The effects of qos level degradation cost on provider selection and task allocation model in telecommunication networks

Firms acquire network capacity from multiple suppliers which offer different Quality of Service (QoS) levels. After acquisition, day-to-day operations such as video conferencing, voice over IP and data applications are allocated between these acquired capacities by considering QoS requirement of each operation. In optimal allocation scheme, it is generally assumed each operation has to be placed into resource that provides equal or higher QoS Level. Conversely, in this study it is showed that former allocation strategy may lead to suboptimal solutions depending upon penalty cost policy to charge degradation in QoS requirements. We model a cost minimization problem which includes three cost components namely capacity acquisition, opportunity and penalty due to loss in QoS

A bi-objective genetic algorithm approach to risk mitigation in project scheduling

A problem of risk mitigation in project scheduling is formulated as a bi-objective optimization problem, where the expected makespan and the expected total cost are both to be minimized. The expected total cost is the sum of four cost components: overhead cost, activity execution cost, cost of reducing risks and penalty cost for tardiness. Risks for activities are predefined. For each risk at an activity, various levels are defined, which correspond to the results of different preventive measures. Only those risks with a probable impact on the duration of the related activity are considered here. Impacts of risks are not only accounted for through the expected makespan but are also translated into cost and thus have an impact on the expected total cost. An MIP model and a heuristic solution approach based on genetic algorithms (GAs) is proposed. The experiments conducted indicate that GAs provide a fast and effective solution approach to the problem. For smaller problems, the results obtained by the GA are very good. For larger problems, there is room for improvement

A biobjective genetic algorithm approach to project scheduling under risk

A problem of risk mitigation in project scheduling is formulated as a bi-objective optimization problem, where the expected makespan and the expected total cost are both to be minimized. The expected total cost is the sum of four cost components: overhead cost, activity execution cost, cost of reducing risks and penalty cost for tardiness. Risks for activities are predefined. For each risk at an activity, various levels are defined, which correspond to the results of different preventive measures. Only those risks with a probable impact on the duration of the related activity are considered here. Impacts of risks are not only accounted for through the expected makespan but are also translated into cost and thus have an impact on the expected total cost. An MIP model and a heuristic solution approach based on genetic algorithms (GAs) is proposed and tested. The experiments conducted indicate that GAs provide a fast and effective solution approach to the proble m. For smaller problems, the results obtained by the GA are very good. For larger problems, there is room for improvement

Four payment models for the multi-mode resource constrained project scheduling problem with discounted cash flows

In this paper, the multi-mode resource constrained project scheduling problem with discounted cash flows is considered. The objective is the maximization of the net present value of all cash flows. Time value of money is taken into consideration, and cash in- and outflows are associated with activities and/or events. The resources can be of renewable, nonrenewable, and doubly constrained resource types. Four payment models are considered: Lump sum payment at the terminal event, payments at prespecified event nodes, payments at prespecified time points and progress payments. For finding solutions to problems proposed, a genetic algorithm (GA) approach is employed, which uses a special crossover operator that can exploit the multi-component nature of the problem. The models are investigated at the hand of an example problem. Sensitivity analyses are performed over the mark up and the discount rate. A set of 93 problems from literature are solved under the four different payment models and resource type combinations with the GA approach employed resulting in satisfactory computation times. The GA approach is compared with a domain specific heuristic for the lump sum payment case with renewable resources and is shown to outperform it

Context-aware LDA: Balancing Relevance and Diversity in TV Content Recommenders

In the vast and expanding ocean of digital content, users are hardly satisfied with recommended programs solely based on static user patterns and common statistics. Therefore, there is growing interest in recommendation approaches that aim to provide a certain level of diversity, besides precision and ranking. Context-awareness, which is an effective way to express dynamics and adaptivity, is widely used in recom-mender systems to set a proper balance between ranking and diversity. In light of these observations, we introduce a recommender with a context-aware probabilistic graphi-cal model and apply it to a campus-wide TV content de-livery system named “Vision”. Within this recommender, selection criteria of candidate fields and contextual factors are designed and users’ dependencies on their personal pref-erence or the aforementioned contextual influences can be distinguished. Most importantly, as to the role of balanc-ing relevance and diversity, final experiment results prove that context-aware LDA can evidently outperform other al-gorithms on both metrics. Thus this scalable model can be flexibly used for different recommendation purposes

Kaynak kısıtlı proje çizelgelemede indirgenmiş nakit akışı maksimizasyonu için bir genetik algoritma yaklaşımı

Bu çalısmada kaynak kısıtlı proje çizelgelemede indirgenmis nakit akısını ençoklamak için gelistirilen bir genetik algoritma sunulmaktadır. Problem hem yenilenebilir hem de yenilenemez kaynaklar göz önüne alınarak tanımlanmaktadır. Kaynakların uygulanmasında sonlu sayıda mod söz konusudur. Genetik algoritmada, çok-bilesenli, düzgün, sıralama temelli bir çaprazlama operatörü kullanılmıstır. Bu çaprazlama operatörünün öncüllük kısıtlarını ihlal etmeyisi önemli bir avantaj sağlamaktadır. Genetik algoritmanın parametrelerinin saptanması için bir meta-seviye genetik algoritma uygulanmıstır. Önerilen algoritmanın sınanması için teknik yazında mevcut 93 problemlik bir test problem kümesi kullanılmıstır. Ayrıca, salt yenilenebilir kaynaklar problemi için, özel amaçlı bir algoritma ile karsılastırma yapılmıs ve önerilen algoritmanın özellikle büyük boyutlu problemlerde basarılı olduğu gösterilmistir

Multiprocessor task scheduling in multistage hyrid flowshops: a genetic algorithm approach

This paper considers multiprocessor task scheduling in a multistage hybrid flow-shop environment. The objective is to minimize the make-span, that is, the completion time of all the tasks in the last stage. This problem is of practical interest in the textile and process industries. A genetic algorithm (GA) is developed to solve the problem. The GA is tested against a lower bound from the literature as well as against heuristic rules on a test bed comprising 400 problems with up to 100 jobs, 10 stages, and with up to five processors on each stage. For small problems, solutions found by the GA are compared to optimal solutions, which are obtained by total enumeration. For larger problems, optimum solutions are estimated by a statistical prediction technique. Computational results show that the GA is both effective and efficient for the current problem. Test problems are provided in a web site at www.benchmark.ibu.edu.tr/mpt-h; fsp

When to recommend What? A study on the role of contextual factors in IP-based TV services

Today’s IP-based TV services commonly strive for personalizing their content offers using complex recommendation systems to match their users’ interests. These systems try to capture the relevance of content recommended to a user, which may also depend on many contextual factors such as time, location, or social company. Nevertheless, in most cases, these factors are either omitted or integrated in recommendation systems without a concrete modeling of what different roles each may play on different users’ experiences. Do users really care about all of these specific factors? How do those factors interact with or influence each other? Can this interaction be modeled commonly for all users or is it more specific to the user profile? To the best of our knowledge, answers to these questions have not been studied in detail yet. In this paper, we introduce the results of a questionnaire and a focus group discussion to elaborate on the influence of contextual factors on IP-based TV services from the users’ point-of-view