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Öğe Artificial intelligence approaches to estimate the transport energy demand in Turkey(Springer Heidelberg, 2021) Turgut, Mert Sinan; Eliiyi, Uğur; Turgut, Oğuz Emrah; Öner, Erdinç; Eliiyi, Deniz TürselIn this study, eight parameters are selected and their historical data are collected to predict the future of the energy demand of Turkey. The initial eight parameters were the gross domestic product (GDP) of Turkey, average annual US crude oil price (COP), inflation for Turkey in percentages (INF), the population of Turkey, total vehicle travel in kilometers for Turkey, total amount of goods transported on motorways, employment for Turkey, and trade of Turkey. However, after these eight parameters data are analyzed using Pearson and Spearman correlation methods, it is found out that five of these parameters are highly correlated. The remaining three parameters are the GDP of Turkey, COP, and INF for Turkey. Afterward, five separate scenarios are developed to forecast the future of the energy demand of Turkey. The first two scenarios involve the third- and fourth-order polynomial fitting, the third and fourth scenarios employ static and recurrent neural networks, and the fifth scenario utilizes autoregressive models to predict the future energy demand of Turkey. The efficient hybridization of the seagull optimization and very optimistic method of minimization metaheuristic algorithms is carried out to achieve the polynomial fitting of the data. The optimization performance of the hybrid algorithm is assessed by applying the algorithm on benchmark optimization problems and comparing the results with that of some other metaheuristic optimizers. Moreover, it is seen that the forecasts of the first scenario agree well with the Ministry of the Energy and Natural Resources estimates.Öğe Cargo allocation and vessel scheduling on liner shipping with synchronization of transshipments(Elsevier Science Inc, 2020) Özcan, Sel; Eliiyi, Deniz Türsel; Reinhardt, Line BlanderA mixed integer linear programming model is presented for the operational level cargo allocation and vessel scheduling problem of a liner shipping company in Turkey, where flow-dependent port-stay lengths, transit times and transshipment synchronizations are considered. The proposed model aims to assign shipments to routes to decrease total tardiness and construct partial vessel schedules for establishing coordination with port authorities to comply with the berthing time windows. In addition to the mathematical model, novel valid inequalities and benders decomposition algorithm are implemented. Performance of the developed algorithm is evaluated on real-life problem instances. The results show that benders decomposition with valid inequalities yields the best performance. (C) 2019 Elsevier Inc. All rights reserved.Öğe A comprehensive review of quay crane scheduling, yard operations and integrations thereof in container terminals(Springer, 2021) Kızılay, Damla; Eliiyi, Deniz TürselOver the past decades, container transportation has achieved considerable growth, and maritime trade now constitutes 80% of the global trade. The vessel sizes increased in parallel, up to 21,400 TEU (Twenty-foot-equivalent unit container). Accordingly, global containerized trade reached up to 150 million TEU in 2017 (UNCTAD 2018). This growth brings the need to use scientific methods to manage and operate container terminals more economically throughout the globe. In order to manage container transshipment and to use large vessels efficiently, the docking time at the container port for each vessel should be minimized. The decrease in the docking time enables the vessel to move to its next destination faster, decreasing turnover time and facilitating more containers to be transported. Container terminals can be divided into five main areas as the berth, the quay, the storage yard, the transport area, and the gate. The vessels must be berthed in suitable positions, after which many containers have to be unloaded or loaded via quay cranes, transshipped by vehicles inside the terminal, and stacked by yard cranes to suitable positions, all by using expensive equipment. With the invention of new technologies, the bottleneck at the berth side is almost overcome; however, the yard and the quayside operations have to be further perfected to obtain efficient plans. In this comprehensive literature review study, we aim to combine the literature on both yard and quayside operations, carefully examining independently studied problems as well as integrated ones. General information about port operations and relevant literature is provided. For the quayside, the literature on quay crane assignment and scheduling problems is investigated, whereas, for the yard side, yard crane scheduling, transport vehicle dispatching and scheduling, vehicle routing and traffic control, and storage location and space planning problems are reviewed in depth. In addition to these individual problems, their integrations are also analyzed, relevant publications and their respective contributions are explained in detail. Besides the milestone papers that lead the literature on container terminals, recent publications and advances are also reviewed, and managerial insights and future research directions are identified.Öğe Constraint programming models for integrated container terminal operations(Elsevier, 2020) Kızılay, Damla; Van Hentenryck, Pascal; Eliiyi, Deniz TürselAlthough operations in container terminals are highly interdependent, they are traditionally optimized by decomposing the overall problem into a sequence of smaller sub-problems, each focusing on a single operation. Recent studies, however, have demonstrated the need and potential of optimizing these interdependent operations jointly. This paper proposes the Integrated Port Container Terminal Problem (IPCTP) that considers the joint optimization of quay crane assignment and scheduling, yard crane assignment and scheduling, yard location assignments, and yard truck assignment and scheduling. The IPCTP aims at minimizing the turnover times of the vessels and maximize terminal throughput. It also considers inbound and outbound containers simultaneously and models the safety distance and the interference constraints for the quay cranes. To solve the IPCTP, the paper proposes several constraint programming (CP) models. Computational results show that CP provides exact solutions in acceptable time to IPCTP instances derived from an actual (small) container terminal in Turkey. For hard IPCTP instances, the CP model can be generalized in a two-stage optimization approach to produce high-quality solutions in reasonable times. (C) 2020 Elsevier B.V. All rights reserved.Öğe A distributed depth first search based algorithm for edge connectivity estimation(IEEE, 2020) Uğurlu, Onur; Akram, Vahid Khalilpour; Eliiyi, Deniz TürselThe edge connectivity of a network is the minimum number of edges whose removal disconnect the network. The edge connectivity determines the minimum number of edge-disjoint paths between all nodes. Hence finding the edge connectivity can reveal useful information about reliability, alternative paths and bottlenecks. In this paper, we propose a cost-effective distributed algorithm that finds a lower bound for the edge connectivity of a network via finding at most c depth-first-search trees, where c is the edge connectivity. The proposed algorithm is asynchronous and does not need any synchronization between the nodes. In the proposed algorithm, the root node starts a distributed depth-first-search algorithm, and the nodes select next node in the tree based on their available edges to maximize the total number of established trees. The simulation results show that the proposed algorithm finds the edge connectivity with an average of 48% accuracy ratio.Öğe An energy-efficient permutation flowshop scheduling problem(Pergamon-Elsevier Science Ltd, 2020) Öztop, Hande; Taşgetiren, M. Fatih; Eliiyi, Deniz Türsel; Pan, Quan-Ke; Kandiller, LeventThe permutation flowshop scheduling problem (PFSP) has been extensively explored in scheduling literature because it has many real-world industrial implementations. In some studies, multiple objectives related to production efficiency have been considered simultaneously. However, studies that consider energy consumption and environmental impacts are very rare in a multi-objective setting. In this work, we studied two contradictory objectives, namely, total flowtime and total energy consumption (TEC) in a green permutation flowshop environment, in which the machines can be operated at varying speed levels corresponding to different energy consumption values. A bi-objective mixed-integer programming model formulation was developed for the problem using a speed-scaling framework. To address the conflicting objectives of minimizing TEC and total flowtime, the augmented epsilon-constraint approach was employed to obtain Pareto-optimal solutions. We obtained near approximations for the Pareto-optimal frontiers of small-scale problems using a very small epsilon level. Furthermore, the mathematical model was run with a time limit to find sets of non-dominated solutions for large instances. As the problem was NP-hard, two effective multi-objective iterated greedy algorithms and a multi-objective variable block insertion heuristic were also proposed for the problem as well as a novel construction heuristic for initial solution generation. The performance of the developed heuristic algorithms was assessed on well-known benchmark problems in terms of various quality measures. Initially, the performance of the algorithms was evaluated on small-scale instances using Pareto-optimal solutions. Then, it was shown that the developed algorithms are tremendously effective for solving large instances in comparison to time-limited model. (C) 2020 Elsevier Ltd. All rights reserved.Öğe Ensemble of metaheuristics for energy-efficient hybrid flowshops: Makespan versus total energy consumption(Elsevier, 2020) Öztop, Hande; Taşgetiren, M. Fatih; Kandiller, Levent; Eliiyi, Deniz Türsel; Gao, LiangDue to its practical relevance, the hybrid flowshop scheduling problem (HFSP) has been widely studied in the literature with the objectives related to production efficiency. However, studies regarding energy consumption and environmental effects have rather been limited. This paper addresses the trade-off between makespan and total energy consumption in hybrid flowshops, where machines can operate a varying speed levels. A bi-objective mixed-integer linear programming (MILP) model and a bi-objective constraint programming (CP) model are proposed for the problem employing speed scaling. Since the objectives of minimizing makespan and total energy consumption are conflicting with each other, the augmented epsilon (epsilon)-constraint approach is used for obtaining the Pareto-optimal solutions. While close approximations for the Pareto-optimal frontier are obtained for small-sized instances, sets of non-dominated solutions are obtained for large instances by solving the MILP and CP models under a time limit. As the problem is NP-hard, two variants of the iterated greedy algorithm, a variable block insertion heuristic and four variants of ensemble of metaheuristic algorithms are also proposed, as well as a novel constructive heuristic. The performances of the proposed seven bi-objective metaheuristics are compared with each other as well as the MILP and CP solutions on a set of well-known HFSP benchmarks in terms of cardinality, closeness, and diversity of the solutions. Initially, the performances of the algorithms are tested on small-sized instances with respect to the Pareto-optimal solutions. Then, it is shown that the proposed algorithms are very effective for solving large instances in terms of both solution quality and CPU time.Öğe Island-based Crow Search Algorithm for solving optimal control problems(Elsevier, 2020) Turgut, Mert Sinan; Turgut, Oğuz Emrah; Eliiyi, Deniz TürselCrow Search Algorithm (CROW) is one of the members of recently developed swarm-based meta-heuristic algorithms. Literature includes different applications of this algorithm on engineering design problems. However, this optimization method suffers from some drawbacks such as premature convergence and trapping into local optima at the early phase of iterations. In order to conquer this algorithm specific inabilities, many research studies have been conducted in the literature dealing with the improvements and enhancements on the search mechanism of CROW. Structured population mechanism plays a vital role in preserving and controlling diversity, and thus increases the solution efficiency in evolutionary algorithms. Among the different types of methods used in structured algorithms, the island model is one of the widely applied solution strategies, in which the population individuals are subdivided into a predefined number of subpopulations. Migration mechanism is the key factor increasing population diversity, which takes place between independently running subpopulations during iterations to exchange valuable and useful solution information. This study embeds the fundamentals of the island model concepts into the Crow Search Algorithm to improve its probing capabilities of the search domain, by means of the periodically interacting subpopulations on the course of iterations. In addition, four different hierarchical migration topologies have been proposed, and their search effectiveness have been evaluated and compared over 45 optimization test functions. The optimization function test set includes classic benchmark optimization problems and CEC 2015 benchmark functions. Furthermore, each hierarchical island model is applied for solving six different optimal control problems in order to investigate their efficiencies on multi-dimensional real world optimization problems. The investigated optimal control problems are parallel reaction, continuous stirred tank reactor, batch reactor consecutive reaction, nonlinear constrained mathematical system, nonlinear continuous stirred tank reactor and nonlinear crane container problems. It is found out that the island model concepts improved the optimization performance of CROW. The proposed island models outperformed or showed similar performance compared to the six selected literature optimizers for 27-29 classic benchmark optimization problems. Moreover, incorporating the master sub-population to the island model improved the optimization capability of the algorithm further in most cases. The island models that employ the master sub-population came up with more favorable results compared to their non-master sub-population peers in all optimal control problems. The island model that includes the master sub-population and has the migration topology entitled 82'' found the most desirable solutions for 4-6 optimal control problems. (C) 2020 Elsevier B.V. All rights reserved.Öğe Joint Forecasting-Scheduling for the Internet of Things(Institute of Electrical and Electronics Engineers Inc., 2019) Nakip, Mert; Rodoplu, Volkan; Güzeliş, C.; Eliiyi, Deniz TürselWe present a joint forecasting-scheduling (JFS) system, to be implemented at an IoT Gateway, in order to alleviate the Massive Access Problem of the Internet of Things. The existing proposals to solve the Massive Access Problem model the traffic generation pattern of each IoT device via random arrivals. In contrast, our JFS system forecasts the traffic generation pattern of each IoT device and schedules the transmissions of these devices in advance. The comparison of the network throughput of Autoregressive Integrated Moving Average (ARIMA), Multi-Layer Perceptron (MLP) and Long-Short Term Memory (LSTM) forecasting models reveals that the optimal choice of the forecasting model for JFS depends heavily on the proportions of distinct IoT device classes that are present in the network. Simulations show that our JFS system scales up to 1000 devices while achieving a total execution time under 1 second. This work opens the way to the design of scalable joint forecasting-scheduling solutions at IoT Gateways. © 2019 IEEE.Öğe Metaheuristic algorithms for the hybrid flowshop scheduling problem(Pergamon-Elsevier Science Ltd, 2019) Öztop, Hande; Taşgetiren, M. Fatih; Eliiyi, Deniz Türsel; Pan, Quan-KeThe hybrid flowshop scheduling problem (HFSP) has been widely studied in the literature, as it has many real-life applications in industry. Even though many solution approaches have been presented for the HFSP with makespan criterion, studies on HFSP with total flow time minimization have been rather limited. This study presents a mathematical model, four variants of iterated greedy algorithms and a variable block insertion heuristic for the HFSP with total flow time minimization. Based on the well-known NEH heuristic, an efficient constructive heuristic is also proposed, and compared with NEH. A detailed design of experiment is carried out to calibrate the parameters of the proposed algorithms. The HFSP benchmark suite is used for evaluating the performance of the proposed methods. As there are only 10 large instances in the current literature, further 30 large instances are proposed as new benchmarks. The developed model is solved for all instances on CPLEX under a time limit, and the performances of the proposed algorithms are assessed through comparisons with the results from CPLEX and the two best-performing algorithms in literature. Computational results show that the proposed algorithms are very effective in terms of solution time and quality. Additionally, the proposed algorithms are tested on large instances for the makespan criterion, which reveal that they also perform superbly for the makespan objective. Especially for instances with 30 jobs, the proposed algorithms are able to find the current incumbent makespan values reported in literature, and provide three new best solutions. (C) 2019 Elsevier Ltd. All rights reserved.Öğe Multi-Channel joint forecasting-scheduling for the internet of things(IEEE-Inst Electrical Electronics Engineers Inc, 2020) Rodoplu, Volkan; Nakip, Mert; Qorbanian, Roozbeh; Eliiyi, Deniz TürselWe develop a methodology for Multi-Channel Joint Forecasting-Scheduling (MC-JFS) targeted at solving the Medium Access Control (MAC) layer Massive Access Problem of Machine-to-Machine (M2M) communication in the presence of multiple channels, as found in Orthogonal Frequency Division Multiple Access (OFDMA) systems. In contrast with the existing schemes that merely react to current traffic demand, Joint Forecasting-Scheduling (JFS) forecasts the traffic generation pattern of each Internet of Things (IoT) device in the coverage area of an IoT Gateway and schedules the uplink transmissions of the IoT devices over multiple channels in advance, thus obviating contention, collision and handshaking, which are found in reactive protocols. In this paper, we present the general form of a deterministic scheduling optimization program for MC-JFS that maximizes the total number of bits that are delivered over multiple channels by the delay deadlines of the IoT applications. In order to enable real-time operation of the MC-JFS system, first, we design a heuristic, called Multi-Channel Look Ahead Priority based on Average Load (MC-LAPAL), that solves the general form of the scheduling problem. Second, for the special case of identical channels, we develop a reduction technique by virtue of which an optimal solution of the scheduling problem is computed in real time. We compare the network performance of our MC-JFS scheme against Multi-Channel Reservation-based Access Barring (MC-RAB) and Multi-Channel Enhanced Reservation-based Access Barring (MC-ERAB), both of which serve as benchmark reactive protocols. Our results show that MC-JFS outperforms both MC-RAB and MC-ERAB with respect to uplink cross-layer throughput and transmit energy consumption, and that MC-LAPAL provides high performance as an MC-JFS heuristic. Furthermore, we show that the computation time of MC-LAPAL scales approximately linearly with the number of IoT devices. This work serves as a foundation for building scalable JFS schemes at IoT Gateways in the near future.Öğe A multiscale algorithm for joint forecasting-scheduling to solve the massive access problem of IoT(IEEE-Inst Electrical Electronics Engineers Inc, 2020) Rodoplu, Volkan; Nakip, Mert; Eliiyi, Deniz Türsel; Guzelis, CuneytThe massive access problem of the Internet of Things (IoT) is the problem of enabling the wireless access of a massive number of IoT devices to the wired infrastructure. In this article, we describe a multiscale algorithm (MSA) for joint forecasting-scheduling at a dedicated IoT gateway to solve the massive access problem at the medium access control (MAC) layer. Our algorithm operates at multiple time scales that are determined by the delay constraints of IoT applications as well as the minimum traffic generation periods of IoT devices. In contrast with the current approaches to the massive access problem that assume random arrivals for IoT data, our algorithm forecasts the upcoming traffic of IoT devices using a multilayer perceptron architecture and preallocates the uplink wireless channel based on these forecasts. The multiscale nature of our algorithm ensures scalable time and space complexity to support up to 6650 IoT devices in our simulations. We compare the throughput and energy consumption of MSA with those of reservation-based access barring (RAB), priority based on average load (PAL), and enhanced predictive version burst-oriented (E-PRV-BO) protocols, and show that MSA significantly outperforms these beyond 3000 devices. Furthermore, we show that the percentage control overhead of MSA remains less than 1.5%. Our results pave the way to building scalable joint forecasting-scheduling engines to handle a massive number of IoT devices at IoT gateways.Öğe Parallel identification of central nodes in wireless multi-hop networks(IEEE, 2020) Eliiyi, Deniz Türsel; Arslan, Hilal; Akram, Vahid Khalilpour; Uğurlu, OnurA wireless multi-hop network is a collection of nodes that communicate by message passing over multiple links. Sending a message to a remote node can consume some energy from all intermediary nodes. In a network, the nodes with minimum distance to all other nodes are called Jordan central nodes. Selecting the central nodes as sink or base station can considerably reduce the overall energy consumption and increase the network life time. This paper proposes a new parallel algorithm to find all central nodes of a network by finding BFS trees of a subset of nodes. The roots of the trees with smallest height are selected as Jordan central nodes. After finding each tree the algorithm eliminates some nodes from the search space. Available processors construct the BFS tree for different nodes in parallel and eliminate a group of unvisited nodes after creating each tree. The implementation results of the algorithm using different number of processors on topologies with up to 250 nodes showed that the proposed algorithm can find all central nodes by examining less than 20% of nodes in less than 0.034 seconds.Öğe A progressive search algorithm for the minimum hitting set problem(2021) Arslan, Hilal; Uğurlu, Onur; Akram, Vahid Khalilpour; Eliiyi, Deniz TürselGiven a finite universe and a collection of the subsets of the universe, the minimum hitting set of thecollection is the smallest subset of the universe that has non-empty intersection with each set in thecollection. Finding the minimum hitting set is an NP-Hard problem that has many real worldapplications. In this study, we propose a progressive search-based approach to find the minimumhitting set of a given collection. The algorithm starts searching for the hitting sets of size 1 andincrease the expected size of the minimum hitting set by a factor of d. After each unsuccessful search,it increases the expected size by d and generate the candidate sets with the expected size. After eachsuccessful search, the algorithm takes the average of last unsuccessful and successful searches andcontinue the searching with the new expected size. The algorithm terminates when the detectedupper bound coincides with the detected lower bound. The effect of different values for d on theperformance of the algorithm has been experimented on various data sets. Experimental resultsreveal that the proposed method effectively computes the minimum hitting set on real-world dataand random dataset.Öğe Vehicle routing with compartments under product incompatibility constraints(Svenciliste U Zagrebu, Fakultet Prometnih Znanosti, 2019) Taşar, Bahar; Eliiyi, Deniz Türsel; Kandiller, LeventThis study focuses on a distribution problem involving incompatible products which cannot be stored in a compartment of a vehicle. To satisfy different types of customer demand at minimum logistics cost, the products are stored in different compartments of fleet vehicles, which requires the problem to be modeled as a multiple-compartment vehicle routing problem (MCVRP). While there is an extensive literature on the vehicle routing problem (VRP) and its numerous variants, there are fewer research papers on the MCVRP. Firstly, a novel taxonomic framework for the VRP literature is proposed in this study. Secondly, new mathematical models are proposed for the basic MCVRP, together with its multiple-trip and split-delivery extensions, for obtaining exact solutions for small-size instances. Finally, heuristic algorithms are developed for larger instances of the three problem variants. To test the performance of our heuristics against optimum solutions for larger instances, a lower bounding scheme is also proposed. The results of the computational experiments are reported, indicating validity and a promising performance of an approach.
