فهرست مقالات Mixed-Integer Nonlinear Programming

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  1 - Data Envelopment Analysis-Discriminant Analysis by imprecise data for more than two groups: apply to the pharmaceutical stock companies
  Sarah Navidi Mohsen Rostamy-Malkhalifeh
  10.30495/ijdea.2023.73900.1198
  One of the interesting subjects that amuse the mind of researchers is surmising the correct classification of a new sample by using available data. Data Envelopment Analysis (DEA) and Discriminant Analysis (DA) can classify data by each one alone. DEA classifies as effi چکیده کامل

  One of the interesting subjects that amuse the mind of researchers is surmising the correct classification of a new sample by using available data. Data Envelopment Analysis (DEA) and Discriminant Analysis (DA) can classify data by each one alone. DEA classifies as efficient and inefficient groups and DA classify by historical data. Merge these two methods is a powerful tool for classifying the data. Since, in the real world, in many cases we do not have the exact data, so we use imprecise data (e.g. fuzzy and interval data) in these cases. So, in this paper, we represent our new DEA-DA method by using Mixed-Integer Nonlinear Programming (MINLP) to classify with imprecise data to more than two groups. Then we represent an empirical example of our purpose method on the Iranian pharmaceutical stock companies' data. In our research, we divided pharmaceutical stock companies into four groups with imprecise data (fuzzy and interval data). Since, most of the classical DA models used for two groups, the advantage of the proposed model is beheld. The result shows that the model can predict and classify more than two groups (as many as we want) with imprecise data so correct. پرونده مقاله
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  2 - Multiple Items Supplier Selection, Economic Lot-sizing, and Order Allocation Under Quantity Discount: A Genetic Algorithm Approach
  Getachew Basa Bonsa Till Becker Abdelkader Kedir
  10.22094/joie.2023.1901314.1751
  The task at hand involves selecting the most suitable supplier(s), determining the optimal lot size, and allocating the total order quantities among the suppliers based on various selection criteria. However, this can become more complex when taking into account quantit چکیده کامل

  The task at hand involves selecting the most suitable supplier(s), determining the optimal lot size, and allocating the total order quantities among the suppliers based on various selection criteria. However, this can become more complex when taking into account quantity discount offers and transportation selection decisions in the selection and order allocation process. To address this challenge, this paper proposes an integrated approach that combines the Analytic Hierarchy Process (AHP) with a multi-objective mixed integer nonlinear program. The approach is designed for a multi-item, capacitated multi-supplier scenario, where the goal is to select suppliers, determine lot sizes, and allocate orders while taking into account unit quantity discounts and intermodal freight costs. The proposed approach aims to minimize costs and the percentage of rejected items, while maximizing the purchasing value. To solve this problem, an efficient genetic algorithm with problem-specific operators is utilized. پرونده مقاله