Capacity Optimization in Dynamically Routing Computer Network Systems

A computer network system is a complex system with a great number of dynamic components. There are many devices in the system, such as computers, routers, lines, hubs, and switches. In addition to these hardware systems, many protocols are integrated to set the rules and provide the way of communication. Due to the nature of the system, it is hard to formulate and solve problems analytically without making any assumptions. One of the prominent problems that occur in computer systems is the line capacity assignment problem. In the previous mathematical models, message routes were predetermined and the dynamic nature of the system was neglected. This study deals with the line capacity assignment problem under a dynamically routing policy. Four different computer network topologies are used and solved by two heuristic algorithms via simulation. A dynamic search approach based on the occupancy rate of lines is used to define the consecutive routes of messages. The performances of harmony search and genetic algorithms via simulation are compared with the results of OptQuest, one of the optimization packet programs embedded in simulation software Arena®

Passenger railway network protection: A model with variable post-disruption demand service

Protecting transportation infrastructures is critical to avoid loss of life and to guard against economic upheaval. This paper addresses the problem of identifying optimal protection plans for passenger rail transportation networks, given a limited budget. We propose a bi-level protection model which extends and refines the model previously introduced by Scaparra et al, (Railway infrastructure security, Springer, New York, 2015). In our extension, we still measure the impact of rail disruptions in terms of the amount of unserved passenger demand. However, our model captures the post-disruption user behaviour in a more accurate way by assuming that passenger demand for rail services after disruptions varies with the extent of the travel delays. To solve this complex bi-level model, we develop a simulated annealing algorithm. The efficiency of the heuristic is tested on a set of randomly generated instances and compared with the one of a more standard exact decomposition algorithm. To illustrate how the modelling approach might be used in practice to inform protection planning decisions, we present a case study based on the London Underground. The case study also highlights the importance of capturing flow demand adjustments in response to increased travel time in a mathematical model

Passenger railway network protection: A model with variable post-disruption demand service

Protecting transportation infrastructures is critical to avoid loss of life and to guard against economic upheaval. This paper addresses the problem of identifying optimal protection plans for passenger rail transportation networks, given a limited budget. We propose a bi-level protection model which extends and refines the model previously introduced by Scaparra et al, (Railway infrastructure security, Springer, New York, 2015). In our extension, we still measure the impact of rail disruptions in terms of the amount of unserved passenger demand. However, our model captures the post-disruption user behaviour in a more accurate way by assuming that passenger demand for rail services after disruptions varies with the extent of the travel delays. To solve this complex bi-level model, we develop a simulated annealing algorithm. The efficiency of the heuristic is tested on a set of randomly generated instances and compared with the one of a more standard exact decomposition algorithm. To illustrate how the modelling approach might be used in practice to inform protection planning decisions, we present a case study based on the London Underground. The case study also highlights the importance of capturing flow demand adjustments in response to increased travel time in a mathematical model

Intermodal Network Design and Expansion for Freight Transportation

Over the last 50 years, international trade has grown considerably, and this growth has strained the global supply chains and their underlying support infrastructures. Consequently, shippers and receivers have to look for more efficient ways to transport their goods. In recent years, intermodal transport is becoming an increasingly attractive alternative to shippers, and this trend is likely to continue as governmental agencies are considering policies to induce a freight modal shift from road to intermodal to alleviate highway congestion and emissions. Intermodal freight transport involves using more than one mode, and thus, it is a more complex transport process. The factors that affect the overall efficiency of intermodal transport include, but not limited to: 1) cost of each mode, 2) trip time of each mode, 3) transfer time to another mode, and 4) location of that transfer (intermodal terminal). One of the reasons for the inefficiencies in intermodal freight transportation is the lack of planning on where to locate intermodal facilities in the transportation network and which infrastructure to expand to accommodate growth. This dissertation focuses on the intermodal network design problem and it extends previous works in three aspects: 1) address competition among intermodal service providers, 2) incorporate uncertainty of demand and supply in the design, and 3) incorporate multi-period planning into investment decisions. The following provides an overview of the works that have been completed in this dissertation. This work formulated robust optimization models for the problem of finding near-optimal locations for new intermodal terminals and their capacities for a railroad company, which operates an intermodal network in a competitive environment with uncertain demands. To solve the robust models, a Simulated Annealing (SA) algorithm was developed. Experimental results indicated that the SA solutions (i.e. objective function values) are comparable to those obtained using GAMS, but the SA algorithm can obtain solutions faster and can solve much larger problems. Also, the results verified that solutions obtained from the robust models are more effective in dealing with uncertain demand scenarios. In a second study, a robust Mixed-Integer Linear Program (MILP) was developed to assist railroad operators with intermodal network expansion decisions. Specifically, the objective of the model was to identify critical rail links to retrofit, locations to establish new terminals, and existing terminals to expand, where the intermodal freight network is subject to demand and supply uncertainties. Addition considerations by the model included a finite overall budget for investment, limited capacities on network links and at intermodal terminals, and due dates for shipments. A hybrid genetic algorithm was developed to solve the proposed MILP. It utilized a column generation algorithm for freight flow assignment and a shortest path labeling algorithm for routing decisions. Experimental results indicated that the developed algorithm can produce optimal solutions efficiently for both small-sized and large-sized intermodal freight networks. The results also verified that the developed model outperformed the traditional network design model with no uncertainty in terms of total network cost. The last study investigated the impact of multi-period approach in intermodal network expansion and routing decisions. A multi-period network design model was proposed to find when and where to locate new terminals, expand existing terminals and retrofit weaker links of the network over an extended planning period. Unlike the traditional static model, the planning horizon was divided into multiple periods in the multi-period model with different time scales for routing and design decisions. Expansion decisions were subject to budget constraints, demand uncertainty and network disruptions. A hybrid Simulated Annealing algorithm was developed to solve this NP-hard model. Model and algorithm’s application were investigated with two numerical case studies. The results verified the superiority of the multi-period model versus the single-period one in terms of total transportation cost and capacity utilization

Um modelo conjunto de localização e operação de estoque em redes dinâmicas

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Produção, Florianópolis, 2010Neste trabalho é proposto um modelo de otimização para o problema de localização de instalações, alocação de demanda e operação de estoque em redes dinâmicas onde parte dos arcos desta rede está sujeita a interrupções que podem ocorrer segundo probabilidades conhecidas, respeitando um processo Markoviano. O modelo é concebido com o objetivo de integrar, numa mesma abordagem, decisões estratégicas (onde localizar) e decisões operacionais (como operar) visando proporcionar a minimização dos custos do sistema, ao mesmo tempo em que se estabelece nível de serviço para atendimento à demanda. Para resolver este modelo é desenvolvida uma estratégia que utiliza programação dinâmica estocástica, simulação, o modelo clássico de lote econômico de compra e uma adaptação do algoritmo heurístico de Teitz & Bart. Tal estratégia é implementada em um programa de computador e testes computacionais são realizados com sucesso em um estudo de caso elaborado a partir de dados hipotéticos. Os resultados obtidos e as análises realizadas demonstram a factibilidade do modelo e a aplicabilidade da estratégia de solução

RESILIENCE OF TRANSPORTATION INFRASTRUCTURE SYSTEMS: QUANTIFICATION AND OPTIMIZATION

Transportation systems are critical lifelines for society, but are at risk from natural or human-caused hazards. To prevent significant loss from disaster events caused by such hazards, the transportation system must be resilient, and thus able to cope with disaster impact. It is impractical to reinforce or harden these systems to all types of events. However, options that support quick recovery of these systems and increase the system's resilience to such events may be helpful. To address these challenges, this dissertation provides a general mathematical framework to protect transportation infrastructure systems in the presence of uncertain events with the potential to reduce system capacity/performance. A single, general decision-support optimization model is formulated as a multi-stage stochastic program. The program seeks an optimal sequence of decisions over time based upon the realization of random events in each time stage. This dissertation addresses three problems to demonstrate the application of the proposed mathematical model in different transportation environments with emphasis on system-level resilience: Airport Resilience Problem (ARP), Building Evacuation Design Problem (BEDP), and Travel Time Resilience in Roadways (TTR). These problems aim to measure system performance given the system's topological and operational characteristics and support operational decision-making, mitigation and preparedness planning, and post-event immediate response. Mathematical optimization techniques including, bi-level programming, nonlinear programming, stochastic programming and robust optimization, are employed in the formulation of each problem. Exact (or approximate) solution methodologies based on concepts of primal and dual decomposition (integer L-shaped decomposition, Generalized Benders decomposition, and progressive hedging), disjunctive optimization, scenario simulation, and piecewise linearization methods are presented. Numerical experiments were conducted on network representations of a United States rail-based intermodal container network, the LaGuardia Airport taxiway and runway pavement network, a single-story office building, and a small roadway network