Energy and content aware multi-homing video transmission in heterogeneous networks,”

Abstract-This paper studies video transmission using a multihoming service in a heterogeneous wireless access medium. We propose an energy and content aware video transmission framework that incorporates the energy limitation of mobile terminals (MTs) and the quality-of-service (QoS) requirements of video streaming applications, and employs the available opportunities in a heterogeneous wireless access medium. In the proposed framework, the MT determines the transmission power for the utilized radio interfaces, selectively drops some packets under the battery energy limitation, and assigns the most valuable packets to different radio interfaces in order to minimize the video quality distortion. First, the problem is formulated as MINLP which is known to be NP-hard. Then we employ a piecewise linearization approach and solve the problem using a cutting plane method which reduces the associated complexity from MINLP to a series of MIPs. Finally, for practical implementation in MTs, we approximate the video transmission framework using a two-stage optimization problem. Numerical results demonstrate that the proposed framework exhibits very close performance to the exact problem solution. In addition, the proposed framework, unlike the existing solutions in literature, offers a choice for desirable trade-off between the achieved video quality and the MT operational period per battery charging. Index Terms-Multi-homing video transmission, video packet scheduling, heterogeneous wireless access medium, precedenceconstrained multiple knapsack problem (PC-MKP)

Non-interior piecewise-linear pathways to l-infinity solutions of overdetermined linear systems

Ankara : Department of Industrial Engineering and the Institute of Engineering and Science of Bilkent University, 1996.Thesis (Master's) -- Bilkent University, 1996.Includes bibliographical references leaves 70-71In this thesis, a new characterization of solutions to overdetermined systems of linear equations is described based on a simple quadratic penalty function, which is used to change the problem into an unconstrained one. Piecewiselinear non-interior pathways to the set of optimal solutions are generated from the minimization of the unconstrained function. It is shown that the entire set of solutions is obtained from the paths for sufficiently small values of a scalar parameter. As a consequence, a new finite penalty algorithm is given for fx, problems. The algorithm is implemented and exhaustively tested using random and function approximation problems. .A comparison with the Barrodale-Phillips algorithm is also done. The results indicate that the new algorithm shows promising performance on random (non-function approximation) problems.Elhedhli, SamirM.S

Interior-point decomposition methods for integer programming : theory and application

Mixed integer programming (MIP) provides an important modeling and decision support tool for a wide variety of real-life problems. Unfortunately, practical MIPs are large-scale in size and pose serious difficulties to the available solution methodology and software.This thesis presents a novel solution approach for large-scale mixed integer programming that integrates three bodies of research: interior point methods, decomposition techniques and branch-and-bound approaches. The combination of decomposition concepts and branch-and-bound is commonly known as branch-and-price, while the integration of decomposition concepts and interior point methods lead to the analytic centre cutting plane method (ACCPM). Unfortunately, the use of interior point methods within branch-and-bound methods could not compete with simplex based branch-and-bound due to the inability of "warm" starting.The motivation for this study stems from the success of branch-and-price and ACCPM in solving integer and non-differentiable optimization problems respectively and the quest for a method that efficiently integrates interior-point methods and branch-and-bound.The proposed approach is called an Interior Point Branch-and-Price method (IP-B&P) and works as follows. First, a problem's structure is exploited using Lagrangean relaxation. Second, the resulting master problem is solved using ACCPM. Finally, the overall approach is incorporated within a branch-and-bound scheme. The resulting method is more than the combination of three different techniques. It addresses and fixes complications that arise as a result of this integration. This includes the restarting of the interior-point methods, the branching rule and the exploitation of past information as a warm start.In the first part of the thesis, we give the details of the interior-point branch-and-price method. We start by providing, discussing and implementing new ideas within ACCPM, then detail the IP-B&P method and its different components. To show the practical applicability of IP-B&P, we use the method as a basis for a new solution methodology for the production-distribution system design (PDSD) problem in supply chain management. In this second part of the thesis, we describe a two-level Lagrangean relaxation heuristic for the PDSD. The numerical results show the superiority of the method in providing the optimal solution for most of the problems attempted

The Effectiveness of Simple Decision Heuristics: Forecasting Commercial Success for Early-Stage Ventures

We investigate the decision heuristics used by experts to forecast that early-stage ventures are subsequently commercialized. Experts evaluate 37 project characteristics and subjectively combine data on all cues by examining both critical flaws and positive factors to arrive at a forecast. A conjunctive model is used to describe their process, which sums "good" and "bad" cue counts separately. This model achieves a 91.8% forecasting accuracy of the experts' correct forecasts. The model correctly predicts 86.0% of outcomes in out-of-sample, out-of-time tests. Results indicate that reasonably simple decision heuristics can perform well in a natural and very difficult decision-making context.judgment, heuristic, forecast, decision making, statistical prediction, early-stage ventures

Classification Models Via Tabu Search: An Application to Early Stage Venture Classification

We model the decision making process used by experts at the Canadian Innovation Centre to classify early stage venture proposals based on potential commercial success. The decision is based on thirty seven attributes that take values in (-1; 0; 1). We adopt a conjunctive decision framework due to Astebro and Elhedhli that selects a subset of attributes and determines two threshold values: one for the maximum allowed negatives (n) and one for minimum required positives (p). A proposal is classified as a success if the number of positives is greater than or equal to p and the number of negatives is less than or equal to n over the selected attributes. Based on data of 561 observations, the selection of attributes and the determination of the threshold values is modeled as a large-scale mixed integer program. Two solution approaches are explored: Benders decomposition and Tabu search. The first was very slow to converge, while the second provided high quality solutions quickly. Tabu Search provides excellent classification accuracy for predicting commercial successes as well as replicating the experts' forecasts, opening the venue for the use of Tabu Search in scoring and classification problems

Optimal Order Batching in Warehouse Management: A Data-Driven Robust Approach

Optimizing warehouse processes has direct impact on supply chain responsiveness, timely order fulfillment, and customer satisfaction. In this work, we focus on the picking process in warehouse management and study it from a data perspective. Using historical data from an industrial partner, we introduce, model, and study the robust order batching problem (ROBP) that groups orders into batches to minimize total order processing time accounting for uncertainty caused by system congestion and human behavior. We provide a generalizable, data-driven approach that overcomes warehouse-specific assumptions characterizing most of the work in the literature. We analyze historical data to understand the processes in the warehouse, to predict processing times, and to improve order processing. We introduce the ROBP and develop an efficient learning-based branch-and-price algorithm based on simultaneous column and row generation, embedded with alternative prediction models such as linear regression and random forest that predict processing time of a batch. We conduct extensive computational experiments to test the performance of the proposed approach and to derive managerial insights based on real data. The data-driven prescriptive analytics tool we propose achieves savings of seven to eight minutes per order, which translates into a 14.8% increase in daily picking operations capacity of the warehouse

Demand Allocation in Systems with Multiple Inventory Locations and Multiple Demand Sources

We consider the problem of allocating demand that originates from multiple sources among multiple inventory locations. Demand from each source arrives dynamically according to an independent Poisson process. The cost of fulfilling each order depends on both the source of the order and its fulfillment location. Inventory at all locations is replenished from a shared production facility with a finite production capacity and stochastic production times. Consequently, supply lead times are load dependent and affected by congestion at the production facility. Our objective is to determine an optimal demand allocation and optimal inventory levels at each location so that the sum of transportation, inventory, and backorder costs is minimized. We formulate the problem as a nonlinear optimization problem and characterize the structure of the optimal allocation policy. We show that the optimal demand allocations are always discrete, with demand from each source always fulfilled entirely from a single inventory location. We use this discreteness property to reformulate the problems as a mixed-integer linear program and provide an exact solution procedure. We show that this discreteness property extends to systems with other forms of supply processes. However, we also show that supply systems exist for which the property does not hold. Using numerical results, we examine the impact of different parameters and provide some managerial insights.production-inventory systems, optimal demand allocation, make-to-stock queues, facility location