Browsing by Author "Eliiyi, Deniz Tursel"

Now showing 1 - 11 of 11

Citation - WoS: 9
A Multi-compartment Vehicle Routing Problem for Livestock Feed Distribution
(SPRINGER INTERNATIONAL PUBLISHING AG, 2017) Levent Kandiller; Deniz Tursel Eliiyi; Bahar Tasar; Kandiller, Levent; Tasar, Bahar; Eliiyi, Deniz Tursel; KF Doerner; I Ljubic; G Pflug; G Tragler
In the well-known Vehicle Routing Problem (VRP) customer demands from one or more depots are to be distributed via a fleet of vehicles. Various objectives of the problem are considered in literature including minimization of the total distance/time traversed by the fleet during distribution the total cost of vehicle usage or minimizing the maximum tour length/time. In this study we consider a multi-compartment VRP with incompatible products for the daily solution of a livestock feed distribution network where each livestock farm requests one type of feed from a single depot and the vehicles have several compartments. The objective is to minimize the total cost of distribution. Although VRP is a well-studied problem in literature multi-compartment VRP is considered only by few authors and our problem differs from the existing ones due to special operational constraints imposed by the situation on hand. We formulate a basic mathematical model for the problem and present possible extensions. We design a computational experiment for testing the effects of uncontrollable parameters over model performance on a commercial solver and report the results. The proposed model can easily be adapted to other distribution networks such as food and fuel/chemicals.
Citation - WoS: 16
Citation - Scopus: 26
A Multiscale Algorithm for Joint Forecasting-Scheduling to Solve the Massive Access Problem of IoT
(Institute of Electrical and Electronics Engineers Inc., 2020) Volkan Rodoplu; Mert Nakıp; D. T. Eliiyi; Cüneyt Güzeliş; Rodoplu, Volkan; Guzelis, Cuneyt; Nakip, Mert; Eliiyi, Deniz Tursel
The 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. © 2020 Elsevier B.V. All rights reserved.
Citation - WoS: 13
Citation - Scopus: 19
Constraint and mathematical programming models for integrated port container terminal operations
(Springer Verlag service@springer.de, 2018) Damla Kizilay; D. T. Eliiyi; Pascal van Hentenryck; Kizilay, Damla; Van Hentenryck, Pascal; Eliiyi, Deniz Tursel; W.-J. van Hoeve
This paper considers the integrated problem of quay crane assignment quay crane scheduling yard location assignment and vehicle dispatching operations at a container terminal. The main objective is to minimize vessel turnover times and maximize the terminal throughput which are key economic drivers in terminal operations. Due to their computational complexities these problems are not optimized jointly in existing work. This paper revisits this limitation and proposes Mixed Integer Programming (MIP) and Constraint Programming (CP) models for the integrated problem under some realistic assumptions. Experimental results show that the MIP formulation can only solve small instances while the CP model finds optimal solutions in reasonable times for realistic instances derived from actual container terminal operations. © 2018 Elsevier B.V. All rights reserved.
Citation - WoS: 13
Citation - Scopus: 14
Energy-efficient single machine total weighted tardiness problem with sequence-dependent setup times
(Springer Verlag service@springer.de, 2018) M. Fatih Tasgetiren; Hande Oztop; Uǧur Eliiyi; D. T. Eliiyi; Quanke Pan; Tasgetiren, M. Fatih; Fatih Tasgetiren, M.; Oztop, Hande; Pan, Quan-Ke; Eliiyi, Ugur; Eliiyi, Deniz Tursel; P. Premaratne , P. Gupta , D. Huang , V. Bevilacqua
Most of the problems defined in the scheduling literature do not yet take into account the energy consumption of manufacturing processes as in most of the variants with tardiness objectives. This study handles scheduling of jobs with due dates and sequence-dependent setup times (SMWTSD) while minimizing total weighted tardiness and total energy consumed in machine operations. The trade-off between total energy consumption (TEC) and total weighted tardiness is examined in a single machine environment where different jobs can be operated at varying speed levels. A bi-objective mixed integer linear programming model is formulated including this speed-scaling plan. Moreover an efficient multi-objective block insertion heuristic (BIH) and a multi-objective iterated greedy (IG) algorithm are proposed for this NP-hard problem. The performances of the proposed BIH and IG algorithms are compared with each other. The preliminary computational results on a benchmark suite consisting of instances with 60 jobs reveal that the proposed BIH algorithm is very promising in terms of providing good Pareto frontier approximations for the problem. © 2018 Elsevier B.V. All rights reserved.
Citation - WoS: 14
Citation - Scopus: 17
Green Permutation Flowshop Scheduling: A Trade- off- Between Energy Consumption and Total Flow Time
(SPRINGER INTERNATIONAL PUBLISHING AG, 2018) Hande Oztop; M. Fatih Tasgetiren; Deniz Tursel Eliiyi; Quan-Ke Pan; Tasgetiren, M. Fatih; Fatih Tasgetiren, M.; Oztop, Hande; Pan, Quan-Ke; Türsel Eliiyi, Deniz; Eliiyi, Deniz Tursel; DS Huang; MM Gromiha; K Han; A Hussain
Permutation flow shop scheduling problem (PFSP) is a well-known problem in the scheduling literature. Even though many multi-objective PFSPs are presented in the literature with the objectives related to production efficiency and customer satisfaction studies considering energy consumption and environmental effects in scheduling is very seldom. In this paper the trade-off between total energy consumption (TEC) and total flow time is investigated in a PFSP environment where the machines are assumed to operate at varying speed levels. A multi-objective mixed integer linear programming model is proposed based on a speed-scaling strategy. Due to the NP-complete nature of the problem an efficient multi-objective iterated greedy (IGALL) algorithm is also developed. The performance of IGALL is compared with model performance in terms of quality and cardinality of the solutions.
Citation - Scopus: 16
Joint Forecasting-Scheduling for the Internet of Things
(Institute of Electrical and Electronics Engineers Inc., 2019) Mert Nakıp; Volkan Rodoplu; Cüneyt Güzeliş; D. T. Eliiyi; Rodoplu, Volkan; Guzelis, Cuneyt; Nakip, Mert; Eliiyi, Deniz Tursel
We 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. © 2020 Elsevier B.V. All rights reserved.
Citation - WoS: 21
Citation - Scopus: 27
Modelling and optimisation of online container stacking with operational constraints
(ROUTLEDGE JOURNALS TAYLOR & FRANCIS LTD, 2019) Ceyhun Guven; Deniz Tursel Eliiyi; Türsel Eliiyi, Deniz; Guven, Ceyhun; Eliiyi, Deniz Tursel
Container transportation plays a critical role in the global shipping network and container terminals need to improve their operations to increase efficiency. Storage yard of a container terminal is a temporary area where the containers stay until they are shipped to their next destination. We concentrate on increasing the efficiency of the storage yard by developing online stacking policies for each incoming container. An unproductive move of a container performed to reach another container stored underneath is called reshuffling. The objective in container stacking problem is to minimise the number of reshuffles thereby increasing the efficiency of terminal operations. Additional weight-related operational constraints bring additional complexity to the online stacking decisions. We propose a mathematical model for the optimal online assignment of an incoming export transit import or empty container. We also propose an optimal online stacking policy and compare it with a random policy through simulation. Additionally lower bounds for the performance measures are obtained through simulation by relaxing the operational constraints of the problem in a third stacking policy. We present and discuss the computational results in terms of four main performance measures.
Citation - WoS: 12
Citation - Scopus: 18
Multi-Channel Joint Forecasting-Scheduling for the Internet of Things
(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2020) Volkan Rodoplu; Mert Nakip; Roozbeh Qorbanian; Deniz Tursel Eliiyi; Rodoplu, Volkan; Qorbanian, Roozbeh; Nakip, Mert; Eliiyi, Deniz Tursel
We 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.
Citation - WoS: 3
Citation - Scopus: 4
Optimisation and heuristic approaches for assigning inbound containers to outbound carriers
(Routledge info@tandf.co.uk, 2017) Cemalettin Öztürk; F. Zeynep Sargut; Mustafa Arslan Ornek; D. T. Eliiyi; Ozturk, Cemalettin; Sargut, F. Zeynep; Ornek, M. Arslan; Türsel Eliiyi, Deniz; Eliiyi, Deniz Tursel
Due to economical and/or geographical constraints most of the time overseas containers cannot be directly shipped to their destinations. These containers visit transhipment ports where they are first unloaded and temporarily stored and then loaded onto smaller vessels (feeders) to be transported to their final destinations. The assignment of these containers to outbound vessels necessitates several factors to be taken into account simultaneously. In this paper we develop a mathematical model to reflect multiple objectives with priorities and to assign these containers to different vessels at the transit container port terminal. Although we solve a single-objective (with the weighted sum of objectives) mathematical model to optimality we also propose two heuristic approaches to solve this complex problem for a transit agency. The first heuristic is shipment based and has four variants differing in how the opportunity costs of the assignments are calculated. The second greedy heuristic is trip based where the goal is to maximise the capacity utilisation of the vessels. The heuristics return very promising solutions in ignorable computational times. We also provide real-life cases and present our conclusions. © 2017 Elsevier B.V. All rights reserved.
Citation - WoS: 20
Citation - Scopus: 33
Trip allocation and stacking policies at a container terminal
(Elsevier, 2014) Ceyhun Güven; D. T. Eliiyi; Guven, Ceyhun; Eliiyi, Deniz Tursel
There are three crucial resources at container terminals, the yard cranes and the vehicles. The main objective of the terminal is the efficient use of these resources while performing different operations. The yard is a temporary storage area where containers remain until transported to their next location by truck train or vessel. Containers are stacked on top of each other in order to utilize the yard space efficiently. However stacking cranes can only directly access those containers at the top of the stack. As a result reshuffling/shifting occurs defined as an unproductive move of a container required to access another container stored underneath. We focus on increasing the efficiency of the yard via consideration of the container stacking optimization problem for transshipment inbound and outbound containers at a container terminal. The objective of the problem is to minimize container storage and retrieval times through avoidance of reshuffles resulting in more efficient loading/unloading operations and in turn minimizing the dwell time of containers. The main inputs are the type weight discharge port/location destined vessel/vehicle of the container and the expected departure time. Different stacking policies are proposed in this study and we also investigate the problem of allocating the transit container to outbound vessels to minimize dwell time at the terminal. Transit containers require multiple sea-trips to reach their final destination. Vessels departing from the terminal and destined for the same port may provide exchangeable trips for this type of container based on their several attributes and capacity restrictions. We consider this problem taking into account several factors that affect container/trip allocation decisions. The solution of this problem also has implications for the stacking problem. © 2016 Elsevier B.V. All rights reserved.
Citation - WoS: 7
Citation - Scopus: 8
Vehicle routing with compartments under product incompatibility constraints, Ürün karişmama kisitlari altinda çok kompartimanli araç rotalama
(Faculty of Transport and Traffic Engineering, 2019) Bahar Taşar; D. T. Eliiyi; Levent Kandiller; Taşar, Bahar; Kandiller, Levent; Türsel Eliiyi, Deniz; Eliiyi, Deniz Tursel
This 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. © 2024 Elsevier B.V. All rights reserved.