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This study addresses order-picking routing optimization in a wide aisle of a mechanized warehouse. Efficiencies of the three popular heuristic policies (return, traversal, and Z-pick) are compared for different pick densities. A modified Z-pick policy with a flexible string length is proposed to increase its efficiency and accommodate lift-truck movement. The simulation-based results show that all three policies are not significantly different in the pick density region of 10–15 percent. The traversal policy performs better at pick densities below the region of indifference, while the return policy outperforms the others at higher pick densities. However, the Z-pick provides a viable universal alternative across all tested pick densities. [ABSTRACT FROM AUTHOR]

One of the most important topics in the field of wireless sensor networks is the development of approaches to improve network lifetime. In this paper, an energy-efficient control and routing protocol for wireless sensor networks is presented. This algorithm is based on reinforcement learning for energy management in the network. This protocol seeks to optimize routing policies to maximize the long-term reward received by each node, using reinforcement learning, which is a machine learning approach. In order to improve the lifetime of wireless sensor network, three energy management approaches have been proposed. The first approach is to navigate correctly using reinforcement learning to reduce the length of the routes and to improve energy consumption. The second approach is to exploit a sleep scheduling technique to improve node energy consumption. The last approach is used to restrict data transmission of each node based on the received data change rate. Simulation results show that in terms of network lifespan, the proposed method significantly outperforms previous reported methods.

In this study, an assessment model for analysing the reliability of a warehouse system, focusing on resource capability, under given combinations of storage, routing, batching and zoning process policies, is proposed. A reliability assessment model is developed in order to evaluate the effect of different combinations of operations policies on warehouse reliability. To better reflect the real order pick up operations at the warehouse, a simulation model based on the operation scenarios of a Hong Kong logistics service company is developed. The simulation results are used for supporting the evaluation of warehouse reliability through using the proposed reliability assessment model. The most important finding indicates that order batching together with zoning policies yields greater warehouse reliability particularly when the daily order volume is large. In addition, another important research finding shows that warehouse performance is not constant and can drop when certain combinations of operations policies are adopted. The degrading rate of the resource (forklift) when used according to a given combination of polices is also defined by the proposed assessment method. Hence, the result helps logistics service providers enhance resource durability through modifying operations policies and by implementing proper preventive maintenance policies within a dynamic operations environment. [ABSTRACT FROM PUBLISHER]

With increasing demands for more flexible services, the routing policies in enterprise networks become much richer. This has placed a heavy burden to the current router forwarding plane in support of the increasing number of policies, primarily due to the limited capacity in TCAM, which further hinders the development of new network services and applications. The scalable forwarding table structures for enterprise networks have therefore attracted numerous attentions from both academia and industry. To tackle this challenge, in this paper we present the design and implementation of a new forwarding table structure. It separates the functions of TCAM and SRAM, and maximally utilizes the large and flexible SRAM. A set of schemes are progressively designed, to compress storage of forwarding rules, and maintain correctness and achieve line-card speeds of packet forwarding. We further design an incremental update algorithm that allows less access to memory. The proposed scheme is validated and evaluated through a realistic implementation on a commercial router using real datasets. Our proposal can be easily implemented in the existing devices. The evaluation results show that the performance of forwarding tables under the proposed scheme is promising. [ABSTRACT FROM AUTHOR]

In radiotherapy, treatment needs to be delivered in time. Long waiting times can result in patient anxiety and growth of tumors. They are often caused by inefficient use of radiotherapy equipment, the linear accelerators (LINACs). However, making an efficient schedule is very challenging, especially when we have multiple types of patients, having different service requirements and waiting time constraints. Moreover, in radiotherapy a patient needs to go through a LINAC multiple times over multiple days, to complete the treatment. In this paper we model the radiotherapy treatment process as a queueing system with multiple queues, and we propose a new class of scheduling policies that are simple, flexible and fair to patients. Numerical experiments show that our new policy outperforms the commonly used policies. We also extend the policy to an adaptive one to deal with unknown and fluctuating arrival rates. Our adaptive policy turns out to be quite efficient in absorbing the effects caused by these changes. Due to the complexity of our problem, we select the parameters of the policies through simulation-based optimization heuristics. Our work may also have important implications for managers in other service systems such as call centers. [ABSTRACT FROM AUTHOR]

Abstract We present a routing system that considers uncertainties, which are prevalent in any real transport system. Given desired departure or arrival times and a utility function representing the traveller's preferences, our method computes not just a single path through the network, but a more sophisticated and adaptive journey plan called routing policy. For each stop and time instance, a policy specifies the list of services that the passenger is recommended to take. We show that the problem of finding an optimal policy is NP-hard. We also give a polynomial-time algorithm for a relaxation of the problem when the number of recommended services is limited at each stop and time. A computational case study for the public transport network of Budapest shows that the obtained routing policies can lead to substantial travel time savings compared to deterministic plans, and that considering multiple service policies leads to an improvement compared to previous solutions using single-service policies. [ABSTRACT FROM AUTHOR]

The end-to-end throughput of multi-hop communication in wireless ad hoc networks is affected by the conflict between forwarding nodes. It has been shown that sending more packets than maximum achievable end-to-end throughput not only fails to increase throughput but also decreases throughput owing to high contention and collision. Accordingly, it is of crucial importance for a source node to know the maximum end-to-end throughput. The end-to-end throughput depends on multiple factors, such as physical layer limitations, medium access control (MAC) protocol properties, routing policy, and nodes’ distribution. There have been many studies on analytical modeling of end-to-end throughput but none of them has taken routing policy and nodes’ distribution as well as MAC layer altogether into account. In this paper, the end-to-end throughput with perfect MAC layer is obtained based on routing policy and nodes’ distribution in 1-D and 2-D networks. Then, imperfections of IEEE 802.11 protocol are added to the model to obtain precise value. An exhaustive simulation is also made to validate the proposed models using ns-2 simulator. Results show that if the distribution to the next hop for a particular routing policy is known, our methodology can obtain the maximum end-to-end throughput precisely. [ABSTRACT FROM AUTHOR]

Foresee traffic conditions and demand is a major issue nowadays that is very often approached using simulation tools. The aim of this work is to propose an innovative strategy to tackle such problem, relying on the presentation and analysis of a behavioural dynamic traffic assignment. The proposal relies on the assumption that travellers take routing policies rather than paths, leading us to introduce the possibility for each simulated agent to apply, in real time, a strategy allowing him to possibly re-route his path depending on the perceived local traffic conditions, jam and/or time already spent in his journey. The re-routing process allows the agents to directly react to any change in the road network. For the sake of simplicity, the agents’ strategy is modelled with a simple neural network whose parameters are determined during a preliminary training stage. The inputs of such neural network read the local information about the route network and the output gives the action to undertake: stay on the same path or modify it. As the agents use only local information, the overall network topology does not really matter, thus the strategy is able to cope with large and not previously explored networks. Numerical experiments are performed on various scenarios containing different proportions of trained strategic agents, agents with random strategies and non strategic agents, to test the robustness and adaptability to new environments and varying network conditions. The methodology is also compared against existing approaches and real world data. The outcome of the experiments suggest that this work-in-progress already produces encouraging results in terms of accuracy and computational efficiency. This indicates that the proposed approach has the potential to provide better tools to investigate and forecast drivers’ choice behaviours. Eventually these tools can improve the delivery and efficiency of traffic information to the drivers. [ABSTRACT FROM AUTHOR]

Abstract: The purpose of this paper is to model and evaluate the performances of a bus network using Colored Petri Nets (CPN) and (max, +) Algebra. Our main contribution is to describe CPN with (max, +) equations in order to evaluate buses timetables and also boarding, disembarking and waiting times of passengers. Furthermore, the influence of buses limited capacity on passengers waiting times is studied. Simulation results will illustrate the applicability of the proposed methodology. [ABSTRACT FROM AUTHOR]

Hybrid wireless network is composed of both ad hoc transmissions and cellular transmissions. Under the $L$ -maximum-hop routing policy, flow is transmitted in the ad hoc mode if its source and destination are within $L$ hops away; otherwise, it is transmitted in the cellular mode. Existing works study the hybrid wireless network capacity as a function of $L$ so as to find the optimal $L$ to maximize the network capacity. In this paper, we consider two more factors: traffic model and base station access mode. Different from existing works, which only consider the uniform traffic model, we consider a traffic model with social behavior. We study the impact of traffic model on the optimal routing policy. Moreover, we consider two different access modes: one-hop access (each node directly communicates with base station) and multi-hop access (node may access base station through multiple hops due to power constraint). We study the impact of access mode on the optimal routing policy. Our results show that: 1) the optimal $L$ does not only depend on traffic pattern, but also the access mode; 2) one-hop access provides higher network capacity than multi-hop access at the cost of increasing transmitting power; and 3) under the one-hop access mode, network capacity grows linearly with the number of base stations; however, it does not hold with the multi-hop access mode, and the number of base stations has different effects on network capacity for different traffic models. [ABSTRACT FROM AUTHOR]

We consider a wireless network where two nodes (source and destination) aim at exchanging a message that must be kept secret from a third eavesdropper node. Other legitimate nodes relay the message from the source to the destination over multiple hops. Channels are subject to fading and only a partial channel state information at the transmitter is available before transmission. Assuming that each transmission has an entangled cost, e.g., delay or energy, we aim at finding the optimal routing policy that minimizes the cost with a constraint on the average secrecy outage probability. We model the problem as the search for a control policy of a stochastic dynamic system that minimizes a discounted expected cost objective, with a discounted expectation constraint. A Lagrange-multiplier based solution is proposed. Numerical results show the merits of the proposed solution over a multi-hop wireless network. [ABSTRACT FROM PUBLISHER]

A multistate network is a stochastic network composed of multistate arcs in which each arc has several possible capacities and may fail due to failure, maintenance, etc. The quality of a multistate network depends on how to meet the customer's requirements and how to provide the service in time. The system reliability, the probability that a given amount of data can be transmitted through a pair of minimal paths (MPs) simultaneously under the time constraint, is a proper index to evaluate the quality of a multistate network. An efficient solution procedure is first proposed to calculate it. In order to further enhance the system reliability, the network administrator decides the routing policy in advance to indicate the first and the second priority pairs of MPs. The second priority pair of MPs takes charge of the transmission duty if the first fails. The system reliability under the routing policy can be subsequently evaluated. [ABSTRACT FROM PUBLISHER]

We consider a new idea for a storage model on n nodes, namely stability of shape. These nodes support K neighborhoods S ⊂ {1, ..., n} and items arrive at the S as independent Poisson streams with rates λ, i = 1, ... , K . Upon arrival at S an item is stored at node j ∈ S where j is determined by some policy. Under natural conditions on the λ we exhibit simple local policies such that the multidimensional process describing the evolution of the number of items at each node is positive recurrent (stable) in shape. [ABSTRACT FROM AUTHOR]

Internet Service Providers (ISPs) have the ability to route their traffic over different network providers. This study investigates the optimal routing strategy under multihoming in the case where network providers charge ISPs according to top-percentile pricing (i.e. based on the θth highest volume of traffic shipped). We call this problem the Top-percentile Traffic Routing Problem (TpTRP). The TpTRP is a multistage stochastic optimization problem. Routing decision for every time period should be made before knowing the amount of traffic that is to be sent. The stochastic nature of the problem forms the critical difficulty of this study. Solution approaches based on Stochastic Integer Programming or Stochastic Dynamic Programming (SDP) suffer from the curse of dimensionality, which restricts their applicability. To overcome this, we suggest to use Approximate Dynamic Programming, which exploits the structure of the problem to construct continuous approximations of the value functions in SDP. Thus, the curse of dimensionality is largely avoided. [ABSTRACT FROM AUTHOR]

Abstract: Real-time information can enable travelers to adapt to changing traffic conditions and make better routing decisions in uncertain networks. In this paper, a generic description of real-time online information is provided based on three schemes using partial online information and one scheme with no online information. A theoretical analysis shows that more error-free information is always better than (or at least as good as) less information for optimal adaptive routing in flow-independent networks. A heuristic algorithm is designed for the optimal adaptive routing problem for all the four information schemes, based on a set of necessary conditions for optimality. The effectiveness of the heuristic algorithm is shown to be satisfactory over the tested random networks. This study is of interest for traveler information system evaluation and design. [Copyright &y& Elsevier]

A multistate network is a stochastic network composed with multistate arcs in which each arc has several possible capacities and may fail due to failure, maintenance, etc. Different from the deterministic case, the minimum transmission time in a multistate network is not a fixed number. We evaluate the probability that a given amount of data/commodity can be sent from a source port to a sink port through a pair of minimal path (MP) simultaneously under the time constraint. Such a probability is named the system reliability. An efficient solution procedure is first proposed to calculate it. In order to enhance the system reliability, the network administrator decides the routing policy in advance to indicate the first and the second priority pairs of MP. Subsequently, we can evaluate the system reliability under the routing policy. An easy criterion is then proposed to derive an ideal routing policy with higher system reliability. We can treat the system reliability as a performance index to measure the transmission ability of a multistate network such as computer, logistics, urban traffic, telecommunication systems, etc. [ABSTRACT FROM AUTHOR]

Abstract: Two attributes, the capacity and the lead time, are involved in the quickest path problem which finds a path with the minimum transmission time. The capacity of each edge is assumed to be deterministic in this problem. However, in many real-life networks such as computer, telecommunication, logistics networks, etc., each edge should be multistate due to failure, maintenance, etc. Such a network is named a multistate network. Hence, the minimum transmission time through a multistate network is not fixed. We evaluate the system reliability that a specified amount of data can be sent through a pair of minimal paths simultaneously within the time threshold. A solution procedure is first proposed to calculate it. In order to boost the system reliability, the network administrator decides the routing policy in advance to indicate the first and the second priority pairs of minimal paths. The second one will be responsible for the transmission duty if the first one fails. According to the routing policy, the system reliability can be subsequently computed. The case to transmit data through more than two minimal paths can be extended easily. [Copyright &y& Elsevier]

Today, network operators typically reason about network behaviour by observing the effects of a particular configuration in operation. This configuration process typically involves logging configuration changes and rolling back to a previous version when a problem arises. Advanced network operators (more each day) use policy-based routing languages to define the routing configuration and tools based on systematic verification techniques to ensure that operational behaviour is consistent with the intended behaviour. These tools help operators to reason about properties of routing protocols. However, these languages and tools work in low-level, i.e. they focus on properties, parameters, and elements of routing protocols. However, network operators receive high-level policies that must be refined to low level parameters before they can be applied. These high-level policies should consider other properties (e.g. extensibility or reasoning capabilities), parameters (e.g. time period, localization or QoS parameters), and elements (e.g. AAA individuals or resources), when the network configuration is defined. We believe that there is a need of broader approaches in languages and tools for defining routing configurations that are more powerful and integrated to other network elements. This article provides the main ideas behind the specification of routing policies using formal languages which enable the description of semantics. These semantics make easier the policy refinement process and allows describing an automated process for doing conflict detection on these policies. [ABSTRACT FROM AUTHOR]

Commercial agreements drive the routing policies used in today's Internet. The two most extensively studied commercial agreements are transit and peering; however, they are only two of many diverse and continuously evolving commercial agreements that ISPs enter into. So far, the only known practical safe and robust routing policy is Gao and Rexford's policy guideline, which is applicable to transit and peering agreements only. It is, therefore, of importance to identify routing policies that are safe and robust and, at the same time, capable of accommodating the diverse commercial agreements existing in the Internet. In particular, this paper investigates the extent to which routing policies can be devised to accommodate complex mutual transit agreements. We propose a series of policy guidelines that allow mutual transit agreements with progressively broader semantics to be established. Those policy guidelines guarantee routing safety and robustness as long as the autonomous system (AS) graph satisfies a corresponding set of precise topological constraints. An experimental evaluation of the proposed policy guidelines demonstrates the benefits they would likely afford in terms of routing reliability if adopted in the current Internet. [ABSTRACT FROM PUBLISHER]

Abstract: This paper deals with route choice models capturing travelers’ strategic behavior when adapting to revealed traffic conditions en route in a stochastic network. The strategic adaptive behavior is conceptualized as a routing policy, defined as a decision rule that maps from all possible revealed traffic conditions to the choices of next link out of decision nodes, given information access assumptions. In this paper, we use a specialized example where a variable message sign provides information about congestion status on outgoing links. We view the problem as choice under risk and present a routing policy choice model based on the cumulative prospect theory (CPT), where utility functions are nonlinear in probabilities and thus flexible attitudes toward risk can be captured. In order to illustrate the differences between routing policy and non-adaptive path choice models as well as differences between models based on expected utility (EU) theory and CPT, we estimate models based on synthetic data and compare them in terms of prediction results. There are large differences in path share predictions and the results demonstrate the flexibility of the CPT model to represent varying degrees of risk aversion and risk seeking depending on the outcome probabilities. [Copyright &y& Elsevier]

Abstract: Several research works have shown that the process of Internet routing configuration is error-prone. The primary method used by operators to determine whether their routing configurations are correct is to try them out in operation. A more advanced method is the static analysis of router configurations before they are deployed. We believe that there is a need of broader approaches to verify router configurations automatically and continuously, that is, without human intervention before and after they are deployed. This paper describes a multi-layer analysis framework for configuration checking, which includes three analysis tasks: static analysis before the configuration is deployed; runtime analysis after the configuration is deployed, which is based on routing event monitoring; and active analysis based on checking external information provided by third-parties. We also present the most relevant details of the implementation of our framework. [Copyright &y& Elsevier]

Abstract: The quickest path problem is to find a path which sends a given amount of data from the source to the sink such that the transmission time is minimized. More specifically, the capacity of each arc in the network is assumed to be deterministic. However, in many real-life networks such as computer systems, telecommunication systems, etc., the capacity of each arc is stochastic due to failure, maintenance, etc. Such a network is named as stochastic-flow network. Hence, the minimum transmission time is not a fixed number. The purpose of this paper is to provide a decision procedure for a stochastic-flow network under the time and budget constraints. We try to evaluate the probability that d units of data can be sent through the network under both time threshold and budget according to the routing policy. Such a probability is named the system reliability, which is a performance index to measure the system quality. An efficient algorithm is proposed to derive the optimal routing policy with highest system reliability. The sensitive analysis can be conducted to improve the most important component which increases the system reliability most significantly. [Copyright &y& Elsevier]

We present a detailed study of the potential impact of border gateway protocol peering session attacks and the resulting exploitation of route flap damping (RFD) that cause network-wide routing disruptions. We consider canonical grid as well as down-sampled realistic autonomous system (AS) topologies and address the impact of various typical service provider routing policies. Our modeling focuses on three dimensions of routing performance sensitivity: 1) protocol aware attacks (e.g., tuned to RFD); 2) route selection policy: and 3) attack-region topology. Analytical results provide insights into the nature of the problem and potential impact of the attacks. Detailed packet-level simulation results complement the analytical models and provide many additional insights into specific protocol interactions and timing issues. Finally, we quantify the potential effect of the BGP graceful restart mechanism as a partial mitigation of the BGP vulnerability to peering session attacks. [ABSTRACT FROM AUTHOR]

Abstract: We study optimal routing policy problems in stochastic time-dependent networks, where link travel times are modeled as random variables with time-dependent distributions. These are fundamental network optimization problems for a wide variety of applications, such as transportation and telecommunication systems. The routing problems studied can be viewed as counterparts of shortest path problems in deterministic networks. A routing policy is defined as a decision rule that specifies what node to take next at each decision node based on realized link travel times and the current time. We establish a framework for optimal routing policy problems in stochastic time-dependent networks, which we believe is the first in the literature. We give a comprehensive taxonomy and an in-depth discussion of variants of the problem. We then study in detail one variant that is particularly pertinent in traffic networks, where both link-wise and time-wise stochastic dependencies of link travel times are considered and online information is represented. We give an exact algorithm to this variant, analyze its complexity and point out the importance of finding good approximations to the exact solution. We then overview several approximations, and present a summary of a theoretical and computational analysis of their effectiveness against the exact algorithm. [Copyright &y& Elsevier]

This paper describes a routing algorithm that assigns each arriving packet to one of several homogeneous parallel servers. The algorithm differs from existing routing policies in that it considers the impact of each arriving packet''s size on the average packet delay. It is derived from mimic optimal routing, which mimics the behavior of numerical solutions to a packet assignment problem. It works under the condition that inter-arrival-time and packet-size distributions are unknown, but some possible candidates are known in advance. The algorithm calculates statistics, such as means of these distributions, from input traffic, so it provides not only good performance under stationary input traffic but also adaptive behavior under dynamic input traffic, where the distributions change with time. [Copyright &y& Elsevier]