decision makers in selecting suitable R&D portfolios, while there is a lack of reliable project information.