Cost Performance Metrics Research Articles

Intelligent location-routing for sustainable reverse supply chain of end-of-life vehicles considering awareness cost and carbon penalty

This paper presents a comprehensive investigation into optimizing the reverse supply chain for end-of-life vehicles (ELVs) with a focus on sustainability considerations, including awareness cost and carbon penalty. A novel three-objective mathematical model is developed, leveraging fuzzy logic to address the complex nature of the problem. Two multi-objective algorithms, the Grey Wolf Optimizer (GWO) based on the Pareto boundary and the NSGA-II algorithm, are deployed to solve the model. A case study in China is conducted to validate the proposed model, with results compared against the outcomes of the algorithms. Additionally, a sensitivity analysis approach is employed to assess the impact of awareness cost and carbon penalty parameters. Furthermore, the Taguchi experimental design method is utilized to identify the optimal combination of parameter values. The findings reveal that the GWO algorithm surpasses NSGA-II in terms of solution quality and diversity, although NSGA-II demonstrates superior performance in uniformity and computational time. The sensitivity analysis highlights the positive correlation between increased awareness cost and various performance metrics, such as the number of collected vehicles, economic profit, and social profit, while also indicating a reduction in environmental impacts. Conversely, escalating carbon penalties leads to a decrease in the acceptance and processing of vehicles within the supply chain, resulting in diminished chain and social profits, despite the decrease in carbon penalties.

Reward (Mis)design for Autonomous Driving (Abstract Reprint)

This article considers the problem of diagnosing certain common errors in reward design. Its insights are also applicable to the design of cost functions and performance metrics more generally. To diagnose common errors, we develop 8 simple sanity checks for identifying flaws in reward functions. We survey research that is published in top-tier venues and focuses on reinforcement learning (RL) for autonomous driving (AD). Specifically, we closely examine the reported reward function in each publication and present these reward functions in a complete and standardized format in the appendix. Wherever we have sufficient information, we apply the 8 sanity checks to each surveyed reward function, revealing near-universal flaws in reward design for AD that might also exist pervasively across reward design for other tasks. Lastly, we explore promising directions that may aid the design of reward functions for AD in subsequent research, following a process of inquiry that can be adapted to other domains.

COMPARATIVE ANALYSIS OF COMMUNICATION TECHNOLOGIES FOR CLOUD-BASED FLOOD RISK PREDICTIVE MODELING IN URBAN AREAS

The increasing occurrence and severity of natural disasters worldwide have emphasized the pressing want for sophisticated technology to enhance catastrophe management and response efforts. The Internet of Things (IoT) has gained significant interest for its ability to potentially transform early warning systems and improve predictive modeling in places prone to disasters. This research article explores the comparison of several communication technologies in the context of a cloud-based predictive modeling system designed for flood risk assessment. The evaluation of cost-effectiveness and performance indicators of various communication systems is centered on a case study. The chosen technologies include Bluetooth, WiFi, LoRa, Zigbee, NB-IoT, and 4G Cellular, each exemplifying distinct methodologies for IoT-enabled communication in crisis management situations. This case study takes place in an area that is prone to flooding. The goal is to use sensor nodes with advanced communication technologies to gather and analyze real-time data. This data will be used to evaluate flood risks and quickly share warnings. This research attempt relies on a well constructed cost analysis framework. The framework provides a thorough overview of expenses associated with each communication method, encompassing hardware acquisition, installation, ongoing maintenance, and data transfer. The project seeks to give stakeholders with practical insights into the economic feasibility of implementing certain communication technologies in the context of flood risk assessment by carefully measuring these cost elements. Moreover, certain criteria for evaluating performance are carefully defined in order to analyze the effectiveness and dependability of any communication technology. Parameters such as range, data rate, power consumption, and overall dependability are analyzed to establish the operational effectiveness of the installed sensor networks. Through rigorous performance assessment, the study aims to reveal the fundamental strengths and limits of each communication technology, therefore aiding decision-makers in their quest to enhance disaster management infrastructure. The outcome of this study endeavor gives significant insights that transcend basic technology comparison. By shedding light on the nuanced interplay between cost considerations, technological capabilities, and performance metrics, the research equips disaster management authorities with the requisite knowledge to make informed decisions regarding the adoption and deployment of IoT-enabled communication technologies. Ultimately, the results of this research effort carry important implications for strengthening disaster resilience and shielding vulnerable populations from the ravages of natural disasters.

Eliminating Food Insecurity in the USA: a Target Trial Emulation Using Observational Data to Estimate Effects on Health-Related Quality of Life.

Food insecurity is associated with many aspects of poor health. However, trials of food insecurity interventions typically focus on outcomes of interest to funders, such as healthcare use, cost, or clinical performance metrics, rather than quality of life outcomes that may be prioritized by individuals who experience food insecurity. To emulate a trial of a food insecurity elimination intervention, and quantify its estimated effects on health utility, health-related quality of life, and mental health. Target trial emulation using longitudinal, nationally representative data, from the USA, 2016-2017. A total of 2013 adults in the Medical Expenditure Panel Survey screened positive for food insecurity, representing 32 million individuals. Food insecurity was assessed usingthe Adult Food Security Survey Module. The primary outcome was the SF-6D (Short-Form Six Dimension) measure of health utility. Secondary outcomes were mental component score (MCS) and physical component score (PCS) of the Veterans RAND 12-Item Health Survey (a measure of health-related quality of life), Kessler 6 (K6) psychological distress, and Patient Health Questionnaire 2-item (PHQ2) depressive symptoms. We estimated that food insecurity elimination would improve health utility by 80 QALYs per 100,000 person-years, or 0.008 QALYs per person per year (95% CI 0.002 to 0.014, p = 0.005), relative to the status quo. We also estimated that food insecurity elimination would improve mental health (difference in MCS [95% CI]: 0.55 [0.14 to 0.96]), physical health (difference in PCS: 0.44 [0.06 to 0.82]), psychological distress (difference in K6: -0.30 [-0.51 to -0.09]), and depressive symptoms (difference in PHQ-2: -0.13 [-0.20 to -0.07]). Food insecurity elimination may improve important, but understudied, aspects of health. Evaluations of food insecurity interventions should holistically investigate their potential to improve many different aspects of health.

Reward (Mis)design for autonomous driving

This article considers the problem of diagnosing certain common errors in reward design. Its insights are also applicable to the design of cost functions and performance metrics more generally. To diagnose common errors, we develop 8 simple sanity checks for identifying flaws in reward functions. We survey research that is published in top-tier venues and focuses on reinforcement learning (RL) for autonomous driving (AD). Specifically, we closely examine the reported reward function in each publication and present these reward functions in a complete and standardized format in the appendix. Wherever we have sufficient information, we apply the 8 sanity checks to each surveyed reward function, revealing near-universal flaws in reward design for AD that might also exist pervasively across reward design for other tasks. Lastly, we explore promising directions that may aid the design of reward functions for AD in subsequent research, following a process of inquiry that can be adapted to other domains.

Additive manufacturing integration in E-commerce supply chain network to improve resilience and competitiveness

In light of recently increased e-commerce, also a result of the COVID-19 pandemic, this study examines how additive manufacturing (AM) can contribute to e-commerce supply chain network resilience, profitability and competitiveness. With the recent competitive supply chain challenges, companies aim to decrease cost performance metrics and increase responsiveness. In this work, we aim to establish utilisation policies for AM in a supply chain network so that companies can simultaneously improve their total network cost and response time performance metrics. We propose three different utilisation policies, i.e. reactive, proactive – both with 3D printing support – and a policy excluding AM usage in the system. A simulation optimisation process for 136 experiments under various input design factors for an (s, S) inventory control policy is carried out. We also completed a statistical analysis to identify significant factors (i.e. AM, holding cost, lead time, response time, demand amount, etc.) affecting the performance of the studied retailer supply chain. Results show that utilising AM in such a network can prove beneficial, and where the reactive policy contributes significantly to the network performance metrics. Practically, this work has important managerial implications in defining the most appropriate policies to achieve optimisation of supply network operations and resilience with the aid of AM, especially in times of turbulence and uncertainty.

Exploring the relationship between performance metrics and cost saving potential of defect prediction models

Context:Performance metrics are a core component of the evaluation of any machine learning model and used to compare models and estimate their usefulness. Recent work started to question the validity of many performance metrics for this purpose in the context of software defect prediction.Objective:Within this study, we explore the relationship between performance metrics and the cost saving potential of defect prediction models. We study whether performance metrics are suitable proxies to evaluate the cost saving capabilities and derive a theory for the relationship between performance metrics and cost saving potential.Methods:We measure performance metrics and cost saving potential in defect prediction experiments. We use a multinomial logit model, decision, and random forest to model the relationship between the metrics and the cost savings.Results:We could not find a stable relationship between cost savings and performance metrics. We attribute the lack of the relationship to the inability of performance metrics to account for the property that a small proportion of very large software artifacts are the main driver of the costs.fact that performance metrics are incapable of accurately considering the costs associate with individual artifacts, which is required due to the exponential distribution of artifact sizes.Conclusion:Any defect prediction study interested in finding the best prediction model, must consider cost savings directly, because no reasonable claims regarding the economic benefits of defect prediction can be made otherwise.

Emergent Approaches to Efficient and Sustainable Polyhydroxyalkanoate Production.

Petroleum-derived plastics dominate currently used plastic materials. These plastics are derived from finite fossil carbon sources and were not designed for recycling or biodegradation. With the ever-increasing quantities of plastic wastes entering landfills and polluting our environment, there is an urgent need for fundamental change. One component to that change is developing cost-effective plastics derived from readily renewable resources that offer chemical or biological recycling and can be designed to have properties that not only allow the replacement of current plastics but also offer new application opportunities. Polyhydroxyalkanoates (PHAs) remain a promising candidate for commodity bioplastic production, despite the many decades of efforts by academicians and industrial scientists that have not yet achieved that goal. This article focuses on defining obstacles and solutions to overcome cost-performance metrics that are not sufficiently competitive with current commodity thermoplastics. To that end, this review describes various process innovations that build on fed-batch and semi-continuous modes of operation as well as methods that lead to high cell density cultivations. Also, we discuss work to move from costly to lower cost substrates such as lignocellulose-derived hydrolysates, metabolic engineering of organisms that provide higher substrate conversion rates, the potential of halophiles to provide low-cost platforms in non-sterile environments for PHA formation, and work that uses mixed culture strategies to overcome obstacles of using waste substrates. We also describe historical problems and potential solutions to downstream processing for PHA isolation that, along with feedstock costs, have been an Achilles heel towards the realization of cost-efficient processes. Finally, future directions for efficient PHA production and relevant structural variations are discussed.

A new generation of direct X-ray detectors for medical and synchrotron imaging applications

Large-area X-ray imaging is one of the most widely used imaging modalities that spans several scientific and technological fields. Currently, the direct X-ray conversion materials that are being commercially used for large-area (> 8 cm × 4 cm without tiling) flat panel applications, such as amorphous selenium (a-Se), have usable sensitivities of up to only 30 keV. Although there have been many promising candidates (such as polycrystalline HgI2 and CdTe), none of the semiconductors were able to assuage the requirement for high energy (> 40 keV) large-area X-ray imaging applications due to inadequate cost, manufacturability, and long-term performance metrics. In this study, we successfully demonstrate the potential of the hybrid Methylammonium lead iodide (MAPbI3) perovskite-based semiconductor detectors in satisfying all the requirements for its successful commercialization in synchrotron and medical imaging. This new generation of hybrid detectors demonstrates low dark current under electric fields needed for high sensitivity X-ray imaging applications. The detectors have a linear response to X-ray energy and applied bias, no polarization effects at a moderate bias, and signal stability over long usage durations. Also, these detectors have demonstrated a stable detection response under BNL’s National Synchrotron Light Source II (NSLS-II) 70 keV monochromatic synchrotron beamline.

Efficient Utilization of IaaS Cloud using Adaptive Evolution Based Technique

In the present era of technology and innovation, computing provides as a service. The service-oriented computing paradigm is a subset of utility and distributed computing paradigm. Hardware specification, platform, and application provide a service to the users in a different time zone. Resource utilization and request completion time is the prime focus of the service providers and consumers. In this work, we proposed a nature-inspired evolution and astrology science-based approach. The Big-Bang Big-Crunch optimization method based task allocation technique focuses on efficient resource utilization of IaaS (Infrastructure as a Service) cloud. We focused on three performance metrics, which may be user-oriented and service provider oriented. Performance metrics measure in terms of average resource utilization cost. Average Finish time, an average start time, and operational cost (customer-oriented) performance metrics are consumer-centric. The simulation performs using five cloud resources, six cloud configurations, and user-defined population size, and number iterations. The selection operator includes a fitness function that depends on estimated resource cost in the duration of execution. BB-BC cost-aware model provides optimal resource utilization, and minimum average finish time, and minimum average start time (ms) of cloudlets. The results exhibit that the proposed astrology, evolution based meta-heuristic approach outperforms the static (first come first serve (FCFS)), dynamic, and meta-heuristic (Genetic cost-aware, Genetic execution time aware and Particle Swarm optimization) approaches.

Eligibility of BPMN Models for Business Process Redesign

Business process redesign (BPR) is an organizational initiative for achieving competitive multi-faceted advantages regarding business processes, in terms of cycle time, quality, cost, customer satisfaction and other critical performance metrics. In spite of the fact that BPR tools and methodologies are increasingly being adopted, process innovation efforts have proven ineffective in delivering the expected outcome. This paper investigates the eligibility of BPMN process models towards the application of redesign methods inspired by data-flow communities. In previous work, the transformation of a business process model to a directed acyclic graph (DAG) has yielded notable optimization results for determining average performance of process executions consisting of ad-hoc processes. Still, the utilization encountered drawbacks due to a lack of input specification, complexity assessment and normalization of the BPMN model and application to more generic business process cases. This paper presents an assessment mechanism that measures the eligibility of a BPMN model and its capability to be effectively transformed to a DAG and be further subjected to data-centric workflow optimization methods. The proposed mechanism evaluates the model type, complexity metrics, normalization and optimization capability of candidate process models, while at the same time allowing users to set their desired complexity thresholds. An indicative example is used to demonstrate the assessment phases and to illustrate the usability of the proposed mechanism towards the advancement and facilitation of the optimization phase. Finally, the authors review BPMN models from both an SOA-based business process design (BPD) repository and relevant literature and assess their eligibility.

Risk examination in the Arabian Gulf Region construction industry from international firms' insights

Risk is an ever-present event on any construction project. International projects are more challenging than domestic projects as they experience a wider range of risks. With the growing amount of construction in the Arabian Gulf Region (AGR) and many more international firms attempting into new international markets, limited research exists to identify and evaluate the impact of risks on projects in this area. This paper provides an overview of the risk associated with international projects. Seventy-four risks encountered in the AGR were identified and their impact on cost and schedule performance metrics was evaluated. Projects for which cost and schedule were both impacted were identified and the correlation between cost and schedule was compared. Some factors were found to have significantly higher impact on cost or schedule. An international risk assessment tool (IRAT) was developed to help multinational firms enhance their visual ability to pre-emptively identify, address, and mitigate risks.

Optimizing Cloud Bandwidth and Cost using Content Aware Chunk Prediction to Improve Efficiency of Retrieval

Bandwidth, an important most demanding computing resource is mainly focused on backup service in cloud computing technology. In addition to backup solutions, communication and transferring of data also involves a considerable amount of bandwidth for efficient throughput. Management of wide area network bandwidth provision and cost is a major challenge in recent periods. Due to enormous content in cloud, cost effective bandwidth provisioning solution should be adopted by the cloud providers for better user satisfaction. Here we use a min chunk hashing technique based on the content for preserving all chunks in order to reduce the overall transfer time for individuals as well as to reduce the resource allocation load on the cloud servers. Thus, content based chunking along with the metadata considerably reduces the cost of bandwidth utilization for individuals. The bandwidth usage is predicted using cloudsim simulator through BwProvisioner class provided in the cloudbus package. A sample of large text files with redundant data is refined, chunked and hashed using Message Digest5 to measure the transfer time and cost performance metrics for experimental values in a dynamic work load environment.

Developing Students’ Understanding of Co-opetition and Multilevel Inventory Management Strategies in Supply Chains: An In-Class Spreadsheet Simulation Exercise

Instructors look for ways to explain to students how supply chains can be constructed so that competing suppliers can work together to improve inventory management performance (i.e., a phenomenon known as co-opetition). An Excel spreadsheet-driven simulation is presented that models a complete multilevel supply chain system—customer, retailer, wholesaler, distributor, and manufacturer—for up to six competing products. Students are provided the opportunity to compare the inventory cost and other key performance metrics of the alternative multilevel supply chain structures. This article explains the simulation model, describes the instructional approach, and presents assessment results from students in an introductory operations management course. Students find that the simulation is easy to use and helps them understand the performance impact of strategic supplier collaborations on supply chain operating performance.

A Low Cost Route Optimization Scheme for Cluster-Based Proxy MIPv6 Protocol

Proxy Mobile IPv6 (PMIPv6) is a network based mobility protocol which has been designed to relieve the mobile nodes (MNs) from participating in the mobility process and to reduce the long handoff latency of the MIPv6 protocol. However, PMIPv6 incurs a long communication path due to the triangle routing problem, in which, all packets sent by MNs are obligated to pass through the local mobility anchor. Several solutions have been proposed to mitigate this issue. However, they still incur high signaling overhead to recover the Route Optimization (RO) status after handoff. In this paper, we propose a Cluster-Based RO (CBRO) scheme for the clustered architecture of the PMIPv6, in which, the Mobile Access Gateways (MAGs) are grouped into clusters with a distinguished Head MAG (HMAG) for each. In the proposed CBRO, the RO process is relied on the HMAGs to reduce the handoff latency while achieving a fast recovery of the optimized path after handoff. The proposed CBRO is evaluated analytically and compared with the basic PMIP and the current RO schemes. The obtained numerical results have shown that the proposed CBRO outperforms all other schemes in terms of signaling cost required to recover the RO status after handoff and the total cost performance metrics.

A cluster-based proxy mobile IPv6 for IP-WSNs

The Sensor Proxy Mobile IPv6 (SPMIPv6) has been designed for IP-based wireless sensor networks mobility to potentially save energy consumption by relieving the sensor nodes from participating in the handoff process. However, SPMIPv6 is dependent on a single and central Local Mobility Anchor (LMA), and thus, it inherited most of the problems observed in the Proxy Mobile IPv6 (PMIPv6) protocol, including long handoff latency, non-optimized communication path, and bottleneck issues. In addition, SPMIPv6 extends the single point of failure to include both the authentication and network information. This study presents an enhanced architecture for SPMIPv6 called Clustered SPMIPv6 (CSPMIPv6) to overcome the problems above. In the proposed architecture, the Mobility Access Gateways (MAGs) are grouped into clusters, each with a distinguished cluster Head MAG (HMAG). The HMAG is mainly designed to reduce the load on LMA by performing intra-cluster handoff signaling and providing an optimized path for data communications. The proposed architecture is evaluated analytically, and the numerical results show that the proposed CSPMIPv6 outperforms both SPMIPv6 and PMIPv6 protocols in terms of LMA load, local handoff delay, and transmission cost performance metrics.

Progress in Performance Improvement and New Research Areas for Cost Reduction of 2G HTS Wires

Second-generation (2G) HTS wires are now being produced routinely in kilometer lengths using Metal Organic Chemical Vapor Deposition (MOCVD) process with critical currents of 300 A/cm. While this achievement is enabling several prototype devices, in order to reach a substantial commercial market, the cost-performance metrics of 2G HTS wires need to be significantly improved in device operating conditions. Zr-doping has been found to be an effective approach to improve in-field critical current performance of MOCVD-based HTS wires. In this work, we have explored modifications to the Zr-doped precursor compositions to achieve three and two-fold increase in deposition rate in research and production MOCVD systems respectively. Production wires made with modified Zr-doped compositions exhibit a self-field critical current density of 50 <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">${\rm MA/cm}^{2}$</tex> </formula> at 4.2 K and a 55 to 65% higher performance than our previous wires with Zr-doping, over magnetic field range of 0 to 30 T. We have also developed an alternate, low-cost technique, namely electrodeposition, to deposit silver overlayer on superconducting film. Wires made with electrodeposited silver are able to sustain the same level of overcurrent as sputtered silver layers. This process has been successfully scaled up to 100 m lengths.

Analytic hierarchy process to assess and optimize distribution network

Effective supply chain distribution network design needs to consider various performance dimensions and product characteristics. Recently, researchers have begun to realize that the decision and integration effort in supply chain design should be driven by a comprehensive set of performance metrics and also product characteristics. In this paper, we relate product characteristics to optimizing supply chain delivery network design and adopt cost and service factor performance metrics as the decision criteria. An analytic hierarchy process (AHP) multi-criteria decision-making methodology is then developed to take into account both qualitative and quantitative factors in the best delivery network design selection. By using AHP methodology we could optimize the selection of delivery network design followed by relevant choices for decision making of Home plus distribution center.

IPTV transport architecture alternatives and economic considerations

Telecommunications companies are under increased pressure due to competition from multiple system operators, and the changing communications and entertainment needs of residential consumers. These pressures are driving the telecommunications companies to design and deploy metro networks capable of delivering broadcast video and VOD services through IP-based broadband connectivity (known as IPTV). One important architectural consideration for these IPTV networks is the core layer 1 transport infrastructure that supports economical delivery of these high-bandwidth services. This article surveys some of the major layer 1 optical transport network alternatives including DWDM, SONET, and Ethernet-on-fiber as well as its passive WDM/DWDM (PWDM) variation. The article analyzes the architectural and cost performance metrics for these transport options, and explores the economic crossover points with regard to the key network and service parameters such as bandwidth demand, number of subscribers, network topology, and operational constraints. DWDM technology, when coupled with native Ethernet aggregation, is found to have significant applicability for IPTV transport networks. DWDM offers value in capital cost and operations, while delivering the required bandwidth and performance. Ethernet aggregation capability within the transport infrastructure adds scalability, flexibility, and cost advantage for the multiservice network applications of the future.

Economic Value of Combined Best Practice Use

This paper measures the value of best practices based on data taken from the Construction Industry Institute Benchmarking and Metrics database. A three-step process provides the basis for measuring the potential benefits of increased best practice use. First, a practice use index is derived to model the way in which best practices are utilized on actual projects. The index combines data from eight best practices to create a single measure of practice utilization. Second, a project performance index, combining cost and schedule metrics, is derived as an indicator of overall project performance. Third, the practice use index is correlated with cost, schedule, and overall project performance metrics. Both owners and contractors benefit from increased practice use. Potential cost savings for owners range from $1.7 to $3.4 million, depending on industry group and project size. Potential cost savings for contractors are higher, averaging $7.2 million for the typical $88 million heavy industrial project. Benefits from schedule reductions are most apparent for owners. On large building projects, schedule reductions for owners average 27 weeks. Finally, improvements in overall project performance on the order of 30% are observed for both owners and contractors.