Ezgi Aktar Demirtas

Analytic network process and multi-period goal programming integration in purchasing decisions

This paper presents a multi-period inventory lot sizing scenario, where there is single product and multiple suppliers. By considering multi-period planning horizon, an integrated approach of Archimedean Goal Programming (AGP) and Analytic Network Process (ANP) is suggested. This integrated approach proposes a two-stage mathematical model to evaluate the suppliers and to determine their periodic shipment allocations given a number of tangible and intangible criteria. In the evaluation stage, the suppliers are evaluated according to 14 criteria that are involved in four control hierarchies; benefit, opportunity, cost and risk (BOCR). In the shipment stage, a multi objective mixed integer linear programming (MOMILP) model is described to solve the order allocation problem. This MOMILP model is suggested to achieve target values of periodic goals: budget, aggregate quality, total value of purchasing (TVP) and demand over the planning horizon, without exceeding vendor production capacities. This multi-period model is solved by using AGP. Finally some computational experiments are conducted to test the performance of the proposed method.

Multi-period lot-sizing with supplier selection using achievement scalarizing functions

In this paper, an integration of Analytic Network Process (ANP) and achievement scalarizing functions is proposed to choose the best suppliers and define the optimum quantities among the selected suppliers by considering tangible-intangible criteria and time horizon. To reflect the decision makers (DMs) preferences more accurate, an additive achievement function is defined consist of several components. In this additive function while unwanted deviations from periodic budget and aggregate quality goals are balanced by Minmax Goal Programming (MGP), and unwanted deviations from total cost, total value of purchasing (TVP) and aggregate quality are minimized by Achimedean Goal Programming (AGP) to provide more acceptable solutions. This multi-period model enables us to reflect DMs preferences more flexible than the other traditional models that use only one type of achievement function. The sensitivity analysis was also performed for different levels of periodic demands. It is also possible to enlarge the sensitivity analyses for other parameters such as different levels of capacity, and different weights of components.

Modeling and analysis of the effects of nano-oil additives on wear properties of AISI 4140 steel material using mixture design

The aim of this study is to investigate the effects of nano-oil additives on the wear properties of AISI 4140 steel material. This paper also introduces an application of mixture design to the development of a new proportion formulation for nano-oil additives dispersed in engine oil (SAE10W) to enhance lubrication. A mixture design procedure was performed on the basis of design, modeling, optimization, and confirmation steps. Based on a simplex lattice design, a cubic model was established as a function of the nanoparticle fractions. The optimal proportions were obtained by considering the cross-sectional area of the wear profile. A 77.81% decrease compared to the base oil was obtained by using ZnO and carbon nanotubes.

A Case Study in Mixture Design: Multi Response Optimization of Glaze Formulation

ABSTRACT The main purpose of this study was to optimize the glaze formulation of a company that produces vitrified products. In this type of process, the chemical composition of the glaze has a critical effect on quality indicators such as coefficient of expansion, R value, and creeping value. The procedure used in this study included design, modeling, optimization, and confirmation steps. By the selection of desired ranges of multiple responses, the optimum glaze formula that minimizes the cost was determined by Derringer-Suich optimization. The verification results for the optimum glaze formula exhibited good agreement between the regression models and experimental measurements.

A Data Envelopment-Based Clustering Approach for Public Sugar Factories in Privatizing Process

Turkish Sugar Inc., a public enterprise including 25 factories, is the first corporation of Turkish industry. According to the government policy, public sugar factories (PSFs) will be privatized as geography-based 6 portfolio groups in two years. As performance measures of PSF affect government, sugar producers, and several unions in privatizing process, a systematic approach is necessary to measure efficiencies and grouping factories. This paper uses a new DEA- (Data Envelopment Analysis-) based clustering approach for measuring efficiency scores of PSF and grouping them instead of geography- based portfolio groups. This new approach can help decision makers in privatizing process. At the same time, target values obtained by dual model can be used to eliminate inefficiencies of some PSFs.