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With the rapid advances in information technology, an increasing number of online reviews are posted daily on the Internet. Such reviews can serve as a promising data source to understand customer satisfaction. To this end, in this paper, we proposed a method for modelling customer satisfaction from online reviews. In the method, customer satisfaction dimensions (CSDs) are first extracted from online reviews based on latent dirichlet allocation (LDA). The sentiment orientations of the extracted CSDs are identified using a support vector machine (SVM). Then, considering the existence of complex relationships among different CSDs and the customer satisfaction, an ensemble neural network based model (ENNM) is proposed to measure the effects of customer sentiments toward different CSDs on customer satisfaction. On this basis, to identify the category of each CSD from the customer's perspective, an effect-based Kano model (EKM) is proposed. Finally, an empirical study, which consists of two parts (phones and cameras), is given to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper investigates a two-stage supply chain consisting of a battery supplier (BS) and an electric vehicle manufacturer (EVM). Considering consumers' sensitivity to the battery driving range, the BS can improve the driving range level by investing, and obtain subsidies if the driving range level exceeds the subsidy threshold set by the government. Meanwhile, the BS may misreport his private information of the investment cost to the EVM. We mainly examine the effect of cost information misreporting and how the BS determines the optimal improvement strategy when the subsidy threshold increases. Firstly, a low (high) subsidy threshold makes the BS raise the driving range level above (below) the subsidy threshold; when the subsidy threshold is moderately raised, the choice of the improvement strategy is controlled by both the degree of raising the subsidy threshold and the technical upper limit. Secondly, participants may show different preferences for the improvement strategy under certain conditions. Finally, cost information misreporting brings adverse effects to participants and reduces the driving range level. [ABSTRACT FROM AUTHOR]

This study addresses cooperative batch scheduling problems based on both serial batch (s-batch) scheduling and parallel batch (p-batch) scheduling. A mixed-integer linear programming model was formulated to describe such problems and a bid construction scheme (BCS) was developed to solve them. In s-batch and p-batch scheduling, the sheet strips function as both bidders and auctioned resources, with different sheet strips forming different batches over multiple rounds of auctioning. Then, a local search operator is applied to optimize the feasible solutions. Numerical experiments were conducted on a series of randomly generated and real-world instances. The results obtained by the proposed BCS were compared with those obtained by four combinations of existing algorithms and analysed by SPSS. The proposed scheme was able to identify feasible solutions quickly and efficiently, proving its the effectiveness, stability, practicality, and suitability for addressing cooperative batch scheduling problems, especially those corresponding to large-scale instances. [ABSTRACT FROM AUTHOR]

This paper addresses cell part scheduling (CPS) problem with transportation capacity constraint. In the problem, parts may need to visit different cells, they have to be transferred by automated guided vehicle (AGV). The objective is to minimise the over-all process make-span. An integer nonlinear programming (INLP) model is formulated to allocate the machines, AGVs and schedule all parts. A reasonable transportation mode is presented, and an auction-based heuristic approach is proposed to solve the problem, which focuses on dealing with cooperation between machines and AGVs during processing and transferring parts. The auction consists of two aspects: auction for AGV and auction for machine. In both auctions, AGVs and machines act as auctioneers respectively, and parts act as bidders. A new improved disjunctive graph model is developed to optimise the feasible solutions obtained by auction-based approach. Numerical experiments were conducted to test the auction-based approach and improved disjunctive graph model. The results demonstrate the effectiveness of proposed auction-based approach and improved disjunctive graph model, also indicate influence of the capacity of AGV on scheduling parts. [ABSTRACT FROM AUTHOR]

As an innovative mode of supply chain finance, financing service offered by a third part logistics (3PL) firm has been practiced over the recent years. This paper investigates the optimal operational strategies of the supply chain system consisting of a supplier, a capital-constrained retailer and a 3PL firm. In this study, we compare the optimal operational strategies under both decentralised and centralised decisions, and put forward the supply chain coordination condition when the 3PL firm offers financing service. Several important results are achieved. First, the supplier's wholesale price strategy has obvious influence on the decisions of the retailer and the 3PL firm. Second, the retailer's ordering quantity under 3PL financing service is more than that without any financing service; and it can exceed the ordering quantity of a well-funded retailer, if and only if the supplier's wholesale price and the 3PL's interest rate meet certain conditions. Finally, we find that under 3PL financing service, the profit of supply chain can achieve Pareto improvement, and the wholesale price contract may realise profit maximisation and channel coordination in the supply chain system under certain condition. [ABSTRACT FROM AUTHOR]

This study develops machine learning methods for the data-driven demand estimation and assortment planning problem by addressing three subproblems, that is, demand forecasting simultaneously considering cross-selling and substitutions, estimation of the cross-selling and substitution effects, and assortment optimization. These three subproblems are transformed into three sequentially related machine learning problems: collective demand forecasting, demand inference for cross-selling and substitutions, and assortment rule mining. For collective demand forecasting, related product features are introduced to consider both the cross-selling and substitution effects, and a collaborative coordinate descent method with a good convergence property is developed to make distributed demand forecasting and a global update of related product features. Using the results, demand inference adopts transfer and semisupervised learning methods to tackle the challenge of missing data in quantifying the cross-selling and substitution effects. For assortment rule mining, the assortment rules bridge the gap between prediction and optimization, and the developed heuristics obtain the best assortment using the prior knowledge discovered in demand inference. The computational results on a real-world database and a semisynthetic database show that collective demand forecasting obtained far better results than the standard demand forecasting methods and some popular graph learning methods, and the developed heuristics identified much better assortments than those obtained with the baseline methods. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: This work was supported by the construction base project of discipline innovation and talent introduction plan of Chinese higher educational institutions (111 project) [Grant B16009] and the National Natural Science Foundation of China [Grant 72031002]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/ijoc.2022.1251. [ABSTRACT FROM AUTHOR]

In military manufacturing enterprises, exceptionally high product performance standards are generally met by using heat treatment followed by machining. These procedures are typically parallel batch-processing (BP) scheduling problems and job-shop problems, respectively. A mixed-integer nonlinear programming model is created to describe the problem with a machine availability constraint, which is divided into two stages: BP and machine processing (MP). Furthermore, an auction-based approach is developed in which jobs are categorized into batches during the BP stage and resources are allocated to operating machines during the MP stage. A local search operator is then applied to optimize the obtained feasible solutions. Benchmark instances are enlarged to adapt to the proposed problem. The approach is tested and compared to existing algorithms, and statistical analysis is performed using SPSS Statistics. The results show that the auction-based approach is effective and stable, and has absolute advantages in solving large-scale instances. [ABSTRACT FROM AUTHOR]