QoE-Oriented Cooperative Broadcast Optimization for Vehicular Video Streaming

The popularity of online vehicular video has caused enormous information requests in Internet of vehicles (IoV), which brings huge challenges to cellular networks. To alleviate the pressure of base station (BS), Roadside Units (RSUs) and vehicle peers are introduced to collaboratively provide broadcast services to vehicle requesters where vehicles act as both service providers and service requesters. In this paper, we propose an efficient framework leveraging scalable video coding (SVC) technique to improve quality of experience (QoE) from two perspectives: (1) maximizing the data volume received by all requesters and (2) determining buffer action based on playback fluency and average playback quality. For (1), potential providers cooperate to determine the precached video content and delivery policy with the consideration of vehicular mobility and wireless channel status. If one provider fails, other sources will complement to provide requested content delivery. Therefore, their cooperation can improve the QoE and enhance the service reliability. For (2), according to buffer occupancy status, vehicle requesters manage buffer action whether to buffer new segments or upgrade the enhancement level of unplayed segment. Furthermore, the optimization of the data volume is formulated as an integer nonlinear programming (INLP) problem, which can be converted into some linear integer programming subproblems through McCormick envelope method and Lagrange relaxation. Numerical simulation results show that our algorithm is effective in improving total data throughput and QoE.

Design of a distribution network for the school lunch program

The national school lunch program (NSLP) is crucial for providing healthy, inexpensive, or free lunches to children, thus benefiting society. Designing a distribution network for the program requires solving a location and routing problem. In this paper, first, we formulate a multi-objective non-linear integer programming formulation of the problem. Next, we develop a two-step approach since the problem is Np-hard. The first stage presents a K-mean clustering method that deals with routing decisions by determining the locations of food processing centers and allocating schools to these centers. The second stage offers a multi-objective mixed-integer linear mathematical model for finding the locations of distribution centers. Besides economic and environmental factors, we optimize travel time in the network as perishable items are involved. A weighted sum approach is presented for different weights of objectives. We provide a real case study in Turkey to demonstrate the applicability of the two-stage approach proposed in this study. The numerical results provide valuable information for decision-makers and authorities to prioritize and prepare action plans.

Location of Urban Logistics Spaces (ULS) for Two-Echelon Distribution Systems

The main concern in city logistics is the need to optimize the movement of goods in urban contexts, and to minimize the multiple costs inherent in logistics operations. Inspired by an application in a medium-sized city in Latin America, this paper develops a bi-objective mixed linear integer programming (MILP) model to locate different types of urban logistics spaces (ULS) for the configuration of a two-echelon urban distribution system. The objective functions seek to minimize the costs associated with distance traveled and relocation, in addition to the costs of violation of time windows. This model considers heterogeneous transport, speed assignment, and time windows. For experimental evaluation, two operational scenarios are considered, and Pareto frontiers are obtained to identify the efficient non-dominated solutions to select the most feasible ones from such a set. A case study of a distribution company of goods for supermarkets in the city of Barranquilla, Colombia, is also used to validate the proposed model. These solutions allow decision-makers to define the configuration of ULS networks for urban product delivery.

Optimal Release Planning Using Machine Learning and Linear Integer Programming for Ideas in a Crowdsourcing Platform

Obtaining and analyzing customer and product information from various sources has become a top priority for major competitive companies who are striving to keep up with the digital and technological progress. Therefore, the need for creating a crowdsourcing platform to collect ideas from different stakeholders has become a major component of a company’s digital transformation strategy. However, these platforms suffer from problems that are related to the voluminous and vast amount of data. Different large sets of data are being spurred in these platforms as time goes by that render them unbeneficial. The aim of this paper is to propose a solution on how to discover the most promising ideas to match them to the strategic decisions of a business regarding resource allocation and product development (PD) roadmap. The paper introduces a 2-stage filtering process that includes a prediction model using a Random Forest Classifier that predicts ideas most likely to be implemented and a resource allocation optimization model based on Integer Linear Programming that produces an optimal release plan for the predicted ideas. The model was tested using real data on an idea crowdsourcing platform that remains unnamed in the paper due to confidentiality. Our prediction model has proved to be 92% accurate in predicting promising ideas and our release planning optimization problem results were found out to be 85% accurate in producing an optimal release plan for ideas.

Modeling Heterogeneous Fleet Vehicle Allocation Problem with Emissions Considerations

Aims: This study proposes a bi-objective linear integer programming model for heterogeneous fleet VAP with emissions considerations. Profit maximization and emissions minimization objectives are employed to handle economic and environmental sustainability purposes. Background: Our literature survey shows that there is no model for the heterogeneous fleet VAP with emissions considerations that simultaneously consider vehicle heterogeneity, penalty costs for unmet demands, and emissions from transportation operations. Objective: The model is employed to also make several scenario analyses on sustainable freight logistics management to understand the trade-offs among economic and environmental objectives. In freight transportation problems, decision-makers need to be able to maintain profitability and to reduce emissions. Methods: In this study, a bi-objective linear integer programming model is proposed for a heterogeneous fleet Vehicle Allocation Problem (VAP) with emissions considerations encountered in the field of sustainable freight transportation. Results: In the numerical analyses, various practical assumptions that can be confronted by decision-makers in real life are discussed. In each analysis, total profit and emissions amounts are revealed along with several other KPIs. The results of the analyses provided in this study could also be useful in terms of understanding the relations among pillars of sustainability in VAPs. Conclusion: It is thought that the proposed model has the potential to aid decision-making processes in sustainable logistics management. In the base case analyses, the total profit obtained under profit maximization is about nine times higher than that obtained under emissions minimization. When the aim is to minimize emissions, the total emissions are found to be nearly one-tenth of that of profit maximization. Supported by also additional scenario analyses, it can be concluded that it might not economically viable to be environmentally-friendly for companies. Therefore, companies have to be encouraged or forced to take environmentally and socially responsible actions through legislation. The analyses demonstrated that various legislative policies on emissions may affect the transportation plans differently in such vehicle allocation systems.

An application of a genetic algorithm to retail staff scheduling

Staff scheduling has received increasing attention over the past few years because of its widespread use, economic significance and difficulty of solution. For most organizations, the ability to have the right staff on duty at the right time is a critically important factor when attempting to satisfy their customers' requirements. The purpose of this study is to develop a genetic algorithm (GA) for the retail staff scheduling problem, and investigate its effectiveness. The proposed GA is compared with the conventional, linear integer programming approach. The GA is tested on a set of six real-world problems. Three are tested using a range of population size and mutation rate parameters. Then all six are solved with the best of those parameters. The results are compared to those obtained with the branch-and-bound algorithm. It is shown that GA can produce near-optimal solutions for all of the problems, and for half of them, it is more successful than the branch-and-bound method.

An application of a genetic algorithm to retail staff scheduling

Staff scheduling has received increasing attention over the past few years because of its widespread use, economic significance and difficulty of solution. For most organizations, the ability to have the right staff on duty at the right time is a critically important factor when attempting to satisfy their customers' requirements. The purpose of this study is to develop a genetic algorithm (GA) for the retail staff scheduling problem, and investigate its effectiveness. The proposed GA is compared with the conventional, linear integer programming approach. The GA is tested on a set of six real-world problems. Three are tested using a range of population size and mutation rate parameters. Then all six are solved with the best of those parameters. The results are compared to those obtained with the branch-and-bound algorithm. It is shown that GA can produce near-optimal solutions for all of the problems, and for half of them, it is more successful than the branch-and-bound method.

An Insight into the Characteristic Equation for an Integer Program

This article enhances properties and applications associated with the characteristic equation (CE) developed to find an optimal and other ranked-optimal solutions of linear integer programming model. These enhanced properties have applications in the analysis of the multi-objective linear integer programs. The paper also identifies why the CE approach is not possible for some special linear programming (LP) models and creates a challenge for further investigation.