Journal of Management Science and Engineering最新文献

Although the carbon pricing policy is a critical driving factor that will help China achieve economic growth, energy transition, and dual climate change mitigation goals, the kind of carbon pricing policy that will complement the country's current development situation remains controversial. We apply the World Induced Technical Change Hybrid (WITCH) model to explore the heterogeneity and synergy of different carbon pricing policies, and the results indicate that it will be challenging to achieve carbon neutrality before 2060. The study find that the combined policy —a mix of carbon tax and carbon market policies — has the optimal emission reduction effect but comes with the highest economic cost, proving to be unsuitable in the long run. The carbon tax policy is an important transitional means to assist in emission reduction, which can serve as an important supplement to carbon market policy and be phased out after the market mechanism matures.

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.

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.

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.

With increasing public environmental awareness, green activities in retail and distribution processes have become crucial tools for retailers to boost demand and enhance competitiveness. This study develops an analytical model to study the green investment choices of two differentiated retailers dealing with a common green manufacturer. It also explores the impacts of these investment choices on the manufacturer's operational decisions, channel efficiency, consumer welfare, and the environment. We derive three main results. First, the powerful retailer always favors green investments, whereas the less powerful (inferior) retailer may either prefer or avoid green investments. The fiercer the inter-retailer competition, the lower the willingness of the inferior retailer to introduce green investments. Second, although all supply chain parties may disagree on their preferences for retailers' green investments, a bilateral green investment (i.e., both retailers make green investments) can reach an incentive alignment for all firms if the differentiation between retailers is low enough and the competition between them is not substantially fierce. Moreover, a bilateral green investment improves consumer welfare and channel efficiency because of the great demand expansion and double marginalization reduction. Third, the retailers' green investments can motivate the manufacturer to produce greener products, but they do not necessarily benefit the environment. We show that the supply chain's economic sustainability aligns with its environmental sustainability only if the environmental improvement efficiency of green investments is substantially high. We further examine the impact of retailers with differentiated green investment abilities and the manufacturer's green investment efficiency to verify the robustness of the main results.

A clear understanding of customer needs is key to the success of new product development and customer-centric product design. Online reviews, particularly initial reviews, are commonly used as effective sources for mining customer requirements. Although customers may post truer and more objective supplementary reviews after a period of product use, such reviews are often overlooked when identifying customer requirements for product design improvement. In this study, we proposed a framework for identifying customer requirements by combining initial and supplementary reviews based on text mining. We categorized the identified customer needs into five product attribute categories using the Kano model. Two case studies were conducted in the laptop and cell phone industries to demonstrate the effectiveness of our method. Thus, dynamic customer requirements and satisfaction can be accurately mined and captured when considering supplementary reviews. In practice, the appropriate use of supplementary reviews may provide valuable guidance for product design and development strategies.

This study proposes a novel and more efficient quantum algorithm for portfolio optimization using quantum combinatorial optimization (QCO) techniques. A recent construction developed in 2021 has sparked the field of financial portfolio optimization through the Quantum Walk Optimization Algorithm (QWOA). In this study, we investigated the complexity and efficiency of quantum optimization algorithms with a special interest in QWOA. The objective is to minimize investment risk by having a good combination of assets in the portfolio. We also focused on reducing the number of iterations while attaining a high-quality resolution through contraction of the solution space to ease computations. The concept of QWOA was extended by constructing a newly outperforming scheme known as the “Quantum Mix Optimization Algorithm (QMOA).” QMOA algorithm codes were provided for the implementation and simulation of numerical results. In addition, the efficiency of QMOA, which is better than the existing QCO algorithms, was discussed. For instance, the least QWOA number of computations required to execute the initial state equation was p > 2, whereas this value was p ≥ 2 in the proposed QMOA.

Under the combined effects of inventory-level-dependent demand (ILDD) and trade credit, the retailer is able to order more quantities to stimulate market demand. However, from the supplier's perspective, two important issues are lacking sufficient attention. First, during the credit period, the retailer's higher order quantities imply increases in both the retailer's account payable and the supplier's opportunity cost of capital. Second, given the supplier's fixed production rate, the increased market demand may drive the capacity utilization to be variable. Thus, by formulating a supplier-dominated system, this paper incorporates trade credit limit (TCL) to address its effects on optimal policies vis-à-vis the item with ILDD. Specifically, three indicators can be proposed to reveal which type of financing policy the retailer should choose. Moreover, based on TCL, the supplier can effectively manage the retailer's order quantity and the corresponding account payable. Additionally, the retailer's maximum allowable order quantity is developed to ensure that the supplier can supply the retailer's order quantity on time. Furthermore, when the effects of ILDD become more significant, the manufacturer will reduce the maximum allowable order quantity to control the retailer's order incentive.