The surge in product returns poses significant financial and environmental challenges, particularly for seasonal goods, as retailers must often discard or liquidate returned items at the end of a short selling season. To address these challenges, we propose a tiered-refund policy integrated with return restoration . Under this policy, the retailer offers full refunds for early returns and only partial refunds for late returns, creating a supply of early returns that can be restored and resold as new. By employing a two-period model to capture the essential dynamic of early and late returns, and accounting for consumer valuation uncertainty and the disutility of returning early, we characterize the retailer’s optimal late-return refund, initial inventory level, and restoration quantities. We investigate the impact of the proposed tiered-refund policy with return restoration relative to two benchmarks: a restrictive short-return-window policy and a lenient full-refund policy. Our analytical results show that, even without restoration, the tiered-refund policy dominates the short-return-window policy, and outperforms the full-refund policy unless consumer valuations are low. When restoration is feasible, the tiered-refund policy becomes even more attractive compared to the full-refund policy for lower consumer valuations and strictly dominates if restoration costs are not prohibitively high. We also find that the operational flexibility created by restoration leads the retailer to lower the optimal late-return refund in order to incentivize early returns. Numerical experiments using practice-calibrated parameters show that the tiered-refund policy with restoration increases profits by an average of 11% relative to the short-return-window policy and 7% relative to the full-refund policy, with maximum gains reaching 38%. Furthermore, the policy reduces total environmental impact by an average of 1.5% and 5% against the respective benchmarks, with reductions of up to 13%. These sustainability gains are driven by converting otherwise discarded returns into usable products and by reducing initial inventory requirements through restoration. Finally, we find that tiered refunds and restoration are complementary in improving profitability.

This paper examines the retailer's selection between KOL (Key Opinion Leader)-live and self-live selling when introducing a live-streaming channel alongside an existing traditional e-channel and investigates how pricing decisions regulate demand and align channel incentives. While KOLs expand market size, they also incur commission fees and may trigger impulsive buying, leading to higher returns. Our results show that the retailer's selection of selling formats depends on both impulsive buying behavior and the channel structure. In the centralized structure, the retailer adopts KOL-live at a high commission fee when KOL's market expansion efficiency is high in the absence of impulsive consumers (ICs). Specifically, the retailer leverages KOL-live for market expansion, while setting a lower price on the traditional e-channel than on the live-streaming channel to divert purchases away from the live-streaming channel and avoid a high commission fee. Otherwise, self-live is adopted. However, ICs discourage the retailer from adopting KOL-live under a high commission fee. When the commission fee is moderate, the retailer can utilize the live-streaming channel (traditional e-channel) to target ICs (Non-Impulsive consumers) by setting a relatively high price for live-streaming channel. In the decentralized structure, the manufacturer can strategically lower the wholesale price to increase the retailer's preference for KOL-live. Moreover, decentralization alleviates the negative impact of ICs.

We consider a problem where a seller presells a product and the demands in the advance and spot periods are stochastic and correlated. There are two types of consumers, informed and uninformed consumers, in the market, depending on their arrival time. Informed consumers who arrive in the advance period are uncertain about their valuation of the product, and the uncertainty will be resolved until the spot period. Considering the correlation between the two types of consumers, by offering a proper advance strategy, the seller can use the advance order information to update his forecast for the spot-period demand and make better inventory decisions accordingly. In this paper, we consider two preorder strategies: the preorder strategy with a price guarantee and the one without a price guarantee. We investigate the seller's optimal advance and spot pricing decisions under the two strategies and investigate the impact of consumers' valuation uncertainty on the seller's decisions. We also conduct numerical experiments to show that the seller should offer a price guarantee when the informed demand uncertainty is large.

Supply stability has become increasingly crucial amid the reshaping of global supply chains. This paper introduces a novel analytical framework to examine the retailer's strategic inventory decisions and the effects on the profits of the manufacturer and the supply chain under three supply chain structures: complete decentralization, partial centralization, and complete centralization. Utilizing a game-theoretic approach, we derive equilibrium outcomes revealing that strategic inventory enhances the manufacturer's profit under complete decentralization. In contrast, this positive effect on the manufacturer's profit is not always observed under partial centralization. Our findings also indicate that the retailer's decision to adopt strategic inventory, along with the impact on the supply chain's profit, is contingent upon the supply stability level and the unit inventory cost. We further analyze the performance of the supply chain under three structures and find that complete centralization always achieves the optimal profit when the retailer does not choose strategic inventory. However, complete centralization does not always achieve the optimal profit when the retailer adopts strategic inventory. These findings provide new insights into the role of strategic inventory across various supply chain structures.

Accelerated by the widespread use of social media, Internet rumors in major public health emergencies will destroy urban resilience. To find the path to improve the effectiveness of government rumor-refutation in major public health emergencies to enhance urban resilience, this paper creatively establishes an assessment research structure of the government's rumor-refutation effectiveness in major public health emergencies, and an assessment criteria system from four perspectives of source, message, channel, and reviewer is constructed, an assessment method incorporating multicriteria decision-making and machine learning methods, i.e., optimal clustering-VIKORSort with hesitant fuzzy linguistic term sets based on combinatorial weighting is proposed. Subsequently, all 102 cases of government rumor-refutation during the COVID-19 lockdown in Wuhan in 2020 are taken as alternatives and assessed. The results show that Wuhan's rumor-refutation effectiveness was not strong. Then, the investigated factors that constrain Wuhan's emergency rumor-refutation effectiveness are diversified. Furthermore, this paper assesses the rumor-refutation effectiveness in Shanghai during the 2022 epidemic, obtains similar problems to Wuhan, and demonstrates the generalizability and robustness of the proposed method. Finally, based on the results, this paper proposes suggestions for improving the government's rumor-refutation effectiveness in major public health emergencies to enhance urban resilience, which is a crucial contribution to combating Internet rumors and improving urban resilience in major public health emergencies.

Facing of the existing reception dilemma of rumor refuting information, crowd identification and feature analysis should be realized through big data analysis. Our approach aims to accurately identify rumor refuters and interpret predictions. Initially, we compare six machine learning models, with results demonstrating the superiority of eXtreme Gradient Boosting (XGBoost) over other advanced models. Subsequently, we introduce Shapley additive explanations (SHAP) to interpret the predictions of complex machine learning models and assess the importance of various features. Our findings underscore that utilizing XGBoost alongside the SHAP approach can offer decision support, enhancing the effectiveness of rumor governance.

In addressing the challenges of climate change, the Chinese government has established a distinct objective to attain its carbon peak by 2030 and strive for carbon neutrality by 2060. This commitment reflects a concerted effort to progressively achieve a state of net-zero carbon dioxide (CO2) emissions. China's agriculture faces the significant task of ensuring food security and contributes to the country's carbon neutrality goals. This study introduces an innovative model for assessing and prioritizing risks in fresh produce supply chain under carbon peaking and carbon neutrality goals in China, employing a hybrid approach combining the Fuzzy Failure Modes and Effects Analysis (FMEA) and Fuzzy VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje). Nine risk indicators related to carbon emissions are identified through literature review and expert opinion. In the Sichuan company case study, R4 energy consumption emerges as the most severe risk criteria in the supply chain, following closely are R6 product transportation and R1 agricultural practices. This framework provides a useful guide for companies and stakeholders in the fresh produce industry to enhance sustainability and reduce risks in their supply chains.

Motivated by the practical needs of improving the rescue efficiency of emergency events and reducing the damage, the problem of emergency material supplier selection arises. This paper aims to propose a novel group decision making framework, this framework can aggregate experts’ opinions from different backgrounds to evaluate emergency material suppliers. Firstly, an evaluation indicator system considering the supplier’s emergency supply capacity is established, to make up for the insufficient applicability of the traditional supplier evaluation indicator system in the field of emergency material supplier evaluation. Then, the game theory integrated weight (GTIW) method is used to gather the initial weights obtained by Maximum Consensus Minimum Hesitation Model (MCMHM) and expert background weights. Moreover, Exponential TODIM (ExpTODIM) method based on Hesitant fuzzy linguistic set (HFLs) named HF-ExpTODIM is introduced to solve the problem of emergency material supplier selection, TODIM is an acronym in Portuguese for Interactive and Multi-criteria Decision Making. Again, a case study of China on emergency medical mask supplier selection is presented, and the corresponding results are discussed. Finally, the practicality of the proposed framework in a real-world context is discussed, including the necessary preparation, operational priorities and caveats. This paper can provide a reference for group decision making of material supplier selection in emergencies.

Low-carb on development is a global trend and a requirement from governments and industries. Many companies produce green products based on brown products to implement green product segmentation strategies, and the platform economy can help them achieve green development. Considering a manufacturer who can choose a pure brown, green low-price segmentation or green high-price segmentation strategy, we construct three corresponding platform supply chain models. By comparing the optimal results of the three models, we observe how the manufacturer performs in terms of economic and ecological benefits, as well as social welfare. Our results show that the pro-portion of green consumers and the green preference of green consumers have a positive impact on the manufacturer's profit. The manufacturer can make the most profit when it produces both brown and green products and sets a higher price for the green products. The green product segmentation improves social welfare, and the manufacturer can achieve a win-win situation in terms of economic and social benefits.

This paper examines remanufacturing decisions in the context of outsourcing, which have important implications for environmental and economic sustainability. Specifically, we model the competition between an experienced Original Equipment Manufacturer (OEM) and an emerging Independent Remanufacturer (IR). The OEM can decide the manufacturing quantities of a brand-new product, and the IR can collect the OEM’s used products and remanufacture them for resale. The information structure is asymmetric, as only the OEM knows the market size. We identify the equilibrium quantities of both firms, which are shown to be strongly influenced by the IR’s cost efficiency and the consumers’ willingness to pay for the IR’s products. Asymmetric information also plays an important role. Is it always better to hide information? Interestingly, the OEM makes the most profit when the IR has full information on the market size. We find that when the market size is high, the OEM’s and IR’s production and encroachment decisions are the same as when both parties have equal information. The OEM also does not benefit from hiding market information from the IR when the market size is low. Indeed, if the IR’s cost efficiency is moderate and the market size is low, the OEM’s profits are actually hurt by hiding market information. Here, the diminished profits from hiding market information arises from the OEM’s substantially reduced production quantity to prevent IR encroachment. The OEM’s production quantity is higher if the OEM shares market information and the IR encroaches on the market. Thus, by sharing information, the OEM’s benefit gained from increased production quantity outweighs the cost of losing its monopoly. Additionally, consumer surplus increases when the IR engages in remanufacturing, while social surplus increases only when either the OEM’s or IR’s product is strongly favored. Even if the IR does not engage in remanufacturing, the resulting OEM monopoly can still lead to a higher environmental impact under certain market conditions. This arises when the OEM lowers production quantities when the IR encroaches on the market, thereby improving the overall environmental impact. Therefore, policymakers seeking to improve environmental and economic sustainability by encouraging IRs must consider these complex competition dynamics and consumer preferences, as they indirectly influence OEMs’ production decisions.

In recent times, fulfillment services in online marketplaces have witnessed a surge in popularity. Dominant e-commerce platforms, such as Amazon.com, now allow small retailers to join their online marketplaces, while concurrently providing comprehensive fulfillment services. In return, small retailers pay a fulfillment fee per unit of sales. We examine the advantages of these fulfillment programs in the context of a dominant retailer that possesses an online marketplace, alongside a small retailer who operates within this marketplace and possesses private fulfillment cost information. We analyze two scenarios, contingent upon the presence or absence of competition between the two retailers, and we derive the optimal fulfillment fees for the dominant retailer. Our findings indicate that the structure of market competition plays a pivotal role in shaping fulfillment fees and associated advantages. In the absence of market competition, the provision of fulfillment services consistently proves advantageous. This is attributed to enhancements in (1) consumer valuation of the small retailer's products and (2) overall system efficiency in fulfillment. Conversely, when there is competition, offering a fulfillment program may not be as lucrative if the improvements in product valuation and the degree of cost heterogeneity are minimal. Furthermore, our findings highlight that enhancements in product valuation can positively impact both retailers and consumer welfare, irrespective of market competition. However, in cases where cost heterogeneity arises due to asymmetric information, it may adversely affect consumer welfare in the absence of competition.

Flood resilience is the principal capability of the flood-affected areas to quickly eliminate the impact of floods. Motivated by the realistic demand of improving the regional flood resilience and reducing negative hazards caused by flood, regional flood resilience evaluation is carried out from the perspective of objective and managerial factors in this paper. Firstly, a novel Multi Criteria Decision Making (MCDM) method G1-EW-MGLA (GEM)-AHPSort II method is proposed by combining AHPSort II method, G1-EW method, and MGLA method. Secondly, the evaluation indicator system of regional flood resilience is constructed from the perspective of objective and managerial factors. Subsequently, the proposed method and indicator system were applied to the case of flood resilience evaluation in Hubei Province, China. The case study analysis results show that in the 17 regions involved in the evaluation, the number of regions with “medium-low resilience” and “low resilience” is close to half, and the flood resilience of the regions with high economic development tends to be a higher level. In addition, under the same case data, the evaluation results of the proposed GEM-AHPSort II method are compared against the EW-AHPSort II method and AHPSort II method. The indicator weights determination process of GEM-AHPSort II method has been improved compared with the other two methods, and the evaluation results are more practical. Finally, some management suggestions for effectively improving the regional flood resilience and reducing flood hazards were put forward. This paper can provide a reference for the government to formulate or improve relevant flood protection strategies.

Counterfeiting is an important challenge in maintaining the security and sustainability of supply chains. This paper examines a supply chain consisting of a luxury goods manufacturer (and a retailer) in the presence of counterfeit goods. Inspired by the reality that both manufacturers and retailers have incentives to implement anti-counterfeiting, this paper combines the psychological impact of anti-counterfeiting efforts on consumers and discusses the impact of anti-counterfeiting efforts on pricing and profits. We find that: (1) anti-counterfeiting has a positive impact on the selling price of brand products and the firms’ profits. However, the impact on wholesale prices varies depending on who implements the anti-counterfeiting strategy. (2) Only when the quality of brand products is higher than the threshold, is the firm willing to input anti-counterfeiting efforts. Manufacturers in a reselling structure are more motivated to fight counterfeits. (3) Implementing anti-counterfeiting in a direct selling structure is the most effective strategy for manufacturers. Under a reselling structure, it is more beneficial for manufacturers to have the retailer input anti-counterfeiting efforts. Our study provides insights into the reasons why some manufacturers establish internal anti-counterfeiting teams under the direct selling structure, while others incentivize retailers to invest in anti-counterfeiting.

Deviations between consumers' information gathering and purchase channels may lead to showrooming and webrooming, where the former refers to obtaining product information in a brick-and-mortar (BM) store but purchasing online while the latter corresponds to the reverse. In this paper, we endogenize con-sumers' information gathering and purchase decisions and characterize the optimal information provision decision for an online retailer in the presence of a rival BM store. For instances where showrooming can arise, we find that the optimal information level decreases with the fraction of consumers who consider showrooming. Despite the popular belief that showrooming is always detrimental to the BM store, our results suggest that showrooming may increase the profit of the BM store and decrease the profit of the online store. In instances with webrooming, we again find that the optimal information level decreases with the fraction of consumers who consider webrooming but that the profit of the online retailer al-ways decreases with the fraction of consumers who consider webrooming. In addition, we consider the price matching strategy of the BM store and its interplay with the online retailer's information decisions. Lastly, we briefly extend our work to study settings with return cost, heterogeneity in online shopping cost, all consumers evaluating the product online first, endogenized pricing decisions for retailers, and a retailer owning both online and offline channels. (c) 2022 Elsevier Ltd. All rights reserved.

Regional water resources coordination (RWRC) plays a prominent role in long-term sustainability. In order to achieve harmonious utilization of regional water resources, it is suggested that local governments should focus on balancing three goals - security, equity, and efficiency, resulting in both ecological and socio-economic benefits in a region. Therefore, this paper aims to develop a comprehensive evaluation methodology for assessing the degree of RWRC. First, a security-equity-efficiency (SEE) evaluation indicator system is established. A novel multi-criteria decision-making (MCDM) approach that integrates decision-making trial and evaluation laboratory (DEMATEL) method and VlseKriterijumska Optimizacija I Kompromisno Resenje (in Serbian, known as VIKOR) method considering two different hesitant fuzzy linguistic term sets (HFLTSs) is then proposed. With this approach, a final ranking is obtained for six alternatives for the RWRC of the relevant regions (i.e., Shandong Province, Henan Province, Hebei Province, Anhui Province, Tian City, Jiangsu Province) in North China Plain, and the six relevant regions receive coordination performance ranking of II, III, V, I, VI, IV. Moreover, comparison analysis and sensitivity analysis are conducted to verify the effectiveness and reliability of the proposed method and dig deep into the RWRC conditions in the studied regions. Finally, some managerial suggestions are provided for strengthening the coordination levels in these regions.

Nowadays, frequent meteorological disasters that cause huge economic losses and ecological damages have swept the world. Thus, research investigates how to overcome the adverse impacts of storm debris flow by exploring sustainable interaction among disaster, economy, and ecology. To achieve this goal, the study analyzes coupling coordination for disaster–economy–ecology system through data-driven technology named Scrapy engine. To be specific, a comprehensive index system of disaster–economy–ecology is established. Accordingly, a projection pursuit method is used to reduce the dimensions of data involved in the system. Then, an integrated weighting method of interval-valued hesitant fuzzy entropy and maximum deviation of weight is utilized. For further analysis of the internal laws in disaster–economy–ecology system, a coupling coordination model based on order preference by similarity to ideal solution is proposed. Moreover, a back-propagation artificial neural network is designed to identify the key influencing factors in disaster–economy–ecology system. Finally, an empirical study is carried out using the panel data related to storm debris flow of 31 provincial areas in China within 11 years to illustrate the study. The study results show that the overall sustainable development of disaster, economy, and ecology in China does not achieve an ideal status. Various measures based on local conditions are required to improve the imbalanced development of disaster–economy–ecology system in different areas of China. At last, strategic suggestions for sustainable development of disaster–economy–ecology system are provided.

In Book Reviews, we review an extensive and diverse range of books. They cover theory and applications in operations research, statistics, management science, econometrics, mathematics, computers, and information systems. In addition, we include books in other fields that emphasize technical applications. The editor will be pleased to receive an email from those willing to review a book, with an indication of specific areas of interest. If you are aware of a specific book that you would like to review, or that you think should be reviewed, please contact the editor. The following books are reviewed in this issue of INFORMS Journal on Applied Analytics, 51(4), July-August: Applications of Operations Research and Management Science for Military Decision Making, William P. Fox, Robert Burks; Behavioral Operational Research: A Capabilities Approach, Leroy White, Martin Kunc, Katharina Burger, Jonathan Malpass.

In Book Reviews, we review an extensive and diverse range of books. They cover theory and applications in operations research, statistics, management science, econometrics, mathematics, computers, and information systems. In addition, we include books in other fields that emphasize technical applications. The editor will be pleased to receive an email from those willing to review a book, with an indication of specific areas of interest. If you are aware of a specific book that you would like to review, or that you think should be reviewed, please contact the editor. The following books are reviewed in this issue of INFORMS Journal on Applied Analytics, 51(3), May-June: Optimization and Control for Systems in the Big-Data Era: Theory and Applications, Tsan-Ming Choi, Jianjun Gao, James H. Lambert, Chi-Kong Ng, Jun Wang; Pricing Lives: Guideposts for a Safer Society, W. Kip Viscusi.

Safety management is the primary issue in global coal mine enterprises. To ensure the safety of coal mine enterprises reasonably and effectively, it’s critical to establish a scientific sorting method to conduct risk grading for coal mine safety management. Firstly, an indicator system of coal mine safety risk grading is given. Secondly, the AHPsort II method is employed to sort the risk levels of coal mine under a fuzzy environment with triangular fuzzy sets. Furthermore, a numerical example is provided to verify the efficiency of the proposed method. Finally, some recommendations based on the evaluation results are proposed for more effective safety management of coal mine.