Cost Trade-offs Research Articles

A Manager and an AI Walk into a Bar: Does ChatGPT Make Biased Decisions Like We Do?

Problem definition: Large language models (LLMs) are being increasingly leveraged in business and consumer decision-making processes. Because LLMs learn from human data and feedback, which can be biased, determining whether LLMs exhibit human-like behavioral decision biases (e.g., neglect, risk aversion, confirmation bias, etc.) is crucial prior to implementing LLMs into decision-making contexts and workflows. To understand this, we examine 18 common human biases that are important in operations management (OM) using the dominant LLM, ChatGPT. Methodology/results: We perform experiments where GPT-3.5 and GPT-4 act as participants to test these biases using vignettes adapted from the literature (“standard context”) and variants reframed in inventory and general OM contexts. In almost half of the experiments, Generative Pre-trained Transformer (GPT) mirrors human biases, diverging from prototypical human responses in the remaining experiments. We also observe that GPT models have a notable level of consistency between the standard and OM-specific experiments as well as across temporal versions of the GPT-3.5 model. Our comparative analysis between GPT-3.5 and GPT-4 reveals a dual-edged progression of GPT’s decision making, wherein GPT-4 advances in decision-making accuracy for problems with well-defined mathematical solutions while simultaneously displaying increased behavioral biases for preference-based problems. Managerial implications: First, our results highlight that managers will obtain the greatest benefits from deploying GPT to workflows leveraging established formulas. Second, that GPT displayed a high level of response consistency across the standard, inventory, and non-inventory operational contexts provides optimism that LLMs can offer reliable support even when details of the decision and problem contexts change. Third, although selecting between models, like GPT-3.5 and GPT-4, represents a trade-off in cost and performance, our results suggest that managers should invest in higher-performing models, particularly for solving problems with objective solutions. Funding: This work was supported by the Social Sciences and Humanities Research Council of Canada [Grant SSHRC 430-2019-00505]. The authors also gratefully acknowledge the Smith School of Business at Queen’s University for providing funding to support Y. Chen’s postdoctoral appointment. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0279 .

A catalogue of land-based adaptation and mitigation solutions to tackle climate change

Informing and engaging all actors in the land sector, including land-owners and managers, researchers, policy-makers and citizens, on the most effective sustainable land-based solutions and behavioural changes is a key strategy for achieving climate change adaptation and mitigation targets at the global as well as at EU and local level. One requisite to support actors in the land sector is to provide them publicly available, reliable and ready-to-use information related to the implementation of Land-based Adaptation and Mitigation Solutions (LAMS). Here we introduce a LAMS catalogue, a collection of meaningful quantitative and qualitative information on 60 solutions characterised according to a set of specifications (e.g., mitigation and adaptation potential, cost of implementation, suitability factors, synergies and trade-offs, drivers and barriers to the implementation). The catalogue offers a reliable, science-based tool useful for different users’ needs, including valuable references for deriving context-specific quantitative inputs to simulate and evaluate the performance of solutions over time using modelling tools, such as Integrated Assessment Models at any scale.

Giant Pacific octopus optimizer based on fitness-distance balance and natural survival method to solve time-cost-quality-labor trade-off problem in construction projects

ABSTRACT To successfully arrange, execute, monitor, and complete a project, a project manager must play a key role in project management. The utilization of technology and artificial intelligence in the engineering industry has grown absolutely essential and guarantees that each nation’s socioeconomic development needs are accomplished. This study proposes the Giant Pacific Octopus Optimizer Based on Fitness-Distance Balance and Natural Survivor Method to tackle the challenge of optimizing the optimal combination of time, cost, quality, and labor tradeoff in the construction industry. The Natural Survivor Method and Fitness Distance Balance selection techniques, along with the Tournament Selection technique, are used in this research to redesign and further enhance the mechanism of the Giant Pacific Octopus Optimizer, which enhances the convergence time by rapidly transitioning from random selection to best candidate selection. The results of Giant Pacific Octopus Optimizer Based on Fitness-Distance Balance and Natural Survivor Method were compared with many well-known, comparable algorithms to demonstrate the viability and effectiveness of the proposed model through case study of construction management, Friedman test, Wilcoxon Signed Rank test, Indicators evaluation and Stability analysis. Based on the results obtained, a robust model that is superior to the evaluated models was developed in this research.

Load-Shifting Strategies for Cost-Effective Emission Reductions at Wastewater Facilities.

Significant hourly variation in the carbon intensity of electricity supplied to wastewater facilities introduces an opportunity to lower emissions by shifting the timing of their energy demand. This shift could be accomplished by storing wastewater, biogas from sludge digestion, or electricity from on-site biogas generation. However, the life cycle emissions and cost implications of these options are not clear. We present a multiobjective optimization framework for comparing cost- and emission-minimizing load-shifting strategies at a California case study facility with a relatively low carbon intensity grid and high spread in peak and off-peak electricity prices. We evaluate cost and emission trade-offs from the optimal flexible operation of both existing infrastructure and optimally sized energy flexibility upgrades. We estimate energy-related emission reductions of up to 9.0% with flexible operation of existing infrastructure and up to 16.8% with optimally sized storage upgrades. Only a fraction of these potential savings are realized under actual industrial energy tariffs and the EPA's recommended social cost of carbon. Energy flexibility may hold promise as a short-term emission-saving solution for the wastewater sector, but the extent of savings is heavily dependent on the cost of carbon, electricity tariffs, and emission intensity of the regional electricity grid.

Optimizing Project Duration and Cost of Support Ship through Time-Cost Trade-Off Analysis and Microsoft Project Application: A Case Study at PT XYZ

The construction of a ship project requires time control to avoid delays because late fines will be imposed. This control can be carried out using project management control tools, namely project scheduling. This research aims to optimize project duration and costs by finding the critical path using the help of Microsoft Project Software and accelerating to avoid project delays using the time cost trade off method. The data collection method was carried out by interviews and direct observation at the project location and obtained through PT XYZ internal parties. The research results showed that there was a delay of 12 days on the Support Ship construction project with 10 critical activities on the production floor. Alternative acceleration is done by adding working hours (overtime) by 2 hours, 3 hours and 4 hours. After calculating the acceleration for critical activities that have the lowest cost slope values, The optimal duration and costs were obtained by adding 3 hours of overtime so that the processing time could be accelerated from 236 days to 228 days with a total acceleration cost of IDR 53,295,000.

Updates on Methods for Body Composition Analysis: Implications for Clinical Practice.

Recent technological advances have introduced novel methods for measuring body composition, each with unique benefits and limitations. The choice of method often depends on the trade-offs between accuracy, cost, participant burden, and the ability to measure specific body composition compartments. To review the considerations of cost, accuracy, portability, and participant burden in reference and emerging body composition assessment methods, and to evaluate their clinical applicability. A narrative review was conducted comparing traditional reference methods like dual-energy X-ray absorptiometry (DXA), magnetic resonance imaging (MRI), and computed tomography (CT) with emerging technologies such as smartphone camera applications, three-dimensional optical imaging scanners, smartwatch bioelectric impedance analysis (BIA), and ultrasound. Reference methods like CT and MRI offer high accuracy and the ability to distinguish between specific body composition compartments (e.g., visceral, subcutaneous, skeletal muscle mass, and adipose tissue within lean mass) but are expensive and non-portable. Conversely, emerging methods, such as smartwatch BIA and smartphone-based technologies, provide greater accessibility and lower participant burden but with reduced accuracy. Methods like three-dimensional optical imaging scanners balance portability and accuracy, presenting promising potential for population-level applications. The selection of a body composition assessment method should be guided by the clinical context and specific application, considering trade-offs in cost, accuracy, and portability. Emerging methods provide valuable options for population-level assessments, while reference methods remain essential for detailed compartmental analysis.

Analysis of Ultrasonic Focusing in Silicon Wafers using the Angular Spectrum Approach and Ray Tracing

Understanding ultrasonic beam focusing in anisotropic media is crucial for optimizing nondestructive evaluation techniques, as many advanced materials exhibit anisotropic properties. This study investigates the focusing behavior of ultrasonic beams in silicon, a representative anisotropic material, across three crystallographic orientations (0°, 22.5°, and 45° offsets from the [1 0 0] axis) using the angular spectrum approach (ASA) and ray tracing to understand computational cost trade-offs between the two models while elucidating new focusing behaviors and profiles. The ASA model is used to construct three-dimensional wave field visualizations and localize focal depths. Ray tracing is then employed to map the focal profiles at the ASA-identified depths. The results demonstrate that focal behavior geometry and depth vary as the anisotropic orientation changes, with foci splitting for the 45° orientation and skewed focusing for the 22.5° orientation. For high-frequency approximations, the ASA method may effectively identify focal depths that can serve as focal planes for ray tracing, potentially reducing computational expense. This approach showed alignment in the present study for 25 MHz, though its validity depends on the material properties under consideration. These findings can be useful in devising alternative experimental configurations that optimize focusing within anisotropic solids.

Application of Time Cost Trade Off Method in Optimizing Time and Cost on Ship Refurbishment Projects KMP Dharma Rucitra 1 and MT Triaksa 17

Application of Time Cost Trade Off Method in Optimizing Time and Cost on Ship Refurbishment Projects KMP Dharma Rucitra 1 and MT Triaksa 17

Enhanced voltage conversion and reduced inductor size in a flying capacitor boost converter compared to conventional boost converter for photovoltaic systems

In photovoltaic (PV) systems, conventional boost converters (CBCs) face limitations at high-duty cycles, including low voltage conversion ratio (VCR), high voltage stress, and reduced efficiency. While magnetic coupling components can enhance VCR, they introduce drawbacks such as reduced power rating and leakage current. To overcome these challenges, this paper proposes the flying capacitor boost converter (FCBC) as an alternative for PV systems. We analyze the performance of CBCs, FCBCs, and multilevel boost converters through MATLAB/SIMULINK simulations. The results demonstrate that the FCBC achieves a significantly higher VCR, requiring a smaller inductor size and potentially exhibiting a smoother output voltage compared to the CBC. Furthermore, the paper explores the trade-offs in complexity and cost associated with the FCBC, highlighting its advantages for high-voltage applications. Future research will focus on a comprehensive analysis of efficiency, control complexity, and the scaling of FCBCs to higher levels.

Multi-objective optimisation for urban road network maintenance based on spatial design network analysis: trade-offs in performance, environmental impacts and costs

ABSTRACT Pavement maintenance decision-making needs to account for multiple conflicting strategic objectives and the complex interdependencies among heterogeneous road sections within road networks. Using spatial design network analysis which allows to capture the complex interdependencies among interconnected road sections, a multi-objective optimisation model is developed for urban road network maintenance. This model integrates multi-dimensional objectives: maximising road network condition, minimising environmental impacts, and minimising agency costs. The model, which uses the non-dominated sorting genetic algorithm II to approximate the Pareto front, is implemented in a real-world urban road network in Shanghai, China. The results show that the trade-offs among the three objectives are more intricate than those found in prior research using traditional decision-making models, which often overlooked the complex interdependencies among heterogeneous road sections. Sensitivity analyses indicate prominently non-linear relationships among objectives: marginal changes in road network condition and environmental impacts fluctuate with different agency costs. As an exploratory effort to develop a network-level multi-objective optimisation model for urban road maintenance through modelling complex interconnections among heterogeneous road sections, this study contributes to a deepened understanding of the complex non-linear associations among performance-environment-cost objectives in pavement maintenance decision-making and offers a network-based approach to perform trade-offs in these conflicting objectives.

Navigating the Golden Triangle: The Need to Jointly Consider Modularization and Interface Choices When Making Performance, Cost, and Schedule Tradeoffs for Complex System Development

ABSTRACTDecomposition is a critical enabler of complex system development, as it enables both task specialization and efficiency through parallel work. The process of decomposing involves partitioning system parameters into tightly coupled modules and managing any cross‐module coupling by designing passive interfaces or through active coordination. A rich literature has developed algorithms and tools to support this process. However, we contend that this view has placed too much emphasis on module selection, and not enough on the interaction with interface design. This perspective has significant implications for lifecycle costs and development time. To that end, this study explores how earlier consideration of interface design can create more valuable options to better navigate performance, cost, and schedule tradeoffs. Specifically, through an abstract simulation experiment, we demonstrate that (1) a sequential approach that first selects modules and then designs interfaces to support those modules, yields lower performance than an integrated approach that considers modules and supporting interfaces simultaneously; and (2) this result is even stronger when schedule and cost are considered as part of the evaluation. In other words, an integrated approach provides more options for project managers seeking to navigate the performance‐cost‐schedule tradeoff known as the golden triangle. These results emphasize the need for a decomposition aid that adopts a holistic view of the optimization problem, accounting for interface creation, intra‐organization collaboration, and valuing nonperformance measures of effectiveness.

Evaluating time-cost trade-off in a resource constraint GERT-type project scheduling problem considering rework

In practice, rework is a major threat to projects’ performance and increases the risk of not delivering the outcomes on time and budget. The classical project scheduling methods are incapable of considering the rework and stochastic structure for projects in the planning phase. The current study models the stochastic projects through a GERT network in which the occurrence of rework is allowed. The aim is to find the optimal execution mode for each activity so that the project is completed within the desired time and the resource constraint is satisfied. We develop a multi-mode time–cost trade-off resource constraint project scheduling problem under uncertainty to find a here-and-now solution to minimise project completion time. The problem is solved using a combination of flow-graph theory and a branch and bound method for the developed integer programming model. As the project structure is stochastic, a distinct set of all possible realised paths is considered. To the best of our knowledge, there is no similar test instance in the literature, hence, the solution methodology is implemented on a randomly generated numerical example. The reported computational results exhibit the sensitivity of objective function to the changes in available resource amount.

End-point RPA-CRISPR/Cas12a-based detection of Enterocytozoon bieneusi nucleic acid: rapid, sensitive and specific

Enterocytozoon bieneusi is a common species of microsporidia that infects humans and animals. Current methods for detecting E. bieneusi infections have trade-offs in sensitivity, specificity, simplicity, cost and speed and are thus unacceptable for clinical application. We tested the effectiveness of a previously reported CRISPR/Cas12a-based method (ReCTC) when used for the nucleic acid detection of E. bieneusi. The limit of detection (LOD) and the specificity of the expanded ReCTC were evaluated using prepared target DNA, and the accuracy of the ReCTC-based detection of E. bieneusi in clinical samples was validated. The ReCTC method was successfully used for the nucleic acid detection of E. bieneusi. The sensitivity test indicated an LOD of 3.7 copies/µl for the ReCTC-based fluorescence and lateral flow strip methods. In specificity test involving other common enteric pathogens, a fluorescent signal and/or test line appeared only when the sample was positive for E. bieneusi. These results demonstrated that the ReCTC method can successfully detect E. bieneusi in clinical samples. The ReCTC method was successfully used to detect E. bieneusi nucleic acid with high sensitivity and specificity. It had excellent performance in clinical DNA samples and was superior to nested polymerase chain reaction. Furthermore, the ReCTC method demonstrated its capability for use in on-site detection.

Partially factorized variational inference for high-dimensional mixed models

Abstract While generalized linear mixed models are a fundamental tool in applied statistics, many specifications, such as those involving categorical factors with many levels or interaction terms, can be computationally challenging to estimate due to the need to compute or approximate high-dimensional integrals. Variational inference is a popular way to perform such computations, especially in the Bayesian context. However, naive use of such methods can provide unreliable uncertainty quantification. We show that this is indeed the case for mixed models, proving that standard mean-field variational inference dramatically underestimates posterior uncertainty in high-dimensions. We then show how appropriately relaxing the mean-field assumption leads to methods whose uncertainty quantification does not deteriorate in high-dimensions, and whose total computational cost scales linearly with the number of parameters and observations. Our theoretical and numerical results focus on mixed models with Gaussian or binomial likelihoods, and rely on connections to random graph theory to obtain sharp high-dimensional asymptotic analysis. We also provide generic results, which are of independent interest, relating the accuracy of variational inference to the convergence rate of the corresponding coordinate ascent algorithm that is used to find it. Our proposed methodology is implemented in the R package vglmer. Numerical results with simulated and real data examples illustrate the favourable computation cost versus accuracy trade-off of our approach compared to various alternatives.

An arithmetic optimization algorithm based on opposition jumping rate for time cost trade-off optimization problems

An arithmetic optimization algorithm based on opposition jumping rate for time cost trade-off optimization problems

UPR deficiency in budding yeast reveals a trade-off between ER folding capacity and maintenance of euploidy.

The Unfolded Protein Response (UPR) was discovered in budding yeast as a mechanism that allows cells to adapt to ER stress. While the Ire1 branch of this pathway is highly conserved, it is not thought to be important for cellular homeostasis in the absence of stress. Surprisingly, we found that removal of UPR activity led to pervasive aneuploidy in budding yeast cells, suggesting selective pressure resulting from UPR-deficiency. Aneuploid UPR-deficient cells grew better than euploid cells, but exhibited heightened general proteostatic stress, a hallmark of aneuploidy in wild-type cells. Modulation of key genes involved in ER proteostasis that were encoded on aneuploid chromosomes, could phenocopy the effects of aneuploidy, indicating that the reason cells require UPR activity to maintain euploidy is to counteract protein folding stress in the ER. In support of this model, aneuploidy in UPR-deficient cells can be prevented by expression of a UPR-independent general ER chaperone. Overall, our results indicate an unexpected role for the UPR in basal cell growth that is sufficiently important for cells to accept the costly trade-off of aneuploidy in the absence of UPR activity.

Modeling the Impact of Additional Electrolyte on Lithium-Ion Convection Cell Performance

Addressing growing energy demands while balancing sustainability imperatives, environmental stewardship, and economic viability stands as a paramount global challenge. Electrochemical technologies, particularly lithium-ion batteries (LIBs), are pivotal in achieving deep decarbonization by facilitating the deployment of electric vehicles and ensuring the reliable delivery of renewable electricity. However, current LIBs fall short of the rigorous requirements for widespread adoption in these critical sectors, as well as in emerging areas such as long-haul trucking and electric vertical takeoff and landing vehicles (eVTOLs).1,2 Breakthroughs in the science and engineering of LIBs are imperative to produce devices capable of delivering high performance without sacrificing safety and cost.In addition to traditional materials-focused approaches, the development of new battery cell architectures, including the integration of electrolyte flow, presents a promising avenue for advancing performance. Prior mathematical modeling has shown forced electrolyte convection can significantly improve heat and mass transfer within LIBs, enabling operation under conditions that challenge conventional formats (e.g., high-rate charge/discharge).3,4 However, to exclude convoluting secondary effects, these studies simplify balance-of-plant systems which, in turn, may not fully describe practical operation. Specifically, these simulations use large electrolyte tanks which buffer against bulk concentration and temperature changes, aiding in the fundamental analyses. Notably, scaling under these conditions could potentially lead to cost-prohibitive and thus impractical systems.5 In this presentation, we investigate the impact of additional electrolyte volume on the complex interplay between electrochemical performance enhancements, fluid dynamic losses, and economic incentives in a convection cell. Through physics-based electrochemical modeling, we demonstrate that electrolyte convection alone can alleviate mass transport limitations even under extreme discharge rates, obviating the need for added electrolyte. In contrast, we find that extra electrolyte is necessary as a heat sink to mitigate cell temperature rise. We extend these results to practical operating conditions, where we conduct a preliminary system-level energy analysis to evaluate the energetic penalties associated with scaling this new architecture. Our findings suggest there exists a narrow added electrolyte range that effectively reduces thermal transport limitations without significantly increasing the system weight and volume. We also construct an accompanying technoeconomic framework to estimate the associated cost tradeoffs, illustrating the disparity between energy-optimized and cost-optimized designs. Ultimately, this work represents an initial exploration into practical design and operation considerations for convection batteries, laying the groundwork for future application-specific investigations.

PERBANDINGAN BIAYA DAN WAKTU ANTARA METODE KONVENSIONAL DENGAN METODE MODULAR MENGGUNAKAN MICROSOFT PROJECT

<p><em>Indonesia's construction sector is experiencing rapid growth, this proven by numerous development projects, including a sub-district building construction project, aiming to optimize community services. </em><em>A project must have three aspects, namely cost, quality and time. Choosing an implementation method is very important </em><em>so that a project can achieve these three goals.</em><em> </em><em>The research method used a quantitative descriptive approach with the aim of comparing cost and time aspects between conventional methods and modular methods. The conventional approach involves working on site using conventional building materials, while the modular approach involves the use of pre-fabricated modules. In analyzing the costs and time of construction of the Grabag Sub-district office building, Purworejo Regency, Microsoft project application was used to calculate the duration of the work. The cost analysis show that building structure work using the conventional method costs Rp.3,086,929,413.11, while the modular method costs Rp.3,646,018,620.52. So the difference in the cost of construction work is Rp.559,089,207.42 or 18.11% higher for work using the modular method. The time analysis obtained building structure work using conventional methods require 150 days while the modular method is 87 days or time efficiency of 63 days or 42% lower for work with modular methods. The results of the Time Cost Trade Off (TCTO) calculation showed that the total project cost for conventional construction was Rp.2,781,017,489 while the cost for modular construction was Rp.3,117,530,328. So a difference of Rp.336,512,839 is higher for modular construction. Therefore, conventional construction is the most economical option for the sub-district building.</em></p>

A new S-box pattern generation based on chaotic enhanced logistic map: case of 5-bit S-box

Cryptography plays consistently an essential role in securing any sort of communications or data broadcast over the network. Since the security of any designed block cipher algorithm is related to its Substitution box (S-box), several efforts have been made by researchers to design a vigorous S-box that maintains flawlessly the cost, performance, and security trade-off. From the literature, we can find a variety of input–output sizes of S-boxes, each of which has its benefits and drawbacks. Therefore, this work introduces a new S-box pattern generation based on the chaotic enhanced logistic map where its chaotic behavior offers good randomness ability, a fact that enhances its unpredictability. In our realization, the intention was the generation of a 5-bit Sbox due to its suitability and cost-effectiveness to be integrated into lightweight cryptosystems. Moreover, the S-box security strength is proved by testing it through numerous cryptanalysis measurements. We can mention non-linearity, bijectivity, linearity, differential cryptanalysis, boomerang attacks resilience, Avalanche effect, and algebraic attacks resilience. The results show that our proposition provides good resistance to the aforementioned attacks and even shows superiority over its 5-bit competitors in terms of non-linearity, differential, Boomerang, and algebraic attack resistance.

Analysis of energy, water, land and cost implications of zero and minimal liquid discharge desalination technologies

Desalination is increasingly essential to ensure access to water as climate change and population growth stress fresh water supplies. Already in use in water-stressed regions around the world, desalination generates fresh water from salty sources, and in doing so forms a concentrated brine that requires disposal. There is a growing push for the adoption of zero/minimal liquid discharge (ZLD/MLD) technologies that recover additional water from this brine, thereby reducing the liquid volumes requiring disposal. In this analysis, we evaluated the cost, energy and sustainability impacts of 7 overarching treatment trains with 75 different configurations. We found ZLD/MLD water recoveries ranging from 32.6% to 98.6%, but with steep energy and cost trade-offs that underscore the crucial roles of ion-specific separations, heat integration and clean energy sources. We explored the key trade-offs between cost, energy and water recovery, elucidating the increasingly tight connections that are central to the energy–water nexus and desalination.