As generative audio models are rapidly evolving, AI-generated audios increasingly raise concerns about copyright infringement and misinformation spread. Audio watermarking, as a proactive defense, can embed secret messages into audio for copyright protection and source verification. However, current neural audio watermarking methods focus primarily on the imperceptibility and robustness of watermarking, while ignoring its vulnerability to security attacks. In this paper, we develop a simple yet powerful attack: the overwriting attack that overwrites the legitimate audio watermark with a forged one and makes the original legitimate watermark undetectable. Based on the audio watermarking information that the adversary has, we propose three categories of overwriting attacks, i.e., white-box, gray-box, and black-box attacks. We also thoroughly evaluate the proposed attacks on state-of-the-art neural audio watermarking methods. Experimental results demonstrate that the proposed overwriting attacks can effectively compromise existing watermarking schemes across various settings and achieve a nearly 100% attack success rate. The practicality and effectiveness of the proposed overwriting attacks expose security flaws in existing neural audio watermarking systems, underscoring the need to enhance security in future audio watermarking designs.

With the development of intelligent transportation technologies, on-demand food delivery has become a prevalent logistics service that brings convenience to our daily life. However, problems such as inappropriate assignment and delayed delivery still exist in current food delivery platforms. To address these issues, this paper presents a Crowdsourced Recommending-and-Grabbing (CRG) system for order allocation in on-demand food delivery service. Unlike other traditional logistics systems, the CRG system considers the preferences of crowdsourced riders and allocates orders by recommending them to suitable crowdsourced riders. Riders can grab their favorite orders from personalized order lists. To optimize the experience of both customers and crowdsourced riders, a hierarchical solution framework composed of prediction and optimization is proposed to generate satisfactory order allocation schemes. First at the prediction part, we predict the preferences of crowdsourced riders by establishing a predictive model using eXtreme Gradient Boosting (XGBoost). Then at the optimization phase, an allocating-and-sequencing algorithm is designed to appropriately expose orders to crowdsourced riders and sequence the order list for each rider. Extensive experiments are conducted on real-world datasets and the comparative results demonstrate the effectiveness of the proposed methods, suggesting that the presented CRG system is promising to facilitate the on-demand food delivery effectively and efficiently.

Online food delivery (OFD) service has developed rapidly due to its great convenience for customers, the enormous markets for restaurants and the abundant job openings for riders. However, OFD platforms are encountering enormous challenges, such as massive demand, inevitable uncertainty and short delivery time. This article addresses an OFD problem with stochastic food preparation time. It is a complex NP-hard problem with uncertainty, large search space, strongly coupled subproblems, and high timeliness requirements. To solve the problem, we design an iterated greedy algorithm with a decomposition-based strategy. Concretely speaking, to cope with the large search space due to massive demands, a filtration mechanism is designed by preliminarily selecting suitable riders. To reduce the risk affected by the uncertainty, we introduce a risk-measuring criterion into the objective function and employ a scenario-sampling method. For timeliness requirements caused by short delivery time, we design two time-saving strategies via mathematical analysis, i.e., an adaptive selection mechanism to choose the method with less computational effort and a fast evaluation mechanism based on the small-scale sampling and machine learning model to speed up evaluation. We also prove an upper bound of the stochastic time cost under risk measurement as one of the baseline features to improve the prediction accuracy. The experiments on real-world data sets demonstrate the effectiveness of the proposed algorithm.

As one representative of the emerging on-demand transport services, the on-demand food delivery (OFD) has penetrated into daily life. Due to its intrinsic complexities, the OFD has attracted the interest of a growing number of logistics researchers. This paper aims at optimizing the OFD process and addresses an OFD problem (OFDP). To overcome the dynamic and large-scale complexity, we abstract the OFDP into a static generalized assignment problem with a rolling horizon strategy. To meet the demand on high service quality and limited computation time, we propose an offline-optimization for online-operation framework based on imitation learning. Under this framework, an imitation learning-enhanced iterated matching algorithm (ILIMA) is proposed, which consists of three basic components: an iterated matching heuristic (IMH) to fast generate solutions, an expert to provide expertise, and a machine learning (ML) model to assist the decision-making process in IMH by mimicking the expert. In the offline-optimization phase, the ML model mines knowledge from the high-quality solutions optimized by the expert; in the online-operation phase, the IMH embedded with the well-trained ML model is deployed online to make decisions in a real OFD scenario. Offline simulation experiments are carried out on real historical data, which validate the superiority of ILIMA compared with existing methods. Moreover, rigorous online A/B tests are conducted on the scheduling system of Meituan, which demonstrates the practical value of ILIMA to improve customer satisfaction and delivery efficiency.

In various practical orthogonal frequency-division multiplexing (OFDM) systems, the estimation accuracy at the receiver is challenging, and, specifically when operate over time-varying channels. This occurs mostly due to the presence of multipath Doppler shifts. Meanwhile, deep learning has quite recently demonstrated its superiority in extracting features information from big data. To this end, in this paper, a deep learning-assisted approach for channel estimation refinement is proposed in OFDM systems, under uplink time-varying channels. By exploitingfully-connected deep neural network (FC-DNN) properly, we successfully design a channel parameter refine network (CPR-Net) which combines deep learning with existing channel estimation algorithms. Simulation results demonstrate that, compared with conventional channel estimation algorithms, the proposed CPR-Net can significantly improve the estimation accuracy of channel parameters and provide more accurate and robust signal recovery performance.

In recent years, the online food ordering (OFO) platforms have arose fast and brought huge convenience to people in daily life. Under the scenario of a realistic OFO platform, this paper addresses an online meal delivery problem (OMDP). To reduce the search space, the OMDP is decomposed into two sub-problems, i.e., the pickup and delivery problem and the order dispatching problem. To solve each sub-problem effectively, a hybrid differential evolution algorithm is proposed, which is fused by the DE-based phase to plan routes and the heuristic-based phase to determine order dispatching schemes. In the DE-based routing phase, a heuristic considering the urgency of orders is designed to generate the initial population with certain quality. Besides, a mutation operator is developed to enhance the exploration and a crossover operator embedded with local search is designed to enhance the exploitation. In the heuristic-based dispatching phase, a regret heuristic is presented to produce good dispatching solutions by introducing the influences between delivery persons. Numerical tests have been carried out and computational results demonstrate the effectiveness of the proposed algorithm.

Route planning serves as the most fundamental part in modern food ordering and delivery system, which is also a classical combinatorial optimization problem. In this paper, we study an online extension of traditional route planning problem where a near-optimal solution has to be generated in a very short time. A simple and effective iterated greedy algorithm is presented along with problem-specific initialization rules, destruction procedure and construction procedure. We also propose a local search method, consisting of two adjustment operators and two neighborhood search operators. With experimental results, we show that our algorithm outperforms the compared evolutionary algorithms and has the capability of providing high-quality solutions within milliseconds.

Considering the uncertainty in the real world online food delivery applications, this paper addresses an online order dispatching problem with fuzzy preparation times (FOODP). According to the characteristics of the problem, the FOODP is decomposed into two sub-problems, an order assignment problem and a fuzzy traveling salesman problem with pickup and delivery. To deal with the two sub-problems efficiently, a two-stage algorithm is proposed by reasonably fusing a modified greedy search (mGS) and the fruit fly optimization algorithm (FOA). In the mGS phase, agreement index is employed in the multi-stage decision to search for robust optimal solutions. In the FOA-based search phase, a modified heuristic is used to generate the initial route. To enhance the exploration of the algorithm, an olfactory search is designed by the cooperation of several problem-specific search operators. Moreover, a specific local intensification is employed to further improve the performance of solutions. Numerical tests and statistical analysis demonstrate the effectiveness and efficiency of the proposed algorithm.

Order dispatching, a key part in real-time food delivery system, has received a lot of attention over the last decade. Under constraints of on-time rate and some other practical constraints, our goal is to maximize the total efficiency of the whole system. As an online algorithm, the running time of the algorithm for order dispatching is limited within milliseconds. At the same time, the problem is highly dynamic and the complexity of the solution space is huge. In this paper, we design two fast heuristics for order dispatching in real-time food delivery. We compare our algorithm with two state-of-the-art algorithms. With numerical results, we show our algorithms are faster than algorithms in the literature and with similar or better solution quality.

In this paper, an estimation of distribution algorithm (EDA)-based memetic algorithm (MA) is proposed for solving the distributed assembly permutation flow-shop scheduling problem (DAPFSP) with the objective to minimize the maximum completion time. A novel bi-vector-based method is proposed to represent a solution for the DAPFSP. In the searching phase of the EDA-based MA (EDAMA), the EDA-based exploration and the local-search-based exploitation are incorporated within the MA framework. For the EDA-based exploration phase, a probability model is built to describe the probability distribution of superior solutions. Besides, a novel selective-enhancing sampling mechanism is proposed for generating new solutions by sampling the probability model. For the local-search-based exploitation phase, the critical path of the DAPFSP is analyzed to avoid invalid searching operators. Based on the analysis, a critical-path-based local search strategy is proposed to further improve the potential solutions obtained in the EDA-based searching phase. Moreover, the effect of parameter setting is investigated based on the Taguchi method of design-of-experiment. Suitable parameter values are suggested for instances with different scales. Finally, numerical simulations based on 1710 benchmark instances are carried out. The experimental results and comparisons with existing algorithms show the effectiveness of the EDAMA in solving the DAPFSP. In addition, the best-known solutions of 181 instances are updated by the EDAMA.

A co-evolutionary teaching-learning-based optimization (CTLBO) algorithm is proposed in this paper to solve the stochastic resource-constrained project scheduling problem (SRCPSP). The activity list is used for encoding, and resource-based policies are used for decoding. Also, a new competition phase is developed to select the best solution of each class as the teacher. To make two classes evolve cooperatively, both the teacher phase and student phase of the TLBO are modified. Moreover, Taguchi method of design of experiments is used to investigate the effect of parameter setting. Computational results are provided based on the well-known PSPLIB with certain probability distributions. The comparisons between the CTLBO and some state-of-the-art algorithms are provided. It shows that the CTLBO is more effective in solving the problems with medium to large variance.

A fruit fly optimization algorithm based on the enhanced non-dominated sorting (ESFOA) is proposed to solve the environmental economic dispatch (EED) problem. To measure the difference between two non-dominated solutions, the concept of the enhanced non-dominance is defined, and the degrees of dominance and non-dominance are presented. To enhance the parallel search ability, multiple fruit flies groups are used to perform evolutionary search in the ESFOA. In the vision-based search process, the best fruit fly is determined according to the enhanced non-dominance value. To guarantee the feasibility of the new solutions, an effective heuristic mechanism to handle constraints is adopted to repair the infeasible solutions. Meanwhile, an external archive is used to store the non-dominated solutions. The influence of parameter setting is investigated based on the Taguchi method of design of experiment, and a suitable parameter setting is suggested. Finally, numerical tests are carried out by using the IEEE 30-bus benchmark. The comparisons to some existing methods by using the technique for order preference by similarity to ideal solution (TOPSIS) demonstrate the effectiveness of the proposed algorithm.

As an extension of the classic job-shop scheduling problem (JSP), the flexible job-shop (FJSP) is NP-hard and has a wide application background. In this paper, a shuffled frog-leaping algorithm (SFLA) based algorithm is proposed for solving the FJSP. First, the SFLA employs a hybrid encoding scheme in which two sequences are used to illustrate both operation order sequence and machines assignment. Second, a special bi-Ievel crossover scheme as a local search scheme based on the critical path is incorporated in the framework of the memetic search. The searching ability of the modified SFLA can be well balanced between exploration and exploitation. Furthermore, the influence of the parameter setting is also investigated and suitable parameters are suggested. Numerical simulation and comparisons based on the well-known benchmark problems indicates the effectiveness of the SFLA for solving the problem.