Hasan Selim

A fuzzy multi-objective covering-based vehicle location model for emergency services

Abstract Timeliness is one of the most important objectives that reflect the quality of emergency services such as ambulance and firefighting systems. To provide timeliness, system administrators may increase the number of service vehicles available. Unfortunately, increasing the number of vehicles is generally impossible due to capital constraints. In such a case, the efficient deployment of emergency service vehicles becomes a crucial issue. In this paper, a multi-objective covering-based emergency vehicle location model is proposed. The objectives considered in the model are maximization of the population covered by one vehicle, maximization of the population with backup coverage and increasing the service level by minimizing the total travel distance from locations at a distance bigger than a prespecified distance standard for all zones. Model applications with different solution approaches such as lexicographic linear programming and fuzzy goal programming (FGP) are provided through numerical illustrations to demonstrate the applicability of the model. Numerical results indicate that the model generates satisfactory solutions at an acceptable achievement level of desired goals. Scope and purpose This paper considers the emergency service vehicles location problem. A multi-objective maximal covering location model is proposed in this paper. The model addresses the issue of determining the best base locations for a limited number of vehicles so that the service level objectives are optimized. Three of the important surrogates that reflect the quality of emergency service systems are considered as objectives in the model: maximization of the population covered by one vehicle, maximization of the population with backup coverage and minimization of the total travel distance from locations at a distance bigger than a prespecified distance standard for all zones. The proposed model allows the incorporation of decision makers imprecise aspiration levels for the goals by means of FGP approach. Thus, the solution efficiency inherent in the FGP approaches is also included into the model. To demonstrate the applicability of the model, numerical examples are provided using different solution approaches.

Determinants of house prices in Turkey: Hedonic regression versus artificial neural network

Determinants of house prices in Turkey are examined in this paper using the 2004 Household Budget Survey Data. In property valuation and housing market research, the locational value is usually analyzed by hedonic methods that use multiple regression techniques on large data sets and require a formality based on microeconomic theory in the analyses. Because of potential non-linearity in the hedonic functions, artificial neural network (ANN) is employed in this study as an alternative method. By comparing the prediction performance between the hedonic regression and artificial neural network models, this study demonstrates that ANN can be a better alternative for prediction of the house prices in Turkey.

A fuzzy rule based expert system for stock evaluation and portfolio construction: An application to Istanbul Stock Exchange

The aim of this study is to construct appropriate portfolios by taking investors preferences and risk profile into account in a realistic, flexible and practical manner. In this concern, a fuzzy rule based expert system is developed to support portfolio managers in their middle term investment decisions. The proposed expert system is validated by using the data of 61 stocks that publicly traded in Istanbul Stock Exchange National-100 Index from the years 2002 through 2010. The performance of the proposed system is analyzed in comparison with the benchmark index, Istanbul Stock Exchange National-30 Index, in terms of different risk profiles and investment period lengths. The results reveal that the performance of the proposed expert system is superior relative to the benchmark index in most cases. Additionally, in parallel to our expectations, the performance of the expert system is relatively higher in case of risk-averse investor profile and middle term investment period than the performance observed in the other cases.

A Dynamic Maintenance Planning Framework Based on Fuzzy TOPSIS and FMEA: Application in an International Food Company

A maintenance planning framework is developed in this study to reduce and stabilize the maintenance costs of the manufacturing companies. The framework is based on fuzzy technique for order preference by similarity to ideal solution(TOPSIS) and failure mode and effects analysis (FMEA) techniques and supports maintenance planning decisions in a dynamic way. The proposed framework is general and can easily be adapted to a host of manufacturing environments in a variety of sectors. To determine the maintenance priorities of the machines, fuzzy TOPSIS technique is employed. In this regard, ‘risk priority number’ obtained by FMEA and ‘current technology’, ‘substitutability’, ‘capacity utilization’, and ‘contribution to profit’ are used as the criteria. Performance of the resulting maintenance plan is monitored, and maintenance priorities of the machines are updated by the framework. To confirm the viability of the proposed framework, a real-world implementation in an international food company is presented. The results of the application reveal that the proposed maintenance planning framework can effectively and efficiently be used in practice. Copyright

Integrating multi-criteria decision making and clustering for business customer segmentation

– The purpose of this paper is to develop a systematic approach for business customer segmentation. , – This study proposes an approach for business customer segmentation that integrates clustering and multi-criteria decision making (MCDM). First, proper segmentation variables are identified and then customers are grouped by using hierarchical and partitional clustering algorithms. The approach extended the recency-frequency-monetary (RFM) model by proposing five novel segmentation variables for business markets. To confirm the viability of the proposed approach, a real-world application is presented. Three agglomerative hierarchical clustering algorithms namely “Ward’s method,” “single linkage” and “complete linkage,” and a partitional clustering algorithm, “k-means,” are used in segmentation. In the implementation, fuzzy analytic hierarchy process is employed to determine the importance of the segments. , – Business customers of an international original equipment manufacturer (OEM) are segmented in the application. In this regard, 317 business customers of the OEM are segmented as “best,” “valuable,” “average,” “potential valuable” and “potential invaluable” according to the cluster ranks obtained in this study. The results of the application reveal that the proposed approach can effectively be used in practice for business customer segmentation. , – The success of the proposed approach relies on the availability and quality of customers’ data. Therefore, design of an extensive customer database management system is the foundation for any successful customer relationship management (CRM) solution offered by the proposed approach. Such a database management system may entail a noteworthy level of investment. , – The results of the application reveal that the proposed approach can effectively be used in practice for business customer segmentation. By making customer segmentation decisions, the proposed approach can provides firms a basis for the development of effective loyalty programs and design of customized strategies for their customers. , – The proposed segmentation approach may contribute firms to gaining sustainable competitive advantage in the market by increasing the effectiveness of CRM strategies. , – This study proposes an integrated approach for business customer segmentation. The proposed approach differentiates itself from its counterparts by combining MCDM and clustering in business customer segmentation. In addition, it extends the traditional RFM model by including five novel segmentation variables for business markets.

A multi-agent system model for supply chains with lateral preventive transshipments

A multi-agent system model to observe the effects of ordering parameters on a supply chain with lateral preventive transshipments.Ordering and premium freight bidding processes of the agents are involved.An application to real world multi-national supply chain considering both supply and demand side uncertainties.Effects of safety stock and supplier flexibility levels on performance are examined by simulation from both agent and system-wide perspectives. In this study, a multi-agent system model is developed to observe the effects of ordering parameters on a supply chain with lateral preventive transshipments. The proposed model involves ordering and premium freight bidding processes of the agents. The model is implemented to a multi-national supply chain considering both supply and demand side uncertainties. Effects of safety stock and supplier flexibility levels on performance are examined by simulation from both agent and system-wide perspective. The results reveal the viability of the proposed model.

Multiobjective Optimization of Biomass to Energy Supply Chains in an Uncertain Environment

Abstract The aim of this study is to design supply chain network for biomass to energy conversion systems for the regions having high potential of animal wastes and energy crops production and to reveal economical and environmental benefits from these systems. To this aim, a fuzzy multiobjective mixed integer linear programming (MILP) model is constructed. The model includes environmental and monetary objectives and it is structured as a multiperiod model in order to consider variation in the parameters. The model is solved by using different fuzzy goal programming (FGP) approaches.

A Multi-objective, simulation-based optimization framework for supply chains with premium freights

A simulation-based optimization framework is developed for inventory optimization.A decomposition-based multi-objective differential evolution algorithm is developed.The proposed framework is implemented to a multi-national automotive supply chain.The framework yields better results than NSGA-II and the current operating condition. In this study, a multi-objective, simulation-based optimization framework is developed for supply chain inventory optimization. In this context, a supply chain consisting of a supplier and a number of plants is considered. The plants use a periodic-review order-up-to level policy and request premium freights from the supplier in case of a risky inventory position. Under this setting, the aim of the study is to determine supplier flexibility and safety stock levels that yield the best performance in terms of holding cost and premium freights. Accordingly, a decomposition-based multi-objective differential evolution algorithm (MODE/D) is developed for the proposed framework. As the proposed framework considers both holding cost and premium freight performance, it enables the managers to determine the best tradeoff between the objectives. Consequently, managers have a broad decision spectrum in determining supplier flexibility and safety stock levels. The proposed framework is implemented to a real world multi-national automotive supply chain. Purposely, the results obtained by the proposed framework with MODE/D are compared with the results of non-dominated sorting genetic algorithm II (NSGA-II) and current operating condition of the supply chain. The results reveal that MODE/D yields better holding cost and premium freight performance than those of NSGA-II and current operating condition of the supply chain.

Biomass to Energy Supply Chain Network Design: An Overview of Models, Solution Approaches and Applications

Energy production from biomass is an alternative and additive way to fossil fuel based energy production to reduce the dependency on limited fossil fuel sources and mitigate the harmful environmental impacts of these systems. One of the major challenges in establishing efficient renewable energy systems is the complex supply chain structure in an uncertain decision environment, various decisions to be made and different conflicting criteria/objectives. This study describes the key issues in decision making for biomass to energy supply chains such as decision levels, uncertainty and sustainability concepts. It also provides a comprehensive review and systematic classification of the current literature on decision making approaches for design, management and operation of biomass to energy supply chains. This study allows readers to identify the decision making methods that satisfy the problem specific requirements and offer a clear vision of the advances in the field.

Simulation optimization approach for customer centric lot streaming problem in job shops

In todays competitive market, time based strategies become important to gain sustainable competitive advantage. Therefore, manufacturing companies need advanced production planning and control (PPC) techniques in order to respond quickly to demand. In addition, customer centricity becomes the key success factor for manufacturing industry. In this regard, integration of customer relationship management (CRM) and PPC decisions is necessary in order to satisfy customers with efficient use of manufacturing capabilities. The aim of this study is to analyze the effect of customer centricity on lot streaming (LS) problem. To this aim, LS problem in job shop environment is dealt with in this study, and traditional LS problem is extended by including preferences of multiple customer segments. It is intended to find a lot splitting policy for each customer segment-product type combination. The objective function is defined as minimization of total weighted percentage deviation from the preferences of customer segments. Simulation optimization approach is used to solve the problem. The results reveal that the proposed approach supports order splitting decisions, and can effectively be used in practice.