Cost Coefficients Research Articles

The cost bearing mechanism for advertising in a capital-constrained supply chain

With rising market competition, increasing numbers of firms are launching adverting to attract customers and promote product sales. The increase in operating costs caused by advertising places greater pressure on small retail businesses that are prone to capital shortages, leading them to seek financing from upstream firms. However, in the financing process, upstream and downstream firms may not be able to acquire all of one another's real information, which inevitably has a significant impact on their operating strategies. In this paper, by constructing a Stackelberg game, we study the effects of information asymmetry on the retailer's initial capital and the manufacturer's financing rate on their advertising strategies. We find that in a capital-constrained supply chain with symmetric information, when the advertising cost coefficient is low, manufacturer advertising is the superior strategy and increases the profits of the retailer and the manufacturer, consumer surplus and social welfare; when it is moderate, retailer advertising is the superior policy; but when it is high, retailer advertising is more beneficial for the manufacturer's profit, consumer surplus and social welfare but is more unfavorable for the retailer's profit. In addition, information asymmetry on the manufacturer's financing rate affects the advertising strategies of the manufacturer and the retailer, but information asymmetry on the retailer's initial capital fails. Additionally, we further extend the model to the Nash game scenario and cooperative advertising scenario and draw some different conclusions. The findings provide new theoretical implications for the formulation of firm advertising strategies.

Recycling Mode Choice in a Textile and Apparel Closed-Loop Supply Chain considering Blockchain

The development of the textile and apparel (T&A) industry has led to an increasing focus on recycling used products. Remanufactured product quality raises consumer concerns, and blockchain can effectively solve this problem. We establish a closed-loop supply chain (CLSC) in which a manufacturer, a retailer, or a third-party recycler collects used T&A products to examine the most efficient recycling mode with and without blockchain and the impact of blockchain on CLSC decisions. The results show that (1) if the manufacturer’s recycling cost coefficient is relatively low, used T&A products are collected directly by the manufacturer. Otherwise, the responsibility for recycling used T&A products falls to the retailer or the third-party recycler. It is noteworthy that the manufacturer’s choice of recycling mode remains unchanged whether a blockchain is implemented or not. (2) The implementation of blockchain by the manufacturer and the retailer can increase profits and consumers also benefit when the cost of validating blockchain units remains below a certain threshold. (3) When the recycling cost coefficient exceeds a certain threshold, the implementation of blockchain increases prices and recycling rates. These findings offer CLSC members’ management insights into how to select the optimal recycling mode and the consequences of implementing blockchain.

Sensitivity Analysis of the Cost Coefficients in Multiobjective Integer Linear Optimization

This paper considers sensitivity analysis of the cost coefficients in multiobjective integer linear programming problems. We define the sensitivity region as the set of simultaneous changes to the coefficients for which the efficient set and its structure remain the same. In particular, we require that the component-wise relation between efficient solutions is preserved and that inefficient solutions remain inefficient, and we show that this is sufficient for the efficient set to be the same upon changes. For a single coefficient, we show that a subset of the inefficient solutions can be excluded from consideration. More specifically, we prove that it suffices to inspect the inefficient solutions of a q-objective problem that are efficient in one of two related q + 1-objective problems. Finally, we show that the sensitivity region is a convex set (an interval). Our approach generalizes to simultaneous changes in multiple coefficients. For illustration, we consider mean-variance capital budgeting and determine the region for which the set of efficient portfolios remains the same, despite a misspecification or a change in the net present values of the projects. Further computational experience with multiobjective binary and integer knapsack problems demonstrates the general applicability of our technique. For instance, we obtain all sensitivity intervals for changes to single coefficients of biobjective problems with 500 binary variables in less than half an hour of CPU time by excluding a large number of inefficient solutions. In fact, the number of excluded solutions is above 100 orders of magnitude larger than the number of remaining solutions. This paper was accepted by Chung Piaw Teo, optimization. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.01406 .

The Stochastic Transportation Problem with Imprecise Data using Lomax Distribution

The stochastic transportation problem with imprecise data is a probabilistic chance-constrained programming (CCP) problem in which the objective function is fuzzy and the supply and demand are random. Three models for the STP with ID with mixed-type restrictions that follow the Lomax distribution (LD) are created in this research. Optimizing the transportation cost in FTP under probabilistic mixed constraints is the goal of the research project. To do this, the probabilistic mixed constraints are transformed into deterministic form using the LD, and the cost coefficient of the fuzzy objective function is changed with alpha cut representation. Numerical examples are presented to demonstrate the suggested models.

Total Costs Coefficients in the Basic Input-Output Tables

The article presents the results of the author's research devoted to the improvements in domestic developments of Input-Output Tables (IO Tables), significantly increasing their analytical potential. According to the author, the level of development of Russian statistics in the field of Input-Output Tables is quite high – the basic accounts contain not only the symmetrical IO Tables, but also symmetrical tables of domestic products and imports that they are derived from. This is the main condition for implementing the methodology and methodological solutions described and elaborated in the article for compiling the tables with the coefficients of total costs of domestic products and the total need for import of intermediate imports estimates.In the first part of the article, the author characterizes conceptually different versions of the intersectoral model with the features of the technological matrix corresponding to these versions – the basis for estimating the values of total costs coefficients in the national economy. Three «extreme» variants of the economy model representation of indicative country are studied. They define the economy as a part of a global economy within the framework of an inter-country model; in the form of a national model without the allocation of the import matrix in a symmetrical IO Tables (traditional method) and in the form of a national model with the possibility of such allocation (alternative method). The substantial differences in the values of total costs coefficients for the options are described and their preference for study of the country's economy is emphasizedThe second part of the article presents the results of a quantitative analysis based on the basic IO Tables for 2016 data. It contains the estimates of the measure of discrepancy in total costs coefficients values according to traditional and alternative methods; the differences in the associated indicators of intermediate products total costs distribution by specific dimensions of final demand. The analytical possibilities of the alternative method are described, the estimates for the decomposition of final products cost into domestic and imported value added are provided.The author explains the analytical relevance of total costs coefficients using an alternative method in forecasting practice and in the macroeconomic effects of management decisions assessment and proposes to supply the basic IO Tables for 2021 with tables with these coefficients and with the necessary methodological explanations

Strategies for Investing in Big Data Information Services in Community Group Buying Supply Chains and Their Comparative Study

This study investigates the investment strategy of big data information service in community group-buying supply chain. Combined with the background of big data information service, it constructs three investment decision models, compares the optimal profits of all parties of the game under different modes, and analyzes the problem of choosing the optimal investment strategy for suppliers, community group-buying platforms and group leaders. It is found that: 1) The community group-buying supply chain invests in big data information service, which has a promotion effect on the supply chain profit, and when the supplier's marginal profit and the head's marginal profit satisfy certain conditions, the investment model is the O-mode, which can make the supply chain realize a long-term win-win situation. The profit of the supply chain is related to the consumer conversion rate, discount rate, cost coefficient of supplier's quality management efforts, cost coefficient of platform's big data marketing, and cost coefficient of group leader's marketing.

A stabilised Benders decomposition with adaptive oracles for large-scale stochastic programming with short-term and long-term uncertainty

Benders decomposition with adaptive oracles was proposed to solve large-scale optimisation problems with a column-bounded block-diagonal structure, where subproblems differ only in the right-hand side and cost coefficients. Adaptive Benders reduces computational effort significantly by iteratively building inexact cutting planes and valid upper and lower bounds. However, Adaptive Benders and standard Benders may suffer severe oscillation when solving degenerate models. Therefore, we propose stabilising Adaptive Benders with the level method and adaptively selecting which subproblems to solve each iteration for more accurate information. In addition, we propose a dynamic level method to improve the robustness of stabilised Adaptive Benders by adjusting the level set each iteration. We compare stabilised Adaptive Benders with the unstabilised versions of Adaptive Benders with one subproblem solved per iteration and standard Benders on a multi-region long-term power system investment planning problem with short-term and long-term uncertainty. The problem is formulated as multi-horizon stochastic programming. Four algorithms were implemented to solve linear programming with up to 1 billion variables and 4.5 billion constraints. The computational results show that: (a) for a 1.00% convergence tolerance, the proposed stabilised method is up to 113.7 times faster than standard Benders and 2.1 times faster than unstabilised Adaptive Benders; (b) for a 0.10% convergence tolerance, the proposed stabilised method is up to 45.5 times faster than standard Benders and unstabilised Adaptive Benders cannot solve the largest instance to convergence tolerance due to severe oscillation and (c) dynamic level method makes stabilisation more robust.

Application of Deep Learning Algorithm in Optimization of Engineering Intelligent Management Control

Currently, there are a series of problems in the management of the construction industry, such as resource waste, substandard quality, and low construction efficiency. In response to this phenomenon, the author proposes a multi-objective optimization control method for construction engineering management projects using deep learning algorithms. This method analyzes the relationship between cost, duration, and quality, and constructs an optimization management model for these three factors. At the same time, the improved SULSTM neural network algorithm is used to optimize the model parameters. The experimental results indicate that, when the value coefficient is 0.2211, the total investment cost and quality coefficient are 412700 yuan and 0.99496 yuan, respectively. When the value coefficient is 0.1976, the total cost and quality coefficient are 456300 yuan and 0.98798 yuan, respectively. When the value coefficient is 0.1990, the total cost and quality coefficient are 456300 yuan and 0.99496 yuan, respectively. Proved that the SUSTM neural network algorithm has faster convergence speed and lower loss values compared to the improved LSTM neural network algorithm. The cost of improving quality has a greater impact on the quality coefficient than the duration, and the total investment cost has a greater impact on the value coefficient than the quality coefficient.

Критерии и показатели оценки параметрических реформ в жилищно-коммунальном хозяйстве Беларуси

The article examines the initial stage of housing and communal services reforming in Belarus. The subject of the study is based on parametric reforms of the sphere. Under parametric reforms the author understands the regulatory effects of state bodies on the object of management within the framework of the current system of economic relations. In the course of parametric reforms, there is a purposeful change in economic norms, standards, tariffs, prices, and the amount of budget financing, which partially stabilizes the situation in the industry under consideration. In the course of the study, the author proposed a scheme for harmonizing the economic interests of housing and communal services company and consumers in the service market and a graphical model of planned loss-making enterprise functioning. These tools make it possible to solve the problems of cross-subsidization when paying for housing and communal services and planned loss-making enterprises in the industry. The author suggests the following criteria for evaluating parametric reforms in housing and communal services of Belarus: paternalism, subsidized costs and budget revenues effectiveness. Based on these criteria, the author substantiates such indicators as paternalism level, subsidization coefficient of costs and budget revenues profitability. These proposals make it possible to justify not only the priority parametric reforms of housing and communal services of Belarus, but also to outline the approaches to structural and institutional reforms.

Decency or devilment: the impact of blockchain technology on product line design

ABSTRACT Blockchain is a secure database shared across a network of all participants, characterised by immutability, openness, transparency, and traceability. Therefore, blockchain technology is widely used in supply chain management, which can increase consumers' understanding of product information when goods arrive at the consumer end and increase the risk of consumer privacy information leakage. Considering a supply chain system that consists of a manufacturer and a platform, this paper studies the strategic interactions between the platform's adoption of blockchain technology and the manufacturer's product line design based on blockchain's positive and negative impacts on consumers. We examine the driving factors of the platform's use of blockchain technology, which are related to the effect of information transparency and privacy concerns caused by blockchain and the marginal production cost coefficient. In particular, a higher marginal production cost coefficient could make the platform more likely to use blockchain technology. We also find that the practice of blockchain technology may incentivize a manufacturer to extend its product line.

Multi-objective optimization of parameters design based on genetic algorithm in annulus aerated dual gradient drilling

AbstractThe optimization of drilling parameters is crucial for resolving the drilling problems in low-pressure and leaky formations using the annulus aerated dual gradient drilling technology. However, the previous studies have mostly focused on engineering applications and wellbore fluid flow models, with less emphasis on parameter optimization. This paper combines the wellbore multiphase flow model with genetic algorithms for the first time, proposing a key parameter optimization method for annulus aerated dual gradient drilling based on genetic algorithms. The study investigates the impact of selection operators on the performance of genetic algorithms and compares genetic algorithms with PSO algorithm and SAA. The results indicate that the convergence and stability of genetic algorithms can be improved by enhancing the selection operators. Compared to the gas–liquid ratio parameter optimization method, the IRSGA optimization method reduces the cost coefficient by 36.46%. Through comparative analysis of different optimization methods, the IRSGA demonstrates over 95% accuracy in large-scale computations. The research findings contribute to the optimization of parameters design under low-cost conditions and are of significant importance for promoting the use of this technology to address the serious issue of lost circulation in drilling technology.

The interplay between blockchain adoption and green operations for a firm

AbstractThis study considers a firm selling only green products or both ordinary and green ones to consumers with or without blockchain technology. We find that the profitability of adoption of this technology for the firm relies heavily on consumers' environmental awareness. It should be adopted when consumers have relatively high environmental awareness. Here, the selling price, sales, and greenness level of green products are relatively higher compared to those in the case without blockchain. Finally, the firm only sells green products after blockchain adoption when consumers' environmental awareness level is relatively high or the development cost coefficient of green products is relatively small.

A solution to multi Objective Stochastic Optimal Power Flow problem using mutualism and elite strategy based Pelican Optimization Algorithm

Wind Energy is rapidly growing into one of the most popular energy options for power generation. However, the unpredictability of wind necessitates the employment of a distribution function when describing the wind environment. In the depiction of wind speeds, the two significant parameters of the Weibull distribution, such as the scale parameter and the shape parameter are often utilized. Therefore, it is essential to choose the most appropriate approach for estimating these parameters. Furthermore, the uncertainty in delegated power from variable wind speeds makes system management challenging. In this paper, the Weibull distribution parameters were determined using the Pelican Optimization Algorithm (POA). POA has gained extensive popularity among the research community and is currently used to address a wide range of optimization problems. However, the exploration and exploitation are not properly balanced in this algorithm. To overcome this problem a novel hybrid algorithm namely Mutualism phase based POA (MPOA) is proposed with the help of mutualism phase of Symbiotic Organisms Search (SOS) algorithm. To demonstrate the efficacy and robustness MPOA is applied on multi-objective Optimal Power Flow (MO-OPF) problems. For this study IEEE 30 bus standard system and modified IEEE 30 bus system consisting of two wind farms are taken. The purpose of this work is to examine the impact of changing the Weibull parameters, reverse and penalty cost coefficients on the overall generation cost while considering all the security aspects.

Sustainable impact analysis of freight pooling strategies on city crowdsourcing logistics platform

This study investigates the effects of freight pooling strategies on urban crowdsourcing logistics, focusing on economic, social, and environmental outcomes. Utilising a mixed-integer linear programming model with an adaptive large neighbourhood search algorithm, our goal is to optimise the cost-efficiency of the freight pooling system. Real-world delivery and driver trajectory data from a major Chinese crowdsourcing logistics platform, along with high-resolution vehicle telematics data, validate our model in five scenarios, each defined by distinct cost coefficients reflecting diverse stakeholder priorities. Results show potential for up to a 21.3% reduction in carbon emissions, a 28.3% decrease in truck activity spatial coverage, and a 7.6% increase in available drivers. However, deadheading trips, due to order consolidation into fewer vehicles without an increase in overall demand, could offset maximum carbon reduction benefits by 16.4%. Other effects on customers’ and drivers’ welfare are explored for a comprehensive quantitative assessment of freight pooling strategies’ sustainability benefits.

A novel concentrated photovoltaic and ionic thermocells hybrid system for full-spectrum solar cascade utilization

Concentrated photovoltaic-thermoelectric generator (CPV-TEG) hybrid system can harvest the low-grade waste heat derived from photovoltaic cells to generate more electric energy. However, it is economically unfeasible due to the expensive cost of thermoelectric generators and higher photovoltaics temperature. Ionic thermocell based on thermogalvanic effect has a more inexpensive cost and ionic Seebeck coefficient of 1– 2 orders of magnitude higher than thermoelectric generators, which is promising for full-spectrum solar cascade utilization. In present work, a novel concentrated photovoltaic and ionic thermocells hybrid system (CPV-iTEC) is reported, aiming to comprehensively evaluate its feasibility. The ionic thermocell adhered directly to the photovoltaic panel consists the π-type multi-channels, electrodes and p/n-type redox electrolytes flowing in multi-channels, which can simultaneously cool photovoltaic panel and output electric energy for waste heat harvesting. A three-dimensional numerical model is proposed and validated by preliminary experiment to investigate the effects of structural and operating parameters on the performance of CPV-iTEC during energy, exergy and economic analyses. Results show that the optimal structural parameters of ionic thermocell are the 2 mm channel height and 1 mm width, showing a 7.18% higher energy efficiency than that of CPV-TEG at 10 concentration ratios. The presence of ionic thermocell can significantly decrease the photovoltaic panel temperature from 351.30 K to 325.14 K, and thus broaden the available concentration ratio from 1– 21 to 1– 39 compared with CPV-TEG, reaching a higher output power of 16.61 W. During the optimization of advanced redox electrolytes/electrodes, the energy efficiency can reach 49.63% at 21 concentration ratios, which is 5.06% higher than that of CPV-TEG, and show a 22.65% lower overall cost. The application of ionic thermocell in the full-spectrum solar cascade utilization of concentrated photovoltaic system is proved to be feasible with significant advantages of low cost, high performance, and flexible operation.

Preferences in adolescents and young people’s sexual and reproductive health services in Nigeria: a discrete choice experiment

BackgroundBarriers to utilization of sexual and reproductive health (SRH) services by adolescents and young people (AYP) have persisted despite evidence that youth-friendly services have a positive effect on contraceptive use, and patient knowledge and satisfaction.ObjectiveThe objective of this study was to elicit, and derive relative valuations for, attributes of SRH services that adolescents and young people value, and their willingness to pay for these services, in public health facilities.MethodsA discrete-choice-experiment (DCE) that was developed using a mixed methods approach was administered to AYP from Ogun State, Southwest Nigeria. The DCE attributes were: the type of staff; physical environment; health worker attitude; cost; waiting time; contraceptive availability; and opening hours. The choice tasks had two unlabeled alternatives and an opt-out option. Panel mixed logit choice model was used to fit the choice data, along with estimation of willingness to pay (WTP). Also, a latent class logit model was used to detect underlying preference heterogeneity among the respondents. Finally, the uptake of the services in health facilities was investigated by estimating the probabilities for selecting hypothetical health facilities under different scenarios.ResultsA total of 859 AYP participated resulting in 6872 choice observations. The physical environment attribute had the highest utility rating relative to the other attributes, followed by preference for a doctor and for a service provider who was open and friendly. The cost and time coefficients were negative, revealing preference for lower cost and shorter waiting time. The latent class model had three classes that varied by their background characteristics. Probability of choosing any of the facility alternatives increased with introduction of more favorable facility characteristics.ConclusionThe pattern of preferences identified are potential targets for service design and delivery optimization that may result in improvements in service acceptability and utilization. These results strengthen the call for involving AYP in decision-making in health interventions for them and developing context-specific SRH programs for AYP in public health facilities.

An analysis of Strategies for Adopting Blockchain Technology in Fresh Product Supply Chain Under Freshness Misreporting

The application of blockchain technology can inhibit producers' freshness misrepresentation in fresh produce supply chains. In this paper, we consider consumers with fresh produce freshness information traceability preference. In a fresh produce supply chain with a single producer and a single seller, the seller's blockchain application decision is studied, the Stackelberg game models of the supply chain members in the two scenarios of the producer misrepresenting freshness and the seller applying blockchain are constructed, and the changes of the equilibrium solutions before and after the application of blockchain technology are analysed. The theoretical proofs and numerical simulation studies show that when the transaction cost is at a certain level, the sellers should choose to apply blockchain technology to improve their own revenue, and the level of applying blockchain technology is affected by the consumer's retrospective preference and the cost coefficient of blockchain technology.

Entry accommodation strategies in a shipping supply chain with corporate social responsibility

Carriers often enter new shipping markets and undertake corporate social responsibility (CSR) activities to attract more shippers. CSR generally refers to the integration of social and environmental factors by a firm into its business decision-making. This paper constructs a shipping supply chain composed of one port, one carrier, and one potential entrant carrier, where both carriers are committed to CSR activities. Two possible channel structures of the incumbent supply chain are considered: centralization and decentralization. Under both structure settings, we study the entry strategy of the entrant, accommodation strategy of the port, and impacts of entry on consumer surplus and social welfare. Our results show that (i) the entry and accommodation strategies depend on the CSR levels of carriers; (ii) downstream entry improves social welfare under centralization but may decrease it under decentralization; (iii) the entrant has more incentive to enter under decentralization than under centralization when the incumbent carrier has a high CSR level. However, the port might have less incentive to accommodate entry under decentralization; (iv) channel decentralization may improve consumer surplus and social welfare when the port accommodates the entry, while hurts these when the port deters the entry. Additionally, we find that under the case of endogenous CSR level, the entry and accommodation strategies depend on the CSR investment cost coefficient; and the entrant has a stronger entering incentive under uniform pricing than under differentiated pricing if the incumbent carrier has a low CSR level.

The Government–Farmer Cooperation Mechanism and Its Implementation Path to Realize the Goals of Optimizing Grain Planting Structure

In order to alleviate the grain supply–demand structural contradictions and ensure the realization of grain planting structure optimization goals, it is necessary to clarify the interactive relationship between multiple entities, establish a cooperation mechanism, and explore its implementation paths. To this end, a differential game model is built to compare and analyze the optimal strategies, optimal benefits, and overall system outcomes for both the government and farmers under three scenarios: the Nash non-cooperative game, the Stackelberg game, and the collaborative cooperation game. Then, key factors and their influencing mechanisms that affect the government–farmer cooperation mechanism are revealed. Finally, the csQCA model is used to explore the implementation paths for different stakeholders to ensure the sound operation of the cooperation mechanism. The results show the following: (1) The government–farmer cooperation mechanism should consist of an inner core system with the government–farmer interaction as the core and an outer system comprising the market environment, cooperation environment, and institutional environment. These two systems should coordinate with each other, respond to each other, and drive progress together. (2) The cooperation mechanism can optimize behavioral enthusiasm, resulting in individual and overall benefits for both the government and farmers. However, its scientific and orderly implementation is affected by factors such as the cost coefficient. Additionally, subsidies serve as a powerful policy tool to enhance farmers’ enthusiasm, thereby increasing the benefits for both parties and maximizing the effectiveness of the cooperation mechanism. (3) There are three implementation paths corresponding to large-scale farmers, rural elites, and small-scale farmers: being led by external policy tools, linkage guidance between decision-making environment and willing subjects, and factor allocation and environmentally driven decision-making. These findings can provide theoretical support and case reference for marginal farmland management and planting structure optimization management in underdeveloped areas.

Decisions of dual-channel fresh agricultural product supply chains based on information sharing

PurposeThis paper studies the optimal decision-making and coordination problem of a dual-channel fresh agricultural product (FAP) supply chain. The purpose is to analyze the impact of information sharing on optimal decisions and propose a coordination mechanism to encourage supply chain members to share information.Design/methodology/approachThe two-echelon dual-channel FAP supply chain includes a manufacturer and a retailer. By using the Stackelberg game theory and the backward induction method, the optimal decisions are obtained under information symmetry and asymmetry and the coordination contract is designed.FindingsThe results show that supply chain members should comprehensively evaluate the specific situation of product attributes, coefficient of freshness-keeping cost and network operating costs to make decisions. Asymmetric information can exacerbate the deviation of optimal decisions among supply chain members and information sharing is always beneficial to manufacturers but not to retailers. The improved revenue-sharing and cost-sharing contract is an effective coordination mechanism.Practical implicationsThe conclusions can provide theoretical guidance for supply chain managers to deal with information asymmetry and improve the competitiveness of the supply chain.Originality/valueThis paper combines the three characteristics that are most closely related to the reality of supply chains, including horizontal and vertical competition of different channels, the perishable characteristics of FAPs and the uncertainty generated by asymmetric demand information.