Adjustment Costs Research Articles

Terahertz quasi-optics imaging systems with easy zoom based on beam-shaping devices and a freeform lens.

Terahertz (THz) waves show outstanding application value in the nondestructive detection of hidden targets that are impenetrable to visible light. However, the uncertain location of hidden targets puts a higher demand on the zoom function of the THz quasi-optical systems, especially when the object is immovable. This paper proposes a continuous zoom system working in the THz band consisting of a negative axicon disk, a segmented axicon, and a freeform lens. The negative axicon disk and segmented axicon shape THz waves into annular beams with adjustable diameters, which are respectively focused at different positions by the zone-designed freeform lens. Both simulation and experimental results indicate that the zoom range of the system can reach 82 mm while maintaining an imaging resolution of 6 mm. Notably, continuous zooming is achieved by merely rotating the negative axicon disk, instead of the lens moving. Moreover, all devices in the system can be fabricated by 3D printing or machining. This approach offers the advantages of simple adjustment and low cost, providing, to our knowledge, a novel perspective for the design and application of THz quasi-optical imaging systems.

BA-CLM: A Globally Consistent 3D LiDAR Mapping Based on Bundle Adjustment Cost Factors.

Constructing a globally consistent high-precision map is essential for the application of mobile robots. Existing optimization-based mapping methods typically constrain robot states in pose space during the graph optimization process, without directly optimizing the structure of the scene, thereby causing the map to be inconsistent. To address the above issues, this paper presents a three-dimensional (3D) LiDAR mapping framework (i.e., BA-CLM) based on LiDAR bundle adjustment (LBA) cost factors. We propose a multivariate LBA cost factor, which is built from a multi-resolution voxel map, to uniformly constrain the robot poses within a submap. The framework proposed in this paper applies the LBA cost factors for both local and global map optimization. Experimental results on several public 3D LiDAR datasets and a self-collected 32-line LiDAR dataset demonstrate that the proposed method achieves accurate trajectory estimation and consistent mapping.

Multi-Stage Optimal Power Control Method for Distribution Network with Photovoltaic and Energy Storage Considering Grouping Cooperation

In view of the current problem of insufficient consideration being taken of the effect of voltage control and the adjustment cost in the voltage control strategy of distribution networks containing photovoltaic (PV) and energy storage (ES), a multi-stage optimization control method considering grouping collaboration is proposed. Firstly, the mechanism by which the access of the PV and ES to the distribution network impacts the node voltage is explored. Then, the unit regulation cost of a photovoltaic inverter and energy storage power is studied. On this basis, the voltage–cost sensitivity is proposed based on the traditional node power–node voltage sensitivity. According to the differences between each group of regulation resources, a multi-stage voltage over-run control strategy based on grouping cooperation is proposed. Finally, the improved 33-node model is taken as an example to verify the correctness and effectiveness of the proposed method.

Increase or decrease: Customer digital transformation and supplier cost stickiness

Customer digital transformation plays a crucial role in shaping supply chain management, particularly affecting the cost stickiness of supplier firms. By analyzing textual data from the top five customers of Chinese listed firms, this study investigates the impact of customer digital transformation on supplier cost stickiness. The findings demonstrate that the spillover effects of customer digital transformation substantially decrease supplier cost stickiness. This decrease is due to decreased adjustment costs, alleviated agency problems, and mitigated management optimism. Additionally, cross-sectional analysis reveals that these effects are more pronounced in suppliers with greater geographic distances from their customers, those within less competitive industries, and those audited by Non-Big Eight accounting firms. This research provides valuable insights for digital initiatives, supply chain management, and cost-related decision-making processes.

The trust incentive mechanism by trust propagation to optimize consensus in social network group decision making

Trust relationship in social network group decision making (SN-GDM) is one of the key factors to promote group consensus. This trust relationship reflects the importance of experts, and the consensus feedback opinion induced by the trust relationship is easier to be accepted by the group. But this trust relationship is often assumed to be static, and therefore the dynamic effect of incentive behavior among experts on trust relationship is not considered. To do that, the purpose of this paper is to develop a trust incentive mechanism to effectively promote group consensus. First, the new discount based Uninorm trust propagation operator with 2-tuple linguistic is presented, which transforms an incomplete trust relationship into a complete trust relationship. And then, the trust incentive mechanism with opinions similarity between experts is constructed, which reflects not only the incentive process among experts, but also the dynamic change of the social trust relationship. Further, the trust incentive mechanism driven consensus optimization model is established to generate experts’ recommendation advices, which not only effectively reduce the total adjustment cost of experts to promote consensus, but also makes the experts continue to motivate the discussion after revising their opinions, leading to dynamic changes in the social trust relationship in the next consensus round. Finally, an example of agricultural UAV brand selection in “Metaverse” intelligent shopping is provided to illustrate the proposed method, and the results show that with the increase of incentive factor, the total adjustment cost decreases, and the best UAV brand also changes alternately and tends to be stable. In addition, comparing with the method without trust incentive, the interaction cost of the proposed method is 13.46% lower, and then our proposed method has certain flexibility and advantages.

Creating employment and reducing emissions: Options for South Africa

ABSTRACT South Africa faces huge challenges of poverty and unemployment, yet at the same time, needs to make a contribution to climate action. Historically, our pattern of development gave rise to the ‘minerals-energy complex’, locking us into low employment and high emissions development. It has proved difficult to change this structure, with political economy interests opposing shifts and high adjustment costs. We analyse policy instruments that can reshape the development path: reducing incentives to capital-intensive and high emissions heavy industry, ending direct and indirect support for cheap electricity, and removing fossil fuel subsidies. Industrial policy should pay attention to creating comparative advantage in more labour-demanding sectors. Agriculture can create employment, while enhancing carbon sinks. Based on our exploration of such policy instruments, we suggest that future development can align employment and mitigation objectives, seeking synergies across industrial, energy, and climate policy, while at the same time managing trade-offs.

Conditional Lead-Time Flexibility in an Assemble-to-Order System

Problem definition: We introduce and formalize a concept termed conditional lead-time flexibility (CLF), which refers to the industry practice where a manufacturer requests that its upstream suppliers dynamically adjust the pipeline orders’ remaining lead times. Over a finite horizon, an assemble-to-order manufacturer makes joint decisions on inventory replenishment and lead-time adjustment to minimize the total discounted expected cost. Methodology/results: The problem is formulated as a multiperiod dynamic programming with an enlarged state space to track the adjustable pipeline orders. The replenishment and lead-time adjustment decisions are made sequentially in each period. The analysis reveals that (i) the optimal cost-to-go function is both convex and additively separable, that (ii) both the optimal replenishment and lead-time adjustment decisions follow general base-stock policies, and that (iii) the convexity and additive separability allow us to perform a derivative analysis on the optimality equations and design an efficient algorithm. Managerial implications: Using real industry data, we numerically find that (i) CLF achieves notable cost savings over a wide range of parameters, that (ii) the value of CLF comes from both the order expediting and deferring, and that (iii) CLF outperforms dual sourcing when the unit adjustment cost is less than 40% of the unit ordering cost from the express source. Our findings underscore the importance of CLF. Funding: T. Deng acknowledges financial support from the National Key Research and Development Program of China [Grant 2022YFB3305602] and the National Natural Science Foundation of China (NSFC) [Grant 72188101]. Supplemental Material: The electronic companion is available at https://doi.org/10.1287/msom.2023.0520 .

Can technological progress abate the negative impact of low carbon policies on economic growth in China?

ABSTRACT Whether technological progress can provide a complementary solution that reduces the economic costs of carbon pricing policies, thereby accomplishing a win-win situation for carbon abatement and economic growth? By developing an improved multi-regional dynamic CGE model that integrates investment adjustment costs into the capital accumulation equation, this study provides a lens through which to evaluate the collective impacts of the national carbon market, carbon tax, and technology progress in China. The findings confirm the substantial potential of carbon pricing policies to curtail CO 2 emissions, highlighting their critical role in climate change mitigation. Yet, it also exposes a trade-off, where these policies might impact GDP growth and exports, necessitating a balanced approach to policy design. The outputs of energy sectors are particularly vulnerable to carbon pricing policies, indicating a need for tailored strategies to support these industries in their transition towards sustainability. The study uncovers that the fusion of carbon pricing policies with technological progress can enhance carbon abatement efforts while softening the economic blow, illuminating a path to sustainable development that balances environmental and economic imperatives.

Minimum cost consensus model with variable cost for multi-criteria large-scale group decision-making considering the tolerance of decision-makers

The objective of multicriteria large-scale group decision-making (LSGDM) is to obtain a decision result that gains acceptance from the majority of decision-makers (DMs). The consensus reaching process (CRP) plays a crucial role in guiding DMs to continuously adjust their opinions through feedback adjustment mechanisms to form a widely accepted decision result. In practical decision-making, adjusting opinions incurs costs and involves limited resources. Simultaneously, DMs have tolerance for modifying their opinions. To this end, this paper proposes a minimum cost consensus model (MCCM) with variable cost to reach consensus efficiently within limited resources and DMs’ tolerance. Firstly, a method to measure the combined similarity among DMs is proposed. Then, an extended local fitness maximization (ELFM) algorithm suitable for multicriteria LSGDM problems is proposed for clustering. Furthermore, an objective method of determining the unit adjustment costs for DMs in the MCCM is provided. For the CRP that considers the tolerance of DMs, this paper introduces the MCCM with variable cost to obtain the adjustment opinions of DMs. During the selection process, we utilize the stochastic multicriteria acceptability analysis (SMAA) method to evaluate alternatives and selection. Finally, the feasibility of the proposed method is verified through an illustrative example, and its advantages are emphasized through comparative analysis.

Geographic industry concentration and asymmetric cost behavior

AbstractThis research diverges from the existing literature on cost stickiness by examining the relationship between a firm's geographic proximity to industry peers, termed industry co‐location, and its asymmetrical cost behavior. We propose that managerial decisions regarding resource adjustment are influenced by the state of resource markets in the firm's vicinity, hypothesizing that a high concentration of industry peers triggers downward asset price spirals during simultaneous resource liquidation. Additionally, we anticipate that industry‐wide demand exceeding fixed capacity exacerbates congestion costs, thereby diminishing bargaining power in local resource markets. We predict that heightened resource adjustment costs linked to industry co‐location discourage managers from reducing capacity commitments, thereby increasing cost stickiness. Our findings support this prediction, demonstrating a positive association between industry co‐location and cost stickiness. Moreover, we observe that cost stickiness intensifies when closely located industry peers experience declines in sales. This paper provides initial evidence illuminating the impact of industry co‐location on cost stickiness and suggests managerial implications to prevent costlier capacity adjustment through local resource markets.

Optimizing integrated berth allocation and quay crane assignment: A distributionally robust approach

In this research, we have formulated a Two-Stage Distributionally Robust Optimization (TDRO) model within the context of a mean–variance ambiguity set, specifically designed to address the challenges in the Integrated Berth Allocation and Quay Crane Assignment Problem (BACAP). A key consideration in this study is the inherent uncertainty associated with ships’ arrival times. During the initial stage, we derive a baseline schedule governing berth allocation and quay crane assignment. Anticipating potential disruptions arising from uncertain arrival delays, the second stage is meticulously formulated to determine the worst-case expectation of adjustment costs within the mean–variance ambiguity set. Subsequently, we undertake an equivalent transformation, converting the general TDRO model into a Two-Stage Robust Second-Order Cone Programming (TRO-SOCP) model. This transformation facilitates the application of the Column and Constraint Generation (C&CG) algorithm, ensuring the derivation of an exact solution. To address the computational intricacies associated with second-order cone programming, we propose two enhancement strategies for upper and lower bounds, aimed at expediting the solution process. Additionally, to contend with large-scale instances, we introduce a refinement and approximation method, transforming the TDRO model into a Mixed-Integer Programming (MIP) model. Furthermore, extensive numerical experiments are executed on both synthetic and real-life instances to validate the superior performance of our model and algorithms. In terms of the total cost, the TDRO model demonstrates superior performance compared with Two-Stage Stochastic Programming (TSP) and Two-Stage Robust Optimization (TRO) models.

Environmental sustainability under production competition with costly adjustments: An appraisal of firms’ behavior with regard to pollution dynamics

The waning efficiency of natural pollution sinks and the predicted consequence of their irreversible transformation into pollution sources within the current century call into question the environmental sustainability of economic activity and, more specifically, its basic engine: horizontal competition. Among factors likely to cast doubt on the environmental sustainability of competition, competing firms’ approaches to pollution control deserve particular attention because myopic approaches that ignore pollution dynamics accelerate such transformations. Another potentially harmful factor is the cost of adjusting production, which can hinder firms’ ability to reduce their polluting emissions in a timely way. In the context of a Cournot market structure, we consider competing firms that incur a cost when adjusting production and are subject to pollution emission taxes. We analyze how production adjustment costs and firms’ choice of myopic or farsighted pollution control policies affect their competitiveness and the long-run sustainability of the environmental system. We show that competition is not compatible with environmental sustainability under a myopic pollution control policy when the maximum threshold of pollution absorption has already been exceeded. Myopic policies cannot neutralize history dependency, and extremely large production adjustment costs exacerbate the problem. In contrast, farsighted pollution control policies can eliminate history-dependency as long as the tax policy is not too restrictive, though they lead to a more polluted environment than myopic policies operating below the maximum absorption efficiency threshold.

Caballero–Engel meet Lasry–Lions: A uniqueness result

In a Mean Field Game (MFG) each decision maker cares about the cross sectional distribution of the state and the dynamics of the distribution is generated by the agents’ optimal decisions. We prove the uniqueness of the equilibrium in a class of MFG where the decision maker controls the state at optimally chosen times. This setup accommodates several problems featuring non-convex adjustment costs, and complements the well known drift-control case studied by Lasry–Lions. Examples of such problems are described by Caballero and Engel in several papers, which introduce the concept of the generalized hazard function of adjustment. We extend the analysis to a general “impulse control problem” by introducing the concept of the “Impulse Hamiltonian”. Under the monotonicity assumption (a form of strategic substitutability), we establish the uniqueness of equilibrium. In this context, the Impulse Hamiltonian and its derivative play a similar role to the classical Hamiltonian that arises in the drift-control case.

Adsorption of a Multicomponent Pharmaceutical Wastewater on Charcoal-Based Activated Carbon: Equilibrium and Kinetics

The treatment of pharmaceutical wastewater is a critical environmental challenge, necessitating efficient removal methods. This study investigates the adsorption of a synthetic multicomponent pharmaceutical wastewater (SPWW) containing methanol, benzene, methylene chloride, 4-aminophenol, aniline, and sulfanilic acid onto charcoal-based activated carbon (AC). Batch experiments were conducted to study the effects of pH, contact time, and initial concentrations of the adsorbates. The results show that longer contact time and higher initial concentrations increase the adsorption capacity, whereas pH shows no significant effect on the adsorption capacity at a value of less than 10, eliminating the need for pH adjustment and reducing process costs. The pseudo-second order (PSO) kinetic model best describes the adsorption process, with intraparticle diffusion playing a key role, as confirmed by the Weber and Morris (W-M) model. Six models describing the adsorption at equilibrium are applied to experimental data, and their parameters are estimated with a nonlinear regression model. Among isotherm models, the Langmuir-Freundlich model provides the best fit, suggesting multilayer adsorption on a heterogeneous granular activated carbon (GAC) surface. The maximum adsorption capacity is estimated to be 522.3 mgC/gAC. Experimental results confirm that GAC could effectively treat highly concentrated pharmaceutical wastewater, achieving up to 52% removal efficiency.

Simultaneous soot parameters fields predictions accuracy improvements in laminar sooting flames from soot radiation measurements — a multi-task learning approach

Machine learning assisted optical diagnostics could reduce conventional extensive experimental data collecting and post-processing costs and time in academic and industrial combustion measurements. In this paper, a novel Trident-Net (T-Net) architecture is designed and assisted for retrieval of high-fidelity soot temperature, volume fraction (SVF), and diameter fields simultaneously from soot radiation measurements in laminar sooting flames. Uniquely, the T-net is subtly fabricated in one branch of the encoder and three branches of decoders, which enables three adjustable cost functions and the corresponding decoder branch for soot three respective parameter fields. Contrasted with previous Back-propagation (BP) and U-net models, the T-Net is more efficient and achieves a higher entire score in terms of individual decoder manipulation. In addition, owing to the generalization performance improvement of multi-task learning, the T-Net model demonstrates decent prediction performance under few-shot learning. Thus, this proposed T-Net model showcases the great opportunities for real-time, in situ, monitoring of practical combustion pollutant emission, by embedded in a response strategy of the detection devices.

New trade theory converges to the old trade theory—An elementary theoretical perspective

AbstractWe introduce factor specificity to reflect the cost of adjustment of reallocating factors between technologies that determine fixed costs and variable costs in a typical Dixit–Stiglitz–Krugman (DSK) model of trade, retaining all other standard specifications of such a framework. We show that such an apparently benign change fundamentally alters the results of the DSK model. The number of varieties and output per variety depends entirely on full employment conditions. Thus, DSK and Heckscher–Ohlin–Samuelson (HOS) models, which represent the major workhorses of old trade theory, converge in the sense that now the number of varieties and outputs per variety are completely determined by factor endowments.

ESG scores, scandal probability, and event returns

The informativeness of environmental, social, and governance (ESG) scores and their actual impact on firms remains understudied. To address this gap in the literature, we make theoretical predictions and conduct empirical research revealing that a high ESG score is associated with a lower probability of ESG scandals and lower stock returns during a scandal event. Our results suggest that ESG scores are heterogeneous but informative, and that a strong ESG reputation may have both positive and negative consequences for firms. Drawing on our findings, we develop a model and showcase that firms face an optimization problem when determining optimal ESG investment levels. Two equilibria may exist based on the trade-off between ESG scandal losses and ESG adjustment costs. Our model explains why certain firms make heterogeneous ESG decisions

Investment allocation in an adjustment-cost production technology framework for two-stage network structures

Effective resource allocation can assist organizations in utilizing infinite resources, thereby improving efficiency and productivity. For an organization, investment is an essential production resource, and how to appropriately allocate investment is one of the most important decisions they need to make. This study aims to rationally allocate investment across each stage of two-stage network structure under two scenarios. We first propose general investment allocation model with adjustment costs to efficiently allocate investments across each stage. Then, given a fixed total investment, we use the DEA-based fixed cost allocation method to achieve the optimal allocation across stages, and we also incorporate two cooperative and noncooperative relationships between two stages. Additionally, capital stock efficiency is defined to analyze the relationship between optimal and actual capital stocks, clarifying the degree to which the optimal capital stock is reached. Finally, the proposed approach is applied to 14 A-share listed companies in China, and suggestions on the optimal amount of investment allocation and capital stock required are provided to facilitate organizational production.

The effect of debt intensity on the asymmetric cost behavior: empirical evidence from local governments

PurposeThis study explores the relationship between debt intensity and cost stickiness at the local government level, a setting characterized by the existence of debt constraints and political influence.Design/methodology/approachBuilding on a theoretical framework informed by the concepts of coercive isomorphism and accountability, the present study focuses on Greek municipalities and applies Anderson et al.’s (2003) extended methodology, as reviewed by Banker and Byzalov (2014), in a sample of 1,366 municipality year observations for the period 2011–2020.FindingsThe results indicate that the degree of cost asymmetry is negatively associated with debt intensity. Periods before elections present the same picture. This negative relationship becomes insignificant in the case of large municipalities, which probably require more resources to support their operations and incur higher adjustment costs for reducing resources. These findings are robust to use alternative types of expenses associated with cost stickiness and a battery of control variables.Originality/valueLittle is known about the impact of debt level and financial constraints on cost behavior in the public sector context. This study takes a fresh look at the relationship between municipal debt structure and cost stickiness, adding to the understanding of cost behavior considering the debt level, financial constraints, resource-adjustment costs and the underlying managerial behavior.

HANK Comes of Age

We study the aggregate and distributional effects of monetary policy in a heterogeneous agent New Keynesian model that explicitly represents the life cycle of households. The model matches the age patterns in the level and dispersion of labor income and financial wealth in the U.S. despite the absence of preference heterogeneity and portfolio adjustment costs. Monetary policy affects the consumption of young households mainly through labor income and the consumption of old households mainly through asset returns. More than half of the aggregate consumption response to an expansionary monetary policy shock comes from those below the age of 40. The shock redistributes welfare from the wealthiest old to the poorest young and increases average welfare of most cohorts.