An Optimal Routing Problem for Attraction with a Fast Pass Constraint

The network routing problem is one of important problems to consider behaviors of multiple flows on a network with traffic. Some network routing problems can be classified into the selfish routing game in which the behaviors of the flows are competitive and the optimal routing problem in which the behaviors of the flows are cooperative. This study focuses on the optimal routing problem. The optimal routing problem is formulated as an optimization problem with a linear equality constraint and lower limit constraints. The learning problem of the support vector machine (SVM) is formulated as an optimization problem with a linear equality constraint and upper and lower limit constraints. The sequential minimal optimization (SMO) is one of the fast solvers for the learning problem. SMO can be applied to solve the optimal routing problem. This study proposes a new fast optimal routing search method using SMO. In the numerical experiments, the effectiveness of the proposed method on the computational cost is verified with the comparison to the search method using the replicator dynamics.

Consideration is given to the optimal circuit routing problem in an existing circuit-switched network. The objective is to find circuit routing which accommodates a given circuit demand while maximizing the residual capacity of the network. In addition, the cost of accommodating the circuit demand should not exceed a given amount. Practical considerations require that a solution be robust to the variations in circuit demand and cost. The objective function for the optimal circuit routing problem is not a smooth one. In order to overcome the difficulties of nonsmooth optimization, the objective function is approximated by smooth concave functions. The optimization algorithm for the circuit routing problem is obtained as a limiting case of the sequence of optimal routing strategies for the corresponding smooth convex optimization problems, and the proof of its convergence to the optimal solution is given. An approach to calculating the optimal multicommodity flow is presented. The optimization algorithm efficiently handles networks with a large number of commodities, satisfies the robustness requirements, and can be used to solve circuit routing problems for large networks. >

This paper introduces the concept of time slot synchronization in time-wave division multiplexed (TWDM) multihop lightwave networks. It is shown that the time slot assignments of the intermediate nodes in a multihop path have significant effect on the end-to-end message delay. This is different from traditional notions in routing, where the hop count and congestion control are the primary concerns. Assuming a TWDM embedding of a given logical topology already exists, we formulate the optimal routing problem for arbitrary propagation delays. We propose a graph unfolding technique which converts this problem into the shortest path routing problem for weighted graphs with well known solutions. We show a method to estimate the buffering cost at the intermediate nodes (in multihop routing) to accommodate high-bandwidth traffic (e.g., video). Next, we address the question: given a logical topology what should the TWDM embedding be so that the routing delays are optimized P This leads us to an iterative approach for TWDM embedding. Given an initial embedding an optimal route is estimated for each pair of nodes. A weighted average of these optimal route distances is used as a metric to evaluate the TWDM embedding. Using a heuristic the TWDM embedding is modified and the process iterated to improve along this metric. Preliminary performance results illustrating the improvement in routing delay are provided. For example, for a 4-cube network on average delay minimization of 10% to 20% is observed.

The problem in transportation is the Vehicle Routing Problem (VRP), the delivery process of bread production at PT. XYZ is known to have not implemented an optimal distribution system. The purpose of writing this thesis is to create a VRP model for the distribution route in PT XYZ, completing it using the saving matrix, nearest insert and nearest neighbour methods. The saving matrix method uses the saving value, the nearest insert method adds a new point to the existing route, while the nearest neighbour method considers the nearest distance. The calculation carried out using the VRP model with the saving matrix method resulted in a distance saving of 82.284 km, then the nearest insert resulted in a distance saving of 93.829 km, and the nearest neighbor resulted in the largest distance saving, namely 94.406 km, with vehicle route 1 Factory-Wonokerto-Pringlangu-Landungsari-Poncol-Factory, vehicle route 2 is Factory-Grogol-Talang-Adiwerna-Slawi-Factory, vehicle route 3 is Factory-Poso-Randugunting-Kraton-Factory, and vehicle route 4 is Factory-Mejasem-Kemantran-Brebes-Factory. The proposed distribution route received the largest decrease, which was 94.406 km, initially the company's mileage was 285.414 km. The calculation of the route saving percentage is 33% and saves costs of Rp. 235,284 thus the optimal route.

We address the problem of optimal routing in overlay networks. An overlay network is constructed by adding new overlay nodes on top of a legacy network. The overlay nodes are capable of implementing any dynamic routing policy, however, the legacy underlay has a fixed, single path routing scheme and uses a simple work-conserving forwarding policy. Moreover, the underlay routes are pre-determined and unknown to the overlay network. The overlay network can increase the achievable throughput of the underlay by using multiple routes, which consist of direct routes and indirect routes through other overlay nodes. We develop a throughput optimal dynamic routing algorithm for such overlay networks called the Optimal Overlay Routing Policy (OORP). OORP is derived using the classical dual subgradient descent method, and it can be implemented in a distributed manner. We show that the underlay queue-lengths can be used as a substitute for the dual variables. We also propose various schemes to gather the information about the underlay that is required by OORP and compare their performance via extensive simulations.

This paper is devoted to the problem of optimal weather routing for transoceanic ship voyages. The goal of optimization is to reduce travel time and fuel consumptions. The optimal route is searched on the admissible set, which comprises routes satisfying all of the imposed static and dynamic constraints. The static constraints are represented mostly by coastal lines and shallow waters. The dynamic constraints are time-varying alarm zones restricted for ship motion due to dangerous weather conditions. The proposed route optimization algorithm uses long-term weather forecast for a sailing region and a mathematical model of ship motion. The key feature of algorithm is the three-dimensional representation of the route, which allows transforming initial finite-dimensional optimization problem to the problem of path finding on special graph.

In this paper, we study an optimal topology and routing problem in the body area network (BAN). Aiming at maximizing the network lifetime, we consider the guarantee of two important quality-of-service (QoS) requirements for BAN applications: the minimum bit error rate and the minimum data rate. Especially, we study the effect of the maximum number of hops that is allowed in routing to the system performance.

Determining an accurate and reliable prediction of the optimal routing paths between source and destination nodes in Mobile Adhoc Network (MANET) is always challenge due to dynamic nature of mobile nodes and the scarcity of network availability. The purpose of this work is to identify reliable optimal paths (ordered) from the set of possible random routing paths. The proposed model leverages fuzzy sets and rough sets for routing path classification. It also utilizes TOPSIS (Technique for order of performance by similarity to ideal solution), a multi criteria desicion analysis method for obtaining best and worst routes in the network. The derived routing path overcomes dynamic traffic routing problem in MANET. The proposed model compared with existing optimal routing models (Genetic Algorithm, Anti-Colony Optimization Algorithm) and found that CPU computing time decreased by 5X and through put (TP) increased by 2X.

New characterizations of optimal state‐dependent routing strategies are obtained for the continuous traffic network model proposed by Segall for linear cost with unity weighting at each node and for constant inputs. The concept of flow relaxation is introduced and is used t o transform the optimal routing problem into an initial flow optimization problem with convex cost and linear constraints. Three algorithms are given for open‐loop computation of the optimal initial flow. The first is a simple iterative algorithm based on gradient descent with bending and it is well suited for decentralized computation. The second algorithm reduces the problem t o a series of max‐flow problems and it computes the exact optimal initial flow in O(lN14) compu‐ tations, where IN1 is the number of nodes in the network. The third algorithm is based on a search for successive bottlenecks in the network.

Dynamic coupling design for nonlinear output agreement and time-varying flow control

Priced timed automata (PTA) are discrete-event system models with temporal constraints and a cost function and are used to pose optimal scheduling and routing problems. To date, solutions to these problems have been found offline and executed open loop.This open-loop control strategy makes it impossible to account for disturbances, i.e., changes in costs or scheduling constraints over time. To address this shortcoming, this work’s first contribution is a closed-loop model predictive control (MPC) framework for PTA, enabling decision-making based on real-time model updates. To ensure the feasibility of an MPC problem, it is often desirable to soften constraints. However, the contemporary PTA theory does not consider soft constraints. Thus, this work’s second contribution is to integrate constraint softening with PTA control by harnessing the capabilities of new solvers enabled by the recasting of the models and control problem into first-order logic by employing modified encoding schemes based on existing works. Finally, the proposed control framework and implementation are demonstrated in a simulation case study on the guidance of a product through a manufacturing system.

Heavy-duty vehicles traveling in platoons consume fuel at a reduced rate. In this paper, we attempt to maximize this benefit by introducing local controllers throughout a road network to facilitate platoon formations with minimal information. By knowing a vehicle's position, speed, and destination, the local controller can quickly decide how its speed should be possibly adjusted to platoon with others in the near future. We solve this optimal control and routing problem exactly for small numbers of vehicles, and present a fast heuristic algorithm for real-time use. We then implement such a distributed control system through a large-scale simulation of the German autobahn road network containing thousands of vehicles. The simulation shows fuel savings from 1-9%, with savings exceeding 5% when only a few thousand vehicles participate in the system. We assume no vehicles will travel more than the time required for their shortest paths for the majority of the paper. We conclude the results by analyzing how a relaxation of this assumption can further reduce fuel use.

Optimizing resource allocation and routing to satisfy service needs is paramount in large-scale networks. Software-defined networking (SDN) is a new network paradigm that decouples forwarding and control, enabling dynamic management and configuration through programming, which provides the possibility for deploying intelligent control algorithms (such as deep reinforcement learning algorithms) to solve network routing optimization problems in the network. Although these intelligent-based network routing optimization schemes can capture network state characteristics, they are prone to falling into local optima, resulting in poor convergence performance. In order to address this issue, this paper proposes an African Vulture Routing Optimization (AVRO) algorithm for achieving SDN routing optimization. AVRO is based on the African Vulture Optimization Algorithm (AVOA), a population-based metaheuristic intelligent optimization algorithm with global optimization ability and fast convergence speed advantages. First, we improve the population initialization method of the AVOA algorithm according to the characteristics of the network routing problem to enhance the algorithm’s perception capability towards network topology. Subsequently, we add an optimization phase to strengthen the development of the AVOA algorithm and achieve stable convergence effects. Finally, we model the network environment, define the network optimization objective, and perform comparative experiments with the baseline algorithms. The experimental results demonstrate that the routing algorithm has better network awareness, with a performance improvement of 16.9% compared to deep reinforcement learning algorithms and 71.8% compared to traditional routing schemes.

We consider an optimal route selection problem for ride-hailing service providers in stochastic and nonlinear road traffic environments described by the Lighthill-Whitham-Richards model. We show that the considered class of optimal control problem has a structure to which Kappen's path-integral control formalism is applicable. The effectiveness of the control synthesis based on the forward-in-time Monte-Carlo sampling suggested by the path-integral control is investigated via numerical studies. The path-integral control framework is generalized to compute a team solution to a two-player route selection problem where two ride-hailing companies collaborate on a shared transportation infrastructure.

The system optimal routing problem has been widely studied for road networks, though less considered for public transit systems. Traditional shortest path–based multimodal itinerary guidance systems may deteriorate the system performance when the assigned lines become congested. The dynamic system optimal routing model for multimodal transit system is formulated for that issue. The transit system is represented by a multilevel graph to explicitly simulate passenger flow and transit system operations. A solution algorithm based on the cross entropy method is proposed, and its performance is compared with the method of successive averages in static and dynamic cases. A numerical study of a simple multimodal transit network provides the basis for comparing system optimal routing and user optimal routing under different congestion levels.