Task Assignment Decisions Research Articles

Dynamic tugboat deployment and scheduling with stochastic and time-varying service demands

Container ports serve as crucial logistics hubs in global supply chains, but navigating ships within such ports is complex due to restricted waterways. Tugboats play a critical role in ensuring safety and efficiency by escorting and towing ships under these conditions. However, the tugboat deployment and scheduling problem has received little attention. To fill the research gap, we propose a new research problem - the dynamic tugboat deployment and scheduling problem, in which not all requests are confirmed initially but dynamically confirmed over time and future tugging demands need to be anticipated when managing the utilization of tugboats. To formulate the problem, we propose an extended Markov decision process (MDP) that incorporates both reactive task assignment decisions and proactive tugboat waiting decisions, creating a reactive and proactive MDP. To solve the advanced MDP model efficiently for real-time decisions, we develop an anticipatory approximate dynamic programming method that incorporates appropriate task assignment and waiting strategies for deploying and scheduling a heterogeneous tugboat fleet and embed the method into an improved rollout algorithm to anticipate future scenarios. The effectiveness, efficiency, and performance sensitivity of the developed modeling and solution methods are demonstrated via extensive numerical experiments for the Singapore container port.

Deep reinforcement learning and ant colony optimization supporting multi‐UGV path planning and task assignment in 3D environments

AbstractWith the development of artificial intelligence, the application of unmanned ground vehicles (UGV) in outdoor hazardous scenarios has received more attention. However, the terrains in these environments are often complex and undulating, which also pose higher challenges to the multi‐UGV path planning and task assignment (MUPPTA) optimization. To efficiently improve the multi‐UGV collaboration in 3D environments, a MUPPTA method is proposed based on double deep Q learning network (DDQN) and ant colony optimization (ACO) to jointly optimize the path planning and task assignment decisions of multiple UGVs. The authors first comprehensively consider the characteristics of the 3D environments, and model the MUPPTA problem as a combinatorial optimization problem. To tackle it, the original problem is decomposed into the multi‐UGV path planning sub‐problem and task assignment sub‐problem, and solve them separately. First, the path planning sub‐problem in the 3D environments is transformed into a Markov decision process (MDP) model, and a multi‐UGV path planning algorithm based on DDQN (MUPP‐DDQN) is proposed to obtain the optimal paths and actual path costs between tasks through extensive offline learning and training. Based on this, a multi‐UGV task assignment algorithm is further proposed based on ACO (MUTA‐ACO) to solve the task assignment sub‐problem and achieve the optimal task assignment solution. Simulation results show that the proposed method is more cost‐effective and time‐saving compared to other comparison algorithms.

Balancing physical workload during workforce scheduling for fair task assignment in a manual warehouse

Warehousing tasks, such as order picking, involve extensive manual materials handling, which can lead to a high physical workload and associated health risks for workers. The goal of this paper is to develop a task assignment decision support model suitable for application in a manually operated warehouse. The model aims to balance the physical workload across the employed order pickers based on an ergonomic indicator, leading to fairer task allocations. The main contribution of this paper is the incorporation of a linearized ergonomic indicator into a typical warehousing task assignment problem that allocates batches to order pickers. The linearized indicator facilitates the ergonomic evaluation during the solution procedure using a commercial solver.

Hierarchical Decision-Making Framework for Multi-UAV Task Assignment via Enhanced Pigeon-Inspired Optimization

Effective task assignment decisions are paramount for ensuring reliable task execution in multi-UAV systems. However, in the development of feasible plans, challenges stemming from extensive and prolonged task requirements are encountered. This paper establishes a decision-making framework for multiple unmanned aerial vehicles (multi-UAV) based on the well-known pigeon-inspired optimization (PIO) algorithm. By addressing the problem from a hierarchical structural perspective, the initial stage involves minimizing the global objective of the flight distance cost after obtaining the entire task distribution and task requirements, utilizing the global optimization capability of the classical PIO algorithm to allocate feasible task spaces for each UAV. In the second stage, building upon the decisions made in the preceding stage, each UAV is abstracted as an agent maximizing its own task execution benefits. An improved version of the PIO algorithm modified with a sine-cosine search mechanism is proposed, enabling the acquisition of the optimal task execution sequence. Simulation experiments involving two different scales of UAVs validate the effectiveness of the proposed methodology. Moreover, dynamic events such as UAV damage and task changes are considered in the simulation to validate the efficacy of the two-stage framework.

Courier satisfaction in rapid delivery systems using dynamic operating regions

Rapid delivery systems where an order is delivered to a customer from a local distribution point within minutes or hours have experienced rapid growth recently and often rely on gig economy couriers. The prime example is a meal delivery system. During an operating day, couriers in such a system are used to deliver orders placed at different restaurants to different customer locations. Operating a rapid delivery network is challenging, primarily due to the high service expectations and the considerable uncertainty in both demand and delivery capacity. We seek to fill a gap in the literature by considering courier satisfaction in a rapid delivery system, which may improve retention/loyalty in a highly competitive environment. Under the premise that couriers prefer to operate in relatively small geographic areas to increase their efficiency, we propose the novel concept of dynamic courier regions: small operating regions for couriers which can be dynamically and temporarily expanded to allow delivery capacity to be shared between neighboring regions when necessary to keep customer service performance metrics high. We propose an optimization-based rolling horizon algorithm for courier management that handles both region resizing and delivery task assignment decisions. Experimental results for realistic settings demonstrate that the proposed algorithm successfully balances customer and courier satisfaction, simultaneously achieving delivery times that are comparable to those of a single operating region and courier satisfaction metrics that are comparable to those achieved by fixed, inflexible regions.

A Dynamic Task Assignment Optimization Method for Multi-AGV System Based on Genetic Algorithm

<p>Aiming at the influence of AGV without considering the working state on task assignment decision in multi-AGV system task assignment, a dynamic task assignment decision method with task completion prediction based on genetic algorithm. When assigning the arrived tasks at each stage, the decision method brings the working AGVs and the idle AGVs into the set of schedulable vehicles at the same time, which expands the scope of the optimal decision, makes the available AGV resources more fully mobilized in the dynamic scheduling process, and improves the efficiency of the whole scheduling system. First, this paper establishes a prediction model for task completion. On this basis, the task assignment decision model of multi-AGV system based on task completion prediction is established, and the coding, fitness function and genetic operation of the genetic algorithm suitable for this problem are designed. Finally, a univariate factor analysis is carried out on the task assignment time interval and the number of AGVs by using an example, which verifies the effectiveness of the task assignment strategy of the multi-AGV system based on task completion prediction. The results show that the genetic algorithm can better solve the task assignment problem with task completion prediction, and can schedule the available AGV resources to a greater extent, which effectively increase the number of tasks completed by the multi-AGV system in one production cycle.</p> <p> </p>

Machine endowment cost model: task assignment between humans and machines

Although research on human–machine task assignment has presently received academic attention, the theoretical foundation of task assignment requires further development. Based on the two-dimensional characteristics of task flexibility and cognition, a machine endowment cost model is built to examine the economic allocation of tasks between humans and machines. The model derives a machine production possibility curve that directly divides all tasks into two categories, one each for machines and humans. The model shows the dynamic of task allocation between humans and machines as the economic environment evolves, such as wage growth and technological development, and provides conditions wherein task polarization may prevail. The model can be applied to human–machine task assignment decisions in industry and services.

QADM: A quality-aware data model for freelancing applications based on recommendation systems

Freelancing systems such as Freelancer and Upwork have recently been increasing in popularity due to their ability to provide efficient solutions. Nevertheless, the success of freelancing depends on the delivery of high-quality freelanced output. Indeed, quality control is a major concern and is affected by the uncertainty stemming from human factors relating to the diversity of workers’ skills and the workers’ changing behaviour over time. This article proposes a quality-aware freelancing data model (QADM) that takes into account both the diversity of worker skills and workers’ changing behaviour. The QADM is comprised of three submodels: skill, task and worker. The main goal is to model worker quality appropriately, and it does so by effectively modelling (1) worker suitability for new tasks, (2) worker reputation, (3) worker accuracy in completed tasks and (4) worker expected accuracy in new tasks. To improve the worker accuracy estimation, a task-to-task similarity algorithm is developed that achieves higher accuracy than Cos(topic), Cos(tf-idf) and Jaccard similarity methods. The quality-aware task assignment decision problem is solved as a top-k task recommendation problem. The results achieved in this article show that the QADM accomplishes a high recommender system mean average precision for the assignment decisions.

AMRAS

The wide adoption of GPS-enabled smart devices has greatly promoted spatial crowdsourcing, where the core issue is how to assign tasks to workers efficiently and with high quality. In this paper, we build a novel visual analysis system for spatial crowdsourcing, namely AMRAS, which can not only intuitively present the task allocation for workers under different time window scales to users (e.g., data analysts and managers) in real-time, but also help users analyze task assignment decision model and its learning process. AMRAS has the following novel features. First, AMRAS provides two user-friendly interfaces that allow users to employ simple and easy-to-use console to perform statistical analysis. Secondly, AMRAS provides three powerful visualization tools, such as the visualization of assignment results, assignment process, and assignment decision model, which not only allow users to intuitively analyze the whole process of task assignment, but also help users discover the computational bottleneck of their task assignment solution. Finally, AMRAS enables online access to real-time data, providing users with instant assignment and instant analysis. We have implemented and deployed AMRAS on Alibaba Cloud and demonstrated its usability and efficiency in real-world datasets. The demonstration video of AMRAS has been uploaded to Google Drive.

Energy and latency aware mobile task assignment for green cloudlets

Edge computing places cloudlets with high computational capabilities near mobile devices to reduce the latency and network congestion encountered in cloud server-based task offloading. However, many cloudlets are required in such an edge computing network, leading to a tremendous increase in carbon emissions of computing networks globally. This increase in carbon emission envisages the need to employ green energy resources to power these cloudlets. This need has led to the concept of Green Cloudlet Networks (GCNs). But GCNs must deal with the problem of the unpredictability of green energy available to them while optimizing the performance (in terms of latency) delivered to the mobile user. This paper proposes a novel task-assignment called Green Energy and Latency Aware Task Assignment (Ge-LATA) for GCNs to address this issue. The primary aim of Ge-LATA is to optimize the latency and the green energy consumed in processing the offloaded tasks from the mobile devices. In this GCN, the cloudlets are connected in a network to process the incoming tasks cooperatively to ensure load-balancing at the cloudlets. Ge-LATA considers various factors like the current load, available green energy, service rate offered by cloudlets, and the distance from the mobile user, leading to optimal decisions in terms of latency and green energy consumed. Simulations are performed using the actual solar insolation data taken from the NREL database. Ge-LATA is tested with other offloading schemes for latency in processing the offloaded tasks and green-energy consumed under different solar insolation scenarios in these simulations. Simulation results show that Ge-LATA achieves up to 31.87% of reduction in the latency while ensuring up to 50.15% of reduction in the energy consumption than other comparable task-assignment schemes.Thus, Ge-LATA suggests that it leads to an optimal task assignment by considering the various factors mentioned above during the task assignment process. Thus, Ge-LATA considers the above-mentioned extensive set of parameters during the task allotment process. It also proposes an efficient green energy allotment scheme that adapts itself to actual weather and network conditions, leading to optimal task assignment decisions in GCNs.

A survey on Benders Decomposition methods applied to Assembly Line Balancing Problems

The assembly line balancing problem (ALBP) assigns tasks to (work)stations to manufacture products. It divides the station loads as evenly as possible since any bottleneck defines the production rate of a flow shop system. The sum of task processing times represents the station load in the classical Simple Assembly Line Balancing Problem (SALBP). Thus, the most loaded station imposes a lower bound on the line's cycle time. However, the simple sum of processing times is only valid under several assumptions. For instance, more realistic ALBPs may contain stochastic data, multiple workers per station, or depend on the production sequence. Hence, the station load computation involves further scheduling decisions for this latter class of problems. In order to tackle these ALBPs with practical extensions, several literature contributions have recently employed Benders decomposition (BD) algorithms. The BD structure allows a division of the formulation into two or more levels. More specifically, this framework permits the decoupling of task assignment decisions and cycle time assessments. In the field of ALBPs, various stochastic and sequence-dependent problems have successfully applied this approach. This paper provides a literature review of these methods, the different formulations, implementation details, and improvement ideas.

Modeling uncertain task compliance in dispatch of volunteers to out-of-hospital cardiac arrest patients

In countries such as Sweden, Italy, Switzerland, and the Netherlands, projects in which volunteers are dispatched to out-of-hospital cardiac arrest (OHCA) patients with the use of mobile phones exist. Once an OHCA case is reported, a notification is sent to the mobile phones of registered volunteers that are in the vicinity of the patient. These projects mostly use static dispatch methods to determine which volunteers should be sent directly to the patient and which ones should pick up an automatic external defibrillator (AED). However, such schemes do not consider uncertainties associated with these task assignment decisions (e.g., if volunteers will do as instructed, or do something else). In this paper, we propose a method for optimized task assignment and dispatch of volunteers to OHCA patients that considers the uncertainty related to volunteers’ actions once assigned a task. We then compare the results of our method to those of a static dispatch method used in an ongoing mobile phone volunteer dispatch project in Sweden and validate them using simulation. Furthermore, we perform a sensitivity analysis on several parameters to investigate their effect on the performance of the proposed method. With the comparative results we show that the proposed method may help increase the survivability of OHCA patients.

Trident

The recent advancements in storage technologies have popularized the use of tiered storage systems in data-intensive compute clusters. The Hadoop Distributed File System (HDFS), for example, now supports storing data in memory, SSDs, and HDDs, while OctopusFS and hatS offer fine-grained storage tiering solutions. However, the task schedulers of big data platforms (such as Hadoop and Spark) will assign tasks to available resources only based on data locality information, and completely ignore the fact that local data is now stored on a variety of storage media with different performance characteristics. This paper presents Trident, a principled task scheduling approach that is designed to make optimal task assignment decisions based on both locality and storage tier information. Trident formulates task scheduling as a minimum cost maximum matching problem in a bipartite graph and uses a standard solver for finding the optimal solution. In addition, Trident utilizes two novel pruning algorithms for bounding the size of the graph, while still guaranteeing optimality. Trident is implemented in both Spark and Hadoop, and evaluated extensively using a realistic workload derived from Facebook traces as well as an industry-validated benchmark, demonstrating significant benefits in terms of application performance and cluster efficiency.

A Fault-Tolerant Workflow Scheduling Algorithm for Grid with Near-Optimal Redundancy

In scheduling workflows in grid environment, concerns such as minimizing the makespan and cost, meeting the time and budget constraints and the possibility of resource failures and so on have motivated researchers to propose numerous scheduling algorithms. Several heuristics and meta-heuristic algorithms have been proposed to address these issues, each of which often only considers one or a few of these criteria. However, less attention has been paid to fault-tolerant scheduling of workflows. Adding fault-tolerance to a workflow scheduling algorithm leads to an inevitable increase in the makespan and cost. Using the resubmission technique may result to an unacceptable increase in the execution time and possible violation of deadline while the replication method increases the execution cost. In this paper, we propose a fault-tolerant workflow scheduling algorithm with near-optimal time and cost overhead. The proposed approach brings a two-fold novelty. First, we assume a stochastic model of workflow with nondeterministic task parameters and use interval arithmetic to model task execution times and propose a new scheduling algorithm in which the task assignment decisions are taken according to the performability fluctuations of the computational resources. Second, we employ an Efficient combination of resubmission and replication techniques to achieve the benefits of both and propose an algorithm for reliable scheduling of scientific workflows with near-optimal additional time and cost. The proposed method, achieves a significant increase in the reliability while the additional execution time and cost is almost negligible.

Reducing transit fleet emissions through vehicle retrofits, replacements, and usage changes over multiple time periods

Bus transit is often promoted as a green form of transportation, but surprisingly little research has been done on how to run transit systems in a green manner. Both vehicle task assignment and purchase models are generally constructed to minimize financial costs. Integrating vehicle task assignment with purchase decisions is made challenging by the different time scales involved. An integer programming approach is used to combine vehicle purchase, retrofit and aggregated task assignment decisions. The formulation is designed to operate in sequence with traditional vehicle task assignment models, to add emissions and long term financial cost elements to the objective, while maintaining computational tractability and feasible input data requirements. In a case study, a transit agency saves money in the long term by using stimulus money to buy CNG infrastructure instead of purchasing only new buses. Carbon prices up to $400/(ton CO 2 equivalent) do not change vehicle purchase decisions, but higher carbon prices can cause more diesel hybrid purchases, at a high marginal cost. Although the motivation and numerical case study are from the US transit industry, the model is formulated to be widely applicable to green fleet management in multiple contexts.

Specialized Task Assignment and the Learning Curve

By building on the analytical model of Zhang (2003) and Hughes et al. (2005), we demonstrate that learning effects are a critical component of task assignment decisions. Just as broad task assignment can result in the benefit of complementarity, specialized task assignment can produce efficiency gains from learning. Since there remains considerable discussion regarding the true shape and slope of the learning curve, we develop a flexible model which allows managers to vary the learning input when forming decisions regarding employee task assignment. Our results merge the substantial literature on the learning curve with the growing debate over proper task assignment.

Cross-training in a cellular manufacturing environment

This study addresses the need for cross-training in a cellular manufacturing environment. It is demonstrated that an effective cross-training situation results if workers and machines are connected, directly or indirectly, by task assignment decisions. The connections between workers and machines (i.e. the qualifications of workers) form 'chains' that can be used to reallocate work from heavily loaded workers to less loaded workers. This provides the possibility of a balanced workload situation among workers, something that is desirable from a social as well as an economic viewpoint. Based on this insight, we have developed an integer programming (IP) model that can be used to select workers to be cross-trained for particular machines. The model may help in trade-offs between training costs and the workload balance among workers in a manufacturing cell. The workload balance indicates the usefulness of labor flexibility in a particular situation. A numerical example is presented to illustrate various elements and features of the model. It also provides further insight into the role of 'chaining' workers and machines. The industrial applicability of the model and directions for future research are also indicated.

Complementarity, Task Assignment, and Incentives

This paper studies the effect of complementarity on task assignment decisions and the design of optimal incentives for employees in charge of multiple tasks. A principal hires two identical agents. Each agent performs two tasks involving complementarities when the tasks vary in the informativeness of their performance measures. The principal tension governing how tasks are assigned lies between the heterogeneity loss due to aggregate performance measures under broad task assignments and the resulting positive information externalities. Although the aggregate performance measures under broad task assignments preclude tailoring the strength of incentives to the nature of the task, these measures are more informative about agent efforts when tasks are complementary. This enhanced informativeness improves contracting efficiency by mitigating the implicit costs of the heterogeneity loss. The analysis applies not only to the task assignment decisions, but also more broadly to other organizational structure decisions.

Incomplete Information, Task Assignment, and Managerial Control Systems

A firm typically assigns multiple tasks it must perform to either internal employees or outside vendors. This paper demonstrates the need to integrate a task assignment decision with the design of a managerial control system as each affects the other. An internal employee is distinguished from an outside supplier on four different informational dimensions: (i) at the time of contracting, the outside supplier has less information about the task environment more often than the internal employee; (ii) the principal observes the employee's information set more frequently than that of the supplier; (iii) the principal can exercise a greater control over information flow to the internal employee than to the outside supplier; and (iv) the principal may share the details of the outside supplier's contract with the internal employee but not vice versa. Under each of these four distinguishing dimensions, the principal is shown to outsource the upstream task and assign the downstream task to the internal employee more often than vice versa. Further, under the last two dimensions of the firm's boundary, the principal can eliminate inefficiencies arising from the agents' contracting with incomplete information by assigning the downstream task to the employee and not providing predecision information to him while assigning the upstream task to the supplier.