Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Science
Title: Heavy Traffic Analysis of the Dynamic Stochastic Inventory-Routing Problem
Abstract: We analyze three queueing control problems that model a dynamic stochastic distribution system, where a single capacitated vehicle serves a finite number of retailers in a make-to-stock fashion. The objective in each of these vehicle routing and inventory problems is to minimize the long run average inventory (holding and backordering) and transportation cost. In all three problems, the controller dynamically specifies whether a vehicle at the warehouse should idle orembark with a full load. In the first problem, the vehicle must travel along a prespecified (TSP) tour of all retailers, and the controller dynamically decides how many units to deliver to each retailer. In the second problem, the vehicle delivers an entire load to one retailer (direct shipping) and the controller decides which retailer to visit next. The third problem allows the additional dynamic choice between the TSP and direct shipping options. Motivated by existing heavy traffic limit theorems, we make a time scale decomposition assumption that allows us to approximate these queueing control problems by diffusion control problems, which are explicitly solved in the fixed route problems, and numerically solved in the dynamic routing case. Simulation experiments confirm that the heavy traffic approximations are quite accurate over a broad range of problem parameters. Our results lead to some new observations about the behavior of this complex system.
Extracted statement — Fleet size: single
Research field: Science
Title: Deliveries in an inventory/routing problem using stochastic dynamic programming
Abstract: An industrial gases tanker vehicle visitsn customers on a tour, with a possible ( n + 1)st customer added at the end. The amount of needed product at each customer is a known random process, typically a Wiener process. The objective is to adjust dynamically the amount of product provided on scene to each customer so as to minimize total expected costs, comprising costs of earliness, lateness, product shortfall, and returning to the depot nonempty. Earliness costs are computed by invocation of an annualized incremental cost argument. Amounts of product delivered to each customer are not known until the driver is on scene at the customer location, at which point the customer is either restocked to capacity or left with some residual empty capacity, the policy determined by stochastic dynamic programming. The methodology has applications beyond industrial gases.
Extracted statement — Demand: Stochastic
Research field: Science
Title: A Markov Decision Model and Decomposition Heuristic for Dynamic Vehicle Dispatching
Abstract: We describe a dynamic and stochastic vehicle dispatching problem called the delivery dispatching problem. This problem is modeled as a Markov decision process. Because exact solution of this model is impractical, we adopt a heuristic approach for handling the problem. The heuristic is based in part on a decomposition of the problem by customer, where customer subproblems generate penalty functions that are applied in a master dispatching problem. We describe how to compute bounds on the algorithm's performance, and apply it to several examples with good results.
Extracted statement — Demand: Stochastic
Research field: Science
Title: Heavy Traffic Analysis of the Dynamic Stochastic Inventory-Routing Problem
Abstract: We analyze three queueing control problems that model a dynamic stochastic distribution system, where a single capacitated vehicle serves a finite number of retailers in a make-to-stock fashion. The objective in each of these vehicle routing and inventory problems is to minimize the long run average inventory (holding and backordering) and transportation cost. In all three problems, the controller dynamically specifies whether a vehicle at the warehouse should idle orembark with a full load. In the first problem, the vehicle must travel along a prespecified (TSP) tour of all retailers, and the controller dynamically decides how many units to deliver to each retailer. In the second problem, the vehicle delivers an entire load to one retailer (direct shipping) and the controller decides which retailer to visit next. The third problem allows the additional dynamic choice between the TSP and direct shipping options. Motivated by existing heavy traffic limit theorems, we make a time scale decomposition assumption that allows us to approximate these queueing control problems by diffusion control problems, which are explicitly solved in the fixed route problems, and numerically solved in the dynamic routing case. Simulation experiments confirm that the heavy traffic approximations are quite accurate over a broad range of problem parameters. Our results lead to some new observations about the behavior of this complex system.
Extracted statement — Demand: Stochastic
Research field: Science
Title: A Price-Directed Approach to Stochastic Inventory/Routing
Abstract: We consider a new approach to stochastic inventory/routing that approximates the future costs of current actions using optimal dual prices of a linear program. We obtain two such linear programs by formulating the control problem as a Markov decision process and then replacing the optimal value function with the sum of single-customer inventory value functions. The resulting approximation yields statewise lower bounds on optimal infinite-horizon discounted costs. We present a linear program that takes into account inventory dynamics and economics in allocating transportation costs for stochastic inventory routing. On test instances we find that these allocations do not introduce any error in the value function approximations relative to the best approximations that can be achieved without them. Also, unlike other approaches, we do not restrict the set of allowable vehicle itineraries in any way. Instead, we develop an efficient algorithm to both generate and eliminate itineraries during solution of the linear programs and control policy. In simulation experiments, the price-directed policy outperforms other policies from the literature.
Extracted statement — Demand: Stochastic
Research field: Science
Title: Dynamic Programming Approximations for a Stochastic Inventory Routing Problem
Abstract: This work is motivated by the need to solve the inventory routing problem when implementing a business practice called vendor managed inventory replenishment (VMI). With VMI, vendors monitor their customers' inventories and decide when and how much inventory should be replenished at each customer. The inventory routing problem attempts to coordinate inventory replenishment and transportation in such a way that the cost is minimized over the long run. We formulate a Markov decision process model of the stochastic inventory routing problem and propose approximation methods to find good solutions with reasonable computational effort. We indicate how the proposed approach can be used for other Markov decision processes involving the control of multiple resources.
Extracted statement — Demand: Stochastic
Research field: Science
Title: Scenario Tree-Based Heuristics for Stochastic Inventory-Routing Problems
Abstract: In vendor-managed inventory replenishment, the vendor decides when to make deliveries to customers, how much to deliver, and how to combine shipments using the available vehicles. This gives rise to the inventory-routing problem in which the goal is to coordinate inventory replenishment and transportation to minimize costs. The problem tackled in this paper is the stochastic inventory-routing problem, where stochastic demands are specified through general discrete distributions. The problem is formulated as a discounted infinite-horizon Markov decision problem. Heuristics based on finite scenario trees are developed. Computational results confirm the efficiency of these heuristics.
Extracted statement — Demand: Stochastic
Research field: Science
Title: A Two-Stage Stochastic Network Model and Solution Methods for the Dynamic Empty Container Allocation Problem
Abstract: Containerized liner trades have been growing steadily since the globalization of world economies intensified in the early 1990s. However, these trades are typically imbalanced in terms of the numbers of inbound and outbound containers. As a result, the relocation of empty containers has become one of the major problems faced by liner operators. In this paper, we consider the dynamic empty container allocation problem where we need to reposition empty containers and to determine the number of leased con tainers needed to meet customers? demand over time. We formulate this problem as a two-stage stochastic network: in stage one, the parameters such as supplies, demands, and ship capacities for empty containers are deterministic; whereas in stage two, these parameters are random variables. We need to make decisions in stage one such that the total of the stage one cost and the expected stage two cost is minimized. By taking advantage of the network structure, we show how a stochastic quasi-gradient method and a stochastic hybrid approximation procedure can be applied to solve the problem. In addition, we propose some new variations of these methods that seem to work faster in practice. We conduct numerical tests to evaluate the value of the two-stage stochastic model over a rolling horizon environment and to investigate the behavior of the solution methods with different implementations.
Extracted statement — Market: Stochastic
Research field: Science
Title: Liner shipping fleet deployment with cargo transshipment and demand uncertainty
Abstract: This paper addresses a novel liner shipping fleet deployment problem characterized by cargo transshipment, multiple container routing options and uncertain demand, with the objective of maximizing the expected profit. This problem is formulated as a stochastic program and solved by the sample average approximation method. In this technique the objective function of the stochastic program is approximated by a sample average estimate derived from a random sample, and then the resulting deterministic program is solved. This process is repeated with different samples to obtain a good candidate solution along with the statistical estimate of its optimality gap. We apply the proposed model to a case study inspired from real-world problems faced by a major liner shipping company. Results show that the case is efficiently solved to 1% of relative optimality gap at 95% confidence level.
Extracted statement — Market: Stochastic
Research field: Science
Title: Enhanced Pictorial Structures for precise eye localization under incontrolled conditions
Abstract: In this paper, we present an enhanced pictorial structure (PS) model for precise eye localization, a fundamental problem involved in many face processing tasks. PS is a computationally efficient framework for part-based object modelling. For face images taken under uncontrolled conditions, however, the traditional PS model is not flexible enough for handling the complicated appearance and structural variations. To extend PS, we 1) propose a discriminative PS model for a more accurate part localization when appearance changes seriously, 2) introduce a series of global constraints to improve the robustness against scale, rotation and translation, and 3) adopt a heuristic prediction method to address the difficulty of eye localization with partial occlusion. Experimental results on the challenging LFW (Labeled Face in the Wild) database show that our model can locate eyes accurately and efficiently under a broad range of uncontrolled variations involving poses, expressions, lightings, camera qualities, occlusions, etc.
Extracted statement — Challenges: Uncontrolled
Research field: Science
Title: An energy-efficient unequal clustering mechanism for wireless sensor networks
Abstract: Clustering provides an effective way for prolonging the lifetime of a wireless sensor network. Current clustering algorithms usually utilize two techniques, selecting cluster heads with more residual energy and rotating cluster heads periodically, to distribute the energy consumption among nodes in each cluster and extend the network lifetime. However, they rarely consider the hot spots problem in multihop wireless sensor networks. When cluster heads cooperate with each other to forward their data to the base station, the cluster heads closer to the base station are burdened with heavy relay traffic and tend to die early, leaving areas of the network uncovered and causing network partition. To address the problem, we propose an energy-efficient unequal clustering (EEUC) mechanism for periodical data gathering in wireless sensor networks. It partitions the nodes into clusters of unequal size, and clusters closer to the base station have smaller sizes than those farther away from the base station. Thus cluster heads closer to the base station can preserve some energy for the inter-cluster data forwarding. We also propose an energy-aware multihop routing protocol for the inter-cluster communication. Simulation results show that our unequal clustering mechanism balances the energy consumption well among all sensor nodes and achieves an obvious improvement on the network lifetime
Extracted statement — Cluster Properties Cluster size: Unequal
Research field: Science
Title: PRODUCE: A Probability-Driven Unequal Clustering Mechanism for Wireless Sensor Networks
Abstract: There has been proliferation of research on seeking for distributing the energy consumption among nodes in each cluster and between cluster heads to extend the network lifetime. However, they hardly consider the hot spots problem caused by heavy relay traffic forwarded. In this paper, we propose a distributed and randomized clustering algorithm that consists of unequal sized clusters. The cluster heads closer to the base station may focus more on inter-cluster communication while distant cluster heads concentrate more on intra-cluster communication. As a result, it nearly guarantees no communication in the network gets excessively long communication distance that significantly attenuates signal strength. Simulation results show that our algorithm achieves abundant improvement in terms of the coverage time and network lifetime, especially when the density of distributed nodes is high.
Extracted statement — Cluster Properties Cluster size: Unequal
Research field: Science
Title: An energy-efficient distributed unequal clustering protocol for wireless sensor networks
Abstract: Due to the imbalance of energy consumption of nodes in wireless sensor networks (WSNs), some local nodes die prematurely, which causes the network partitions and then shortens the lifetime of the network. The phenomenon is called “hot spot” or “energy hole” problem. For this problem, an energy-aware distributed unequal clustering protocol (EADUC) in multihop heterogeneous WSNs is proposed. Compared with the previous protocols, the cluster heads obtained by EADUC can achieve balanced energy, good distribution, and seamless coverage for all the nodes.Moreover, the complexity of time and control message is low. Simulation experiments show that EADUC can prolong the lifetime of the network significantly.
Extracted statement — Cluster Properties Cluster size: Unequal
Research field: Science
Title: An Energy-Aware Distributed Unequal Clustering Protocol for Wireless Sensor Networks
Abstract: Due to the imbalance of energy consumption of nodes in wireless sensor networks (WSNs), some local nodes die prematurely, which causes the network partitions and then shortens the lifetime of the network. The phenomenon is called “hot spot” or “energy hole” problem. For this problem, an energy-aware distributed unequal clustering protocol (EADUC) in multihop heterogeneous WSNs is proposed. Compared with the previous protocols, the cluster heads obtained by EADUC can achieve balanced energy, good distribution, and seamless coverage for all the nodes. Moreover, the complexity of time and control message is low. Simulation experiments show that EADUC can prolong the lifetime of the network significantly.
Extracted statement — Cluster Properties Cluster size: Unequal
Research field: Science
Title: UHEED - An Unequal Clustering Algorithm for Wireless Sensor Networks
Abstract: Prolonging the lifetime of wireless sensor networks has always been a determining factor when designing and deploying such networks. Clustering is one technique that can be used to extend the lifetime of sensor networks by grouping sensors together. However, there exists the hot spot problem which causes an unbalanced energy consumption in equally formed clusters. In this paper, we propose UHEED, an unequal clustering algorithm which mitigates this problem and which leads to a more uniform residual energy in the network and improves the network lifetime. Furthermore, from the simulation results presented, we were able to deduce the most appropriate unequal cluster size to be used.
Extracted statement — Cluster Properties Cluster size: Unequal
Research field: Science
Title: Multihop Routing Protocol with Unequal Clustering for Wireless Sensor Networks
Abstract: In order to prolong the lifetime of wireless sensor networks, this paper presents a multihop routing protocol with unequal clustering (MRPUC). On the one hand, cluster heads deliver the data to the base station with relay to reduce energy consumption. On the other hand, MRPUC uses many measures to balance the energy of nodes. First, it selects the nodes with more residual energy as cluster heads, and clusters closer to the base station have smaller sizes to preserve some energy during intra-cluster communication for inter-cluster packets forwarding. Second, when regular nodes join clusters, they consider not only the distance to cluster heads but also the residual energy of cluster heads. Third, cluster heads choose those nodes as relay nodes, which have minimum energy consumption for forwarding and maximum residual energy to avoid dying earlier. Simulation results show that MRPUC performs much better than similar protocols.
Extracted statement — Cluster Properties Cluster size: Unequal
Research field: Science
Title: An energy aware fuzzy unequal clustering algorithm for wireless sensor networks
Abstract: In order to gather information more efficiently, wireless sensor networks (WSNs) are partitioned into clusters. The most of the proposed clustering algorithms do not consider the location of the base station. This situation causes hot spots problem in multi-hop WSNs. Unequal clustering mechanisms, which are designed by considering the base station location, solve this problem. In this paper, we introduce a fuzzy unequal clustering algorithm (EAUCF) which aims to prolong the lifetime of WSNs. EAUCF adjusts the cluster-head radius considering the residual energy and the distance to the base station parameters of the sensor nodes. This helps decreasing the intra-cluster work of the sensor nodes which are closer to the base station or have lower battery level. We utilize fuzzy logic for handling the uncertainties in cluster-head radius estimation. We compare our algorithm with some popular algorithms in literature, namely LEACH, CHEF and EEUC, according to First Node Dies (FND), Half of the Nodes Alive (HNA) and energy-efficiency metrics. Our simulation results show that EAUCF performs better than the other algorithms in most of the cases. Therefore, EAUCF is a stable and energy-efficient clustering algorithm to be utilized in any real time WSN application.
Extracted statement — Cluster Properties Cluster size: Unequal
Research field: Science
Title: An Empirical Study on the Environmental Kuznets Curve for China’s Carbon Emissions: Based on Provincial Panel Data
Abstract: Abstract Based on the Environmental Kuznets Curve theory, the authors choose provincial panel data of China in 1990–2007 and adopt panel unit root and co-integration testing method to study whether there is Environmental Kuznets Curve for China’s carbon emissions. The research results show that: carbon emissions per capita of the eastern region and the central region of China fit into Environmental Kuznets Curve, but that of the western region does not. On this basis, the authors carry out scenario analysis on the occurrence time of the inflection point of carbon emissions per capita of different regions, and describe a specific time path.
Extracted statement — EKC Turnaround point(s): Western
Research field: Science
Title: Transitioning from Low-Income Growth to High-Income Growth: Is There a Middle Income Trap?
Abstract: Is there a “middle-income trap”? Theory suggests that the determinants of growth at low and high income levels may be different. If countries struggle to transition from growth strategies that are effective at low income levels to growth strategies that are effective at high income levels, they may stagnate at some middle income level; this phenomenon can be thought of as a “middle-income trap.” Defining income levels based on per capita gross domestic product relative to the United States, we do not find evidence for (unusual) stagnation at any particular middle income level. However, we do find evidence that the determinants of growth at low and high income levels differ. These findings suggest a mixed conclusion: middle-income countries may need to change growth strategies in order to transition smoothly to high income growth strategies, but this can be done smoothly and does not imply the existence of a middle-income trap.
Extracted statement — approach: Relative
Research field: Science
Title: RIGHT HEPATOLOBECTOMY FOR PRIMARY MESENCHYMOMA OF THE LIVER*
Abstract: SUMMARYA case report of a large primary malignant mesenchymoma of the liver is presented. This tumor was successfully removed with normal liver tissue surrounding the tumor by right hepatolobectomy. The pathologic characteristics and clinical behavior of tumors falling into this general category are
Extracted statement — Site: Right
Research field: Science
Title: Distributed clustering with directional antennas for wireless sensor networks
Abstract: This paper proposes a decentralized algorithm for organizing an ad hoc sensor network into clusters with directional antennas. The proposed autonomous clustering scheme aims to reduce the sensing redundancy and maintain sufficient sensing coverage and network connectivity in sensor networks. With directional antennas, random waiting timers, and local criterions, cluster performance may be substantially improved and sensing redundancy can be drastically suppressed. The simulation results show that the proposed scheme achieves connected coverage and provides efficient network topology management.
Extracted statement — Method: Centralized
Research field: Science
Title: A centralized energy-efficient routing protocol for wireless sensor networks
Abstract: Wireless sensor networks consist of small battery powered devices with limited energy resources. Once deployed, the small sensor nodes are usually inaccessible to the user, and thus replacement of the energy source is not feasible. Hence, energy efficiency is a key design issue that needs to be enhanced in order to improve the life span of the network. Several network layer protocols have been proposed to improve the effective lifetime of a network with a limited energy supply. In this article we propose a centralized routing protocol called base-station controlled dynamic clustering protocol (BCDCP), which distributes the energy dissipation evenly among all sensor nodes to improve network lifetime and average energy savings. The performance of BCDCP is then compared to clustering-based schemes such as low-energy adaptive clustering hierarchy (LEACH), LEACH-centralized (LEACH-C), and power-efficient gathering in sensor information systems (PEGASIS). Simulation results show that BCDCP reduces overall energy consumption and improves network lifetime over its comparatives.
Extracted statement — Method: Centralized