Classic supply chain network design models usually assume that all demands are adequately fulfilled, although in practice most companies are selective in their target markets. By not fulfilling all demands, the unit profit margin is no longer constant as in the conventional cost-minimization network design models, because the size of demands is unknown. Additionally, current studies primarily focus on physical flows within the supply chain network, largely neglecting financial flows. However, payment delays are not uncommon in practice and can significantly affect various costs in supply chains. To address these issues, we propose a network design model with flexible demand fulfillment and delayed payments under capital constraints. It integrates the decisions of selective demand fulfillment, payment terms, safety stock levels, and multi-echelon inventory replenishment policies to maximize total profit. Efficient algorithms are developed by exploiting the structural properties of the model. In-depth analysis of the numerical study suggests that it is not always optimal to fulfill all demand sources. Moreover, accounting for payment delays leads to an optimal solution with more demand sources fulfilled, longer replenishment intervals, and eventually higher system wide profit. The impact of this financial arrangement further increases as the difference in the corresponding costs of capital between the firm and demand sources grows.

As environmental awareness grows, companies are rolling out emission-reduction initiatives to offer more low-carbon products. The two commonly adopted strategies for companies are direct carbon reduction investment (R strategy) and carbon offset (O strategy), and they differ in costs and consumer perception of their effects: the former requires substantial upfront technological investment and is therefore highly sensitive to demand fluctuations, whereas the latter involves demand-dependent costs but may suffer from credibility concerns. The choice between them is further complicated by demand uncertainty, which is often amplified by information asymmetry, as retailers typically possess more accurate demand information. We develop a game-theoretic model in which a manufacturer chooses a low-carbon strategy, while a retailer decides whether to share demand information. Our results show that the optimal strategy depends jointly on market potential demand, consumer acceptance of carbon offsets, and information availability. Carbon reduction is preferred when demand is sufficiently high or consumer acceptance of offsets is low, whereas carbon offset becomes more attractive under weak demand due to lower downside risk. Information sharing reduces demand uncertainty and lowers the threshold for manufacturers to adopt carbon reduction, thereby promoting genuine emission abatement. Moreover, under a hybrid strategy, the presence of an offset mechanism encourages deeper carbon-reduction efforts by mitigating investment risk and strengthening incentives for technological abatement.

Applications of the blockchain technology are rapidly emerging across sectors. By redefining information flow, blockchain technology promises to instill trust among supply chain members, improve transaction efficiency, thus reshaping the structure and configuration of supply chains. The practical exploratory use of blockchain has shown immense potential in supply chain management. In order to explore the new changes brought by blockchain technology to supply chain network design issues, this study focuses on the supply chain network design problem in the context of blockchain technology. Specifically, we consider a supply chain network comprised of a number of distribution centers (DCs) and retailers. The operation of this physical network is supported by blockchain-based information infrastructure. As the interrelated relationship between these two networks, it is necessary to optimize them simultaneously to improve the profitability and information exchange level. We propose an objective function based on previous studies to maximize the profits of the entire supply chain network, with decision variables including DC locations, retailer assignment, inventory replenishment policy, and blockchain adoption level. At the same time, to characterize the interrelation between the two networks, the demand and certain cost parameters of the model become endogenous depending on the blockchain adoption level, which significantly increases the complexity of the solution algorithm. We reformulate the model and use the polymatroid cutting plane approach to address this issue. We conducted calculations and numerical analysis. The computational results show that the proposed method can solve practically sized problems, and integrating blockchain design reduces the system-wide cost. We discuss managerial insights based on a range of numerical studies and solve a case study using publicly available instance data.

As consumer demand for fresh products continues to rise, the inefficiencies in cold chain logistics have emerged as a pressing issue, resulting in substantial food waste and compromised product quality. Meanwhile, logistics companies face the dual challenge of reducing costs and carbon emissions while ensuring product freshness. In response to these challenges, this paper proposes a novel target-oriented framework that leverages an underperformance riskiness index to optimize cold chain routing decisions. The primary objective is to minimize the risk of not meeting the target freshness level while accounting for costs and carbon emissions. To address the complexity that arises from stochastic arrival times, a linear decision rule is incorporated into the model. The robust counterpart of the problem is reformulated as a mixed-integer linear programming model, which is then solved efficiently using a Benders decomposition approach. Extensive computational experiments are conducted on realistic instances to evaluate the performance of our proposed approach. A comparative analysis with two benchmark models is also performed. The experimental results reveal that our target-oriented robust optimization framework generates high-quality solutions. It effectively reduces both the likelihood and magnitude of violations of the target freshness level, while maintaining relatively low costs and carbon emissions.

The sharing of security information among firms to improve their defense against hackers is usually encouraged by government departments and industry associations. However, there exists a negative impact of facilitating the learning of hackers to reduce attack costs. Besides, due to the existence of user inconvenience, a high defense level will bring a high inconvenience penalty to firms. Such impacts and penalties under different hacker attack regimes have not received attention in the literature. This paper constructs a game-theoretic model between two competitive firms and one hacker to examine the impacts of security information sharing and inconvenience penalty under different attack modes. We first find that even though the sharing of security information can improve defense level, security information should be shared moderately. The improved defense level can bring inconvenience on users and thus a penalty on firms, but this inconvenience penalty, we next show, may help alleviate the competition in security investment and benefit each firm under random attacks. Then, we reveal that the effects of security elements remain almost unchanged from random attacks to targeted attacks. We find that firms may suffer less and hackers may benefit less under targeted attacks even though they are widely deemed to be more harmful than random attacks.

In an increasingly competitive market, most online tour operators have launched personalized travel itinerary recommendation services. Different from previous studies, this research considers the travel itinerary planning problem under total time limit and uncertain traffic time. This problem requires two stages of decision -making: firstly, selecting the tourist attractions to visit from the candidate attractions based on maximizing the popularity utility of tourists; secondly, planning the tourist attraction visit sequence under stochastic traffic time to maximize the tourist activity utility. Therefore, a two-stage stochastic optimization model with chance constraint is constructed and then solved by the sample average approximation (SAA) method. In order to verify the effectiveness of our model, we introduced two benchmark models for comparative analysis. The results show that in the worst case, our model increases the reliability level of travel itineraries by almost 40% compared with the two benchmark models. In addition, case studies of two large cities, i.e., Nanjing and Beijing, indicate that a tour operator can optimize travel itinerary recommendations by improving tourists utility as well as their own profitability without much loss of reliability.

Many online retailers have adopted drop-shipping as their main order fulfillment strategy. Against this backdrop, this paper studies the service time and pricing decisions in an online retailing system in which the single retailer is served by either monopolistic or duopolistic suppliers. The suppliers are dominant in the Stackelberg game. We employ the guaranteed service framework to model the intricate relationship between service time and inventory. The model stipulates that the delivery of online orders must be completed within guaranteed service time. The equilibrium service times and prices are derived for both cases of monopolistic and duopolistic suppliers. In addition, we systematically analyze the impact of operational and market factors on service time, respectively, and rich insights have been obtained. Interestingly, we find that the presence of competition does not necessarily lead to more rapid delivery service. Suppliers in the duopoly market tend to adopt a price-based competitive strategy, i.e., they are better off by lowering price at the expense of longer service time, especially for the new entrant. However, as competition intensifies to some extent, suppliers are recommended to leverage on more responsive service to maintain or expand the customer base.

As a representative enterprise of domestic social e-commerce platform, Pinduoduo has successfully opened up a blue ocean with social as its main profit, and in just a few years, it has jumped into the top three e-commerce companies and become the top three e-commerce companies in China alongside JD.COM and Ali. Unique marketing strategy is the key to its success. Therefore, based on 4R marketing theory, this paper makes an in-depth study on Pinduoduo's marketing strategy, aiming at digging the key factors behind its success, providing reference for other social e-commerce platforms and ensuring the stable and sustainable development of the e-commerce industry in the future.

Rapid delivery of emergency supplies is of vital importance in disaster relief, and innovative means of delivery has been actively explored. Recently, a novel delivery approach that takes advantage of both trucks and drones is emerging in the commercial field. This synchronized truck-drone delivery (STDD) mode can benefit from the large capacity of trucks as well as the speed and flexibility of drones, which makes it promising for sending ungently-needed supplies to disaster-stricken sites, especially the areas severely damaged and thus inaccessible by trucks. This paper attempts to optimize the synchronized truck-drone paths for quick responses in disaster relief, when accounting for the priority of different disaster-stricken sites. We propose an integer programming model and develop exact algorithm to solve small-scale problems. For large-scale problems, we propose an algorithm based on dynamic programming. We verify that the STDD mode is more efficient than truck-only delivery. Further numerical analysis suggests that a drone with higher speed and longer endurance is not always the best choice. The efficiency of rescue efforts relies on careful calibration of the speed ratio of the drone to the truck and the maximum flight distance of the drone. Finally, the application of the model is illustrated by a case study of the 2008 earthquake at Wenchuan County in Sichuan Province of China.

Shopping without much experience on target items is not unusual in social commerce (s-commerce). Inexperienced users are often influenced by user reviews when making purchasing decisions, which can be easily distorted due to low review quality. To address this issue, this study integrates two important review dimensions into purchasing decision model, i.e. the trust relationships to experienced users and the reliability of experienced users' reviews. A purchasing decision model for s-commerce is proposed based on trust computation and item reviews. The trust degree of inexperienced users to experienced ones is computed based on item reviews and propagated using the parameterized Hamacher t-norm operator. Based on the item review information, inexperienced users can make the final purchasing decisions on target items with trust information to experienced users and their reliability on item reviews. A numerical example with a data subset of Epinions.com is provided and the comparative analysis is conducted to verify the effectiveness of the proposed method. This study proposes an inexperienced purchasing decision method considering the risk attitude of users with flexible trust propagation parameters, which can provide a new perspective for the s-commerce recommendations.

This study considers a two-echelon supply chain that is comprised of an outside vendor, multiple distribution centers (DCs), and multiple retailers. The retailers have access to trade credit offered by upstream suppliers. We propose an integrated DC-retailer network design model that optimizes trade credit terms and safety stock levels, in addition to the decisions of DC locations, DC-retailer assignments, and inventory replenishment policies typically considered in the literature. The operating and handling cost is concave and non-decreasing to capture economies of scale whereas most existing studies simply assume that such cost is linear in demand. Trade credit financing cost is characterized in a way that preserves the important mathematical structure of the classic warehouse retailer network design model. Leveraging on the submodularity property of the cost components, we developed a polymatroid cutting-plane solution algorithm, which is effective for practically sized problem instances in numerical experiments. The results show that incorporating trade credit financing into supply chain network design may substantially reduce the total cost. Further, our study suggests a more consolidated supply chain network when either the safety stock or financing cost increases. Interestingly, as financing cost rises, a high-volume and low-frequency reorder pattern is favored, but the opposite is recommended when financing gain increases. The variation of each individual cost component is also analyzed for an in-depth understanding of the impact of operational and financial parameters on supply chain optimization.

Due to the weak withdrawal capacities of conventional nail joints, using double-headed screw joints as reliable connections in bamboo structures is investigated for the first time. A two-step test program is presented in this paper. In the first step, a double shear test is carried out to investigate the influences of the end distance and bamboo grain direction on the performance of double-headed screw joints. The test shows that there are four main failure modes of double-headed screw joints: double-headed screw shear failure, bearing failure of the hole wall, tensile failure of the bamboo cover panel and shear failure of the cover panel end. In the second step of test, the proposed double-headed screw joints are applied to three single-layer single-span bamboo shear walls, and low-cycle reversed loading tests are applied to the walls with double-headed screw spacings of 50 mm, 100 mm and 150 mm. The failure mode, hysteretic behaviour and energy dissipation performance of the shear walls are discussed. Test results show that the two main failure modes of the bamboo shear walls are the tensile failure of the edge of the wall and shear failure of the double-headed screws. Among the different spacings, the bearing capacity and effective stiffness of the wall with a double-headed screw spacing of 50 mm are the largest, the ductility and energy dissipation capacity of the bamboo shear wall with a double-headed screw spacing of 100 mm are the largest, and the bearing capacity and ductility of the bamboo shear wall with a double-headed screw spacing of 150 mm are the worst.

Attraction recommendation is a key functionality offered by tour operators. The main stakeholders of attraction recommendations include tourists and tour operators. The former use recommendations to make travel decisions, and the latter manage recommendations for their own benefits. Most existing attraction recommendation methods focus on providing recommendations that best match tourists' preferences, yet overlook the benefits of tour operators. To address this gap, we conduct a two-phase study that focuses on cost-based attraction recommendations under stochastic tourist demand from the perspective of tour operators. In the first phase, we obtain preliminary recommendation solutions that best match tourists' topic preferences. Then, with the consideration of cost factors, a stochastic programming model with a joint chance constraint is proposed to refine the preliminary recommendation solutions in the second phase, and a tractable model based upon Sample Average Approximation (SAA) method is further presented. To assess the performance of the proposed method, comprehensive experiments are conducted with both simulated instances and real-world data. The results indicate that the proposed optimization model can significantly reduce tour operators' recommendation cost while maintaining a high service level and tourist satisfaction. (c) 2020 Elsevier Ltd. All rights reserved.

With the rapid development of information technology, the real-time monitoring and management of the supply chain can be better realized only by quickly obtaining the operation information of each link of the supply chain. The digital twin technology proposed in recent years provides a new idea for the whole process management of supply chain. This paper give a definition about digital twin supply chain and propose a concerned model, and analyze the applications of digital twin technology usage in links of whole supply chain, like procurement, manufacture, storage, logistic and sales part as well as give a prospect of further development.

Responsiveness has emerged as a new dimension of differentiation as e-commerce continues to evolve. However, the pursuit of rapid service is not without limits. An obvious consequence is higher costs, and a second but less considered one is higher carbon emissions. Against this backdrop, our research investigates service time and price decisions for an e-tailer. A holistic social welfare perspective that accounts for the e-tailer's profit, consumer surplus as well as the environmental impact of carbon emissions is adopted. Equilibrium service time and price are derived for certain operational settings. In view of the complex interplay between service strategies and carbon emissions, we propose the carbon compensation index that measures the degree of recovery from the environmental impact through carbon tax in monetary terms. The optimal service strategies under different regulatory and market environments are illustrated. Further, our results reveal that excessive taxation could be detrimental to overall social welfare.

Extant studies predict continuance intention primarily from a hindsight perspective, and most existing theories cannot adequately explain the puzzling phenomenon that users sometimes continue to use an information system despite low satisfaction. To address this gap, this paper argues that it is necessary to incorporate forward-looking considerations into information system continuance models, especially for rapidly evolving technologies such as mobile applications. Based on the appraisal theory of emotions, this study specifically focuses on the role of hope in the formation of continuance intention. Empirical results based on a questionnaire survey of mobile app users show that hope can exert significant influence on continuance intention as well as exploratory use, controlling for the effects of previous usage patterns. Moreover, the positive impact of hope on continuance intention is stronger when the degree of personal innovativeness in IT is low, and vice versa. Further, confirmation and involvement are identified as positive antecedents of hope with the former exhibiting a stronger influence. Theoretical and managerial implications of this study are discussed.

To improve practical application of modern bamboo structures, strengthening the bamboo engineering material is necessary to overcome insufficient stiffness. As an essential step in developing fibre-reinforced polymer–bamboo engineering material composite structures aimed at increasing the structural stiffness, the bonding behaviour at the interface of the fibre-reinforced polymer and bamboo engineering materials should be investigated in detail because currently there is a lack of research. In this article, bonding behaviour is studied between basalt fibre-reinforced polymer bar and bamboo engineering material including laminated and reconstituted bamboo and between basalt fibre-reinforced polymer sheets and laminated bamboo. Failure patterns are categorized, and the load–slip curves are discussed. Based on the failure pattern and strain variation, recommended bond lengths were proposed for the basalt fibre-reinforced polymer bar–bamboo engineering material and basalt fibre-reinforced polymer sheet–laminated bamboo composite specimens, respectively. In addition, a simplified three-phase bond–slip model was proposed for the basalt fibre-reinforced polymer bar–bamboo engineering material composite specimen.

A steel–bamboo SI (skeleton–infill) system with steel frame as the skeleton and bamboo box as the filler is proposed, which realizes the requirements of building assembly and sustainable development. As a first step in studying the seismic behavior of the steel–bamboo SI (skeleton–infill) system, a simplified plane steel frame–bamboo infilled wall structure is tested under low-cycle reversed loading in this paper. The deformation mode, failure mode, hysteretic behavior and energy dissipation performance of the system are discussed. The test showed that the steel frame and the bamboo infilled wall were well connected and could work together. Then, the formulas for calculating the lateral stiffness of the system and the axial stiffness of the equivalent diagonal brace which replaced the infilled wall are obtained by theoretical analysis. The error between theoretical calculation and test results was about 1.3%, which proved the correctness of the proposed formula.

Motivated by a recent survey suggesting fast delivery among the most desired features that drive people to shop online, this study aims to investigate service competition in the context of inventory and environmental constraints. We consider a price and service time sensitive market in which two retailers sell substitutable types of products. They compete horizontally for the same group of customers, and they decide independently the service time guaranteed to the consumers. Both retailers adopt stationary base-stock policy. We find that when carbon emissions are not regulated, the choice of service time is closely related to the focus of competition. When price competition intensifies, service competition is deprioritized, and longer service time is used. When service competition is fierce, service time is reduced for greater market share. However, when carbon emissions are regulated by carbon tax, greater level of price competition leads to shorter service time while higher level of service competition results in longer service time, which is opposite to the unregulated situation. Furthermore, we conclude that simply imposing carbon tax may not bring the expected reduction in carbon emissions if consumers are insensitive to their carbon footprints and willing to absorb the carbon cost for speedy service. (C) 2017 Elsevier Ltd. All rights reserved.

This study focuses on two future-oriented emotions, hope and anticipated regret, to predict continued use of information systems. Empirical results based on two studies show that these emotions can exert independent and additive effects on continued use, controlling for previous use behaviour and satisfaction. Interestingly, the effect of hope on continued use is insignificant during initial use but becomes significant at the later stage. Furthermore, disconfirmation and involvement are identified as antecedents of hope and anticipated regret. These findings suggest that incorporating forward-looking variables into models of continued use is necessary and that their influence can be dynamic in nature.