A FARM-TO-DOOR DELIVERY MODE FOR ORGANIC VEGETABLES UNDER MOBILE COMMERCE IN METROPOLISES OF CHINA

This paper presents a farm-to-door delivery mode for organic vegetables, which connects farmers and customers directly, under the circumstance of mobile commerce (M-commerce). In recent years, the need of organic vegetables is growing constantly in China. Meanwhile, the farm-to-door delivery mode widely spread in metropolises as people there barely have time to go to food markets on weekdays. However, the terrible traffic condition makes it impossible to conduct the delivery in day time. So vegetables have to be delivered very early in the morning (usually 3:00-7:00 A.M.), which makes the owner unable to attend delivery. And in the traditional delivery mode, the absence of delivery may lead to the package missing in China. Aiming at solving these practical issues in China, an SMS-based interaction system is integrated in the delivery mode for informing, endorsing, confirming, tracing and complaining. Intelligent cupboards are used as a buffer to realize the asynchronously endorsement. This is a new business mode that extends the frontiers of the M-commerce. It can greatly reduce the intermediate links of vegetable distribution and simplify the food purchasing in people’s daily life. This application of mobile technology would have a huge potential in market

A Decision Tree Approach for Assessing and Mitigating Background and Identity Disclosure Risks

The Facebook/Cambridge Analytica data scandal shows a type of privacy threat where an adversary attacks on a massive number of people without prior knowledge about their background information. Existing studies typically assume that the adversary knew the background information of the target individuals. This study examines the disclosure risk issue in privacy breaches without such an assumption. We define the background disclosure risk and re-identification risk based on the notion of prior and conditional probabilities respectively, and integrate the two risk measures into a composite measure using the Minimum Description Length principle. We then develop a decision-tree pruning algorithm to find an appropriate group size considering the tradeoff between disclosure risk and data utility. Furthermore, we propose a novel tiered generalization method for anonymizing data at the group level. An experimental study has been conducted to demonstrate the effectiveness of our approach

A method integrating simulation and reinforcement learning for operation scheduling in container terminals

The objective of operation scheduling in container terminals is to determine a schedule that minimizes time for loading or unloading a given set of containers. This paper presents a method integrating reinforcement learning and simulation to optimize operation scheduling in container terminals. The introduced method uses a simulation model to construct the system environment while the Q-learning algorithm (reinforcement learning algorithm) is applied to learn optimal dispatching rules for different equipment (e.g. yard cranes, yard trailers). The optimal scheduling scheme is obtained by the interaction of the Q-learning algorithm and simulation environment. To evaluate the effectiveness of the proposed method, a lower bound is calculated considering the characteristics of the scheduling problem in container terminals. Finally, numerical experiments are provided to illustrate the validity of the proposed method

A Heuristic Solution Approach to Order Batching and Sequencing for Manual Picking and Packing Lines considering Fatiguing Effect

In this research, we study an extended version of the joint order batching and scheduling optimization for manual vegetable order picking and packing lines with consideration of workers’ fatiguing effect. This problem is faced by many B2C fresh produce grocers in China on a daily basis which could severely decrease overall workflow efficiency in distribution center and customer satisfaction. In this order batching and sequencing problem, the setup time for processing each batch is volume-dependent and similarity dependent, as less ergonomic motion is needed in replenishing and picking similar orders. In addition, each worker’s fatiguing effect, usually caused by late shift and repetitive operation, which affects order processing times, is assumed to follow a general form of logistic growth with respect to the start time of order processing. We develop a heuristic approach to solve the resultant NP-hard problem for minimization of the total completion time. For order batching, a revised similarity index takes into account not only the number of common items in any two orders but also the proportion of these items based on the vegetable order feature. To sequence batches, the genetic algorithm is adapted and improved with proposed several efficient initialization and precedence rules. Within each batch, a revised nondecreasing item quantity algorithm is used. The performance of the proposed heuristic solution approach is evaluated using numerical instances generated from practical warehouse operations of our partnering B2C grocer. The efficiency of the proposed heuristic approach is demonstrated

Review of Green Supply Chain Management

Today green supply chain management (GSCM) has caused increasing emphasis, as a kind of modern management mode which takes environmental impact and resource efficiency into a comprehensive consideration within the entire supply chain. A systematic literature review of green supply chain is presented in this paper, from the perspectives of concept and connotation, operations, performance evaluation and the applications in specific industries. The paper also identifies some research fields need to be studied in the near future

To Discount or Not to Discount: A Game-Theoretic Analysis of the Pricing and Survival Dilemma in Luxury E-commerce

Selling conspicuous products online is challenging for both brands (brands) and third-party e-commerce platforms (platforms). For brands, the trade-off between offering online discount and maintaining high-end brand image gives no easy answer, which leads to wide application of Minimum Advertised Pricing (MAP) policy to restrain their retailers. For platforms, lack of authority in controlling prices leaves them in a struggling-to-survive situation. Platforms could resort to Platform Discount promotion only when brands agree to participate. This interaction between MAP policy and platform discount, even though it is crucial for conspicuous brands and platforms, has not been studied in the current literature. To fill this gap, we analyze interaction between the brands and third-party e-commerce platforms using a game-theoretic model

Vehicle routing with heterogeneous service types : Optimizing post-harvest preprocessing operations for fruits and vegetables in short food supply chains

This study focuses on the post-harvest preprocessing of fruits and vegetables, aiming to provide an effective way to conduct preprocessing operations in short food supply chains. We consider both a heterogeneous fleet of mobile preprocessing units and the possibility to pick up products for centralized preprocessing. The resulting problem is a variant of the classic heterogeneous fleet vehicle routing problems with time windows (HFVRPTW), with the additional consideration of multi-depot and heterogeneous service types, which we refer to as HFVRPTW-MDHS. These additional considerations are important to include in the development of more efficient food supply chains, but lead to a challenging routing problem. In this paper, we formulate the HFVRPTW-MDHS using a mixed-integer linear programming model. Due to the complexity of the model, we propose a customized adaptive large neighborhood search (ALNS) metaheuristic. We design a multi-level struct-based solution representation to improve the efficiency of the ALNS and develop customized methods for solution evaluation, feasibility checks, and neighborhood search. Comparing our results with the results of an exact algorithm and solutions in the existing literature, we find that our ALNS algorithm can obtain high-quality solutions quickly when solving HFVRPTW-MDHS and related variants of the VRP. Finally, we study the application of our approach in the case of precooling, which is a commonly used preprocessing operation, to illustrate the effectiveness of our approach in a relevant practical context