A Benders Based Rolling Horizon Algorithm for a Dynamic Facility Location Problem

This study presents a well-known capacitated dynamic facility location problem (DFLP) that satisfies the customer demand at a minimum cost by determining the time period for opening, closing, or retaining an existing facility in a given location. To solve this challenging NP-hard problem, this paper develops a unique hybrid solution algorithm that combines a rolling horizon algorithm with an accelerated Benders decomposition algorithm. Extensive computational experiments are performed on benchmark test instances to evaluate the hybrid algorithm’s efficiency and robustness in solving the DFLP problem. Computational results indicate that the hybrid Benders based rolling horizon algorithm consistently offers high quality feasible solutions in a much shorter computational time period than the stand-alone rolling horizon and accelerated Benders decomposition algorithms in the experimental range

A review on Reliability, Security and Memory Management of Numerous Operating Systems

With the improvement of technology and the growing needs of computer systems, it is needed to ensure that operating systems are able to provide the required functionalities. To provide these functionality operating systems are designed to maintain some design factors such as scalability, security, reliability, performance, memory management, energy efficiency. However, none of these factors can be achieved directly without facing any challenges. This research studied several design issues that are connected to each other in terms of providing an effective result. Therefore, this review article tried to reveal the major issues, which are independently more complex to solve at once. Finally, this research provides a guideline to overcome the challenges for future researchers by studying many research articles based on these design issues

Clustering Network Data Using Mixed Integer Linear Programming

Network clustering provides insights into relational data and feeds certain machine learning pipelines. We present five integer or mixed-integer linear programming formulations from literature for a crisp clustering. The first four clustering models employ an undirected, unweighted network; the last one employs a signed network. All models are coded in Python and solved using Gurobi solver. Codes for one of the models are explained. All codes and datasets are made available. The aim of this chapter is to compare some of the integer or mixed-integer programming network clustering models and to provide access to Python codes to replicate the results. Mathematical programming formulations are provided, and experiments are run on two different datasets. Results are reported in terms of computational times and the best number of clusters. The maximum diameter minimization model forms compact clusters including members with a dominant affiliation. The model generates a few clusters with relatively larger size. Additional constraints can be included to force bounds on the cluster size. The NP-hard nature of the problem limits the size of the dataset, and one of the models is terminated after 6 days. The models are not practical for networks with hundreds of nodes and thousands of edges or more. However, the diversity of models suggests different practical applications in social sciences

Multi Objective PSO with Passive Congregation for Load Balancing Problem

High-level architecture (HLA) and Distributed Interactive Simulation (DIS) are commonly used for the distributed system. However, HLA suffers from a resource allocation problem and to solve this issue, optimization of load balancing is required. Efficient load balancing can minimize the simulation time of HLA and this optimization can be done using the multi-objective evolutionary algorithms (MOEA). Multi-Objective Particle Swarm Optimization (MOPSO) based on crowding distance (CD) is a popular MOEA method used to balance HLA load. In this research, the efficiency of MOPSO-CD is further improved by introducing the passive congregation (PC) method. Several simulation tests are done on this improved MOPSO-CD-PC method and the results showed that in terms of Coverage, Spacing, Non-dominated solutions and Inverted generational distance metrics, the MOPSO-CD-PC performed better than the previous MOPSO-CD algorithm. Hence, it can be a useful tool to optimize the load balancing problem in HLA

Design and Analysis of High Gain Low Power CMOS Comparator

The comparator is the most significant component of the analog-to-digital converter, voltage regulator, switching circuits, communication blocks etc. Depending on the various design schemes, comparator performance varied upon target applications. At present, low power, high gain, area efficient and high-speed comparator designed methods are necessary for complementary metal oxide semiconductor (CMOS) industry. In this research, a low power and high gain CMOS comparator are presented which utilized two-stage differential input stages with replication of DC current source to achieve higher gain, higher phase margin, higher bandwidth, and lower power consumption. The simulated results showed that, by using a minimum power supply of 1.2 V, the comparator could generate higher gain 77.45 dB with a phase margin of 60.08°. Moreover, the modified design consumed only 2.84 µW of power with a gain bandwidth of 30.975 MHz. In addition, the chip layout area of the modified comparator is found only 0.0033 mm2

Metrics for Assessing Overall Performance of Inland Waterway Ports: A Bayesian Network Based Approach

Because ports are considered to be the heart of the maritime transportation system, thereby assessing port performance is necessary for a nation’s development and economic success. This study proposes a novel metric, namely, “port performance index (PPI)”, to determine the overall performance and utilization of inland waterway ports based on six criteria, port facility, port availability, port economics, port service, port connectivity, and port environment. Unlike existing literature, which mainly ranks ports based on quantitative factors, this study utilizes a Bayesian Network (BN) model that focuses on both quantitative and qualitative factors to rank a port. The assessment of inland waterway port performance is further analyzed based on different advanced techniques such as sensitivity analysis and belief propagation. Insights drawn from the study show that all the six criteria are necessary to predict PPI. The study also showed that port service has the highest impact while port economics has the lowest impact among the six criteria on PPI for inland waterway ports

A High-Speed and Low-Offset Dynamic Latch Comparator

Circuit intricacy, speed, low-offset voltage, and resolution are essential factors for high-speed applications like analog-to-digital converters (ADCs). The comparator circuit with preamplifier increases the power dissipation, as it requires higher amount of currents than the latch circuitry. In this research, a novel topology of dynamic latch comparator is illustrated, which is able to provide high speed, low offset, and high resolution. Moreover, the circuit is able to reduce the power dissipation as the topology is based on latch circuitry. The cross-coupled circuit mechanism with the regenerative latch is employed for enhancing the dynamic latch comparator performance. In addition, input-tracking phase is used to reduce the offset voltage. The Monte-Carlo simulation results for the designed comparator in 0.18 μm CMOS process show that the equivalent input-referred offset voltage is 720 μV with 3.44 mV standard deviation. The simulated result shows that the designed comparator has 8-bit resolution and dissipates 158.5 μW of power under 1.8 V supply while operating with a clock frequency of 50 MHz. In addition, the proposed dynamic latch comparator has a layout size of 148.80 μm×59.70 μm

A high speed current dq PI controller for PMSM drive

Rješenje PI regulatora struje dq utemeljeno na Field Programmable Gate Array (FPGA) predlaže se u ovom istraživanju, a obično se provodi na računalu s procesorom digitalnog signala (DSP). Glavni problem kod DSP temeljenog rješenja je vrijeme izvršenja, koje je obično u rasponu od mikrosekundi, kao i dostizanju njegovih fizičkih granica. Stoga, dovršavanje izvršenja unutar nanosekundi postaje veliki izazov za sve istraživača, što može biti učinjeno smanjenjem ciklusa sata. Uvođenje ukupnog kontrolnog algoritma u FPGA sigurno će dramatično smanjiti vrijeme izvršenja kao zalog za postojanost motora. Rezultat pokazuje da predložena FPGA izvedba treba samo 68 ns korištenog vremena za operativnu frekvenciju od 30 MHz i točnost od 99,9 %, što je najniži računalni ciklus ovoga doba.A Field Programmable Gate Array (FPGA) based solution of current dq PI controller is proposed in this research, which is usually implemented in digital signal processor (DSP) based computer. The main problem in DSP based solution is the execution time, which is usually in microseconds range as well as reaching its physical limits. Therefore, completing the execution within nanoseconds becomes a major challenge to all researchers, which can be done by reducing the clock cycles. Implementing the overall controlling algorithm into FPGA will certainly reduce the execution time dramatically to pledge the steadiness of the motor. The result shows that the proposed FPGA performance requires only 68 ns of execution time for operating frequency of 30 MHz and accuracy of 99,9 %, which is the lowest computational cycle for the era

Models for a carbon constrained, reliable biofuel supply chain network design and management

This dissertation studies two important problems in the field of biomass supply chain network. In the first part of the dissertation, we study the impact of different carbon regulatory policies such as carbon cap, carbon tax, carbon cap-and-trade and carbon offsetmechanism on the design and management of a biofuel supply chain network under both deterministic and stochastic settings. These mathematical models identify locations and production capacities for biocrude production plants by exploring the trade-offs that exist between transportations costs, facility investment costs and emissions. The model is solved using a modified L-shaped algorithm. We used the state of Mississippi as a testing ground for our model. A number of observations are made about the impact of each policy on the biofuel supply chain network. In the second part of the dissertation, we study the impact of intermodal hub disruption on a biofuel supply chain network. We present mathematical model that designs multimodal transportation network for a biofuel supply chain system, where intermodal hubs are subject to site-dependent probabilistic disruptions. The disruption probabilities of intermodal hubs are estimated by using a probabilistic model which is developed using real world data. We further extend this model to develop a mixed integer nonlinear program that allocates intermodal hub dynamically to cope with biomass supply fluctuations and to hedge against natural disasters. We developed a rolling horizon based Benders decomposition algorithm to solve this challenging NP-hard problem. Numerical experiments show that this proposed algorithm can solve large scale problem instances to a near optimal solution in a reasonable time. We applied the models to a case study using data from the southeast region of U.S. Finally, a number of managerial insights are drawn into the impact of intermodal-related risk on the supply chain performance