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Vendor credit line ( VCL ) is a common instrument to fund retailers with capital constraints, wherein a supplier extends credit for a retailer's purchases and limits credit size for the sake of default risk. Our study investigates the operational and financial decisions in a supply chain consisting of a supplier and a capital-constrained retailer, wherein the supplier sets credit limits for the retailer's ordering, and then examines the financing choice between VCL and bank loan. We derive the equilibrium credit limits, order quantity and financing mode and conclude that, the credit limits linearly increase with the capital level, and both credit limits and capital level are decisive to the order quantity and financing mode. In the illustrating examples, we identify the decision regions contingent on critical fractiles under different demand distributions (e.g., normal, uniform and exponential) and find the financing choice hinging on the supplier's profit margin if the capital level stays low, otherwise VCL dominates. Finally, sensitivity analysis on key parameters is employed, suggesting that VCL is more preferable as the wholesale price increases or as the retail price and production cost decreases. [ABSTRACT FROM AUTHOR]

With data-driven optimization, this study investigates the sellers' inventory replenishment and financial decisions, and lenders' interest rate decisions in online retailing platforms. Moreover, we focus on the annual large-scale promotion, which requires massive capital in a short period. While scholars studying the data-driven inventory replenishment problem hardly consider capital-constrained sellers, these problems are important because the seller's capital level can significantly influence the order quantity and generate different effects on inventory management. Hence, we propose two novel data-driven game-theoretic approaches (including separated and integrated methods) using machine learning and deep learning methods to optimize inventory replenishment and financial decisions for the sellers who obtain financial support from the online platform. Moreover, we propose a data-driven game-theoretic model for the online platform to optimize their interest rate considering the market potential. We explore the real retailing transaction data containing 199,390 weekly sales records. We find that the seller and lender can benefit when the seller chooses integrated machine learning and quantile regression method. However, we find that only a low capital level can motivate the seller to choose to borrow from the lender. Interestingly, our results also suggest that the lender has the motivation to build a data-driven system that helps sellers optimize inventory decisions. Our work identifies the optimal interest rate and inventory decision under the data-driven method. We propose data-driven decision support tools by evaluating the values of both the lender's and the seller's profit and provide new management insights on joint inventory and financing decisions. [ABSTRACT FROM AUTHOR]

This paper explores the optimal joint decision of product information disclosure and ordering in a blockchain-enabled luxury supply chain. Using analytical models, we investigate the optimal joint decision of information disclosure and ordering under three scenarios (i.e., wholesale contracts only, revenue-sharing (RS) contracts only, and a hybrid of these two types of contracts). Furthermore, we extend our study to examine the impacts of the number of competing retailers and the retailers' fairness concerns on supply chain members' optimal decisions. Lastly, the theoretical results are checked and illustrated by numerical examples with sensitivity analysis. The main findings are as follows: (1) As the proportion of information-sensitive consumers in the market increases, the level of product information disclosure of supply chain members increases in varying degrees, while supply chain members' order quantities and profits first decrease and then increase in varying degrees. (2) When a RS contract is acceptable for all supply chain members, all members benefit from the cooperation between the manufacturer and retailers. (3) Although all supply chain members may benefit from an increase in the number of retailers, when the number of retailers is greater than a certain threshold, retailers would be caught in a "prisoner's dilemma" of product information disclosure due to consumer information overload. Moreover, to maximize business profits, manufacturers should sometimes strictly limit and control the number of their reseller partners, rather than blindly expand their markets. (4) Retailers may benefit from their own fairness concerns if and only if the level of fairness concerns is sufficiently low, otherwise such concerns would be harmful to all supply chain members. [ABSTRACT FROM AUTHOR]

High-sugar diet causes health problems, many of which can be addressed with the use of sugar substitutes. Rubusoside and rebaudiosides are interesting molecules, considered the next generation of sugar substitutes due to their low-calorie, superior sweetness and organoleptic properties. However, their low abundance in nature makes the traditional plant extraction process neither economical nor environmental-friendly. Here we engineer baker's yeast Saccharomyces cerevisiae as a chassis for the de novo production of rubusoside and rebaudiosides. In this process, we identify multiple issues that limit the production, including rate-liming steps, product stress on cellular fitness and unbalanced metabolic networks. We carry out a systematic engineering strategy to solve these issues, which produces rubusoside and rebaudiosides at titers of 1368.6 mg/L and 132.7 mg/L, respectively. The rubusoside chassis strain here constructed paves the way towards a sustainable, large-scale fermentation-based manufacturing of diverse rebaudiosides. Rubusoside and rebaudiosides are considered the next generation of sugar substitutes. In this article, the authors report the engineering of Saccharomyces cerevisiae, remodelling the complex metabolic networks by a modular engineering approach, obtaining rubusoside and rebaudiosides at titers of around 1.4 g/L and 100 mg/L, respectively. [ABSTRACT FROM AUTHOR]

Repeat buyer prediction is crucial for e-commerce companies to enhance their customer services and product sales. In particular, being aware of which factors or rules drive repeat purchases is as significant as knowing the outcomes of predictions in the business field. Therefore, an interpretable model with excellent prediction performance is required. Many classifiers, such as the multilayer perceptron, have exceptional predictive abilities but lack model interpretability. Tree-based models possess interpretability; however, their predictive performances usually cannot achieve high levels. Based on these observations, we design an approach to balance the predictive and interpretable performance of a decision tree with model distillation and heterogeneous classifier fusion. Specifically, we first train multiple heterogeneous classifiers and integrate them through diverse combination operators. Then, classifier combination plays the role of teacher model. Subsequently, soft targets are obtained from the teacher and guide training of the decision tree. A real-world repeat buyer prediction dataset is utilized in this paper, and we adopt features with respect to three aspects: users, merchants, and user–merchant pairs. Our experimental results show that the accuracy and AUC of the decision tree are both improved, and we provide model interpretations of three aspects. [ABSTRACT FROM AUTHOR]

Multi-deep automated storage and retrieval systems (AS/RSs) have seen many implementations in warehouses due to their high floor space utilization. However, the rack size, which will fundamentally affect the operating efficiency of the storage and retrieval machine in the system, should be optimized analytically. While extensive studies have been made to analyze the system performance and optimize operating policies for single-deep and double-deep AS/RSs by travel-time models. No analytical models are available for multi-deep AS/RSs. To fill this gap, we develop travel-time models for this system, considering the random storage policy and two class-based storage policies in a multi-deep AS/RS: the one that zoning only on picking face and the one that zoning on both picking face and the depth direction. Based on the travel-time models, we derive the formulation of the optimal system size, to minimize the expected travel time of S/R machine. Simulation models are built to validate the analytical models and the results show that the maximum relative error between analytical result and simulation result is 4.3%. Numerical experiments are conducted to find the optimal size of a multi-deep AS/RS and compare the performance of these storage policies. The results show that class-based storage policy always outperform the random storage policy in terms of expected travel time, and the class-based storage policy that zoning on both picking face and the depth direction may not be better than that only zoning on the picking face, while it may be harder to handle in the system. [ABSTRACT FROM AUTHOR]

The nucleoside antibiotic toyocamycin (TM), which was produced by Streptomyces diastatochromogenes 1628, was found to be highly efficient against a broad range of plant pathogenic fungi. Despite its importance, little is known about the regulation TM biosynthesis. In this study, toyA, located in the TM biosynthetic gene cluster, was identified as a regulatory gene encoding a large ATP-binding regulator of the LuxR family (LAL-family). The role of toyA in TM biosynthesis in S. diastatochromogenes 1628 was investigated by gene deletion, complementation, and over-expression. Gene disruption of toyA resulted in almost loss of TM production. TM production in complemented strain was restored to the level comparable to that in the wild-type strain S. diastatochromogenes 1628. Over-expression of toyA separately controlled by promoter SPL57, SPL21, and permE* in wild-type strain S. diastatochromogenes 1628 led to a 2-fold, 1-fold, and 80% increase in TM production compared with wild-type strain S. diastatochromogenes 1628, respectively. Quantitative RT-PCR analysis revealed that the transcriptional level of toy structural genes was downregulated in the ΔtoyA mutant but restored in complemented strain and further upregulated in the toyA over-expression strain. The detection results from GFP reporter system in Escherichia coli and GUS reporter system and GUS activities in S. albus J1074 and S. diastatochromogenes 1628 showed that ToyA activated the expression of toyB and toyE operon directly and activated the expression of other toy structural genes indirectly. These results indicate that ToyA is essential for TM biosynthesis controlling the expression of structural genes. [ABSTRACT FROM AUTHOR]

We study a new multiple-lift compact automated parking system, which is a new technology with a circular structure using carousel equipped with the shuttles for the horizontal movement and multiple lifts equipped with the platforms for the vertical movement. This paper investigates dedicated and random shuttles operating policies. We divided the carousel into areas based on the number of lifts. We propose a dedicated shuttle operating policy under which each area shuttles can only serve one dedicated lift. A random shuttle operating policy under which shuttles can serve any lift. We get expected throughput time models for the two shuttle operating policies under the same system physical configurations based on queuing network models. We compare the analytical models with simulation models, and the results show that the proposed models provide accurate estimates. A numerical experiment is conducted to illustrate the trade-offs between dedicated shuttle and random shuttle operating policies. The results show that the retrieval time in random shuttle operating policy is larger than the retrieval time in dedicated shuttle operating policy. We also get the optimal system structure, including the number of the lifts. [ABSTRACT FROM AUTHOR]

In today's competitive manufacturing market, effective production planning and scheduling are crucial to streamline production and increase profit. Successful production planning can achieve efficient capacity utilization and fulfill customer demand in a timely manner. For assemble-to-order companies, the assembly production planning is mainly driven by customer orders. In literature, the master production schedule which assigns production orders of individual models to production intervals is generally treated independently from the product sequencing, which might lead to local optimization for the final assembly schedule. In this paper, both order scheduling and mixed-model sequencing are taken into account simultaneously to formulate the final assembly schedule. Three objectives are concurrently considered including, maximizing net profit earned from orders, reducing sequence-dependent setup time between different models and leveling material usage. A novel multi-objective hybrid artificial bee colony (MHABC) algorithm combined with some steps of genetic algorithm and the Pareto optimality is developed to solve the current problem. Experiments are conducted and performance of the proposed MHABC algorithm is examined with the improved strength Pareto evolutionary algorithm (SPEA2). The results indicate that the proposed MHABC performs better as compared to the SPEA2 and gives better Pareto optimal solutions. Finally, a practical case problem from an engineering machinery company is solved with the proposed approach for simultaneous order scheduling and mixed-model sequencing. [ABSTRACT FROM AUTHOR]

In conventional automated storage and retrieval systems (AS/RS), storage and retrieval (S/R) machine travels simultaneously in the horizontal and vertical directions. However, S/R machine cannot support overly heavy loads, such as sea containers, so a new AS/RS, called split-platform AS/RS (SP-AS/RS), was introduced and studied in recent years. The SP-AS/RS employs vertical and horizontal platforms, which move independently, and are capable of handling heavy loads. The vertical platform which represents an elevator (or lift) with the elevator's lifting table carries the load up and down among different tiers and the horizontal platform which represents the shuttle carrier or the shuttle vehicle can access all cells of the tier in which it belongs to. Single command cycle (SC) and dual command cycle (DC) are two main operating modes in AS/RSs. However, travel time models in all previous articles related to the SP-AS/RS are only for the SC. In this study, we first present a continuous travel time model for the DC in the SP-AS/RS under input and output (I/O) dwell point policy and validate its accuracy by computer simulations. Our model and simulation results both show that the square-in-time rack incurs the smallest expected travel time. After comparing with the existing model for the SC, we find that the DC is better than the SC in terms of the expected travel time. [ABSTRACT FROM AUTHOR]