Mixed integer nonlinear programming (MINLP)-based bandwidth utility function on internet pricing scheme with monitoring and marginal cost

The development of the internet in this era of globalization has increased fast. The need for internet becomes unlimited. Utility functions as one of measurements in internet usage, were usually associated with a level of satisfaction of users for the use of information services used. There are three internet pricing schemes used, that are flat fee, usage based and two-part tariff schemes by using one of the utility function which is Bandwidth Diminished with Increasing Bandwidth with monitoring cost and marginal cost. Internet pricing scheme will be solved by LINGO 13.0 in form of non-linear optimization problems to get optimal solution. The optimal solution is obtained using the either usage-based pricing scheme model or two-part tariff pricing scheme model for each services offered, if the comparison is with flat-fee pricing scheme. It is the best way for provider to offer network based on usage based scheme. The results show that by applying two part tariff scheme, the providers can maximize its revenue either for homogeneous or heterogeneous consumers

Network Migration Problem: A Logic-based Benders Decomposition Approach Driven by Column Generation and Constraint Programming

Telecommunication networks frequently face technological advancements and need to upgrade their infrastructure. Adapting legacy networks to the latest technology requires synchronized technicians responsible for migrating the equipment. The goal of the network migration problem is to find an optimal plan for this process. This is a defining step in the customer acquisition of telecommunications service suppliers, and its outcome directly impacts the network owners' purchasing behaviour. We propose the first exact method for the network migration problem, a logic-based Benders decomposition approach that benefits from a hybrid constraint programming-based column generation in its master problem and a constraint programming model in its subproblem. This integrated solution technique is applicable to any integer programming problem with similar structure, most notably the vehicle routing problem with node synchronization constraints. Comprehensive evaluation of our method over instances based on six real networks demonstrates the computational efficiency of the algorithm in obtaining quality solutions. We also show the merit of each incorporated optimization paradigm in achieving this performance

TUNING OPTIMIZATION SOFTWARE PARAMETERS FOR MIXED INTEGER PROGRAMMING PROBLEMS

The tuning of optimization software is of key interest to researchers solving mixed integer programming (MIP) problems. The efficiency of the optimization software can be greatly impacted by the solver’s parameter settings and the structure of the MIP. A designed experiment approach is used to fit a statistical model that would suggest settings of the parameters that provided the largest reduction in the primal integral metric. Tuning exemplars of six and 59 factors (parameters) of optimization software, experimentation takes place on three classes of MIPs: survivable fixed telecommunication network design, a formulation of the support vector machine with the ramp loss and L1-norm regularization, and node packing for coding theory graphs. This research presents and demonstrates a framework for tuning a portfolio of MIP instances to not only obtain good parameter settings used for future instances of the same class of MIPs, but to also gain insights into which parameters and interactions of parameters are significant for that class of MIPs. The framework is used for benchmarking of solvers with tuned parameters on a portfolio of instances. A group screening method provides a way to reduce the number of factors in a design and reduces the time it takes to perform the tuning process. Portfolio benchmarking provides performance information of optimization solvers on a class with instances of a similar structure

INTEGRATED SOLID WASTE MANAGEMENT: A MULTICRITERIA APPROACH

The paper presents the first results of a long term research aimed at producing a decision support system to deal with the integrated solid waste management planning at regional level. In the last years urban waste management has received a strong attention from the public authority in Italy culminating in a new national law, which has priorities such as waste prevention (waste avoidance and reduction) reuse and recycling. Italian Legislation requires to consider not only a series of waste management options aimed at source reduction but also to integrate the environmental soundness with economical viability and social equity. To support this integrated solid waste management it is necessary to ascertain the environmental, economic and social impacts associated with various waste management options so that decision makers can trade them off to achieve a better waste management strategy. To deal with the problem a three level process is suggested: zoning of the territory, implementation of the waste plan, Environmental Impact Assessment (EIA) on the new facilities. The paper focuses, in a non technical way, on a dynamic mixed integer linear programming model to be used in the second phase of the previous process. A multicriteria approach has been adopted to manage waste as an integrated system of recollection, transportation, recovery and disposal activities. At the moment four objective functions have been defined: total cumulative distance, total discounted net cost, total cumulative impact on traffic due to waste transportation, total cumulative landfilling. The model includes different types of collection, as well as different technologies. The model gives the possibility to locate in the same site more facilities. In this way it is possible to construct waste integrated platforms which permit to reduce costs and impacts. The model chooses the sites to be developed, the types of technology that will be installed on such sites, and the schedule of activity. In accordance with the input concentration for each technology it is possible to specify the appropriate output coefficients. The model computes the yields of the intermediate technologies directly from the model parameters, such parameters are exogenously determined, case by case, on the basis of the technical information; all the yields are automatically recomputed by the model when they vary. In this way high flexibility is introduced into the model. According to the preference of the decision maker specific constraints can be introduced in order to limit the admitted technologies; such restrictions have yearly validity. In this way a good representation of a dynamic situation can be reached. The main aspects that can be studied in space and time are: waste recollection at municipality, destination of each type of waste, technologies operating at facilities, landfilling, material and energy recovering, cost, traffic impact due to waste transportation. The results of a first application referred to the Province of Ravenna, in Emilia Romagna Region, Northern Italy are presented in the final section.Environmental Economics and Policy,

Pricing Network Edges to Cross a River.

We consider a Stackelberg pricing problem in directed networks:Tariffs (prices) have to be defined by an operator, the leader, for a subset of the arcs. Clients, the followers, choose paths to route their demand through the network selfishly and independently of each other, on the basis of minimal total cost. The problem is to find tariffs such as to maximize the operator''s revenue. We consider the case where each client takes at most one tariff arc to route the demand.The problem, which we refer to as the river tarification problem, is a special case of problems studied previously in the literature.We prove that the problem is strongly NP-hard.Moreover, we show that the polynomially solvable case of uniform tarification yields an m--approximation algorithm, and this is tight. We suggest a new type of analysis that allows to improve the result to \bigO{\log m}, whenever the input data is polynomially bounded. We furthermore derive an \bigO{m^{1-\varepsilon}}--inapproximability result for problems where the operator must serve all clients, and we discuss some polynomial special cases. Finally, a computational study with instances from France Telecom suggests that uniform pricing performs better in practice than theory would suggest.operations research and management science;

A pricing optimization modelling for assisted decision making in telecommunication product-service bundling

Product service bundle (PSB) is a marketing strategy that offers attractive product-service packages with competitive pricing to ensure sustained profitability. However, designing suitable pricing for PSB is a non-trivial task that involves complex decision-making. This paper explores the significance of pricing optimization in the telecommunication industry, focusing on product-service bundling (PSB). It delves into the challenges associated with pricing PSB and highlights the transformative impact of big data analytics on decision-making for PSB strategies. The study presents a data-driven pricing optimization model tailored for designing appropriate pricing structures for product-service bundles within the telecommunication services domain. This model integrates customer preference knowledge and involves intricate decision-making processes. To demonstrate the feasibility of the proposed approach, the paper conducts a case study encompassing two design scenarios, wherein the results reveal that the model offers competitive pricing compared to existing telecommunication service providers, facilitating PSB design and decision-making. The findings from the case study indicate that the data-driven pricing optimization model can significantly aid PSB design and decision-making, leading to competitive pricing strategies that open avenues for new market exploration and ensure business sustainability. By considering both product and service features concurrently, the proposed model provides a pricing reference for optimal decision-making. The case study validates the feasibility and effectiveness of the approach within the telecommunication industry and highlights its potential for broader applications. The model's capability to generate competitive pricing strategies offers opportunities for new market exploration, ensuring business growth and adaptability