Matching Policy Design for Gig Platforms with “Priority” Features

Workflow scheduling is one of the most popular and challenging problems in cloud computing. However, among the studies on cloud workflow scheduling, very few consider the fairness among workflow tasks which could significantly delay the workflows and hence deteriorates user satisfaction. In this paper, we propose a workflow scheduling algorithm based on stable matching game theory to minimize workflow makespan and ensure the fairness among the tasks. The local optimization methods based on critical path and task duplication are developed to improve the performance of the algorithm. In addition, a novel evaluation metric is proposed to measure the fairness among workflow tasks. Comprehensive experiments are conducted to compare the performance of the proposed algorithm with other four representative algorithms. Experimental results demonstrate that our algorithm outperforms the other compared algorithms in terms of all three performance metrics under different workflow applications.

The Southern Expressway plays a vital role in national road network in Sri Lanka. Vehicles on Expressways can travel at uniform speeds throughout their journey offering advantages on fuel consumption and reduced travel time. The expressways ensure a hassle free safe journey due to the minimum obstructions to the traffic.At present, all the interchanges along the Southern Expressway operate with manually operated toll gates. The efficiency of current toll gates was studied for the existing exit ramp lengths and it investigated whether their current efficiencies are sufficient for higher vehicle volume of the future traffic. To investigate the current toll gate efficiency on the capacity of Southern Expressway, the necessary data were collected from relevant organizations. From the collected data, peak days and peak hours were identified for each and every exit ramp of the Southern Expressway. By considering the peak hours, critical exit ramps were identified. After identifying critical exit ramps of the interchanges, traffic studies were conducted to obtain arrival and service rates in peak hours during the peak days. Then, after analyzing survey data, maximum queue lengths and longest individual delays for critical exit ramps along the Southern Expressway were found. Simultaneously the analysis was carried-out for a different number of gates.Finally, maximum queue lengths and longest individual delays were compared with existing ramp lengths, and shortcomings were identified. By using suitable growth rates for the Southern Expressway, future traffic conditions were predicted. Subsequently, by using the maximum vehicular queue lengths, individual delays that could occur in the future were computed. Hence, these values were used to analyze the effect of the capacity of Southern Expressway due to the present toll gate arrangement. Therefore, it was able to recommend suitable off ramp lengths considering queue lengths and deceleration lengths. Recommendations were also proposed for future improvements to the service rates at toll gates.

With the ongoing energy crisis, the development and utilization of clean energy is an issue that the world is increasingly confronted with. In addition, the number of electric vehicles in both domestic and international markets have steadily increased, making efficient charging solutions essential. Queueing theory provides an effective approach to optimizing charging station locations and capacity allocation, helping to minimize wait times, improve charging pile utilization, and meet electric vehicles charging demands. Research based on queueing models suggests that strategically allocating charging piles and refining queuing mechanisms, such as reservation systems or priority scheduling, can alleviate congestion and enhance service quality. Additionally, integrating queueing theory with energy storage technology enables electric vehicles charging stations to participate in grid dispatching. Through intelligent regulation, these stations can help balance power demand during peak hours, improving grid stability and addressing further challenges related to electric vehicles infrastructure. This work highlights the importance of queueing theory in optimizing the charging stations.

This study aims to determine and analyze service waiting times, identify the root causes of long queues, and develop a strategy to improve service performance at the 5361137-gas station (SPBU) at the Surabaya-Gresik Toll Rest Area. The research method used is a mixed-methods approach with an exploratory sequential design. This study combines quantitative analysis using Queuing Theory to measure system performance (arrival rates and service times) and descriptive qualitative analysis using a Fishbone Diagram. Data were collected through direct observation, interviews, and g-form techniques. The results indicate that the current queuing system performance is in a critical or severe condition, indicated by a server utilization rate of 0.94 to 1.02 during peak hours. The average time spent by vehicles in the system is 14.3 minutes, of which 9.6 minutes (67%) is spent waiting in the queue. Fishbone diagram analysis revealed that the root cause of the main problem lies in the complex interaction of factors: Machine factors (EDC signal failure and pump repair downtime), Human and Method factors (implementation of static shifts and reactive maintenance), and Environmental factors (narrow layouts that hinder large vehicle maneuvers). As a solution, this study formulated a hybrid improvement strategy that includes short-term business process engineering (the use of Floating Staff and lane segregation) and long-term investment in additional pumps to change the queuing model from Single Channel to Dual Channel. This strategy is expected to reduce the utility level to a safe zone below 0.80 with a target waiting time of 3–5 minutes.

With the advancement and widespread adoption of cloud computing technology, its application in the educational sector has deepened, showcasing unique advantages in the sharing of art education resources particularly. The essence of Chinese traditional culture, calligraphy education, is in urgent need of innovation and dissemination through modern technological means. However, existing research on the application of cloud computing in art education, especially in the sharing of calligraphy education resources, remains insufficient, with a lack of targeted demand modeling and resource-sharing mechanism studies. This study explores user needs on art education resource-sharing platforms through regression analysis, constructing demand modeling to provide a scientific, data-driven resource allocation scheme. Furthermore, in response to the specific requirements for sharing art education resources under a cloud computing environment, this research employs matching theory to propose a sharing mechanism aimed at optimizing resource allocation and enhancing efficiency. Through these investigations, not only is the application research of cloud computing in the field of art education enriched, but also theoretical support and implementation strategies for the practice of art education resource sharing are also provided. Research on the art education resource sharing mechanism based on matching theory has led to the development of a modeling method that precisely captures user needs and behavioral patterns. The model has been optimized to enhance its applicability in scenarios involving art education. Empirical results demonstrate the superiority of this model in several areas, namely, the satisfaction in resource sharing, the resource sharing rate, and the social utility. Notably, the model also exhibits significant advantages in computational efficiency. These findings provide a scientific basis and practical tools for resource sharing in the realm of art education.

In-store order fulfilment in omni-channel supermarkets with heterogeneous workforce: A bi-objective optimisation approach

Cross-camera label estimation from a set of unlabelled training data is an extremely important component in unsupervised person re-identification (re-ID) systems. With the estimated labels, existing advanced supervised learning methods can be leveraged to learn discriminative re-ID models. In this paper, we utilize the graph matching technique for accurate label estimation due to its advantages in optimal global matching and intra-camera relationship mining. However, the graph structure constructed with non-learnt similarity measurement cannot handle the large cross-camera variations, which leads to noisy and inaccurate label outputs. This paper designs a Dynamic Graph Matching (DGM) framework, which improves the label estimation process by iteratively refining the graph structure with better similarity measurement learnt from intermediate estimated labels. In addition, we design a positive re-weighting strategy to refine the intermediate labels, which enhances the robustness against inaccurate matching output and noisy initial training data. To fully utilize the abundant video information and reduce false matchings, a co-matching strategy is further incorporated into the framework. Comprehensive experiments conducted on three video benchmarks demonstrate that DGM outperforms state-of-the-art unsupervised re-ID methods and yields competitive performance to fully supervised upper bounds.

In this study, the traffic parameters were collected from three work zones in Iran in order to evaluate the queue length in the work zones. The work zones were observed at peak and non-peak hours. The results showed that abrupt changes in Freeway Free Speed (FFS) and arrival flow rate caused shockwaves and created a bottleneck in that section of the freeway. In addition, acceleration reduction, abrupt change in the shockwave speed, abrupt change in the arrival flow rate and increase in the percentage of heavy vehicles have led to extreme queue lengths and delay. It has been found that using daily traffic data for scheduling the maintenance and rehabilitation projects could diminish the queue length and delay. Also, by determining the bypass for heavy vehicles, the delay can be significantly reduced; by more than three times. Finally, three models have been presented for estimating the queue length in freeway work zones. Moreover, the procedure shown for creating a queue length model can be used for similar freeways.

The coexistence of Ultra-Reliable and Low-Latency Communications (URLLC) and enhanced Mobile BroadBand (eMBB) brings significant challenges for service pairing and resource allocation in beyond fifth-generation (B5G) wireless networks. To meet the reliability requirement of URLLC services and improve the fairness of eMBB services, we first develop a supervised learning-based resource allocation policy for eMBB services. Then, a two-phase resource allocation framework is proposed for URLLC services: 1) eMBB/URLLC service pairing, and 2) URLLC resource allocation. In the first phase, matching theory pairs eMBB and URLLC services for better fairness. In the second phase, URLLC resource allocation policy is optimized by a constrained unsupervised learning algorithm. Simulation results show that our proposed framework can achieve better trade-offs among fairness, throughput, and reliability compared with two existing baselines. For example, a dynamic proportional fairness algorithm can meet the reliability requirement of URLLC when its average packet arrival rate is below 0.7 packets/mini-slot. The proposed algorithm can support URLLC services with an average packet arrival rate of 1.6 packets/mini-slot

Impact of Workforce Flexibility on Customer Satisfaction: Empirical Framework & Evidence from a Cleaning Services Platform

In this paper, we describe a resource matcher (RM) developed for the on-line resource matching in heterogeneous grid environments. RM is based on the principles of evolutionary algorithms (EA) and supports dynamic resource sharing, job priorities and preferences, job dependencies on multiple resource types, and resource specific and site-wide policies. We describe the evolutionary algorithm and the models used for representing the resource requirements, preferences, and policies. We evaluate three different methods for bootstrapping RM. We then describe a evolutionary matcher (EM) service $a Web service implementation of RM for on-line resource matching. Preliminary performance results indicate that the EM service is efficient in speed and accuracy and can keep up with high job arrival rates - an important criterion for on-line resource matching systems. The service oriented architecture makes the EM service scalable and extensible and can be integrated with already existing grid services in a straightforward manner

Stochastic Processing Networks (SPNs) [24] are ubiquitous in engineering with applications in Data Centers (eg. packet routing), Telecommunication (eg. call centers), Transportation, etc. Stochastic Matching Networks (SMNs) are newly emerging variation of SPNs where 'jobs' and 'servers' arrive in the system and are matched using a decision rule. SMNs have applications in Online matching markets (eg. uber platform), Healthcare (eg. kidney transplant), Quantum Networks, Assemble-to-order (eg. manufacturing plants) and many other crucial problems. The aim of my research is to design provably efficient decision-making policies for Stochastic Processing Networks (SPNs) and Stochastic Matching Networks (SMNs) and develop analytical tools to obtain performance guarantees on their throughput and delay.

We study a courier dispatching problem in an on-demand delivery system in which customers are sensitive to delay. Specifically, we evaluate the effect of temporal pooling by comparing systems using the dedicated strategy, with which only one order is delivered per trip, versus the pooling strategy, with which a batch of consecutive orders is delivered on each trip. We capture the courier delivery system’s spatial dimension by assuming that, following a Poisson process, demand arises at a uniformly generated point within a service region. With the same objective of revenue maximization, we find that the dispatching strategy depends critically on customers’ patience level, the size of the service region, and whether the firm can endogenize the demand. We obtain concise but informative results with a single courier and assuming that customers’ underlying arrival rate is large enough, meaning a crowded market, such as rush hour delivery. In particular, when the firm has a growth target and needs to achieve an exogenously given demand rate, using the pooling strategy is optimal if the service area is large enough to fully exploit the pooling efficiency in delivery. Otherwise, using the dedicated strategy is optimal. In contrast, if the firm can endogenize the demand rate by varying the delivery fee, using the dedicated strategy is optimal for a large service area. The reason is that it is optimal for the firm to sustain a relatively low demand rate by charging a high fee for a large service radius: within this large area, the pooling strategy leads to a long wait because it takes a long time for multiple orders to accumulate. Moreover, with an exogenous demand rate to meet, customers’ patience level has no impact on the dispatch strategy. However, when the demand rate can be endogenized, the dedicated strategy is preferable if customers are impatient. Furthermore, we extend our model to account for social welfare maximization, a hybrid contingent delivery policy, a general arrival rate that does not have to be large, a nonuniform distribution of orders in the service region, and multiple couriers. We also conduct numerical analysis and simulations to complement our main results and find that most insights in our base model still hold in these extensions and numerical studies. This paper was accepted by Jeannette Song, operations management. Funding: This work was supported by the Natural Sciences and Engineering Research Council of Canada [Grants RGPIN-2015-06757 and RGPIN-2021-04295]. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4858 .

In an online order picking system, customer orders arrive in real time and the picking information is updated dynamically. One challenging problem is how to process customer orders in a timely manner. In this paper, a nonparametric heuristic method, Green Area, is presented to address the real-time online order batching problems. By nonparametric, we mean that our method is independent of the parameters of a warehouse layout and the characteristics of customer orders; these parameters facilitates the implementation in real life. The advantages of this method are verified under different scenarios by simulations. Specifically, the influences of the arrival rate, the number of order pickers and the number of orders in the order service time are discussed. The results demonstrate that the Green Area method leads to shorter order service times than traditional methods for optimal batch sizes. Finally, we demonstrate that the Green Area method can be applied to online order picking systems with variable arrival rates.

Optimizing the public transportation network is one of the important means to alleviate traffic congestion. We propose an enhanced algorithm based on the harmonic search algorithm to address the optimization problem of fixed station bus networks. The algorithm uses a decimal priority encoding scheme to encode discrete bus stations and specifies the probability of each variable being selected and decoding constraints during the decoding process. Add the lines waiting for adjustment to the initialized sound library and give them high priority. We introduce an interrupt mechanism in the harmony search algorithm to prevent invalid decoding from consuming solution time. Comprehensive experiments show that this method has better performance in terms of passenger direct arrival rate and efficiency.