Cost Trade-offs Research Articles

Disease Bundling or Specimen Bundling? Cost- and Capacity-Efficient Strategies for Multidisease Testing with Genetic Assays

Problem definition: Infectious disease screening can be expensive and capacity constrained. We develop cost- and capacity-efficient testing designs for multidisease screening, considering (1) multiplexing (disease bundling), where one assay detects multiple diseases using the same specimen (e.g., nasal swabs, blood), and (2) pooling (specimen bundling), where one assay is used on specimens from multiple subjects bundled in a testing pool. A testing design specifies an assay portfolio (mix of single-disease/multiplex assays) and a testing method (pooling/individual testing per assay). Methodology/results: We develop novel models for the nonlinear, combinatorial multidisease testing design problem: a deterministic model and a distribution-free, robust variation, which both generate Pareto frontiers for cost- and capacity-efficient designs. We characterize structural properties of optimal designs, formulate the deterministic counterpart of the robust model, and conduct a case study of respiratory diseases (including coronavirus disease 2019) with overlapping clinical presentation. Managerial implications: Key drivers of optimal designs include the assay cost function, the tester’s preference toward cost versus capacity efficiency, prevalence/coinfection rates, and for the robust model, prevalence uncertainty. When an optimal design uses multiple assays, it does so in conjunction with pooling, and it uses individual testing for at most one assay. Although prevalence uncertainty can be a design hurdle, especially for emerging or seasonal diseases, the integration of multiplexing and pooling, and the ordered partition property of optimal designs (under certain coinfection structures) serve to make the design more structurally robust to uncertainty. The robust model further increases robustness, and it is also practical as it needs only an uncertainty set around each disease prevalence. Our Pareto designs demonstrate the cost versus capacity trade-off and show that multiplexing-only or pooling-only designs need not be on the Pareto frontier. Our case study illustrates the benefits of optimally integrated designs over current practices and indicates a low price of robustness. Funding: This work was supported by the National Science Foundation [Grant 1761842]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0296 .

Retinal Biometric for Securing JPEG-Codec Hardware IP Core for CE Systems

Fake/pirated hardware intellectual property (IP) cores integrated in consumer electronics systems can cause reliability hazards and jeopardize the safety of end consumer. Robust detection and isolation of such pirated IP cores before integration is therefore crucial. Existing approaches do not provide robust security against replication or evasion of IP detection resulting into higher probability of coincidence (Pc) and lesser tamper tolerance (TT) compared to the proposed approach. Further, compared to existing biometric based hardware security methodologies, the proposed approach provides more distinctive features and does not require image enhancement; while compared to non-biometric based hardware security methodologies, the proposed approach provides natural uniqueness and non-replicability of features through retinal image. This paper presents a novel retina biometric-based hardware security methodology for securing JPEG compression-decompression (CODEC) hardware IP core against IP piracy. The proposed result reports the following: (a) variation in Pc metric for different sizes of embedded retinal signature and different retinal images (b) variation of TT for proposed approach for different retinal images (c) security comparison of proposed approach with facial, fingerprint biometric and encrypted digital signature-based hardware security approaches (d) Pc-design cost tradeoff for proposed retinal biometric approach. Results indicate enhancement in security at zero design overhead.

Analysis of time and cost using time cost trade off method in pressure vessel project

One of the construction companies got a Pressure Vessel project job. During the process of working on the project, it was delayed from the predetermined date so that the company had to pay a fine of 0.2% every day. So it is necessary to analyze project scheduling in order to get the optimal time and cost of working on this project. The methods used in this research are Time Cost Trade Off (TCTO), Precedence Diagram Method (PDM) and crashing. Acceleration is carried out on all critical paths contained in fabrication activities. This study uses alternatives to accelerate the addition of 50% and 75% labor. The results of this study obtained the optimal time and cost with the alternative of adding 75% labor. The resulting acceleration duration is 75 days faster than the actual duration, with costs incurred of Rp. 161,386,836, -, resulting in a 56% decrease from the actual duration cost of Rp. 286,961,745, -. Analysis of the S-curve found that the accelerated duration can be completed faster than the actual duration because, the curve line on the accelerated duration is more sloping closer to the y-axis indicating greater progress each week.

RASM: Resource-Aware Service Migration in Edge Computing based on Deep Reinforcement Learning

Multi-access Edge Computing (MEC) paradigm allows devices to offload their intensive service tasks that require high Quality of Experience (QoE). Devices mobility forces services to migrate between MECs to maintain QoE in terms of delay. The decision on when to migrate a service requires a cost and QoE tradeoff, and destination MEC selection needs to be done upon latency and resource availability constraints to minimize migrations. To this end, we propose a novel Resource-Aware Service Migration (RASM) mechanism using Deep Q-Network (DQN) to make migration decisions by achieving tradeoff between the QoE in terms of delay and migration cost. Moreover, DQN learns the best policy for maximizing QoE by selecting the migration destination based on the MECs proximity to the device and estimated resource availability at the servers using queuing model. Results show faster convergence to optimal policy, reduced average end-to-end service delay by 27%, and smaller service rejection rate by 24% comparing to the state-of-the-art.

Analisa Percepatan Proyek Pembuatan Mesin Grinding Cat Dengan Time Cost Trade Off Method (Studi Kasus : PT TDI Sidoarjo)

The case study in this research is a project to manufacture a paint paste grinding machine at PT Pabrik Cat Tunggal Djaja Indah. In its implementation, this project is considered to have a delay of 2 weeks, with a total project completion time of 120 days, so project acceleration is needed. The scheduling method is carried out using PDM (Precendence Diagram Method) with the help of Microsoft Project 2016 software to find the critical path, then after knowing the critical activities, crashing is carried out with the alternative of adding working hours or overtime and adding optimum workers. The Time Cost Trade Off method will analyze the time after crashing. The optimal duration to complete this project is 107 days with an acceleration of 13 working days with a percentage of 10.38% and an optimal total cost of Rp 262,947,693 with a cost efficiency of 3.56%.

Omni-fulfillment network design for supply chain service platform considering cost, service and resilience tradeoffs

Supply chain service platform (SCSP) plays an indispensable role in omni-fulfillment supply chain network services in the digital economy era. In this context, this paper studies the omni-fulfillment supply chain network design considering cost, service, and resilience tradeoffs. The problem is modeled as a multi-objective mixed integer nonlinear programming (MOMINLP) model based on omni-fulfillment services. To improve the solving efficiency, the model is linearized, and a tailored multi-objective optimization algorithm is developed. The significant advantage of this algorithm is that the multi-loop skipping mechanism is embedded in the Epsilon-constraint method to obtain the Pareto front faster. The effectiveness of the proposed decision-making framework is verified by real-case data. Numerical results show that the proposed algorithm outperforms AUGMECON2 algorithm in terms of problem size and computational time. In the case of order segmentation, the shorter the delivery time of goods, the higher the cost of the supply chain network. Furthermore, implementing capacity redundancy in the central warehouse can mitigate the impact of different disrupting scenarios.

Assisted ambulation to improve health outcomes for older medical inpatients (AMBULATE): study protocol for a randomized controlled trial

BackgroundHospitalized older adults spend as much as 95% of their time in bed, which can result in adverse events and delay recovery while increasing costs. Observational studies have shown that general mobility interventions (e.g., ambulation) can mitigate adverse events and improve patients’ functional status. Mobility technicians (MTs) may address the need for patients to engage in mobility interventions without overburdening nurses. There is no data, however, on the effect of MT-assisted ambulation on adverse events or functional status, or on the cost tradeoffs if a MT were employed. The AMBULATE study aims to determine whether MT-assisted ambulation improves mobility status and decreases adverse events for older medical inpatients. It will also include analyses to identify the patients that benefit most from MT-assisted mobility and assess the cost-effectiveness of employing a MT.MethodsThe AMBULATE study is a multicenter, single-blind, parallel control design, individual-level randomized trial. It will include patients admitted to a medical service in five hospitals in two regions of the USA. Patients over age 65 with mild functional deficits will be randomized using a block randomization scheme. Those in the intervention group will ambulate with the MT up to three times daily, guided by the Johns Hopkins Mobility Goal Calculator. The intervention will conclude at hospital discharge, or after 10 days if the hospitalization is prolonged. The primary outcome is the Short Physical Performance Battery score at discharge. Secondary outcomes are discharge disposition, length of stay, hospital-acquired complications (falls, venous thromboembolism, pressure ulcers, and hospital-acquired pneumonia), and post-hospital functional status.DiscussionWhile functional decline in the hospital is multifactorial, ambulation is a modifiable factor for many patients. The AMBULATE study will be the largest randomized controlled trial to test the clinical effects of dedicating a single care team member to facilitating mobility for older hospitalized patients. It will also provide a useful estimation of cost implications to help hospital administrators assess the feasibility and utility of employing MTs.Trial registrationRegistered in the United States National Library of Medicine clinicaltrials.gov (# NCT05725928). February 13, 2023.

Solving Time-Cost Tradeoff Problem with Resource Constraint Using Fuzzy Mathematical Model

Scheduling considered being one of the most fundamental and essential bases of the project management. Several methods are used for project scheduling such as CPM, PERT and GERT. Since too many uncertainties are involved in methods for estimating the duration and cost of activities, these methods lack the capability of modeling practical projects. Although schedules can be developed for construction projects at early stage, there is always a possibility for unexpected material or technical shortages during construction stage. The objective of this research is to build a fuzzy mathematical model including time cost tradeoff and resource constraints analysis to be applied concurrently. The proposed model has been formulated using fuzzy theory combining CPM computations, time-cost trade off analysis and resource constraint. MATLAB software has been adopted to perform ranking process, for each case, that facilitates obtaining the optimum solution. This research infers that it is possible to perform time-cost trade off analysis with resource restriction simultaneously, which ensures achieving scheduling optimum solution reducing the effort and the time when performing these techniques in succession using traditional methods.

A Three-Phase Phase-Modular Single-Ended Primary-Inductance Converter Rectifier Operating in Discontinuous Conduction Mode for Small-Scale Wind Turbine Applications

Small-scale wind turbines play an important role in distributed generation since customers can use their houses, farms, and business to produce electric energy. The development of the power electronics system that processes the electric energy from small-scale wind turbines is a concern due to cost, simplicity, efficiency, and performance trade-offs. This paper presents the results of applying a three-phase phase-modular single-ended primary-inductance converter rectifier to processing the energy of a small-scale wind turbine system. The rectifier was designed according to the specifications of a commercial small-scale wind turbine system and tested in an emulator workbench, providing experimental data on the operation of the rectifier in this application. The rectifier can process the energy of a non-sinusoidal three-phase system since the permanent magnet synchronous generator has trapezoidal waveforms. The results show that the rectifier has the advantages of (i) using the inductance of the generator as the input filter inductor of the rectifier, (ii) providing input currents with the same shape as the voltages and in phase without the use of a current control system, (iii) simplicity of control of the DC output voltage and PWM modulation, and (iv) phase-modular characteristics that allow operating with phase fault without any additional control techniques. Due to the operation in discontinuous conduction mode, low efficiency in high power and/or low input voltage specifications are disadvantages.

Online Service Request Duplicating for Vehicular Applications

Vehicles on roads have increasingly powerful computing capabilities and edge nodes are being widely deployed. They can work together to provide computing services for onboard driving systems, passengers, and pedestrians. Typical applications in vehicular systems have service requirements such as low latency and high reliability. Most studies in vehicular networks concerning latency and reliability focus on vehicular communication at the network level. Based on these fundamental works, an increasing proportion of vehicles boast complex applications that require service-level end-to-end performance guarantees. Several works guarantee service-level latency or reliability while new and innovative applications are demanding a joint optimization of the above two metrics. To address the critical challenges induced by the joint modeling of latency and reliability, system uncertainty, and performance and cost trade-off, we employ service request duplication to ensure both latency and reliability performance at the service level. We propose an online learning-based service request duplication algorithm based on a multi-armed bandit framework and Lyapunov optimization theory. The proposed algorithm achieves an upper-bounded regret compared to the oracle algorithm. Simulations are based on real-world datasets and the results demonstrate that the proposed algorithm outperforms the benchmarks.

Mathematical Multiobjective Optimization Model for Trade-Offs in Small-Scale Construction Projects

As construction projects become more complex and challenging to execute, project managers need to develop work schedules that consider as many objectives as are critical to project success. A work schedule designed to optimize all these objectives is likely to enhance project performance. However, achieving a balance among these objectives is difficult because they frequently conflict with each other. Many optimization models have been developed over the years that can factor in different objectives. Although previous optimization studies have contributed to the body of knowledge by individually adding one or two objectives to the classical time–cost trade-off (TCT) problem, none of these studies simultaneously optimized more than three or four objectives at a time. Clearly, there is a need for a multiobjective optimization model to generate work schedules that increase the likelihood of achieving as many project objectives as necessary. This study was motivated by the need to fill this research gap. The objective of this study was to develop a mathematical multiobjective optimization model for the time–cost–quality–schedule flexibility-resource fluctuation trade-off problem in small-scale construction projects. The proposed model is novel because it (1) considers five important objectives of construction project scheduling at the same time, (2) quantifies all objectives to provide an accurate reflection of construction site conditions, and (3) objectively determines the optimal compromise solution using a distance-based method rather than subjectively from a set of Pareto-optimal solutions. The applicability of the proposed model is demonstrated with an illustrative example. The key results of this study are as follows: (1) the optimal solution obtained depends on the project manager’s priorities relative to the objectives; and (2) in optimization problems with more than two objectives, considering the objectives one at a time may result in misleading solutions, because the effects of the other objectives are ignored in this optimization. Consequently, multiple objectives must be considered simultaneously rather than one at a time. The proposed model is expected to provide project managers with a work schedule that balances five or more objectives, increasing the chances of successful project performance.

Quantifying the energetic cost tradeoffs of photovoltaic pumping systems for Sub-Saharan African smallholder farms

As solar technology has matured, irrigation using photovoltaic pumping systems (PVPSs) has gained popularity in developing markets as an effective means to alleviate poverty and increase food security. Yet, there remains a barrier to adoption; the upfront costs of PVPSs pose a financial burden for many low-income farmers. In a PVPS, the capital cost of the solar array contributes a large portion of upfront system costs. The solar pump is the largest energy consumer in the system, thus its efficiency directly impacts the size and cost of the solar array. There is a limited quantitative understanding of how solar pump efficiency affects the capital cost of the solar array. This study presents a technoeconomic framework to directly quantify the impact of solar pump efficiency on the cost of the solar array in a PVPS, for a range of hydraulic operating conditions. New empirical efficiency scaling laws were created by characterizing the efficiencies of 4-inch multistage centrifugal borehole pumps and induction motors. The utility of the technoeconomic framework is demonstrated through a case study comparing solar pump architectures with motors of different efficiencies. Results indicate that, despite the increased motor cost, the use of high-efficiency motors in solar pumps may lead to an overall cost reduction in a PVPS. Counter to the conventional capital cost-driven process, this work demonstrates that an efficiency-driven design process could improve low-cost, solar-powered system design. Engineers and system designers can leverage the presented framework during the design process to make informed decisions to achieve more cost-effective PVPSs.

The Transaction Costs of the South African Levy– Grant System for Skills Development

In this article, I seek to explore the ratio between the administrative transaction costs faced by organisations in South Africa for claiming skills development grants and the grants that they achieve. This simple trade-off of costs versus gains is a key factor in whether firms choose to participate in the training-related activities targeted by this system. Anecdotal sources have claimed that excessive administrative costs have led firms to under-claim grants available to them. However, no prior empirical research exists on such costs, presenting a gap in the literature. Accordingly, in this article, I explore the proportion of administrative transaction costs to direct gains in 14 case studies of South African organisations regarding their 2016 activities and expenditures. The results found half of the firms studied to have costs in excess of direct gains from grants. The remaining firms exhibited extreme variability in the cost–gain ratio from very low to near parity. Some evidence suggests that larger firms may enjoy a slight cost–benefit advantage. This research provides some support for the claim that the levy–grant system for skills development may present many organisations with excessive costs compared to gains. The high levels of variability suggest that policymakers should consider cost structures carefully and mitigate policy instruments.

A new method to assess sustainability of agricultural systems by integrating ecosystem services and environmental impacts

Agricultural stakeholders disagree on how to put the general concept of sustainability into practice. But most recognize the need for assessment methods to characterize the current situation and gauge progress yet to be made. Many sustainability assessment methods for agricultural systems have been published, but very few integrate ecosystem services (ES). And yet, ES indicators are key to better integrating biological processes to support the agroecological transition. Although researchers have attempted to link the concept of environmental impact (EI) and ES through life cycle analysis (LCA), gaps remain when it comes to doing so within a sustainability assessment method. Here a new method based on an original conceptual indicator framework embedding new predictive indicators of EI and ES indicators was developed. This framework covers 22 environmental themes and integrates few basic economic and social indicators. The method was implemented on 33 farms with different certifications and production specifications. All farms were members of an agricultural cooperative in north-eastern France. We selected 250 plots to cover the farms’ crop rotations. The correlation analysis showed no systemic trade-off between EI or ES and economic indicators. Meanwhile, the joint assessment of ES and EI (like the multivariate analysis), delivered more nuanced results than a classical opposition between intensive and organic farming. Some farms with a moderate or even good level of ES often do not leverage them, as shown by their high EI. The principle component analysis and subsequent hierarchical ascendant classification allowed us to distinguish five clusters providing a more complex picture than simply opposing conventional and organic agriculture. Finally, the positive deviance analysis revealed that economic, environmental and social considerations can be balanced in a way that seems to be more sustainable than, for example, an organic farm that has very successfully reduced EI but at a cost of several economic and social trade-offs.

The dual effect of anthropomorphism on customers’ decisions to use artificial intelligence devices in hotel services

ABSTRACT This study investigated the impact of anthropomorphism on customers’ decisions to use artificial intelligence (AI) devices in hotels, along with other traditional predictors. The study’s model considered the co-occurrence of positive and negative effects of anthropomorphism, including social presence and identity threats, which compete to control evaluative attitudes toward AI devices. Structural equation modeling was used to analyze the model using data from 363 participants. The results showed that anthropomorphism positively enhances social presence, leading to higher performance expectancy and positive emotions toward AI devices. Anthropomorphism also entails identity threats, resulting in increased effort expectancy and negative emotions. These trade-offs highlight the need for hotels to consider how customers perceive and respond to anthropomorphized AI devices during service delivery and to choose the level of anthropomorphism accordingly. This study highlights the benefits and cost trade-offs inherent in anthropomorphism and explains how they affect the evaluative attitudinal responses toward AI devices.

Adaptive selection slime mould algorithm in time–cost–quality–environmental impact trade-off optimization

Artificial intelligence technology is now regarded as one of the most significant innovations, aiding humans in finding solutions to a wide range of problems. Because to inventions with such cutting-edge and exceptional features, this field is receiving a lot of attention from all around the world. In this study, the hybrid model adaptive selection slime mould algorithm (ASSMA) is applied to address the project’s multi-objective time, cost, quality, and environment trade-off problem. ASSMA is contrasted with previous algorithms such as multiple-objective swarm algorithm, the opposition-based multi-objective development algorithm, and the slime mould algorithm to emphasize the outcomes of the proposed model. Using performance parameters that evaluate model quality, it is anticipated that this study will greatly outperform and expand upon previous models.

Upstream Logistics Optimization from Shanghai, China to Kasur, Pakistan: An Implementation of Mixed-Integer Linear Programming

Our foremost purpose with this research is to specify a new method of freight optimization that shippers can utilize to satisfy their budget conditions while confronting the market’s necessities. This paper designs an optimization model for tactical planning that minimizes the cost of highway transportation operations. The findings and analysis of China and Pakistan’s shipping sectors shed light on the implications of cost trade-offs between multimodal and intermodal transportation. As this study is based on the ExWorks incoterms, we developed a mixed-integer linear programming (MILP) model to formulate the cost-minimization problem, bearing in mind the internal cost constraints of transportation. The Pareto optimal solutions are generated by a multi-objective genetic algorithm (MOGA) in the MATLAB optimization solver with the support of the Pareto fitness function to balance the costs of multimodal and intermodal transportation in delivering 40 ft container units from China to Pakistan. The solution to the problem demonstrates that the cost of multimodal freight transportation is higher than that of intermodal freight transportation. However, significant changes occur when rail carrying capacity is increased. The cost of intermodal transportation for a 40 ft container from Shanghai, China, to Kasur, Pakistan, is 4712 USD, while multimodal transportation costs 7119 USD. Additionally, this study can be expanded by examining transit times associated with multimodal and intermodal freight distribution for imports and exports.

A fuzzy goal programming model for time, cost, and quality trade-off problem in metro construction projects considering sustainable development

When it comes to sustainable development in a country, transportation plays a significant role. It goes without saying that among various modes of transportation, metro has obtained an outstanding place in the intra-city transportation sector. Thus, accurately assessing the development condition of the urban metro system is inevitable to achieve its sustainable development. Given the importance of this issue, the main purpose of this study is to apply a new fuzzy model to the trade-offs of time, cost, and quality when planning metro construction projects to help achieve sustainable development. This model focuses on three pillars of sustainability: social, economic, and environmental. The research methodology used in this study is a survey and mathematical modelling. For this purpose, a new fuzzy goal programming model is developed. Finally, the model’s efficiency is investigated using sensitivity analysis. For decision-makers, the proposed model provides highly useful results. They are able to fulfil the social and environmental pillars of sustainable development by significantly reducing the completion time and improving the quality of the project. Moreover, by controlling the increase in costs, they can meet the economic pillar. The presented model provides appropriate answers to the problem by the scenarios considered by project managers.

Aggregate economic policy uncertainty and corporate political contribution disclosure

PurposeUnlike other types of corporate disclosure, corporate political disclosure (CPD), which is the disclosure of corporate political contributions and the related governing policies and oversight mechanisms, does not provide completely new information to stakeholders. Some of the information disclosed in CPD is available from other public records (e.g. the Federal Election Committee website or OpenSecrets website). Given this unique feature of CPD, it is interesting to investigate the cost and benefit tradeoff for firms of altering their CPD practice in response to policy and political uncertainty.Design/methodology/approachThis study employs recently developed indexes of aggregate economic policy uncertainty (EPU) and a novel dataset of CPD transparency to examine the impact of EPU on CPD transparency and how the proprietary cost of corporate political activities moderates this association. The sample consists of S&P 500 companies from the 2012 to 2019 period.FindingsThe authors document that firms mitigate the heightened information asymmetry associated with higher aggregate EPU by increasing CPD transparency. The positive association between EPU and CPD is less pronounced for firms that are more sensitive to EPU, for firms that more actively manage EPU through corporate political contributions or lobbying activities and for firms that are followed by more analysts. The authors also find that more transparent CPD helps to mitigate the information asymmetry caused by heightened EPU. This study’s results hold when the authors control for other types of voluntary corporate disclosure.Originality/valueThis study contributes to the emerging literature on the determinants of CPD transparency by identifying EPU's positive impact on CPD transparency. This study also provides empirical evidence that the proprietary costs arising from the controversial nature of corporate political activities dampen firms' incentives to provide transparent CPD in response to heightened EPU, and that information on corporate political activities gathered and processed by financial analysts seems to lower the marginal benefit to companies of publicizing CPD on their own website.

Cost and water footprint trade-off in a supply chain optimization model.

Water footprint (WF) is an appropriate tool to help any water-intensive industrial system to adapt to climate change. WF is a metric where the direct and indirect freshwater consumption of a country, firm, activity, or product are quantified. Most of existing WF literature emphasizes the assessment of products, not theoptimal decision making in the supply chain. To address this research gap, a bi-objective optimization model is developed for supplier selection in a supply chain that minimizes costs and WF. Apart from determining the sources of raw materials to use in producing the products, the model also determines the actions to be taken by the firm in case of supply shortages. The model is demonstrated using three illustrative case studies which show that WF embedded in the raw materials can influence the actions to be taken when addressing issues on raw material availability. The WF becomes significant in the decisions in this bi-objective optimization problem when it is given a weight of at least 20% (or the weight of the cost is at most 80%) for case study 1 and at least 50% for case study 2. When the assigned weight in cost reaches the point where WF becomes significant, the increase in the assigned weight inWF has an inverse impact on the total cost. Case study 3 demonstrates the stochastic variant of the model. The online version contains supplementary material available at 10.1007/s10098-023-02549-5.