Incorporating congestion in preventive healthcare facility network design

Preventive healthcare aims at reducing the likelihood and severity of potentially life-threatening illnesses by protection and early detection. The level of participation to preventive healthcare programs is a crucial factor in terms of their effectiveness and efficiency. This paper provides a methodology for designing a network of preventive healthcare facilities so as to maximize participation. The number of facilities to be established and the location of each facility are the main determinants of the configuration of a healthcare facility network. We use the total (travel, waiting and service) time required for receiving the preventive service as a proxy for accessibility of a healthcare facility, and assume that each client would seek the services of the facility with minimum expected total time. At each facility, which we model as an M/M/1 queue so as to capture the level of congestion, the expected number of participants from each population zone decreases with the expected total time. In order to ensure service quality, the facilities cannot be operated unless their level of activity exceeds a minimum workload requirement. The arising mathematical formulation is highly nonlinear, and hence we provide a heuristic solution framework for this problem. Four heuristics are compared in terms of accuracy and computational requirements. The most efficient heuristic is utilized in solving a real life problem that involves the breast cancer screening center network in Montreal. In the context of this case, we found out that centralizing the total system capacity at the locations preferred by clients is a more effective strategy than decentralization by the use of a larger number of smaller facilities. We also show that the proposed methodology can be used in making the investment trade-off between expanding the total system capacity and changing the behavior of potential clients toward preventive healthcare programs by advertisement and education.     

Tree knapsack approaches for local access network design

In the process of solving many forms of the local access network design problem, the basic model of the tree knapsack problem (TKP) is used as a building block for the search engine of the solution strategy. Various solution strategies can be used to solve this problem. An approach that use standard software coupled with enhanced modelling is presented for the TKP. Enhanced modelling is used to partition the TKP into sub-problems that is easier to solve using standard off the shelve software. The basic approach is described and empirical work is presented. Empirical comparisons are also given relating this approach with some algorithms suggested by other authors.     

Representation of finite games as network congestion games

Weighted network congestion games are a natural model for interactions involving finitely many non-identical users of network resources, such as road segments or communication links. However, in spite of their special form, these games are not fundamentally special: every finite game can be represented as a weighted network congestion game. The same is true for the class of (unweighted) network congestion games with player-specific costs, in which the players differ in their cost functions rather than their weights. The intersection of the two classes consists of the unweighted network congestion games. These games are special: a finite game can be represented in this form if and only if it is an exact potential game.     

Signal design for an isolated intersection during congestion

This paper presents an algorithm for the signal design of an isolated intersection that will be able to alleviate long queues during severe congestion conditions, which cause both lengthy delays and harsh environmental damage. During such periods, the total effective green light is a scarce resource; its best allocation is crucial for the smooth operation of the intersection and sometimes even for a large network. The aim of the procedure presented here is to maximize the average throughput of the intersection. By achieving this goal, the number of vehicles in the queue is reduced at the fastest possible rate, and the period of congestion is shortened. The maximum throughput is achieved when the marginal flow in each phase is equal to the average throughput. The algorithm developed takes into account the decreasing flow rates of existing queues during long periods of green.     

Self-adaptive congestion control for multiclass intermittent connections in a communication network

A Markovian model for intermittent connections of various classes in a communication network is established and investigated. Any connection alternates between being OFF (idle) or ON (active, with data to transmit), and evolves in a way depending only on its class and the state of the network, in particular for the routes it uses among the network nodes to transmit data. The congestion of a given node is a functional of the throughputs of all ON connections going through it, and causes losses to these connections. Any ON connection reacts to its losses by self-adapting its throughput in TCP-like fashion so as to control network congestion. The connections interact through this feedback loop. The system constituted of their states (either OFF, or ON with some throughput) evolves in Markovian fashion, and since the number of connections in each class is potentially huge, a mean-field limit result with an adequate scaling is proved so as to reduce dimensionality. The limit is a nonlinear Markov process given by a McKean?CVlasov equation, of dimension the number of classes. It is then proved that the stationary distributions of the limit can be expressed in terms of the solutions of a finite-dimensional fixed-point equation.     

Dynamics of a congestion control model in a wireless access network

In this paper, a congestion control algorithm with heterogeneous delays in a wireless access network is considered. We regard the communication time delay as a bifurcating parameter to study the dynamical behaviors, i.e., local asymptotical stability, Hopf bifurcation and resonant codimension-two bifurcation. By analyzing the associated characteristic equation, the Hopf bifurcation occurs when the delay passes through a sequence of critical value. Furthermore, the direction and stability of the bifurcating periodic solutions are derived by applying the normal form theory and the center manifold theorem. In the meantime, the resonant codimension-two bifurcation is also found in this model. Some numerical examples are finally performed to verify the theoretical results.     

A simulation-based assessment of route guidance benefits under variable network congestion conditions

The purpose of this study is to establish a quantitative relationship between network congestion and travel-time reduction benefits of a real-time route guidance user service. The approach of the study is to employ the INTEGRATION traffic simulation model and a 2,000 link network based on the Detroit, Michigan roadway system in a series of experiments. While holding the capacity of the roadway fixed, the value of route guidance is evaluated over a range of increasing demand levels. Network demand patterns and trip characteristics are comparable to current national averages. Measures of congestion such as average system commute speed either match or exceed current national averages. Congestion metrics measured for the lightest demand scenario match most current empirical national average data, while the heaviest demand scenario appears roughly comparable with 1994 Tokyo conditions.Results from this study indicate that route-guided vehicles benefit regardless of level of congestion, however, the amount of trip time savings achieved is highly dependent on network congestion conditions. Average benefits for route guided vehicles over unguided vehicles in the A.M. peak period range between 8–26% depending on overall traffic volume.The results indicate a two-part linear relationship between route guidance benefit and network congestion. As congestion increases in the network, benefits of route guidance increase until average network speed drops below 20mph. Beyond that point, benefits decline (but remain positive). This 20mph threshold in our network is the point where the dynamically growing and shifting mass of queued vehicles around bottlenecks begins to impede access to alternative routes for guided vehicles network-wide.In a related experiment, route guided vehicles that receive reliable data on network conditions (including incidents or demand variation) gain 3–9% travel time savings over unguided vehicles that follow optimal routes based on average time-variant network congestion conditions. Route guided vehicles may exploit information about unexpected delays in the network related to incidents as well as variability in daily traffic patterns. This experiment was conducted to isolate the value of route guidance with respect to experienced commuter traffic, rather than an aggregated model of driver behavior including both familiar and unfamiliar drivers.The preliminary results of this study have implications for ITS benefit assessment. First, the benefits of route guidance are directly related to the level of recurrent congestion in a network. Thus, a near-term poor market for route guidance may evolve over time into a good market for these services. Likewise, a good market for a route guidance user service may deteriorate if overall network congestion reaches very high levels. Second, a route guidance user service provides benefits compared to both a model of aggregated unguided traveler behavior and a model of experienced commuter behavior, regardless of congestion levels. Third, route guided vehicles are demonstrated to gain benefit by avoiding the worst congestion in the network. This minimization in day-to-day variability in commute time may be the most significant benefit of the route guidance system for the familiar driver.     

A non-linear congestion network model for planning internal movement in the Hajj

A non-linear congestion network model is developed for the Hajj case. The Hajj is one of the World's largest mass movements, according to religious rituals. Every year about two and a half million people gather to perform religious rituals. The objective of the model is to minimize traffic congestion and overcrowding of holy sites. The constraints of the model are religious, spatial and time constraints. The model developed and applied for this case assisted in easing overcrowding and planning for expansions in routes and holy sites. The model is effective in providing quantitative background for general policy decision on the Hajj transportation.     

Spatial distribution complexities of traffic congestion and bottlenecks in different network topologies

Recently, urban traffic congestion has become a popular social problem. The generation and the propagation of congestion has close relation with the network topology, the traffic flow, etc. In this study, based on the traffic flow propagation method, we investigate the time and space distribution characteristics of the traffic congestion and bottlenecks in different network topologies (e.g., small world, random and regular network). The simulation results show that the random network is an optimal traffic structure, in which the traffic congestion is smaller than others. Moreover, the regular network is the worst topology which is prone to be congested. Additionally, we also prove the effects of network with community structure on the traffic system and congestion bottlenecks including its generation, propagation and time–space complexities. Results indicate that the strong community structure can improve the network performance and is effective to resist the propagation of the traffic congestion.     

The multi-layered network design problem

We address the problem of designing a network built on several layers. This problem occurs in practical applications but has not been studied extensively from the point of view of global optimisation, since the problem of designing a single-layered network is complex. An example of an application is the design of a virtual network (Internet Protocol) built on a sparse optical transport network.     

Multi-period reverse logistics network design

The configuration of the reverse logistics network is a complex problem comprising the determination of the optimal sites and capacities of collection centers, inspection centers, remanufacturing facilities, and/or recycling plants. In this paper, we propose a profit maximization modeling framework for reverse logistics network design problems. We present a mixed-integer linear programming formulation that is flexible to incorporate most of the reverse network structures plausible in practice. In order to consider the possibility of making future adjustments in the network configuration to allow gradual changes in the network structure and in the capacities of the facilities, we consider a multi-period setting. We propose a multi-commodity formulation and use a reverse bill of materials in order to capture component commonality among different products and to have the flexibility to incorporate all plausible means in tackling product returns. The proposed general framework is justified by a case study in the context of reverse logistics network design for washing machines and tumble dryers in Germany. We conduct extensive parametric and scenario analysis to illustrate the potential benefits of using a dynamic model as opposed to its static counterpart, and also to derive a number of managerial insights.     

The hierarchical network design problem

In this paper the authors introduce the hierarchical network design problem (HNDP). The object of the HNDP is to identify the least cost, two-level hierarchical network. The network must include a primary path from a predetermined starting node to a predetermined terminus node. In addition, each node not on the primary path must be connected to some node on that path by means of a secondary path. The problem is initially formulated as an integer linear program. An heuristic is then presented which employs a K shortest path algorithm, and a minimum spanning tree algorithm. Heuristic results of two sample problems are presented and compared to the results obtained by solving the integer LP formulation. Potential applications of the formulation are also discussed.     

General network design: A unified view of combined location and network design problems

This paper presents a unified framework for the general network design problem which encompasses several classical problems involving combined location and network design decisions. In some of these problems the service demand relates users and facilities, whereas in other cases the service demand relates pairs of users between them, and facilities are used to consolidate and re-route flows between users. Problems of this type arise in the design of transportation and telecommunication systems and include well-known problems such as location-network design problems, hub location problems, extensive facility location problems, tree-star location problems and cycle-star location problems, among others. Relevant modeling aspects, alternative formulations and possible algorithmic strategies are presented and analyzed.     

An analytic finite capacity queueing network model capturing the propagation of congestion and blocking

Analytic queueing network models often assume infinite capacity queues due to the difficulty of grasping the between-queue correlation. This correlation can help to explain the propagation of congestion. We present an analytic queueing network model which preserves the finite capacity of the queues and uses structural parameters to grasp the between-queue correlation. Unlike pre-existing models it maintains the network topology and the queue capacities exogenous. Additionally, congestion is directly modeled via a novel formulation of the state space of the queues which explicitly captures the blocking phase. The model can therefore describe the sources and effects of congestion.     

Integrated service network design for a cross-docking supply chain network

This paper considers an integrated service network design problem for a given set of freight demands that is concerned with integration of locating cross-docking (CD) centers and allocating vehicles for the associated direct (transportation) services from origin node to a CD center or from a CD center to the destination node. For the vehicle allocation, direct services (sub-routes) should be determined for the given freight demands, and then the vehicle allocation has to be made in consideration of routing for the associated direct service fulfillment subject to vehicle capacity and service time restriction. The problem is modeled as a path-based formulation for which a tabu-search-based solution algorithm is proposed. To guarantee the performance of the proposed solution algorithm, strong valid inequalities are derived based on the polyhedral characteristics of the problem domain and an efficient separation heuristic is derived for identifying any violated valid inequalities. Computational experiments are performed to test the performance of the proposed solution algorithm and also that of a valid-inequality separation algorithm, which finds that the solution algorithm works quite well and the separation algorithm provides strengthened lower bounds. Its immediate application may be made to strategic (integrated) service network designs and to tactical service network planning for the CD network.     

Survivable network design with shared-protection routing

In this paper we study the problem of designing a survivable telecommunication network with shared-protection routing. We develop a heuristic algorithm to solve this problem. Recent results in the area of global re-routing have been used to obtain very tight lower bounds for the problem. Our results indicate that in a majority of problem instances, the average gap between the heuristic solutions and the lower bounds is within 5%. Computational experience is reported on randomly generated problem instances with up to 35 nodes, 80 edges and 595 demand pairs and also on the instances available in SNDlib database.