Journal of Management Science and Engineering最新文献

Additive manufacturing (AM) has attracted significant attention in recent years based on its wide range of applications and growing demand. AM offers the advantages of production flexibility and design freedom. In this study, we considered a practical variant of the batch-processing-machine (BPM) scheduling problem that arises in AM industries, where an AM machine can process multiple parts simultaneously, as long as the two-dimensional rectangular packing constraint is not violated. Based on the set-partitioning formulation of our mixed-integer programming (MIP) model, a branch-and-price (B&P) algorithm was developed by embedding a column-generation technique into a branch-and-bound framework. Additionally, a novel labelling algorithm was developed to accelerate the column-generation process. Ours is the first study to provide a B&P algorithm to solve the BPM scheduling problem in the AM industry. We tested the performance of our algorithm using a modern MIP solver (Gurobi) and real data from a 3D printing factory. The results demonstrate that for most instances tested, our algorithm produces results similar or identical to those of Gurobi with reasonable computation time and outperforms Gurobi in terms of solution quality and running time on some large instances.

E-commerce is a typical form of retail digitalization that introduces online uncertainty and product returns. To decrease the negative influence of online uncertainty, the largest Chinese e-commerce company, the Alibaba Group, invited an insurance company to develop return-freight insurance (RFI), a new kind of insurance, to compensate for consumers' losses in the event of online product returns. Complimentary RFI can increase consumer confidence in the retailer and attract more demand. Retailers who offer complimentary RFI demonstrate to consumers that their products and services are too good to incur excessive product returns. However, some low-quality online retailers can mimic competitors’ behavior by offering complimentary RFI to consumers. This study aims to introduce an innovative online return policy based on RFI and to explore whether low-quality online retailers would use complimentary RFI as their return strategy to mislead consumers. Using signaling theory, we built a conceptual economic model that includes three exogenous pricing variables: RFI, insurance premium, and compensation. These variables play different roles in the model because consumers cannot observe the insurance premium, but the compensation can be. The main finding of this study is that innovative complimentary RFI could be abused by low-type retailers when the premium and compensation are appropriate. Interestingly, compensation plays different roles for retailers with different product values: low-type retailers use complimentary RFI as a noise tool. When the product works for the consumer and the insurance profit is not too high, the compensation for the low-quality product should be larger than that for the high-quality product, which is different from conventional wisdom. Although high-type online retailers may use complimentary RFI as a product quality signal, there is still a significant risk that nefarious elements will use it to create product quality noise.

This study used dummy variables to measure the influence of day-of-the-week effects and structural breaks on volatility. Considering day-of-the-week effects, structural breaks, or both, we propose three classes of HAR models to forecast electricity volatility based on existing HAR models. The estimation results of the models showed that day-of-the-week effects only improve the fitting ability of HAR models for electricity volatility forecasting at the daily horizon, whereas structural breaks can improve the in-sample performance of HAR models when forecasting electricity volatility at daily, weekly, and monthly horizons. The out-of-sample analysis indicated that both day-of-the-week effects and structural breaks contain additional ex ante information for predicting electricity volatility, and in most cases, dummy variables used to measure structural breaks contain more out-of-sample predictive information than those used to measure day-of-the-week effects. The out-of-sample results were robust across three different methods. More importantly, we argue that adding dummy variables to measure day-of-the-week effects and structural breaks can improve the performance of most other existing HAR models for volatility forecasting in the electricity market.

When selling multiple products with asymmetric uncertainty, should the seller disclose product information so that customers do not have to incur any cost to resolve their uncertainties; if so, which product should the seller choose? To address these questions, we consider a monopolist selling two substitutable products to a group of consumers. Each consumer has asymmetric uncertainty regarding the two products. A total of four different information provision structures are considered based on whether the seller discloses information about each product with the aim of determining which strategy provides the seller with the greatest revenue. We derive several interesting results. First, the optimal information provision strategy depends on the magnitude of uncertainty in relation to the product with lower uncertainty. Specifically, if the uncertainty regarding the product with lower uncertainty is sufficiently small, it is optimal for the seller to provide information about the product with higher uncertainty, otherwise, the seller should provide information about both products. Second, when only one product's information should be revealed, it is optimal for the seller to choose the product with higher uncertainty and charge a higher price. Third, withholding information on both products is never optimal for the seller. Finally, our main model is extended by examining the Mean-Preserving Spread setting, and the robustness of our main results is confirmed. Furthermore, we examine the situation in which a monopolist sells a single product with two main attributes. We find that each of the four information provision strategies can be optimal under various scenarios.

With strong government advocacy and encouragement, many manufacturers hope to enter the remanufacturing market. Manufacturers who have entered the remanufacturing market hope to increase their profits through effective decision-making. Using game-theoretic models, this study investigates manufacturers’ conditions for introducing remanufactured products and the production decisions after the introduction by constructing a consumer utility model. Our study demonstrates that manufacturers’ decision-making method directly affects their decision to introduce remanufactured products. If a manufacturer plans to introduce remanufactured products, they should adopt a centralized decision-making method for the two products. Under this decision-making method, when the ratio of the government subsidy to the cost of new products is not too large or too small, the manufacturer can introduce remanufactured products. Additionally, the range of the ratio of the government subsidy to the cost of new products is related to the difference between the ratio of the cost of remanufactured products to that of new products and the substitutability of the remanufactured products. Therefore, when formulating a subsidy, the government should control it within a reasonable range and formulate differentiated subsidy strategies based on different enterprises’ specific conditions to give full play to the benefits of the government subsidy. Moreover, after the manufacturer has introduced remanufactured products, the consumer surplus and manufacturer’s profit increase with the government subsidy. However, social welfare increases only when the government subsidy is within a reasonable range. Furthermore, compared with subsidizing consumers, it is found that subsidizing the manufacturer does not affect their profit, the consumer surplus, and social welfare; however, the range within which the manufacturer can introduce remanufactured products narrows.

Using monthly data from the Shenzhen Stock Exchange's ‘Hudongyi’ platform and comment letters from December 2014 to December 2018, this study investigates the influence of interactive information disclosure on non-penalty regulatory review risk. The findings reveal that the richness and activeness of interactive information disclosure are positively associated with regulatory review risk. Moreover, the non-penalty regulatory review is effective as it significantly reduces the probability of receiving a comment letter in the subsequent three periods. The timeliness of interactive information disclosure is negatively associated with regulatory review risks. Additionally, we find that newspaper media coverage partially mediates the relationship between interactive information disclosure and regulatory review risk. For companies with low levels of internal governance, in low-competitive industries, and state-owned companies, the positive relationship between the number of investor questions and regulatory review risk is strengthened. These findings enrich the literature on the determinants of regulatory review risk and the economic consequences of interactive information disclosure in emerging markets.

This study extends the multi-asset model of Huang et al. (2017), who examine only two types of investors, by adding a new investor type with partial information on the correlation coefficient and re-explores the limited participation phenomenon under correlation ambiguity. We investigate whether asset allocations depend on incomplete information under market equilibrium—specifically, whether investors with less information might hold greater equilibrium positions than investors with more information. We find that, as the true correlation coefficient (and the maximum correlation coefficient for ambiguity-averse investors) increases and asset quality increases, investors with less information escape from low- to high-quality assets, thus exhibiting a flight-to-quality trading pattern in equilibrium.

This study considers a supply chain consisting of a commodity supplier and a final product manufacturer with uncertain demand. In addition to purchasing from the supplier through a forward contract, the manufacturer can adjust their inventory by trading the commodity in an online spot market after observing the actual demand. However, the spot market is imperfect in that transactions cannot be certainly realized and come with additional transaction costs. Furthermore, the spot price is volatile such that overly relying on the spot market is unwise. To investigate how the spot market affects the decisions and coordination in a supply chain, we develop a game-theoretical model incorporating spot trading. We derive the optimal ordering decision in a centralized supply chain, as well as the supplier's and manufacturer's equilibrium pricing and ordering decisions in a decentralized supply chain. The impact of the imperfect spot market on the optimal decisions and profits is analyzed. This study also demonstrates how the supply chain can be coordinated in the presence of an imperfect spot market. Finally, a numerical analysis is performed to examine the analytical results. Our results indicate that the spot market can generally improve the performance of the centralized supply chain and benefit the manufacturer in the decentralized one. However, it can be detrimental to the supplier. The supply chain can be coordinated by a revenue-sharing contract, and both parties' profits can be improved. Our findings suggest that the manufacturer could take advantage of the spot market, and the supplier should attempt to integrate or coordinate the supply chain to share the benefits of spot trading.

Efforts to provide alternative resources and technologies for producing liquid fuel have recently been intensified. Different levels of dependence on oil imports and carbon prices have a significant impact on the composition of the cost-minimizing portfolio of technologies. Considering such factors, how should China plan its future liquid fuel industry? The model for supporting the technology portfolio and capacity configuration that minimizes the total system cost until 2045 is described in this study. The results obtained for different carbon prices and levels of dependence on oil import indicate that the oil-to-liquid fuel (OTL) will remain dominant in China's liquid fuel industry over the next three decades. If the carbon price is low, the coal-to-liquid fuel (CTL) process is competitive. For a high carbon price, the biomass-to-liquid fuel (BTL) technology expands more rapidly. The results also reveal that developing the BTL and CTL can effectively reduce the oil-import dependency; moreover, a high carbon price can lead to the CTL being replaced with the low-carbon technology (e.g., BTL). Improvement in energy raw material conversion and application of CO₂ removal technologies are also effective methods to control carbon emissions for achieving the carbon emission goals and ultimately emission reduction targets.

In this paper, we propose a novel model for pricing double barrier options, where the asset price is modeled as a threshold geometric Brownian motion time changed by an integrated activity rate process, which is driven by the convolution of a fractional kernel with the CIR process. The new model both captures the leverage effect and produces rough paths for the volatility process. The model also nests the threshold diffusion, Heston and rough Heston models. We can derive analytical formulas for the double barrier option prices based on the eigenfunction expansion method. We also implement the model and numerically investigate the sensitivities of option prices with respect to the parameters of the model.