A distribution-free newsvendor model with balking penalty and random yield

Purpose: The purpose of this paper is to extend the analysis of the distribution-free newsvendor problem under the circumstance of customer balking, which usually occurs when customers are reluctant to buy products if the available inventory falls below a threshold level. Design/methodology/approach: A new tradeoff tool is provided as a replacement of the traditional one to weigh the holding cost and the goodwill costs segment: apart from the shortage penalty, the balking penalty is introduced. Furthermore, such research methodology is employed in the case of random yield. Findings: A model is presented for determining both an optimal order quantity and a lower bound of the profit under the worst possible distribution of the demand. We also study the effects of shortage penalty and the balking penalty on the bias of the optimal order quantity, which have been largely bypassed in the existing distribution-free single period models with balking. Numerical examples are presented to illustrate the result. Originality/value: The incorporation of balking penalty and random yield represents an important improvement in inventory policy performance for distribution-free newsvendor problem when customer balking occurs and the distributional form of demand is uncertain

TripleRE: Knowledge Graph Embeddings via Tripled Relation Vectors

Translation-based knowledge graph embedding has been one of the most important branches for knowledge representation learning since TransE came out. Although many translation-based approaches have achieved some progress in recent years, the performance was still unsatisfactory. This paper proposes a novel knowledge graph embedding method named TripleRE with two versions. The first version of TripleRE creatively divide the relationship vector into three parts. The second version takes advantage of the concept of residual and achieves better performance. In addition, attempts on using NodePiece to encode entities achieved promising results in reducing the parametric size, and solved the problems of scalability. Experiments show that our approach achieved state-of-the-art performance on the large-scale knowledge graph dataset, and competitive performance on other datasets

Rover: An online Spark SQL tuning service via generalized transfer learning

Distributed data analytic engines like Spark are common choices to process massive data in industry. However, the performance of Spark SQL highly depends on the choice of configurations, where the optimal ones vary with the executed workloads. Among various alternatives for Spark SQL tuning, Bayesian optimization (BO) is a popular framework that finds near-optimal configurations given sufficient budget, but it suffers from the re-optimization issue and is not practical in real production. When applying transfer learning to accelerate the tuning process, we notice two domain-specific challenges: 1) most previous work focus on transferring tuning history, while expert knowledge from Spark engineers is of great potential to improve the tuning performance but is not well studied so far; 2) history tasks should be carefully utilized, where using dissimilar ones lead to a deteriorated performance in production. In this paper, we present Rover, a deployed online Spark SQL tuning service for efficient and safe search on industrial workloads. To address the challenges, we propose generalized transfer learning to boost the tuning performance based on external knowledge, including expert-assisted Bayesian optimization and controlled history transfer. Experiments on public benchmarks and real-world tasks show the superiority of Rover over competitive baselines. Notably, Rover saves an average of 50.1% of the memory cost on 12k real-world Spark SQL tasks in 20 iterations, among which 76.2% of the tasks achieve a significant memory reduction of over 60%.Comment: Accepted by KDD 202

Fermentation improves flavors, bioactive substances, and antioxidant capacity of Bian-Que Triple-Bean Soup by lactic acid bacteria

The ancient traditional Chinese drink Bian-Que Triple-Bean Soup made by fermentation (FTBS) of Lactococcus lactis subsp. lactis YM313 and Lacticaseibacillus casei YQ336 is a potential functional drink. The effect of fermentation on the flavor and biological activity of FTBS was evaluated by analyzing its chemical composition. Five volatile flavors were detected in modified FTBS. Fermentation decreased the proportion of nonanal (beany flavor substances) but significantly increased the total flavone contents, phenol contents and many bioactive small molecule substances in FTBS. The changes of these substances led to the significant improvement of FTBS sensory evaluation, antioxidant activity and prebiotic potential. This research provides a theoretical basis for the application of Lactic acid bacteria (LAB) in the fermentation of edible plant-based foods and transformation from traditional food to industrial production

Comparisons of Pre-Sale Strategies for a Fresh Agri-Product Supply Chain with Service Effort

Preselling strategies have been a common marketing tool, but research on this selection is limited. Thus, we examine three pre-sale strategies of a manufacturer who produces and sells a seasonal product to a retailer: (1) supplier pre-sale strategy—the supplier carries out preselling by opening a direct channel; (2) retailer pre-sale strategy—the retailer purchases pre-sale products from the supplier and sells them in online and offline channels; and (3) joint pre-sale strategy—the retailer acts as a pre-sale platform, providing order information and pre-sale services. For each preselling mode, we construct the Stackelberg game model aiming to maximize profits and obtain optimal service levels and pricing decisions. We find that the two latter scenarios, that is, to cooperate with the retailer (retailer pre-sale and joint pre-sale strategies), could highly gain more profit for the supplier compared with the former. For the supplier, the joint pre-sale strategy will likely be the dominant strategy because of its wide-range applicability. On the contrary, the retailer is swaying between retailer pre-sale and joint pre-sale strategies under different conditions. For the supplier, leadership always guarantees a high profit, but if the joint pre-sale strategy is adopted, then the existence of a profit-sharing ratio will narrow the profit gap between the two players

Flora of China.

v.2 (no. 1471-1671

Chinese economic trees,

(1921

Green Manufacturing Strategy Considering Retailers’ Fairness Concerns

This paper addresses the problem of green manufacturing decision making for a green dual-channel supply chain (SC). In the investigated SC, the manufacturer will decide whether to adopt green manufacturing under the influence of the retailer’s fairness concern-based dual-channel. Thus, we discuss two decision scenarios: the no green manufacturing strategy with retailer fairness (NM model), and green manufacturing with retailer fairness (GM model). Our study has several findings: Firstly, adopting a green manufacturing strategy is not always beneficial to supply-chain members when a retailer has fairness. In particular, when fairness is at a relatively high level, the manufacturer will not adopt green manufacturing. Secondly, under green manufacturing, the product’s green degree and subsidies have a positive impact on the price and demand and the members’ profit and utility. Besides, the subsidies and retailer fairness have a counter effect on the optimal decision. Thirdly, comparing the two scenarios (NM & GM), we found that the channel price of the GM model is lower than the NM model. Finally, from the perspective of the supply chain system, the system tends toward the manufacturer adopting green manufacturing and maintaining retailer fairness concerns at a lower level