Didem Cinar

Scenario analysis using Bayesian networks: A case study in energy sector

This paper provides a general overview of creating scenarios for energy policies using Bayesian Network (BN) models. BN is a useful tool to analyze the complex structures, which allows observation of the current structure and basic consequences of any strategic change. This research will propose a decision model that will support the researchers in forecasting and scenario analysis fields. The proposed model will be implemented in a case study for Turkey. The choice of the case is based on complexities of a renewable energy resource rich country. Turkey is a heavy energy importer discussing new investments. Domestic resources could be evaluated under different scenarios aiming the sustainability. Achievements of this study will open a new vision for the decision makers in energy sector.

A 2-phase constructive algorithm for cumulative vehicle routing problems with limited duration

Heuristics for cumulative vehicle routing problem with time limit are developed.Clarke & Wright Algorithm is enhanced by considering load flow.Proposed version improves significantly solution quality at some expense of CPU time.A new hybrid constructive heuristic including clustering is proposed.Hybrid algorithm improves CPU time significantly and obtains fairly good solutions. The Clarke & Wright (C&W) algorithm is one of the most widely used classical heuristics in capacitated Vehicle Routing Problems (VRPs) in which a linear function of distance is considered as the objective function. The C&W algorithm is very simple and easy to implement, and produces fairly good solutions very fast. In this study, the C&W algorithm is adopted for the cumulative VRP with limited duration (CumVRP-LD) where load is also considered in the objective function as well as distance. The most common applications of cumulative VRPs are the determination of routing policies that minimize total fuel consumption. A 2-phase constructive heuristic approach including the K-means clustering algorithm is proposed to improve the computational performance of the modified C&W algorithm for CumVRP-LD. The main contribution of this study is the definition of a new extended formulation that captures truck-load and travel distance by considering the unique characteristics of the problem and to develop a fast and easy implemented constructive algorithm for CumVRP-LD. Such approaches are necessary for the development of systems that respond fast, possibly online, to changes in the real problem situations.

Reduction of CO2 Emissions in Cumulative Multi-Trip Vehicle Routing Problems with Limited Duration

In recent years, as a result of the increase in environmental problems, green logistics has become a focus of interest by researchers, governments, policy makers, and investors. In this study, a cumulative multi-trip vehicle routing problem with limited duration (CumMTVRP-LD) is modelled by taking into account the reduction of CO 2 emissions. In classical vehicle routing problems (VRP), each vehicle can perform only one trip. Because of the high investment costs of additional vehicles, organizations allow the vehicles to perform multiple trips as in multi-trip vehicle routing problems (MTVRP), which reflects the real requirements better than the classical VRP. This study contributes to the literature by using a mixed integer programming (MIP) formulation and a simulated annealing (SA) based solution methodology for CumMTVRP-LD, which considers the minimization of fuel consumption as the objective function. According to preliminary computational results using benchmark problems in the literature, the proposed methodology obtained promising results in terms of solution quality and computational time.

A Cumulative Belief Degree Approach for Prioritization of Energy Sources: Case of Turkey

Energy planning is difficult to model owing to its complex structure, with numerous decision makers, criteria, and scenarios. Fortunately, decision-making methods can be helpful for the sustainable development of energy, by the evaluation of different energy sources with regard to multiple aspects, for example, economic, environmental, political etc. In this study, a methodology based on a cumulative belief degree approach is proposed for the prioritization of energy sources. The approach enables the use of all types of evaluations, without the loss of any information. It also allows for incomplete expert evaluations which may occur in the energy sources prioritization problem. Turkey, like many countries, generates most of energy from fossil fuels, which are imported mostly from other countries. However, the enormous increase in oil prices, and an emerging energy demand, owing to economic growth and environmental issues, is forcing Turkey to improve its sustainable energy planning. Therefore, the proposed methodology is applied to the energy sources prioritization of Turkey. Results show that solar power and wind should be considered as the priori sources of energy in Turkey.

Fuzzy Decision Trees

Decision trees are one of the most widely used classification techniques because of their easily understandable representation. In the literature, various methods have been developed to generate useful decision trees. ID3 and SLIQ algorithms are two of the important algorithms generating decision trees. Although they have been applied for various real life problems, they are inadequate to represent ambiguity and vagueness of human thinking and perception. In this study, fuzzy ID3 and fuzzy SLIQ algorithms, which generate fuzzy decision trees, are discussed as well as their enhanced versions. Their performances are also tested using simple training sets from the literature.

Computational Intelligence: Past, Today, and Future

Computational intelligence is defined as the study of the design of intelligent agents. Since its relation to other branches of computer science is not well-defined, computational intelligence means different things to different people. In this chapter the history of computational intelligence with a wide literature review will be first given. Then, a detailed classification of the existing methodologies will be made. Later, the international computational intelligence journals will be handled and the characteristics of these journals will be examined. As an example, a special literature review on computational intelligence in complex decision systems will be also given. The direction of computational intelligence in the future will be evaluated.

A Taxonomy for the Flexible Job Shop Scheduling Problem

This chapter aims at developing a taxonomic framework to classify the studies on the flexible job shop scheduling problem (FJSP). The FJSP is a generalization of the classical job shop scheduling problem (JSP), which is one of the oldest NP-hard problems. Although various solution methodologies have been developed to obtain good solutions in reasonable time for FSJPs with different objective functions and constraints, no study which systematically reviews the FJSP literature has been encountered. In the proposed taxonomy, the type of study, type of problem, objective, methodology, data characteristics, and benchmarking are the main categories. In order to verify the proposed taxonomy, a variety of papers from the literature are classified. Using this classification, several inferences are drawn and gaps in the FJSP literature are specified. With the proposed taxonomy, the aim is to develop a framework for a broad view of the FJSP literature and construct a basis for future studies.

Team based labour assignment methodology for new product development projects

We develop a team based labour assignment methodology for NPD projects.Proposed hierarchical methodology integrates VSM, DSM and clustering techniques.The aim is to eliminate waste and create flow to reduce lead time.A real life application is performed to evaluate the proposed methodology.The results tested by simulation have promised significant lead time reduction. This study explores the organizational aspects of new product development projects and proposes a new team-based labour assignment methodology. The proposed hierarchical methodology focusses on the project value stream and aims to shorten lead time through waste reduction. Lean product development tools, such as clustering and design structure matrix tools, are integrated with the methodology. A detailed real-life case study is presented and the proposed methodology is evaluated using discrete event simulation. Experiment results show that the proposed methodology and team-based structure provide superior lead time performance when compared to conventional organizational setting. This study contributes to existing literature by presenting evidence of the effect of teams on NPD lead time performance.

Scheduling the truckload operations in automated warehouses with alternative aisles for pallets

Graphical abstractDisplay Omitted HighlightsScheduling of truck load operations problem in automated warehouses is investigated.The problem includes both the sequencing of pallets and selection of aisles.A real life problem is modelled as a flexible job shop scheduling problem.A hybrid genetic algorithm is applied to raise the warehouse management system.Flexibility of aisle selection improves the total loading time and throughput. In this study, the scheduling of truck load operations in automated storage and retrieval systems is investigated. The problem is an extension of previous ones such that a pallet can be retrieved from a set of alternative aisles. It is modelled as a flexible job shop scheduling problem where the loads are considered as jobs, the pallets of a load are regarded as the operations, and the forklifts used to remove the retrieving items to the trucks are seen as machines. Minimization of maximum loading time is used as the objective to minimize the throughput time of orders and maximize the efficiency of the warehouse. A priority based genetic algorithm is presented to sequence the retrieving pallets. Permutation coding is used for encoding and a constructive algorithm generating active schedules for flexible job shop scheduling problem is applied for decoding. The proposed methodology is applied to a real problem arising in a warehouse installed by a leading supplier of automated materials handling and storage systems.

Evaluating Supply Chain Design Models for the Integration of Biomass Co-firing in Existing Coal Plants Under Uncertainty

Co-firing biomass with a primary fuel in existing power plants is a cost effective environmental strategy. Co-firing implementations are carried out considering the impact of emissions reduction, logistic-related costs, plant efficiency and incentive savings. The literature on mathematical programming models for co-firing can be grouped in two categories: supply chain network design and determination of optimal fuel combination. Our literature review reveals that there is a need for models to show the effect of uncertainties in both of these categories. In this chapter, we carry out a traditional sensitivity analysis and propose a stochastic mixed-integer linear programming model within a single-period planning framework for a supply chain design problem which integrates biomass co-firing in existing coal plants. Purchase costs of coal, biomass and the amount of available biomass are considered as the uncertain parameters. The performance of the proposed model is evaluated by computational tests using data from the State of Mississippi. The computational results reveal that the benefit of the two-stage stochastic programming approach is marginal although a considerable uncertainty is taken into account in the model, while our sensitivity analysis shows that strategies making the co-firing option profitable under uncertainties can be revealed by the model.