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With regard to product variety and cost competition, just-in-time (JIT) part-supply has become a critical issue in automobile assembly lines (AALs). This paper aims to investigate a multiple server scheduling problem (MSSP) encountered in the JIT part-supply process of AALs. Parts are stored in boxes and allotted from the JIT-supermarket to consumptive stations with a multiple server system. The schedule is to dispatch and sequence material boxes on each server for minimizing line-side inventory levels.

A mixed integer linear programming (MILP) model is established to formulate the proposed MSSP to pave the way for CPLEX procedure. Considering the high complexity of MSSP, a hybrid ant colony optimization (HACO) approach is developed by integrating basic ant colony optimization (ACO) with local optimizers that comprise of a fast local search and a tailored breadth-first tree search method.

Both CPLEX and HACO approach are capable of solving small-scale instances to optimality within reasonable computation time. The proposed HACO has been well enhanced with the embedded fast local search and tailored breadth-first tree search, and it performs robustly in a statistically significant manner when applied to real-world scale instances.

No stock-outs constraints and weighted line-side inventory level are considered in this paper, and the MSSP is solved satisfactorily to facilitate an efficient JIT part-supply of the AAL. In terms of the algorithm design, a tree search-based local optimizer is embedded into ACO to combine the mechanisms of ACO and problem-specific optimization.

In a complex semiconductor manufacturing system (SMS) environment, the implementation of dynamic production scheduling and dispatching strategies is critical for SMS distributed collaborative manufacturing events to make quick and correct decisions. The purpose of this paper is to assist manufacturers in achieving the real time dispatching and obtaining integrated optimization for shop floor production scheduling.

In this paper, an integrated model is designed under assemble to order environment and a framework of a real time dispatching (IRTD) system for production scheduling control is presented accordingly. Both of the scheduling and ordering performances are integrated into the days of inventory based dispatching algorithm, which can deal with the multiple indicators of dynamic scheduling and ordering in this system to generate the “optimal” dispatching policies. Subsequently, the platform of IRTD system is realized with four modules function embedded.

The proposed IRTD system is designed to compare the previous constant work in process method in the experiment, which shows the better performance achievement of the IRTD system for shop floor production dynamic scheduling and order control. The presented framework and algorithm can facilitate real time dispatching information integration to obtain performance metrics in terms of reliability, availability, and maintainability.

The presented system can be further developed to generic factory manufacturing with the presented logic and architecture proliferation.

The IRTD system can integrate the real time customer demand and work in process information, based on which manufacturers can make correct and timely decisions in solving dispatching strategies and ordering selection within an integrated information system.

In client/server distributed systems, the server is often the bottleneck. Improving the server performance is thus crucial for improving the overall performance of distributed information systems. Real‐time system is required to complete its work and deliver its services on a timely basis. The purpose of this paper is to propose a new scheduling algorithm for real‐time distributed system (client/server model) to achieve the above‐mentioned goal.

The ant colony optimization (ACO) algorithms are computational models inspired by the collective foraging behavior of ants. They provide inherent parallelism and robustness. Therefore, they are appropriate for scheduling of tasks in soft real‐time systems. During simulation, results are obtained with periodic tasks, measured in terms of success ratio and effective CPU utilization; and compared with results of earliest deadline first (EDF) algorithm in the same environment.

Analysis and experiments show that the proposed algorithm is equally efficient during underloaded conditions. The performance of EDF decreases as the load increases, but the proposed algorithm works well in overloaded conditions also. Because of this type of property, the proposed algorithm is more suitable for the situation when future workload of the system is unpredictable.

The application of ACO algorithms for scheduling of client/server real‐time distributed system, never found before in the literature. The new concept proposed in this paper will be of great significance to both theoretical and practical research in scheduling of distributed systems in the years to come.

The purpose of this paper is to improve the efficiency of loading and discharging operations in container terminals. Accounting for an increase in the size of ships, the yard truck (YT) routing and scheduling problem has become an important issue to terminal operators.

A (binary) integer programming model is developed using the time-space network technique to optimally move YTs between quay cranes (QC) and yard cranes (YC) in the time and space dimensions. The objective of the model is to minimize the total operating cost, and the model employs the M/M/S model in the queuing theory to determine the waiting time of YTs. The developed model can obtain the optimal number of YTs and their scheduling and routing plans simultaneously, as shown by the computational results.

The results also show that the model can be applied to practical operations. In this research, an experimental design of the QC and YC operation networks was considered with the import and export containers carried by YTs. The model can be used to tackle a real world problem in an international port, and the analysis results could be useful references for port operators in actual practice.

The purpose of this research only focusses on YTs routing and scheduling problem, however, the container terminal operation problems are interrelated with berth allocation and yard stacking plan. The managerial application of this study is to analyze the trade-off between truck numbers and truck waiting time can be used for terminal operators to adjust the truck assignment. This research can assist an operator to determine the optimal fleet size and schedule in advance to avoid wasted costs and congestion in the quayside and yard block.

This research solves the YT scheduling and routing problem for container discharging and loading processes with a time-space network model, which has not been previously reported, through an empirical research.

The purpose of this paper is to propose a scheduling method called the “Hierarchical Asynchronous Harmonic Broadcasting (H‐AHB)” method, to reduce the waiting time for heterogeneous clients.

The authors analyze and evaluate the performance of the proposed H‐AHB method.

It was confirmed that the proposed method gives shorter average waiting time than the conventional methods.

A future direction of this study will involve making a scheduling method where the server broadcasts multiple videos.

In general broadcasting systems, the server broadcasts the same data repetitively and clients wait until the first portion of the data is broadcast. Although the server can deliver the data to many clients concurrently, clients have to wait until their desired data are broadcast.

The H‐AHB method further reduces waiting time by scheduling an effective broadcast that considers the number of clients' available channels.

The purpose of this study is truck scheduling and assignment of trucks to the doors simultaneously since these issues were considered mainly separately in the previous research. Also, the door service time and its impact on truck scheduling were not taken into account, so this research endeavors to cover this gap.

In this research, a novel model has been presented for simultaneous truck scheduling and assignment problem with time window constraints for the arrival and departure of trucks, mixed service mode dock doors and truck queuing. To resolve the developed model, two meta-heuristic algorithms, namely, genetic and imperialist competitive algorithms, are presented.

The computational results indicate that the proposed framework leads to increased total costs, although it has a more accurate planning; moreover, these indicate that the proposed algorithms have different performances based on the criteria considered for the comparison.

There are some limitations in this research, which can be considered by other researchers to expand the current study, among them the specifications of uncertainty about arrival times of inbound and outbound trucks, number of merchandises which has been loaded on inbound trucks are the main factors. If so, by considering this situation, a realistic scheme about planning of cross docking system would be acquired. Moreover, the capacity of temporary storage has been considered unlimited, so relaxing this limitation can prepare a real and suitable situation for further study. Examining the capacity in the front of each type of doors of cross-dock and executive servers are the other aspects, which could be expanded in the future.

In this study, a mathematical programing model proposed for truck scheduling to minimize total costs including holding, truck tardiness and waiting time for queue of trucks caused by the interference of each carrier’s movement. At the operational levels, this research considered a multi-door cross-docking problem with mixed service mode dock doors and time window constraints for arrival and departure time of trucks. Moreover, M/G/C queue system was developed for truck arrival and servicing of carriers to trucks.

Time waiting for service is a major concern for consumers, and excessive waiting for a pre‐scheduled appointment is especially annoying. This is an on‐going problem because appointment scheduling is a challenging task, mainly due to the uncertainties associated with service times. Prior studies have focused mainly on a single scheduling period (i.e. either a morning or afternoon); this paper uses a more realistic model that represents an on‐going, multi‐period scheduling environment where clients can be scheduled days or even weeks into the future. Two main objectives will be considered; the best scheduling rule to use in a multi‐period environment, and the best placement of appointment slots that are left open for urgent clients. Both of these have been studied in a single period environment, and results here will be compared to those. It will be shown that in some cases earlier findings from the one‐period environment are robust and perform well in a multi‐period environment, while in other cases the one‐period findings do not apply.

Virtual machines (VMs) are suggested by the providers of cloud services as the services for the users over the internet. The consolidation of VM is the tactic of the competent and smart utilization of resources from cloud data centers. Placement of a VM is one of the significant issues in cloud computing (CC). Physical machines in a cloud environment are aware of the way of the VM placement (VMP) as the mapping VMs. The basic target of placement of VM issue is to reduce the physical machines' items that are running or the hosts in cloud data centers. The VMP methods have an important role in the CC. However, there is no systematic and complete way to discuss and analyze the algorithms. The purpose of this paper is to present a systematic survey of VMP techniques. Also, the benefits and weaknesses connected with selected VMP techniques have been debated, and the significant issues of these techniques are addressed to develop the more efficient VMP technique for the future.

Because of the importance of VMP in the cloud environments, in this paper, the articles and important mechanisms in this domain have been investigated systematically. The VMP mechanisms have been categorized into two major groups, including static and dynamic mechanisms.

The results have indicated that an appropriate VMP has the capacity to decrease the resource consumption rate, energy consumption and carbon emission rate. VMP approaches in computing environment still need improvements in terms of reducing related overhead, consolidation of the cloud environment to become an extremely on-demand mechanism, balancing the load between physical machines, power consumption and refining performance.

This study aimed to be comprehensive, but there were some limitations. Some perfect work may be eliminated because of applying some filters to choose the original articles. Surveying all the papers on the topic of VMP is impossible, too. Nevertheless, the authors are trying to present a complete survey over the VMP.

The consequences of this research will be valuable for academicians, and it can provide good ideas for future research in this domain. By providing comparative information and analyzing the contemporary developments in this area, this research will directly support academics and working professionals for better knowing the growth in the VMP area.

The gathered information in this paper helps to inform the researchers with the state of the art in the VMP area. Totally, the VMP's principal intention, current challenges, open issues, strategies and mechanisms in cloud systems are summarized by explaining the answers.

Cloud computing gives several on-demand infrastructural services by dynamically pooling heterogeneous resources to cater to users’ applications. The task scheduling needs to be done optimally to achieve proficient results in a cloud computing environment. While satisfying the user’s requirements in a cloud environment, scheduling has been proven an NP-hard problem. Therefore, it leaves scope to develop new allocation models for the problem. The aim of the study is to develop load balancing method to maximize the resource utilization in cloud environment.

In this paper, the parallelized task allocation with load balancing (PTAL) hybrid heuristic is proposed for jobs coming from various users. These jobs are allocated on the resources one by one in a parallelized manner as they arrive in the cloud system. The novel algorithm works in three phases: parallelization, task allocation and task reallocation. The proposed model is designed for efficient task allocation, reallocation of resources and adequate load balancing to achieve better quality of service (QoS) results.

The acquired empirical results show that PTAL performs better than other scheduling strategies under various cases for different QoS parameters under study.

The outcome has been examined for the real data set to evaluate it with different state-of-the-art heuristics having comparable objective parameters.

This paper aims to develop a service‐oriented distributed multi‐robot system based on manufacturing message specification (MMS) and new‐generation distributed object technology – web services for realizing remotely monitoring and controlling multiple heterogeneous robots in the internet environment.

The study presents robot communication model and distributed multi‐robot monitoring and control software structure based on MMS and web services. In particular, monitoring and control software design of MMS concepts in web services environment using Unified Modeling Language model is discussed in detail. In addition, to verify the validity of the proposed design method, a multi‐robot prototype system for robot flexible assemble cell has been achieved. Its Server software is implemented in C++ with Visual Studio.NET being the development environment and Client software is programmed in Java with Borland JBuilder 9 being the development tool.

Finds that the communication structure following MMS can make the multi‐robot monitoring and control system have perfect robustness, interoperability and reconfigurability. Besides, web services technology can conveniently realize MMS services, also can successfully resolve the remote multi‐robot monitoring and control problem among cross‐network, cross‐platform and heterogeneous systems.

Provides an easy and low‐cost method for realizing heterogeneous multi‐robot remote driving. The web‐based distribution of the presented system is critical in enabling capabilities such as e‐manufacturing, e‐diagnostics and e‐maintenance.

The proposed system can be seamlessly integrated into other automated manufacturing systems or management systems in plug‐and‐play fashion. The combination of MMS and web services is in favor of real manufacturing equipments being embedded in the network, so the presented systematic methodology can be a useful reference for constructing web‐based reconfigurable manufacturing systems.

Provides robot communication model based on MMS and web services and presents service‐oriented distributed remote multi‐robot monitoring and control software architecture.