Heuristic Scheduling Approach Research Articles

Mitigating Co-Activity Conflicts and Resource Overallocation in Construction Projects: A Modular Heuristic Scheduling Approach with Primavera P6 EPPM Integration

This paper proposes a heuristic approach for managing complex construction projects. The tool incorporates Primavera P6 EPPM and Synchro 4D, enabling proactive clash detection and resolution of spatial conflicts during concurrent tasks. Additionally, it performs resource verification for sufficient allocation before task initiation. This integrated approach facilitates the generation of conflict-free and feasible construction schedules. By adhering to project constraints and seamlessly integrating with existing industry tools, the proposed solution offers a comprehensive and robust approach to construction project management. This constitutes, to our knowledge, the first dynamic digital twin for the delivery of a complex project.

A heuristic scheduling approach for fog-cloud computing environment with stationary IoT devices

The emergence of the Internet of Things (IoT) paradigm has led to the rise of a variety of applications with different characteristics and Quality of Service (QoS) requirements. Those applications require computational power and have time sensitive requirements. Cloud computing paradigm provides an illusion to consumers with unlimited computation resource power. However, cloud computing fails to deliver on the time-sensitive requirements of applications. The main challenge in the cloud computing paradigm is the associated delays from the edge IoT device to the cloud data center and from the cloud data center back to the edge device. Fog computing extends limited computational services closer to the edge device to achieve the time sensitive requirement of applications. This work proposes a scheduling solution which adopts the three-tier fog computing architecture in order to satisfy the maximum number of requests given their deadline requirements. In this work, an optimization model using mixed integer programming is introduced to minimize deadline misses. The model is validated with an exact solution technique. The scheduling problem is known to be an NP-hard, and hence, exact optimization solutions are inadequate for a typical size problem in fog computing. Given the complex nature of the problem, a heuristic approach using the genetic algorithm (GA) is presented. The performance of the proposed GA was evaluated and compared against round robin and priority scheduling. The results show that the deadline misses of the proposed approach is 20%–55% better than the other techniques.

Scheduling Policies for Quantum Key Distribution Enabled Communication Networks

The upsurge of ubiquitous data-driven applications makes the need for super-secure communication networks more urgent than ever. Conventional encryption algorithm oriented cryptology would be hacked by cryptanalysis, hence is challenging to provide unconditional security. To tackle this unprecedented issue, quantum key distribution (QKD) has been envisioned as a promising physical layer security technology for the upcoming 6G era due to the unique properties of quantum physics, i.e., no-cloning and uncertainty. To accommodate as many security-sensitive applications as possible, in this letter, the secure key rate maximization problem for QKD enabled communication networks is investigated. By transforming the original problem into a maximum weight perfect matching problem of an undirected and non-bipartite graph, the associated quantum devices scheduling policy is optimally determined, along with the feasibility analysis as well as two simpler heuristic scheduling approaches. Numerical results demonstrate the performance of the devised scheduling strategies from comprehensive perspectives, such as achievable secure key rate and outage probability.

Online Scheduling for Dynamic VM Migration in Multicast Time-Sensitive Networks

With the development of hardware virtualization and cloud computing, modern industry has a tendency to upgrade from the traditional industrial networks to virtual machine (VM) based networks. To provide firm latency guarantees for control messages in these networks, the time-sensitive network (TSN) is a promising technology due to its determinacy for real-time applications. However, TSN faces the challenge of providing a rapid response to dynamic transmission requirement changes incurred by VM migrations. In this paper, we proposed an online scheduling approach to deal with dynamic VM migrations in multicast TSN. In this approach, we devise a novel online scheduling framework [minimal distance tree (MDT) construction - heuristic breadth first search] containing an offline scheduling phase and an online rescheduling phase. While the offline phase introduces a MDT to increase reusable scheduling results, the online phase proposes a heuristic scheduling approach to reuse the results of the offline phase as much as possible to accelerate the rescheduling process. Experiments show that our framework can provide a rapid response to dynamic VM migrations compared with the existing approaches where the amount of control data does not exceed 50% of the bandwidth.

Flexible versus robust lot-scheduling subject to random production yield and deterministic dynamic demand

ABSTRACTWe consider the problem of scheduling production lots for multiple products competing for a common production resource that processes the product units serially. The demand for each product and period is assumed to be known with certainty, but the yield per production lot is random as the production process can reach an out-of-control state while processing each single product unit of a lot. A service-level constraint is used to limit the backlog in the presence of this yield uncertainty. We address the question of how to determine static production lots and how to schedule these lots over the discrete periods of a finite planning horizon. The scheduling problem is characterized by a trade-off between the cost of holding inventory and the cost of overtime, whereas the production output is uncertain. For this purpose, we develop a rigid and robust planning approach and two flexible heuristic scheduling approaches. In an extensive numerical study, we compare the different approaches to assess the cost of operating according to a robust plan as opposed to a flexible policy.

Improved Hyper-Heuristic Scheduling with Load-Balancing and RASA for Cloud Computing Systems

Nowadays cloud computing has turned into a key innovation and has become a great solution for indulging a flexible utility oriented , online allocation and storage of computing resources and client’s information in lower expense, oninterest and dynamically scalable framework on pay per use premise. This technology is a new pattern emerging in IT environment with immense necessities of framework and resources. Job Scheduling Problem is an essential issue. For efficient usage and managing resources, administrations, scheduling plays a critical role. This paper apportion the performance enhancement of HyperHeuristic Scheduling Approach to schedule cloudlets and resources, by taking account of both , computation time and transmission cost with two detection operators. Load Balancing and RASA concept is applied for efficient Load Scheduling, resource utilization and thereby enhancing the overall performance of cloud computing environment. The numerical investigations of HHSA were performed on CloudSim. Experimental results generated via simulation shows that enhanced heuristic scheduling approach is much better than individual heuristic approach in terms of minimizing makespan time.

A Heuristic Scheduler for Port-Constrained Floating-Point Pipelines

We describe a heuristic scheduling approach for optimizing floating-point pipelines subject to input port constraints. The objective of our technique is to maximize functional unit reuse while minimizing the following performance metrics in the generated circuit: (1) maximum multiplexer fanin, (2) datapath fanout, (3) number of multiplexers, and (4) number of registers. For a set of systems biology markup language (SBML) benchmark expressions, we compare the resource usages given by our method to those given by a branch-and-bound enumeration of all valid schedules. Compared with the enumeration results, our heuristic requires on average 33.4% less multiplexer bits and 32.9% less register bits than the worse case, while only requiring 14% more multiplexer bits and 4.5% more register bits than the optimal case. We also compare our results against those given by the state-of-art high-level synthesis tool Xilinx AutoESL. For the most complex of our benchmark expressions, our synthesis technique requires 20% less FPGA slices than AutoESL.

A Generic Approach for the Graph-based Integrated Production and Intermodal Transport Scheduling with Capacity Restrictions

The performance of global manufacturing supply chains depends on the interaction of production and transport processes. Currently, the scheduling of these processes is done separately without considering mutual requirements, which leads to non- optimal solutions. An integrated scheduling of both processes enables the improvement of supply chain performance. The integrated production and transport scheduling problem (PTSP) is NP-hard, so that heuristic methods are necessary to efficiently solve large problem instances as in the case of global manufacturing supply chains. This paper presents a heuristic scheduling approach which handles the integration of flexible production processes with intermodal transport, incorporating flexible land transport and maritime transport running a given timetable. The method is based on a graph that allows a reformulation of the PTSP as a shortest path problem for each job, which can be solved in polynomial time. The proposed method is applied to a supply chain scenario with a manufacturing facility in Brazil and shipments to customers in Germany. The obtained results show that the approach is suitable for the scheduling of large-scale problems and can be flexibly adapted to different scenarios.

Rate-Distortion Optimized Interactive Light Field Streaming

High-quality, photorealistic image-based rendering datasets are typically too large to download entirely before viewing, even when compressed. It is more suitable to instead stream the required image data to a remote user who can start interacting with the dataset immediately. This paper presents an interactive light field streaming system and proposes packet scheduling for transmitting the encoded image data in a rate-distortion optimized manner. An interactive light field streaming system must have low user latency. The system presented in this paper predicts the future user viewing trajectory to mitigate the effects of the low-latency constraints. Experimental results show that view prediction can improve performance, and that this improvement is limited by the prediction accuracy. The proposed packet scheduling algorithm considers network conditions and rate-distortion cost, knowledge of sent and received images, and the distortion for a set of images, to optimize the rendered image quality for the remote user. Rate-distortion optimized scheduling can be implemented either at the receiver or the sender. It is shown that this rate-distortion optimized packet scheduling can significantly improve performance over a heuristic scheduling approach. Experimental results also show that the encoding prediction dependency structure affects streaming performance both through the compression efficiency of the encoding and also through any decoding dependencies that may be introduced

Holonic Job Shop Scheduling Using a Multiagent System

Manufacturing job shop scheduling is a notoriously difficult problem that lends itself to various approaches - from optimal algorithms to suboptimal heuristics. We combined popular heuristic job shop-scheduling approaches with emerging AI techniques to create a dynamic and responsive scheduler. We fashioned our job shop scheduler's architecture around recent holonic manufacturing systems architectures and implemented our system using multiagent systems. Our scheduling approach is based on evolutionary algorithms but differs from common approaches by evolving the scheduler rather than the schedule. A holonic, multiagent systems approach to manufacturing job shop scheduling evolves the schedule creation rules rather than the schedule itself. The authors test their approach using a benchmark agent-based scheduling problem and compare performance results with other heuristic-scheduling approaches.

Scheduling approach for concurrent product development processes

A heuristic scheduling approach is proposed for the concurrent product development processes, in which some crucial parameters are determined using the genetic algorithm (GA). Scheduling concurrent activities is of great significance to shorten development lead-time and minimize costs, since it can eliminate unnecessary redesign periods and guarantee that serial activities can be executed as concurrently as possible. To ensure the optimal concurrent scheduling and the effective utilization of resources along with efficient delivery of information, some key factors such as time order, information, resources, lead-time and overlapping time of activities, which enable concurrent activities to be executed successfully, are taken into account. Their weighting factors are fixed by the GA. In addition, the scheduling module includes role allocation and feedback revision. A simulation example, which is part of an aircraft product development process, shows the feasibility of the algorithm.

Fast heuristic scheduling based on neural networks for real-time systems

As most of the real-time scheduling problems are known as hard problems, approximate or heuristic scheduling approaches are extremely required for solving these problems. This paper presents a new heuristic scheduling approach based on a modified Hopfield-Tank neural network to schedule tasks with deadlines and resource requirements in a multiprocessor system. In this approach, fast heuristic scheduling is achieved by performing a heuristic scheduling policy in conjunction with backtracking on the neural network. The results from our previous work, using the same neural network architecture without backtracking, are included here as a case with zero backtracking. Extensive simulation, which includes comparison with the conventional heuristic approach, is used to validate the effectiveness of our approach.

Learning-based scheduling in a flexible manufacturing flow line

We develop a bilevel framework for scheduling a circuit board (PCB) assembly plant which uses surface mount technology for inserting electronic components. Machine learning techniques are utilized for developing a system that adaptively utilizes the most appropriate heuristics given the current state of the system. We consider heuristics for both part-release and dispatching at machines. The results demonstrate the effectiveness of our approach compared to some other existing heuristic scheduling approaches. >