Integrated Scheduling Strategy Research Articles

Container Terminal Berth and Yard Collaborative Allocation for Import and Export Synchronous Operations by Computational Logistics

Container terminal operators have practical and clear requirements on improving operational efficiency and service level to address running challenges. The resource allocation and collaborative scheduling of quayside berth and storage yard are vital issues in the optimization of container terminal handling systems (CTHSs). The allocation of berth in quayside and blocks on storage yard directly affects the running efficiency of CTHS. Consequently, we focus on the berth and yard collaborative allocation for import and export synchronous operations (BYCA-IESOs) and propose the BYCA-IESO research model based on synchronous or asynchronous integrated scheduling strategy by computational logistics. The BYCA-IESO model is a mixed integer quadratic programming model to minimize multiple time consumption of calling liners and the total horizontal transferring distances of yard trailers at container terminals. Furthermore, we design other two allocation policies for comparison on the BYCA-IESO, which are both multistage scheduling strategy profiles. CPLEX 20.1.0 is applied to implement and solve the model and tactics of BYCA-IESO. Accordingly, the feasibility, validity, and relative merits of the models and strategies are obtained and verified by analyzing and comparing multiple numerical experimental results.

Integrated Scheduling Strategy for Private Electric Vehicles and Electric Taxis

Private electric vehicles (PREVs) and electric taxis (ETs) are two of the most common electric vehicles (EVs) in the cities. It is important to regulate the charging load file of PREVs and ETs, which has a significant impact on the power grid. Most of the existing works optimize the charging load profiles of PREVs and ETs separately since their charging behaviors are different. However, the separated optimization methods may not find out the optimal profile of the total charging load. To address this problem, in this article, we propose a two-stage integrated scheduling strategy, which can achieve a desired load profile of PREVs and ETs. In the first stage, according to the charging models of PREVs and ETs, we formulate an integrated optimization problem to determine the optimal feasible total load profile and the respective optimal load profiles of PREVs and ETs. In the second stage, we formulate two 0–1 optimization problems, respectively, which aim to select PREVs and ETs in the most need of charge to track the reference load fed from the first stage. We propose a generic algorithm to solve the two problems, which has fast convergence speed and requires low data size of communication. Finally, the simulation results validate the effectiveness of the proposed algorithm. Moreover, we demonstrate that proposed integrated scheduling strategy performs better than the separated scheduling strategies.

A Resource Scheduling Strategy in Cloud Computing Based on Multi-agent Genetic Algorithm

Resource scheduling strategies in cloud computing are used either to improve system operating efficiency, or to improve user satisfaction. This paper presents an integrated scheduling strategy considering both resources credibility and user satisfaction. It takes user satisfaction as objective function and resources credibility as a part of the user satisfaction, and realizes optimal scheduling by using genetic algorithm. We integrate this scheduling strategy into Agent subsequently and propose a cloud computing system architecture based on Multi-agent. The numerical results show that this scheduling strategy improves not only the system operating efficiency, but also the user satisfaction. DOI: http://dx.doi.org/10.11591/telkomnika.v11i11.3497 Full Text: PDF

Dynamic query scheduling in parallel data warehouses

AbstractParallel processing is a key to high performance in very large data warehouse applications that execute complex analytical queries on huge amounts of data. Although parallel database systems (PDBSs) have been studied extensively in the past decades, the specifics of load balancing in parallel data warehouses have not been addressed in detail.In this study, we investigate how the load balancing potential of a Shared Disk (SD) architecture can be utilized for data warehouse applications. We propose an integrated scheduling strategy that simultaneously considers both processors and disks, regarding not only the total workload on each resource but also the distribution of load over time. We evaluate the performance of the new method in a comprehensive simulation study and compare it to several other approaches. The analysis incorporates skew aspects and considers typical data warehouse features such as star schemas with large fact tables and bitmap indices. Copyright © 2003 John Wiley & Sons, Ltd.

A Scheduling Strategy for a Distributed Hard Real-Time Programming Environment

Abstract In this paper we discuss the integrated scheduling strategy adopted to try to met the timing requirements of distributed programs written in STER. Programs in STER include periodic modules (or tasks) with precedence relations, aperiodic modules and modules without timing requirements (non real-time modules). Modules can share remote resources using servers. A client module can access a server using single service requests or multiservice accesses. In a multiservice access a server is allocated in exclusive mode to the client for a given sequence of service requests. The scheduling strategy discussed in this paper consists of three levels: off-line scheduling of periodic modules and on-line scheduling of aperiodic and non real-time modules. To off-line search for a feasible schedule, a known heuristic algorithm was extended to support the concepts of module, server and multiservice accesses to servers. To increase the schedulability of aperiodic modules, the off-line scheduling algorithm implements some simple schemes to balance the load between the sites of a distributed system and, instead of rigid start times, defines scheduling windows for periodic tasks.