Integer Programming Optimization Research Articles

RAP via hybrid genetic simulating annealing algorithm

This paper aims to solve Redundancy allocation problem (RAP). It is a significant complex optimization and non-linear integer programming problem of reliability engineering. RAP includes the choices of components and the suitable amount of redundant subsystems for maximizing reliability of the system under given restrictions like cost, weight, volume etc. It is difficult to solve non-linear complex problems. In this paper, the RAP is solved by the combination of genetic and simulating algorithm that is called Hybrid Genetic Simulating Annealing Algorithm (HGSAA). It can be observed that superiority of both the algorithms are combined and form an adequate algorithm which ignores the individual weakness. Comparative analysis of HGSAA with existing methods such as Heuristic Algorith, Constraint Optimization Genetic Algorithm, Hybrid Particle Swarm Optimization and Constraint Optimization Genetic Algorithm are presented in this study. RAP is also solved by Branch and Bound method to validate the result of HGSAA. The developed algorithm is programmed by Matlab.

Fully decoupled reliability-based optimization of linear structures subject to Gaussian dynamic loading considering discrete design variables

Reliability-based optimization (RBO) offers the possibility of finding an optimal design for a system according to a prescribed criterion while explicitly taking into account the effects of uncertainty. However, due to the necessity of solving simultaneously a reliability problem nested in an optimization procedure, the corresponding computational cost is usually high, impeding the applicability of the methods. This computational cost is even further enlarged when one or several design variables must belong to a discrete set, due to the requirement of resorting to integer programming optimization algorithms. To alleviate this issue, this contribution proposes a fully decoupled approach for a specific class of problems, namely minimization of the failure probability of a linear system subjected to an uncertain dynamic load of the Gaussian type, under the additional constraint that the design variables are integer-valued. Specifically, by using the operator norm framework, as developed by the authors in previous work, this paper shows that by reducing the RBO problem with discrete design variables to the solution of a single deterministic optimization problem followed by a single reliability analysis, a large gain in numerical efficiency can be obtained without compromising the accuracy of the resulting optimal design. The application and capabilities of the proposed approach are illustrated by means of three examples.

Optimizing design and dispatch of a renewable energy system

Renewable energy technologies are becoming increasingly important due to their cost-competitiveness, and because of enhanced climate concerns. We demonstrate the capabilities of an integer-programming optimization model that minimizes capital (investment) and operational costs, and utility charges, while adhering to system sizing constraints, demand requirements, and interoperability characteristics of the systems chosen. The model recommends an optimally sized mix of renewable energy, conventional generation, and energy storage technologies, while simultaneously optimizing the corresponding dispatch strategy. Our case studies explore several venues, i.e., a small campus and a local hospital, with complex utility rate tariffs, multi-technology integration opportunities, and incentives for renewable power production. Using an optimization model, versus applying rules of thumb, can produce millions of dollars in savings over a 25-year time horizon and result in thousands of kilowatts of installed renewable energy.

Deployment and Migration of Virtualized Services with Joint Optimization of Backhaul Bandwidth and Load Balancing in Mobile Edge-Cloud Environments

Mobile edge-cloud computing environments appear as a novel computing paradigm to offer effective processing and storage solutions for delay sensitive applications. Besides, the container based virtualization technology becomes solicited due to its natural lightweight and portability as well as its small migration overhead that leads to seamless service migration and load balancing. However, with the mobility property, the users’ demands in terms of the backhaul bandwidth is a critical parameter that influences the delay constraints of the running applications. Accordingly, a Binary Integer Programming (BIP) optimization problem is formulated. It minimizes the users’ perceived backhaul delays and enhances the load-balancing degree in order to offer more chance to accept new requests along the network. Also, by introducing bandwidth constraints, the available user backhaul bandwidth after the placement are enhanced. Then, the adopted methodology to design two heuristic algorithms based on Ant Colony System (ACS) and Simulated Annealing (SA) is presented. The proposed schemes are compared using different metrics,and the benefits of the ACS-based solution compared to the SA-based as well as a genetic algorithm (GA) based solutions are demonstrated. Indeed, the normalized cost and the total backhaul costs are given by more optimal values using the ACS algorithm compared to the other solutions.

Energy-Saving and Resource-Efficient Algorithm for Virtual Network Function Placement With Network Scaling

As an significant paradigm change in network service provisioning, network function virtualization (NFV) can accommodate sophisticated and heterogeneous network services by placing miscellaneous virtual network functions (VNFs) on the specific software-based middleboxes. However, with every-growing energy and construction cost, network operators still face several challenges in terms of adaptation to fluctuant network load and the reduction of energy consumption. Specifically, it is still an open problem to resource-efficiently place VNFs on substrate network while saving network's energy consumption. In this article, the problem of VNF placement is investigated for the optimization of energy consumption and resource utilization. We first present the formulations of power and resource utilization, and then we formulate the VNF placement problem as a binary integer programming (BIP) optimization problem. A novel VNF placement algorithm, named as VPANS, is designed to achieve the efficient VNF placement solution based on matching theory and the dynamical adjustment of the substrate network's scale while it is redundant or inadequate for real-time load. The performance evaluation shows that compared with other three contrast algorithms, our proposed VPANS algorithm not only achieve a maximum power gain of 34.67 percent, but also achieve a better resource utilization and revenue.

Long term transmission expansion planning to improve power system resilience against cascading outages

A Transmission Expansion Planning (TEP) problem is proposed to find the optimal configuration of the transmission network with considering security and resilience constraints. Due to the importance of blackouts and cascading failure, the proposed Resilient TEP (RTEP) minimizes the effects of cascading outages in term of load curtailment. In order to estimate the size of cascading outages, based on the total lost load, an iterative algorithm is proposed to analyze and simulate the steady state mechanism of cascading outages. A set of initiating events are defined as the triggering points of the cascading failure and the consequent chain of related outages are identified to determine the blackout size. In addition to the resilience constraints, the security constraints based on N-1 security criterion are also considered. Based on the Benders Decomposition (BD) algorithm, a multi-stage solution procedure is developed to handle the investment decisions, security constraints, and resilience requirements efficiently. In order to achieve the optimal resilient TEP configuration, all the master and sub-problems are formulated as Mixed Integer Programming optimization models. The proposed RTEP is implemented over the IEEE 24-bus test system.

Drivers’ range anxiety and cost of new EV chargers in Amsterdam: a scenario-based optimization approach

ABSTRACT Due to the sharp growth in the adaptation of electric vehicles (EV) in the Netherlands and the objectives of the Dutch Climate Accord is to encourage electric mobility, in the coming decades a substantial number of new EV charging facilities needs to be provided. Efficient planning of EV charging infrastructure is coupled with the notion of range anxiety, which is likely to be severely high in case of soon-to-be EV drivers. This study aims to estimate the cost of developing a new charging infrastructure under five scenarios of range anxiety in Amsterdam East. Employing a Linear Integer Programming optimization model, on the basis of geographic data on car registration, existing EV chargers, and electricity substations, it is obtained that if drivers use 90% of their battery before using a charging facility, the existing charging infrastructure needs to be expanded by only 31% to accommodate almost seven times larger number of EVs – the threshold set by the European Union (EU) legislation on the deployment of alternative fuel infrastructure. If drivers use only 30% of the batteries; however, an increase of 167% in infrastructure is inevitable (accounting for almost five million euro of cost). Second, at any point along the range anxiety spectrum, if the interval between charging session increases for 1 day, the overall cost decreases by more than 30%. These findings are discussed, and two policy approaches are proposed: (1) information technology approach; (2) demand-response approach, on the basis of EU legislation on energy efficiency and deployment of alternative fuel infrastructure.

A framework for optimizing sustainment logistics for a US Army infantry brigade combat team

Purpose The purpose of this paper is to help optimize sustainment logistics for US Army brigade combat teams, which may face challenges in transporting their assigned assets. Design/methodology/approach This paper develops a simulation framework with an integrated integer programming optimization model. The integer-programming model optimizes sustainment outcomes of supported battalions on a daily basis, whereas the simulation framework analyzes risk associated with shortfalls that may arise over the entire duration of a conflict. Findings This work presents a scenario reflecting the steady resupply of an infantry brigade combat team during combat operations and presents an in-depth risk analysis for possible fleet compositions. Originality/value The risk curves obtained allow decision-makers and commanders to optimize vehicle fleet design in advance of a conflict.

Improved quantum state tomography for systems with XX+YY couplings and Z readouts

Quantum device characterization via state tomography plays an important role in both validating quantum hardware and processing quantum information, but it needs the exponential number of the measurements. For the systems with XX+YY-type couplings and Z readouts, such as superconducting quantum computing (SQC) systems, traditional quantum state tomography (QST) using single-qubit readout operations at least requires $3^n$ measurement settings in reconstructing an $n$-qubit state. In this work, I proposed an improved QST by adding 2-qubit evolutions as the readout operations and obtained an optimal tomographic scheme using the integer programming optimization. I respectively apply the new scheme on SQC systems with the Nearest-Neighbor, 2-Dimensional, and All-to-All connectivities on qubits. It shows that this method can reduce the number of measurements by over 60% compared with the traditional QST. Besides, comparison with the traditional scheme in the experimental feasibility and robustness against errors were made by numerical simulation. It is found that, the new scheme has good implementability and it can achieve comparable or even better accuracy than the traditional scheme. It is expected that the experimentalist from the related fields can directly utilize the ready-made results for reconstructing quantum states involved in their research.

Mixed integer programming for dynamic tower crane and storage area optimization on construction sites

Construction site layout planning, the arrangement of temporary facilities and equipment on site, is a fundamental part of construction preparation. In construction, the operation of tower cranes has a great impact on construction process execution and preparation costs. Therefore, tower cranes should be chosen and placed such that they enable smooth construction processes at minimal cost. Since the majority of loads on site are transported either from or to a storage area, the transport routes taken between tower cranes and storage areas have a high impact. This paper presents a mathematical model that computes cost-optimal positions for both tower cranes and storage areas. The model consists of two linked mixed integer programs, allowing for the detailed quality assessment of the obtained solutions. Time dependency is considered by subdividing the construction process into several construction phases. Conditions on site and construction elements can be retrieved from a building information model. Particular advantages of our approach include the close approximation of complex shapes via convex hulls. Available positions on site are represented by a fine grid and calculated during runtime, which allows for freer placement instead of restricting to a fixed number of possible locations given as input. Using mixed integer programming offers tremendous modeling possibilities and a rigorous quality guarantee for the obtained solutions. In contrast to existing heuristic approaches, the returned solutions are provably optimal up to the chosen optimality gap. A case study is provided to demonstrate the practical applicability of the proposed model.

A Comparative Study of Several Metaheuristic Algorithms to Optimize Monetary Incentive in Ridesharing Systems

The strong demand on human mobility leads to excessive numbers of cars and raises the problems of serious traffic congestion, large amounts of greenhouse gas emissions, air pollution and insufficient parking space in cities. Although ridesharing is a potential transport mode to solve the above problems through car-sharing, it is still not widely adopted. Most studies consider non-monetary incentive performance indices such as travel distance and successful matches in ridesharing systems. These performance indices fail to provide a strong incentive for ridesharing. The goal of this paper is to address this issue by proposing a monetary incentive performance indicator to improve the incentives for ridesharing. The objectives are to improve the incentive for ridesharing through a monetary incentive optimization problem formulation, development of a solution methodology and comparison of different solution algorithms. A non-linear integer programming optimization problem is formulated to optimize monetary incentive in ridesharing systems. Several discrete metaheuristic algorithms are developed to cope with computational complexity for solving the above problem. These include several discrete variants of particle swarm optimization algorithms, differential evolution algorithms and the firefly algorithm. The effectiveness of applying the above algorithms to solve the monetary incentive optimization problem is compared based on experimental results.

Bandwidth On-Demand for Multimedia Big Data Transfer Across Geo-Distributed Cloud Data Centers

Multimedia content is massively generated from various applications and devices, and processed in cloud data centers. Multimedia service providers prefer that their data are processed in data centers close to users in order to offer them high performance and reliable multimedia services that meet the requirements specified in the Service Level of Agreement (SLA). This requires transferring huge data sets of video streams, games content, images etc. across geographically distributed cloud data centers using underutilized bandwidth in backbone transport networks. As the amount of multimedia content increases, the demand to transfer big data sets across data centers increases as well. As such, the leftover bandwidth that appears at different times and for different durations in the backbone network becomes insufficient to satisfy the rapidly increasing demand for multimedia big data transfer. This challenge led to the creation of multi-rate Bandwidth on-Demand (BoD) service offerings for communication between geographically distributed cloud data centers. In this paper, we focus on BoD services which are offered by the Dense Wavelength Division Multiplexing (DWDM) layer because of its huge capacity. We propose a BoD broker which employs a scheduling algorithm that considers various deadlines of multimedia big data transfer requests. The broker in our model leverages the concept of standby wavelengths to minimize peak traffic and accommodate time requirements of delay-tolerant and delay-intolerant transfer requests. We also study strategies of routing and wavelength assignment using Mixed Integer Programming (MIP) optimization to rapidly handle volumes of multimedia big data transfer requests.

FAST AND REGULARIZED RECONSTRUCTION OF BUILDING FAÇADES FROM STREET-VIEW IMAGES USING BINARY INTEGER PROGRAMMING

Abstract. Regularized arrangement of primitives on building façades to aligned locations and consistent sizes is important towards structured reconstruction of urban environment. Mixed integer linear programing was used to solve the problem, however, it is extremely time consuming even for state-of-the-art commercial solvers. Aiming to alleviate this issue, we cast the problem into binary integer programming, which omits the requirements for real value parameters and is more efficient to be solved. Firstly, the bounding boxes of the primitives are detected using the YOLOv3 architecture in real-time. Secondly, the coordinates of the upper left corners and the sizes of the bounding boxes are automatically clustered in a binary integer programming optimization, which jointly considers the geometric fitness, regularity and additional constraints; this step does not require a priori knowledge, such as the number of clusters or pre-defined grammars. Finally, the regularized bounding boxes can be directly used to guide the façade reconstruction in an interactive environment. Experimental evaluations have revealed that the accuracies for the extraction of primitives are above 0.82, which is sufficient for the following 3D reconstruction. The proposed approach only takes about 10% to 20% of the runtime than previous approach and reduces the diversity of the bounding boxes to about 20% to 50%.

Optimization of open pit to underground transition depth: An idea for reducing waste rock contamination while maximizing economic benefits

As open pit (OP) mines grow downward, more waste rocks are produced. Thus, the waste removal and management costs increase exponentially. On the other hand, waste rocks spoil natural surfaces, water resources, and local biodiversity surrounding mining areas. These reasons motivate the shareholders to shift from their routine mining method toward an underground (UG) operation by which less waste rocks are extracted. The depth of this transition should be determined so as to achieve maximum profit from the entire project (OP & UG). The present study aims to evaluate the amount of waste rock reduction while optimizing the transition depth. To this end, an integer programming (IP) optimization model was executed on a real orebody. The results indicated that waste rock generation decreases up to 7.5 million tons while the net present value (NPV) of the project increases up to 0.28%. In addition, due to this optimization, around 4.5% of natural surfaces, which had been initially allocated to the disposal site, remained uncontaminated. Further, based on acid base accounting (ABA) test, it was concluded that waste rocks do not have acid generation potential and, thus, can be used as backfilled material.

Feeder Layout Optimization and Feeding Sequence Optimization in High Speed and High Precision Placement Machine

As for the multi-optimization mutual coupling process optimization for mounting production with multiple 1210 or1812 resistor components and the relatively low nozzle requirement, a fast multi-objective hierarchical optimization method with low matching requirement for parallel nozzle and placement sequence optimization is proposed. In the hierarchical structure, the nozzle optimization is constructed as a 0-1 integer programming optimization model for the first master hierarchy. In the second hierarchical, feeder optimization and picking order are parallel optimized based on the first hierarchy optimization. The respective problems related algorithm strategies have been proposed. The engineering experiments show that this Feeder layout optimization and Feeding sequence optimization method can significantly improve the overall performance of the multi-head placement machine with high speed and high precision.

Applied Research of Hierarchical Multi-objective Optimization Method in High Speed and High Precision Placement Machine

In practical engineering, placement optimization of multi-head in the high speed and high precision Surface Mount Technology (SMT) is a multi-objective optimization problem. It mainly includes the feeder distribution optimization, the component placement sequence optimization and the nozzle configuration optimization. Optimization problems are complicated and mutual coupling between them. In this paper, the hierarchical multi-objective optimization model is developed. In the hierarchical structure, the nozzle optimization is constructed as a 0-1 integer programming optimization model for the first master hierarchy. In the second hierarchical, component mounting sequence, feeder optimization and picking order are parallel optimized based on the first hierarchy optimization. The respective problems related algorithm strategies have been proposed. The engineering experiments show that this hierarchical multi-objective optimization method can significantly improve the overall performance of the multi-head placement machine with high speed and high precision.

On SDN-Driven Network Optimization and QoS Aware Routing Using Multiple Paths

Software Defined Networking (SDN) is a driving technology for enabling the 5th Generation of mobile communication (5G) systems offering enhanced network management features and softwarization. This paper concentrates on reducing the operating expenditure (OPEX) costs while i) increasing the quality of service (QoS) by leveraging the benefits of queuing and multi-path forwarding in OpenFlow, ii) allowing an operator with an SDN-enabled network to efficiently allocate the network resources considering mobility, and iii) reducing or even eliminating the need for over-provisioning. For achieving these objectives, a QoS aware network configuration and multipath forwarding approach is introduced that efficiently manages the operation of SDN enabled open virtual switches (OVSs). This paper proposes and evaluates three solutions that exploit the strength of QoS aware routing using multiple paths. While the two first solutions provide optimal and approximate optimal configurations, respectively, using linear integer programming optimization, the third one is a heuristic that uses Dijkstra short-path algorithm. The obtained results demonstrate the performance of the proposed solutions in terms of OPEX and execution time.

Tabu search and simulated annealing for resource-constrained multi-project scheduling to minimize maximal cash flow gap

In reality, a contractor may implement multiple projects simultaneously and in such an environment, how to achieve a positive balance between cash outflow and inflow by scheduling is an important problem for the contractor has to tackle. For this fact, this paper investigates a resource-constrained multi-project scheduling problem with the objective of minimizing the contractor's maximal cash flow gap under the constraint of a project deadline and renewable resource. In the paper, we construct a non-linear integer programming optimization model for the studied problem at first. Then, for the NP-hardness of the problem, we design three metaheuristic algorithms to solve the model: tabu search (TS), simulated annealing (SA), and an algorithm comprising both TS and SA (SA-TS). Finally, we conduct a computational experiment on a data set coming from existing literature to evaluate the performance of the developed algorithms and analyze the effects of key parameters on the objective function. Based on the computational results, the following conclusions are drawn: Among the designed algorithms, the SA-TS with an improvement measure is the most promising for solving the problem under study. Some parameters may exert an important effect on the contractor's maximal cash flow gap.

Trajectory Optimization for UAVs’ Efficient Charging in Wireless Rechargeable Sensor Networks

Unmanned aerial vehicle (UAV)-aided Wireless Rechargeable Sensor Network (WRSN) is a promising application in providing sustainable power supply to the rechargeable sensor nodes (SNs). Constructing a trajectory for the UAV to traverse all SNs with the cheapest cost is an important issue in UAV-aided WRSN. Although some exact algorithms and heuristic methods have been proposed, they cannot achieve a superb result for the large-scale scene within the tolerable time. In this paper, we study the UAV‘s trajectory optimization problem from a novel viewpoint that the designed trajectory should maximize the UAV's energy utilization efficiency. The maximum UAV's energy utilization efficiency problem is decomposed as integer programming and non-convex optimization problems. For the problem that UAV‘s charging position is fixed, we have speeded the algorithm's performance by limiting the search direction, the initial search position, and the search space. In the other case, where the power transfer efficiency is unchangeable within a certain distance, a polynomial-time randomized approximation scheme (PRAS) is presented to find the approximate minimum number of hovering locations. We have presented TPA-FCP and TPA-ERC to solve the above problems, respectively. The numerical results verify that our proposed algorithms effectively reduce the length of optimal trajectory and the time complexity. Besides, the energy carried by the UAV for the specified task is predictable, which provides valuable information for arranging the UAV's flight task.

An Outsourcing Model for Alert Analysis in a Cybersecurity Operations Center

A typical Cybersecurity Operations Center (CSOC) is a service organization. It hires and trains analysts, whose task is to perform analysis of alerts that were generated while monitoring the client’s networks. Due to ever-increasing financial and infrastructure burden on a CSOC driven by the rapidly growing demand for security services, it would become prohibitively expensive to continually expand the size of a CSOC to meet the demands in the future. An alternative solution is to outsource the alert analysis process to on-demand analysts, to provide scalable CSOC service to its clients with features, such as (1) higher throughput, (2) higher quality, and (3) more economical service than the current in-house service. The current outsourcing model is not cost effective and an exact optimization model is computationally inefficient. This article presents a novel two-step sequential mixed integer programming optimization method that is used in the development of a new decision-support business model for outsourcing the alert analysis process. It is demonstrated that through this model, a CSOC can effectively deliver its alert management services with the above-mentioned features. Results indicate that the model is scalable, computationally viable, real-time implementable, and can deliver CSOC services that meet the service-level agreement (SLA) between the CSOC and its client. In addition, the article provides valuable insights into the cost of operating the new business process outsourcing model for cybersecurity services.