Hasan Hüseyin Turan

Using heuristic algorithms for capacity leasing and task allocation issues in telecommunication networks under fuzzy quality of service constraints

Nowadays, every firm uses telecommunication networks in different amounts and ways in order to complete their daily operations. In this article, we investigate an optimisation problem that a firm faces when acquiring network capacity from a market in which there exist several network providers offering different pricing and quality of service (QoS) schemes. The QoS level guaranteed by network providers and the minimum quality level of service, which is needed for accomplishing the operations are denoted as fuzzy numbers in order to handle the non-deterministic nature of the telecommunication network environment. Interestingly, the mathematical formulation of the aforementioned problem leads to the special case of a well-known two-dimensional bin packing problem, which is famous for its computational complexity. We propose two different heuristic solution procedures that have the capability of solving the resulting nonlinear mixed integer programming model with fuzzy constraints. In conclusion, the efficiency of each algorithm is tested in several test instances to demonstrate the applicability of the methodology.

Integrated Optimization for Stock Levels and Cross-Training Schemes with Simulation-Based Genetic Algorithm

A spare part supply system for repairable spares in a repair shop is modeled as a set of heterogeneous parallel servers that have the ability to repair only certain types of repairables. The proposed model minimizes the total cost of holding inventory for spare parts, cost for backorder arising from downtime of the system due to the lack of spare parts and the cost of crosstraining for servers. Simulation-based Genetic Algorithm (GA) is proposed to optimize inventory levels and to determine the best skill assignments to servers, i.e., cross-training schemes. When methodologys performance is compared with total enumeration, tight optimality gaps are obtained.

A bandwidth sourcing and task allocation model in telecommunications under stochastic QoS guarantees

Graphical abstractDisplay Omitted HighlightsBandwidth provider selection and task allocation problem is modeled from firms perfective at managerial level.Delay and jitter are considered as random variables and integrated into model via chance constraints.A novel heuristic algorithm and a tight lower bound method are proposed.Firms optimal strategies are investigated under different scenarios. In this study, we model bandwidth provider selection and task allocation problem as an expected cost minimization problem with stochastic constraints. Two important parameters of quality of service (QoS), namely delay and jitter are considered as random variables to capture stochastic nature of telecom network environment. As solution methodology, stochastic model is converted into its deterministic equivalent and then a novel heuristic algorithm is proposed to solve resulting non-linear mixed integer programming model. We analyze how different prices, quality and task distribution affect the optimal behavior of the firm. Finally, performance of solution procedure is tested by several randomly generated scenarios and by a relaxation of this problem as tight lower bound.

Simulation based particle swarm optimization of cross-training policies in spare parts supply systems

We study a single location supply system for repairable spare parts. The system consists of a multi-server repair shop and inventory with ready-to-use spare parts. When a failed part is received, a new (or as-good-as-new) replacement part is sent back, and the failed part is forwarded to the repairshop. In the case of unavailability of spare parts, failed requests are backordered and fulfilled when a ready-for-use part of the same type is received from the repairshop. The repair shop has several multi-skilled parallel servers (technicians) that are capable of handling certain types of spares. In this paper, we propose a Particle Swarm Optimization heuristic combined with Discrete-Event Simulation for optimizing the cross-training policy (skill assignment scheme) while minimizing the total system cost (consisting of inventory costs, backorder penalty cost, server cost and skill cost).

A fuzzy long-term investment planning model for a genco in a hybrid electricity market considering climate change impacts

We study the long-term generation capacity investment problem of an independent power generation company (GenCo) that functions in an environment where GenCos perform business with both bilateral contracts (BC) and transactions in the day-ahead market (DAM). A fuzzy mixed integer linear programming model with a fuzzy objective and fuzzy constraints is developed to incorporate the impacts of imprecision/uncertainty in the economic environment on the calculation of the optimal value of the GenCo’s objective function. In formulating the fuzzy objective function we also include the potential impacts of climate change on the energy output of hydroelectric power plants. In addition to formulating and solving the capacity planning/investment problem, we also performed scenario-based (sensitivity) analysis to explore how investment decisions of the GenCos change when fuzziness (tolerance) in the maximum energy output of hydroelectric units and/or drought expectation increases. The proposed model is novel and investigates the effects of factors like drought expectations of climate changes, hydroelectric power plant investments, and other power generation technology investment options.

A Pooling Strategy for Flexible Repair Shop Designs.

We discuss the design problem of a repair shop in a single echelon repairable multi-item spare parts supply system. The repair shop consists of several parallel multi-skilled servers, and storage facilities for the repaired items. The effectiveness of repair shops and the total cost of a spare part supply system depend highly on the design of repair facility and the management of inventory levels of the spare parts. In this paper, we concentrate on a design scheme known as pooling. A repair shop can be considered as a pooled structure if the spare parts can be divided into clusters such that each part type is unambiguously assigned to a single cluster (cell). Nonetheless, it is both an important and tough combinatorial optimization question to determine which type of spares to pool together. We propose a sequential solution heuristic to find the best pooled design by considering inventory allocation and capacity level designation of the repair shop. The numerical experiments show that the suggested solution approach has a reasonable algorithm run time and yields considerable cost reductions.

Provider selection and task allocation in telecommunications with QoS degradation policy

The information age that we are living in is characterized by exponentially increasing needs and corresponding means to access, transmit and use data in a variety of business settings. Fast growing demand, which is translated to market opportunities, has led to the emergence of many new and well-established firms entering into the telecommunications market, resulting in a crowded, highly competitive business environment with numerous providers and carriers offering a wide range of data services. Today’s firms use telecommunication networks in a variety of ways to carry out their daily communications such as video conferencing, voice over IP and other data-intensive transmissions. In this paper, we report on a study in which we investigate a cost optimization problem that a firm encounters when acquiring network bandwidth from a telecommunication market that consists of many backbone providers offering different combinations of pricing policies and quality of service (QoS) levels. After the acquisition of network resources (bandwidth), firms allocate these resources to their daily data transmissions (tasks) according to the QoS requirement of the tasks. In an optimal allocation scheme, it is generally presumed that each task has to be assigned to a network resource which is capable of providing an equal or higher level of QoS than required by the task. However, it is shown with the proposed heuristic approach (presented herein) that QoS degradations during the allocation of tasks can lead to more favorable outcomes, especially when certain cost penalty policies are applied to the reduction of QoS requirements.

Stochastic fuzzy multi-objective backbone selection and capacity allocation problem under tax-band pricing policy with different fuzzy operators

In this paper, we investigate a multi-objective optimization problem that a telecom bandwidth broker (BB) faces when acquiring and selling bandwidth in an uncertain market environment in which there exists several backbone providers (BPs) and end users. The proposed model incorporates two important goals: maximizing expected profit and minimizing expected loss capacity within realistic constraints such as BPs’ capacity, meeting the end users’ bandwidth requests and satisfying the Quality of Service requests of end users’, considering stochastic capacity loss rates of BPs. The fuzzy set theory and stochastic programming techniques are employed to handle the non-deterministic nature of telecommunication network setting due to the presence of vagueness and randomness of information. The model is formulated in such a way that it simultaneously considers the randomness in demand and determines the allocation of end users’ bandwidth requests into purchased capacity based on tax-band pricing scheme. As solution strategies, two different fuzzy operators, namely max–min and weighted additive max–min, are integrated into a resulting two-stage multi-objective stochastic linear programming model. Then, algorithms are provided to solve and to compare methodologies with deterministic approaches. Finally, the proposed algorithms are tested on several randomly generated test scenarios to provide managerial insight to decision makers of BB companies.

A Genetic Algorithm for the Pooling-Inventory-Capacity Problem in Spare Part Supply Systems

We study a pooling-inventory-capacity problem that arises in the design of repair shops for repairable spare part logistic systems. We formulate the problem as a stochastic nonlinear integer programming model and propose a two-stage sequential solution algorithm. At the first stage, a genetic algorithm (GA) generates a set of feasible pooled repair shop design schemes. A pooled design can be viewed and modeled as the union of mutually exclusive and total exhaustive multi-class multi-server queueing systems. Thus, we exploit this fact and optimize each queueing system separately. In the second stage, optimal inventory and capacity levels for each independent system are calculated by using a queueing approximation technique and a local greedy heuristic. Finally, the performed numerical experiments show that proposed two-stage approach achieves high-quality solutions in reasonable time.

An Integrated Fuzzy Multi-criteria Approach for Evaluation of Energy Resources: A Case Study of Turkey

The energy consumption of the world has been increasing in a consistent trend. This increment is faster than overall energy supply. The construction of additional power plants is essential to keep up with meeting the growing total energy demand. However, the effects of these power plants on social life and local economy should be considered during the planning stage. In this chapter, a Delphi method integrated fuzzy multi-criteria decision-making methodology is proposed to evaluate and to compare power plant alternatives. This approach is based on the fuzzy logarithmic least squares method and performs good solutions under implicit information conditions. The methodology is tested in a case study to find the optimal plant type for Turkey. The numerical results together with conducted sensitivity analysis provide insights on the strength and feasibility of the proposed method.