Endüstri Mühendisliği Bölümü Yayın Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/279
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Article Citation - WoS: 11Citation - Scopus: 10Ranking Using Promethee When Weights and Thresholds Are Imprecise: a Data Envelopment Analysis Approach(Taylor & Francis Ltd, 2022) Eryilmaz, Utkan; Karasakal, Orhan; Karasakal, EsraMulticriteria decision making (MCDM) provides tools for the decision makers (DM) to solve complex problems with multiple conflicting criteria. Scalarization of criteria values requires using weights for criteria. Determining weights creates controversy as they are influential on the final ranking and challenges the DM as they are hard to elicit. PROMETHEE method is widely used in MCDM for ranking the alternatives and appropriate in situations when there is limited information on the preference structure of the DM. The DM should provide exact values for parameters such as criteria weights and thresholds of preference functions. Data Envelopment Analysis (DEA) is used for measuring the relative efficiency of alternatives in a non-parametric way without requiring any weight input. In this study, we propose two novel PROMETHEE based ranking approaches that address the determination of weight and threshold values by using an approach inspired by DEA. The first approach can deal with imprecise specification of criteria weights, and the second approach can utilize both imprecise weights and thresholds. The proposed approaches provide the DM substantial flexibility on the required level of information on those parameters. An illustrative example and a real-life case study are presented to show the utility of the proposed approaches.Article Citation - WoS: 1Citation - Scopus: 1Cycle Cost Considerations in a Continuous Review Inventory Control Model(Taylor & Francis Ltd, 2021) Yildirim, Gonca; Konur, DincerIn this study, the continuous review order-quantity-re-order point (Q, R) model is analysed with cycle cost considerations. First, we formulate the maximum cycle cost of a given (Q, R) policy using a distribution-free approach. Then, two approaches are introduced to minimize the maximum cycle cost: (i) adjusting R of a given (Q, R) policy and (ii) designing a new (Q, R) policy. Optimum inventory control decisions are characterized for each approach. A set of numerical studies is presented to compare the outcomes of both approaches to three long-term cost minimization approaches, namely the cost minimizing (Q, R) policy, the distribution-free minmax (Q, R) policy, and the distribution-free (Q, R) policy based on the maximum entropy principle. Our numerical results demonstrate the viability of the two approaches introduced and discuss implications of penalty costs and lead time demand's coefficient of variation. Later, we formulate a bi-objective model with the objectives of expected cost and maximum cycle cost minimizations and propose a bi-directional method to approximate the set of Pareto efficient solutions. Numerical examples are presented to illustrate the algorithm and demonstrate the Pareto front.