Deployment Decisions Research Articles

Equilibrium strategies of channel structure and RFID technology deployment in a supply chain with manufacturer encroachment

The rapid growth of online marketplaces and the advance of Internet technology have enabled manufacturers to more easily engage in direct selling through their own online stores. However, as any retail channel suffers misplacements, the manufacturer's direct sales channel will also face a critical inventory misplacement problem if it chooses encroachment. Radio-frequency identification (RFID) proven to be the most effective technology to eliminate the inventory misplacement problem. This paper investigates the strategic interaction between manufacturer encroachment and RFID deployment decisions in a supply chain consisting of a manufacturer and a reseller. We find that the manufacturers’ encroachment strategies are determined by channels’ misplacement rates, RFID deployment decisions and channel competition intensity. Specifically, the retail (direct) channel's misplacement problem encourages (discourages) the manufacturer to encroach; in addition, if only the retail (if it is direct) channel employs RFID, the encroachment cost threshold increases (decreases) with the RFID tag price; moreover, the more intense the channel competition is, the less likely the manufacturer will be to encroach. Further, when encroachment has occurred, channel competition will be a crucial factor in the direct channel's RFID deployment decision, but it will not affect the deployment of RFID in the retail channel.

Impact of Incorporating Returns into Pre‐Disaster Deployments for Rapid‐Onset Predictable Disasters

Initial deployment decisions are critical to humanitarian organizations as they pre‐position resources and prepare for predictable disasters, such as hurricanes, floods, and wildfires. Deploying too few resources results in unmet demand, bad publicity, and unhappy donors. However, deploying a large number of resources can also result in bad publicity and unhappy donors due to high costs and wasted resources when disasters do not occur as expected and deployed items are not managed efficiently. We use a stochastic optimization model to investigate the initial deployment decision and resulting costs. The stochastic model forces the initial deployment decision to be made after the disaster warning, but before the disaster occurs and true demand is realized. We find that a full optimization model (incorporating returns and disposal into the model) will increase the initial deployment level from the level obtained without incorporating returns and disposal (referred to as a partial optimization model). This allows more beneficiaries to be served when using a full optimization model. We also find that fixed return costs do not have a large impact on initial deployment levels, but can result in a more consolidated pre‐positioning strategy. Using the results of our models, we present several other interesting managerial insights that may be useful for humanitarian organizations in pre‐positioning inventory for predictable disasters.

A comparison of methods for determining performance based employee deployment in production systems

Employee deployment is a crucial process in production systems. Based on qualification and individual performance of employees, deployment decisions can lead to ambiguous outcomes. This paper first reviews the state of the art and further compares two methods based on combinatorial analysis for employee deployment. Therefore, this paper emphasizes the costs and benefits of a Brute Force and an alternative Greedy method. When considering the qualification and individual performance of each employee, both algorithms provide working solutions. In direct comparison, the outcome of the alternative Greedy algorithm is more efficient in terms of calculation time whereas the Brute Force method provides the combination with the global optimum. This means calculation time as well as quality of outcome differ. The exponential growth of employee allocation possibilities depends on the amount of employees and leads to high calculation times, when using a Brute Force method. The comparison of both methods reveal that the proposed alternative Greedy algorithm reaches nearly as high outcomes as the Brute Force does, with significantly less calculation time. Furthermore, this paper offers an insight into the impact of deployment decisions within production systems.

Deploying cobots in collaborative systems: major considerations and productivity analysis

Collaborative robots (cobots) are important components of the Industry 4.0 paradigm and smart manufacturing. Cobots are known for their ability to interact with the operators in a shared workspace. Due to their spread in the last decade, cobot research proliferated. However, most individual studies focused on specific aspects of cobot deployment, and only scant attention was given to their evaluation (mostly not based on productivity criteria). Thus, better support is needed for cobot acquisition and deployment decisions. This paper answers this need by presenting a summary of the major considerations related to cobots acquisition and deployment, and providing a productivity analysis procedure that supports cobot acquisition and deployment decisions. Defining the cobots’ required characteristics and capabilities, effectively narrows the possible selection of cobots. However, it does not give information as to the economic value of acquiring and deploying a specific cobot. So, in addition to cobots’ characteristics and capabilities, this paper presents computational techniques to analyse and support this decision for a single workstation per se, and for a station in an assembly line. The difference between these two cases is discussed and analysed, and corresponding models are presented with computational examples.

Multi-Objective Wireless Sensor Network Deployment Problem with Cooperative Distance-Based Sensing Coverage

This paper investigates the multi-objective problem of deploying wireless sensor networks with cooperative distance-based sensing coverage. This problem considers deploying a number of sensor nodes to cove multiple target points on a deployment area. Based on the locations of target points and the sensor nodes with their own inner and outer coverage radii, the distance-based sensing coverages of target nodes by sensor nodes are divided into three categories: full coverage (i.e., within the inner coverage radius), no coverage (i.e., outside the outer coverage radius), and partial coverage (i.e., between the inner and outer radii). Furthermore, this paper additionally considers the cooperative sensing coverage in which the sensing coverage of a target point is provided by more than one sensor node. The decision of sensor deployment in this paper is to select sensor nodes from potential sensor node positions so as to simultaneously maximize the collective sensing coverage of all target points and minimize the total distances between each target point and the selected sensor node(s). This paper first formulates this problem as a multi-objective optimization model, and then develops a solution procedure to determine the best non-dominated solution set for the problem model. Numerical experiments for the concerned problem by the proposed solution approach are demonstrated.

BCOOL: A Novel Blockchain Congestion Control Architecture Using Dynamic Service Function Chaining and Machine Learning for Next Generation Vehicular Networks

This paper presents the first, novel, dynamic, resilient, and consistent Blockchain COngestion ContrOL (BCOOL) system for vehicular networks that fills the gap of trustworthy Blockchain congestion prediction systems. BCOOL relies on the heterogeneity of Machine Learning, Software-Defined Networks and Network Function Virtualization that is customized in three hybrid cloud/edge-based On/Offchain smart contract modules and ruled by an efficient and reliable communication protocol. BCOOL’s first novel module aims at managing message and vehicle trustworthiness using a novel, dynamic and hybrid Blockchain Fog-based Distributed Trust Contract Strategy (FDTCS). The second novel module accurately and proactively predicts the occurrence of congestion, ahead of time, using a novel Hybrid On/Off-Chain Multiple Linear Regression Software-defined Contract Strategy (HOMLRCS). This module presents a virtualization facility layer to the third novel K-means/Random Forest-based On/Off-Chain Dynamic Service Function Chaining Contract Strategy (KRF-ODSFCS) that dynamically, securely and proactively predicts VNF placements and their chaining order in the context of SFCs w.r.t users’ dynamic QoS priority demands. BCOOL exhibits a linear complexity and a strong resilience to failures. Simulation results show that BCOOL outperforms the next best comparable strategies by 80% and 100% reliability and efficiency gains in challenging data congestion environments. This yields to fast, reliable and accurate congestion prediction decisions, ahead of time, and optimizes transaction validation processing time. Globally, the Byzantine resilience, complexity and attack mitigation strategies along with simulation results prove that BCOOL securely predicts the congestion and provides real-time monitoring, fast and accurate SFC deployment decisions while lowering both capital and operational expenditures (CAPEX/OPEX) costs.

Choosing the Flag in the Name of Peace: Why Have the Baltic States (Re)turned to the United Nations?

ABSTRACT The twenty-first century peacekeeping landscape is defined by the multiplicity of institutional frameworks under which international operations are deployed. Small states with limited resources cannot meet every demand for troop contributions and face the inevitable dilemma of choosing whose ‘flag’ to carry. For the first decade after joining NATO and the EU, the Baltic States gave a clear priority to NATO and U.S.-led military operations, but since the 2014 Ukrainian crisis gradually shifted their focus to the UN framework. Drawing on interviews with policy-makers in the three Baltic countries this article aims to explain this recent shift. It reviews the main theoretical assumptions about troop contributions to the UN peacekeeping operations and burden-sharing behaviour among NATO allies, and suggests that the recent troop deployment decisions of the Baltic States are best explained by a realist focus on national security concerns. The seemingly ‘internationalist’ context of UN peacekeeping operations simply happened to be most suitable for a bilateral exchange with their European allies, namely Germany and France, that in turn contributed troops to NATO's Baltic flank.

Evaluating factors influencing infrasonic signal detection and automatic processing performance utilizing a regional network.

Physical and deployment factors that influence infrasound signal detection and assess automatic detection performance for a regional infrasound network of arrays in the Western U.S. are explored using signatures of ground truth (GT) explosions (yields). Despite these repeated known sources, published infrasound event bulletins contain few GT events. Arrays are primarily distributed toward the south-southeast and south-southwest at distances between 84 and 458 km of the source with one array offering azimuthal resolution toward the northeast. Events occurred throughout the spring, summer, and fall of 2012 with the majority occurring during the summer months. Depending upon the array, automatic detection, which utilizes the adaptive F-detector successfully, identifies between 14% and 80% of the GT events, whereas a subsequent analyst review increases successful detection to 24%-90%. Combined background noise quantification, atmospheric propagation analyses, and comparison of spectral amplitudes determine the mechanisms that contribute to missed detections across the network. This analysis provides an estimate of detector performance across the network, as well as a qualitative assessment of conditions that impact infrasound monitoring capabilities. The mechanisms that lead to missed detections at individual arrays contribute to network-level estimates of detection capabilities and provide a basis for deployment decisions for regional infrasound arrays in areas of interest.

Early Uncertainty Quantification for an Improved Decision Support Modeling Workflow: A Streamflow Reliability and Water Quality Example

Effective decision making for resource management is often supported by combining predictive models with uncertainty analyses. This combination allows quantitative assessment of management strategy effectiveness and risk. Typically, history matching is undertaken to increase the reliability of model forecasts. However, the question of whether the potential benefit of history matching will be realized, or outweigh its cost, is seldom asked. History matching adds complexity to the modeling effort, as information from historical system observations must be appropriately blended with the prior characterization of the system. Consequently, the cost of history matching is often significant. When it is not implemented appropriately, history matching can corrupt model forecasts. Additionally, the available data may offer little decision-relevant information, particularly where data and forecasts are of different types, or represent very different stress regimes. In this paper, we present a decision support modeling workflow where early quantification of model uncertainty guides ongoing model design and deployment decisions. This includes providing justification for undertaking (or forgoing) history matching, so that unnecessary modeling costs can be avoided and model performance can be improved. The workflow is demonstrated using a regional-scale modeling case study in the Wairarapa Valley (New Zealand), where assessments of stream depletion and nitrate-nitrogen contamination risks are used to support water-use and land-use management decisions. The probability of management success/failure is assessed by comparing the proximity of model forecast probability distributions to ecologically motivated decision thresholds. This study highlights several important insights that can be gained by undertaking early uncertainty quantification, including: i) validation of the prior numerical characterization of the system, in terms of its consistency with historical observations; ii) validation of model design or indication of areas of model shortcomings; iii) evaluation of the relative proximity of management decision thresholds to forecast probability distributions, providing a justifiable basis for stopping modeling.

Characterization and analysis of cloud-to-user latency: The case of Azure and AWS

With the growing adoption of cloud infrastructures to deliver a variety of IT services, monitoring cloud network performance has become crucial. However, cloud providers only disclose qualitative information about network performance, at most. This hinders efficient cloud adoption, resulting in uncertainties about the behavior of hosted services, and sub-optimal deployment choices. In this work, we focus on cloud-to-user latency, i.e. the latency of network paths interconnecting datacenters to worldwide-spread cloud users accessing their services. Specifically, we performed a 14-day measurement campaign from 25 vantage points deployed via the Planetlab infrastructure (emulating spatially-spread users) and considering services running in distinct locations on the infrastructures of Amazon Web Services and Microsoft Azure. First, our experimentation allows us to provide an in-depth performance characterization (based on multiple probing methods and fine-grained sampling rate) of such networks as perceived by users spread worldwide, highlighting both spatial and temporal latency trends. Then, our analysis is exploited with design purposes to support both cloud customers and providers with the assessment of cloud-network performance (via badness detection & imputation tools) and the making of deployment decisions (via the evaluation of multi-cloud benefits). The dataset gathered from the campaign is publicly released to foster reproducibility.

Joint DNN Partition Deployment and Resource Allocation for Delay-Sensitive Deep Learning Inference in IoT

Nowadays, the widely used Internet-of-Things (IoT) mobile devices (MDs) generate huge volumes of data, which need analyzing and extracting accurate information in real time by compute-intensive deep learning (DL) inference tasks. Due to its multilayer structure, the deep neural network (DNN) is appropriate for the mobile-edge computing (MEC) environment, and the DL tasks can be offloaded to DNN partitions deployed in MEC servers (MECSs) for speed-up inference. In this article, we first assume the arrival process of DL tasks as Poisson distribution and develop a tandem queueing model to evaluate the end-to-end (E2E) inference delay of DL tasks in multiple DNN partitions. To minimize the E2E delay, we develop a joint optimization problem model of partition deployment and resource allocation in MECSs (JPDRA). Since the JPDRA is a mixed-integer nonlinear programming (MINLP) problem, we decompose the original problem into a computing resource allocation (CRA) problem with fixed partition deployment decision and a DNN partition deployment (DPD) problem that optimizes the optimal-delay function related to the CRA problem. Next, we design a CRA algorithm based on Markov approximation and a low-complexity DPD algorithm to obtain the near-optimal solution in the polynomial time. The simulation results demonstrate that the proposed algorithms are more efficient and can reduce the average E2E delay by 25.7% with better convergence performance.

Molecular surveillance reveals the presence of pfhrp2 and pfhrp3 gene deletions in Plasmodium falciparum parasite populations in Uganda, 2017\u20132019

BackgroundHistidine-rich protein-2 (HRP2)-based rapid diagnostic tests (RDTs) are the only RDTs recommended for malaria diagnosis in Uganda. However, the emergence of Plasmodium falciparum histidine rich protein 2 and 3 (pfhrp2 and pfhrp3) gene deletions threatens their usefulness as malaria diagnostic and surveillance tools. The pfhrp2 and pfhrp3 gene deletions surveillance was conducted in P. falciparum parasite populations in Uganda.MethodsThree-hundred (n = 300) P. falciparum isolates collected from cross-sectional malaria surveys in symptomatic individuals in 48 districts of eastern and western Uganda were analysed for the presence of pfhrp2 and pfhrp3 genes. Presence of parasite DNA was confirmed by PCR amplification of the 18s rRNA gene, msp1 and msp2 single copy genes. Presence or absence of deletions was confirmed by amplification of exon1 and exon2 of pfhrp2 and pfhrp3 using gene specific PCR.ResultsOverall, pfhrp2 and pfhrp3 gene deletions were detected in 29/300 (9.7%, 95% CI 6.6–13.6%) parasite isolates. The pfhrp2 gene was deleted in 10/300 (3.3%, 95% CI 1.6–6.0%) isolates, pfhrp3 in 9/300 (3.0%, 95% CI 1.4–5.6%) while both pfhrp2 and pfhrp3 were deleted in 10/300 (3.3%, 95% CI 1.6–6.0%) parasite isolates. Proportion of pfhrp2/3 deletions was higher in the eastern 14.7% (95% CI 9.7–20.0%) compared to the western region 3.1% (95% CI 0.8–7.7%), p = 0.001. Geographical location was associated with gene deletions aOR 6.25 (2.02–23.55), p = 0.003.ConclusionsThis is the first large-scale survey reporting the presence of pfhrp2/3 gene deletions in P. falciparum isolates in Uganda. Roll out of RDTs for malaria diagnosis should take into consideration the existence of pfhrp2/3 gene deletions particularly in areas where they were detected. Periodic pfhrp2/3 surveys are recommended to inform future decisions for deployment of alternative RDTs.

Recurring Episodic Model of Human Capital Resource Deployment

Drawing upon the resource-based view of the firm, human capital resource (HCR) and strategic human resource management (SHRM) scholars argue that organizations which secure and develop better pools of HCRs, through formal SHRM systems, are better positioned in their competitive environment. However, HCRs represent capacities to act, not action itself. Recent work has addressed this by highlighting the importance of appropriately deploying employees to realize the potential value of HCRs. Yet, the informal learning and teams literatures suggest that deployments would also greatly influence the further development of HCR over time. This presents a potential paradox for organizational decision makers as deploying employees for optimal current performance may be at odds with deploying to develop HCR for the future. Drawing upon the dynamic equilibrium model of organizing (Smith & Lewis, 2011), we address this paradox and advance a recurring episodic model of HCR deployment decisions. We argue that individual differences and situational demands pertinent to decision makers (i.e., managers) shift the balance of immediate performance versus longer-term development concerns, which may be addressed through an oscillating deployment approach. We argue that the two goals may be jointly optimized over time, and detail the nature of different developmental deployments.

Utilizing an Analytical Hierarchy Process with Stochastic Return On Investment to Justify Connected Vehicle-Based Deployment Decisions

With the increasing interest in connected vehicles (CV), it becomes all the more important to support decisions by transportation agencies to invest in Connected Vehicle to Infrastructure (V2I) applications. This paper presents a method that can be used to justify the investment in CV-based safety applications considering the availability of existing solutions. The method utilizes a combination of stochastic return on investment (ROI) analysis and a multi-criteria decision-analysis (MCDA) procedure to account for uncertainties, to consider effects that cannot be converted to dollar values, and to account for stakeholder priorities. The stochastic ROI analysis is applied using Monte Carlo simulations and included as part of the selection criteria in the MCDA method using the Analytical Hierarchy Process (AHP). This paper applies the method to support the deployment of CV-based applications to address transportation safety concerns on urban arterials. These applications can be categorized as CV-based support of signalized intersection safety, CV-based support of unsignalized intersection safety, and CV-based hazard warning applications. The results of the Monte Carlo simulation analysis for a project case study indicated the cost-effectiveness of these applications. The results of the AHP analysis indicate that utilizing V2I applications is 41.3% more favorable than utilizing the investigated existing solutions to address safety concern on the arterial facility that is the subject of the case study.

Geochemical characteristics and genesis of the Middle Devonian and the Middle Permian natural gas in the Sichuan Basin, China

Breakthroughs have been made for the natural gas exploration of the Middle Devonian and the Middle Permian formations in the Sichuan Basin in recent years. Nevertheless, its natural gas source is still not clear, which then affects the next exploration deployment decision. A comprehensive study is conducted on the geochemical characteristics of the Middle Devonian and the Middle Permian natural gas, the biomarkers of reservoir bitumen, and the source rocks. The results show that these natural gases are secondary cracking dry gas that includes more than 86% of methane content, a small amount of ethane, propane, and a non-hydrocarbon gas (e.g., nitrogen, carbon dioxide, and hydrogen sulfide). The natural gas maturity varies slightly in different regions. The carbon and hydrogen isotope values of natural gas are as follows: δ13C1 ranges from −35.7‰ to −27.3‰, δ13C2 ranges from −38.7‰ to −26.6‰, δ13C3 ranges from −37‰ to −26.5‰, δ2HCH4 ranges from −141‰ to −138‰, and δ2HC2H6 ranges from −164‰ to −112‰. The isotope values indicate that the genetic type of natural gas is the sapropelic type and the mixture type is dominated by sapropelic type. The difference between natural gas values of δ13C1, δ13C2, and δ2HCH4 in different regions is related to the contribution ratio of source rocks from different ages. The gases of the Middle Devonian and the Middle Permian of the Shuangyushi structure in Northwestern Sichuan, the Middle Permian of Southwestern Sichuan, and the Middle Permian of Paleo-uplift in Central Sichuan are mainly derived from the Cambrian Qiongzhusi Formation and the Middle-Lower Permian source rocks. The Middle-Upper Permian natural gases of the volcanic reservoir in Southwestern Sichuan is mainly from the Qiongzhusi source rock. The Middle Permian natural gases of the Hewanchang structure in Northwestern Sichuan, Eastern Sichuan, and Southern Sichuan are mainly derived from the Silurian Longmaxi source rock. The research results have important guiding significance for developing exploration and deployment solutions for natural gas of the Middle Devonian and the Middle Permian formation in the Sichuan Basin.

When does politics stop at the water’s edge? A QCA of parliamentary consensus on military deployment decisions

AbstractWhy do some military deployment decisions lead to high levels of political contestation, whereas others do not? Or, put differently, when does parliamentary consensus on the use of force abroad exist? In this article, we aim to answer this question by focusing on the varying levels of consensus in national parliaments when taking military deployment decisions. We do so by examining conditions that were derived from research on the domestic-level determinants of the use of force, parliamentary voting, and opposition behaviour. These conditions were included in an integrated theoretical framework, which we tested with fuzzy set Qualitative Comparative Analysis. The results of our analysis show that the international legal status and the objectives of the military operation are of crucial importance for explaining the pattern of political contestation. However, domestic variables need to be taken into account as well to fully explain the level of political contestation of military deployment decisions.

Institutional arrangements and airport solar PV

Combining a unique dataset of 488 public airports and interviews with managers and stakeholders at four airports in the United States, this study investigates how airports’ institutional arrangements shape their solar photovoltaic (PV) deployment decisions. The findings indicate that airports operated by general-purpose governments (i.e., city, county, or state governments) are more likely to deploy solar PV than airports operated by special-purpose governments (i.e., port or airport authorities). Airports more involved in the professional associations (e.g., the American Association of Airport Executives) are also more likely to deploy solar PV, but this relationship is stronger for special-purpose airports. Additionally, airport solar PV is less likely to appear in the service area of cooperatives than in the service area of investor-owned utilities. These findings help policymakers understand how airport governance, utility ownership, and involvement in professional associations may promote or hinder renewable energy transitions at airports and other large publicly used properties.

한국군 해외파병 결정에 관한 전문가 집단의 의식 분석

한국군 해외파병 결정에 관한 전문가 집단의 의식 분석

Evaluation of Replication Mechanisms on Selected Database Systems

This paper is focused on comparing database replication over spatial data in PostgreSQL and MySQL. Database replication means solving various problems with overloading a single database server with writing and reading queries. There are many replication mechanisms that are able to handle data differently. Criteria for objective comparisons were set for testing and determining the bottleneck of the replication process. The tests were done over the real national vector spatial datasets, namely, ArcCR500, Data200, Natural Earth and Estimated Pedologic-Ecological Unit. HWMonitor Pro was used to monitor the PostgreSQL database, network and system load. Monyog was used to monitor the MySQL activity (data and SQL queries) in real-time. Both database servers were run on computers with the Microsoft Windows operating system. The results from the provided tests of both replication mechanisms led to a better understanding of these mechanisms and allowed informed decisions for future deployment. Graphs and tables include the statistical data and describe the replication mechanisms in specific situations. PostgreSQL with the Slony extension with asynchronous replication synchronized a batch of changes with a high transfer speed and high server load. MySQL with synchronous replication synchronized every change record with low impact on server performance and network bandwidth.

Bounds on ship deployment in container shipping with time windows

This paper extends and complements the liner shipping literature by examining the ship deployment decision in the presence of time windows at container ports. A set of tight bounds on the number of ships that can be deployed is derived, revealing a number of fundamental insights into the impact of time windows on ship deployment. The bounds in this paper can also enable the extension of single-route shipping models that have appeared in the literature to the system-wide level by taming the increase in computational requirements.