Current Scheduling Methods Research Articles

Effectiveness of Dynamic Insertion Scheduling Strategy for Demand-Responsive Paratransit Vehicles Using Agent-Based Simulation

This paper deals with the scheduling of paratransit vehicles. The current scheduling method utilized by paratransit providers is to provide a door-to-door ride for those customers who have made reservations. Thus, the paratransit providers know in advance the pickup and drop-off locations of each customer. Using this information, they are able to determine a route for each vehicle to minimize the total operating costs. In the current scheduling method, vehicles are not allowed to pick up unscheduled customers. This practice often leads to low seat utilization. To address this shortcoming, this paper explores the idea of allowing vehicles to pick up unscheduled customers who are in close proximity to the prescheduled stops (referred to as the dynamic response area or DRA). To this end, this paper develops an agent-based simulation model to evaluate the effectiveness of this strategy. The model was tested using the Chicago network. The results of the simulation experiments indicate that (1) the proposed strategy is able to serve more customers using the same fleet size, and (2) the proposed strategy will not significantly affect the scheduled customers’ in-vehicle travel time.

Resource Planning and Allocation in Distributed Cloud Networks using Voids in Scheduled Intervals

The significant objective of research in the distributed cloud networks is resource planning and allocation using voids in scheduled intervals, which grabbed several researchers’ attention in contemporary literature. Minimum failures of resource scheduling, completion of a robust task and fair usage of the resource were the important parameters of resource scheduling schemes. Therefore, this paper projected scalable-resource scheduling method for distributed CC environments, which aimed to attain the metrics of scheduling. The projected method is known as "Resource Planning and Allocation in Distributed Cloud Networks using Voids in Scheduled Intervals (RPA-DCN)," where resources are scheduled towards the corresponding task so that, the optimum using of idle time of resource is attained. The projected method performs scheduling in sequential order, and they were allocation of optimum resource when no single resource is identified to allocate, then optimum manifold idle resources are allocated with a considerable filling of schedule intervals. This paper discusses (a) pre-requisite of resource scheduling schemes, (b) current scheduling methods found in recent literature, (c) methods & materials utilized and method of projected resource scheduling scheme (d) and its significance over other standard methods. The analysis of performance for the proposal conducted by metrics cross-validation such as load vs. loss, optimality of task completion & process-overhead in the resource-scheduling. Here, the simulation outcomes exhibit that the projected method is robust and scalable under modified metrics

Integrated Scheduling and Capacity Planning with Considerations for Patients' Length-of-Stays.

Motivated by the shortcoming of current hospital scheduling and capacity planning methods which often model different units in isolation, we introduce the first dynamic multi-day scheduling model that integrates information about capacity usage at more than one location in a hospital. In particular, we analyze the first dynamic model that accounts for patients' length-of-stay and downstream census in scheduling decisions. Via a simple and innovative variable transformation, we show that the optimal number of patients to be allowed in the system is increasing in the state of the system and in the downstream capacity. Moreover, the total system cost exhibits decreasing marginal returns as the capacity increases at any location independently of another location. Through numerical experiments on realistic data, we show that there is substantial value in making integrated scheduling decisions. In contrast, localized decision rules that only focus on a single location of a hospital can result in up to 60% higher expenses.

Distributed Dynamic Scheduling for Cyber-Physical Production Systems Based on a Multi-Agent System

The world is experiencing a new industrial revolution characterized by intelligent manufacturing. Cyber-physical production systems (CPPSs) have become a research focus due to their proposed use as a solution to the development of flexible and reactive systems. The application of current centralized scheduling methods is difficult because of the enhanced precision control mode of a CPPS. Therefore, this paper focuses on distributed optimal scheduling based on multi-agent systems. First, the goals and constraints of the system are set, a two-layer decision model and the required indicators are designed to ensure the overall optimization effect, and the roles and functions of different agents are then set. Second, the dynamic decision cycle and the multistage negotiation mechanism based on the contract net protocol are studied to ensure the quality of negotiation. A rescheduling algorithm is designed to guarantee adaptability in the case of disturbance in the system. Finally, the applicability and superiority of the strategies are demonstrated via experiments and case studies.

Energy-Efficient Job-Shop Dynamic Scheduling System Based on the Cyber-Physical Energy-Monitoring System

Nowadays, a large amount of energy is consumed by buildings in the city. Among them, industrial energy consumption accounts for a large proportion due to the high power of manufacturing devices in the job-shop. To achieve higher energy-efficiency, lots of scheduling methods in the job-shop are developed. However, most of the current scheduling method is based on the prior-experimental data and the schedule of the production cycle cannot be adjusted according to energy status of manufacturing devices. Thus a dynamic scheduling system based on the cyber-physical energy monitoring system is proposed to improve the energy efficiency of the job-shop. First, a novel process of “scheduling-monitoring-updating-optimizing” is implemented in the proposed system. In the scheduling model, tool aging condition is considered along with the geometry information of the work-piece to estimate energy consumption of the manufacturing process. At last, a modified genetic algorithm with multi-layer coding is applied to generate an energy-efficient schedule. The proposed system is implemented in a production cycle which contains 36 operations of six work-pieces and ten machine tools to prove its validity.

Classified scheduling algorithm of big data under cloud computing

ABSTRACTIn order to realize the optimization of resource scheduling in the cloud computing environment and improve the utilization ratio of network data resource, we need to carry out the data resource scheduling in the cloud computing environment. But when using the current scheduling method for data resource scheduling in cloud computing environment, we schedule the current task through determining the resource scheduling cost function, however, we do not take into account the comprehensive evaluation cost of performance of user data, resulting in data network resource scheduling is poor. Therefore, this paper proposed a method of scheduling network data resources in cloud computing environment based on particle swarm optimization. Firstly, this method dynamically clusters cloud computing network data resources. By considering the network bandwidth, the bandwidth utilization, the current network running state, the method introduces the particle swarm apportion model in the process of building the network data resource scheduling model, and predicts the execution speed of task based on network nodes, and schedules and arranges task sets of different data resources. We obtain the total weight of data network resource scheduling tasks and take it as parameters to calculate the fitness value, on the basis of particle swarm optimization algorithm, we optimize different network data resource scheduling tasks. Experimental results show that the proposed method is suitable for the scheduling of network data resources in cloud computing environment, and has advantages in scheduling efficiency and resource utilization.

A dispatching rule and a random iterated greedy metaheuristic for identical parallel machine scheduling to minimize total tardiness

This paper addresses a real-life production scheduling problem with identical parallel machines, originating from a plant producing Acrylonitrile-Butadiene-Styrene (ABS) plate products. In the considered practical scheduling problem, ABS plate has some specific specifications and each specification has several different levels. Because there is at least one different level of specification between two ABS plate products, it is necessary to make a set-up adjustment on each machine whenever a switch occurs from processing one ABS plate product to another product. As tardiness leads to extra penalty costs and opportunity losses, the objective of minimising total tardiness has become one of the most important tasks for the schedule manager in the plant. The problem can be classified as an identical parallel machine scheduling problem to minimise the total tardiness. A dispatching rule is proposed for this problem and evaluated by comparing it with the current scheduling method and several existing approaches. Moreover, an iterated greedy-based metaheuristic is developed to further improve the initial solution. The experimental results show that the proposed metaheuristic can perform better than an existing tabu search algorithm, and obtain the optimal solution for small-sized problems and significantly improve the initial solutions for large-sized problems.

Scheduling with multi-attribute set-up times on unrelated parallel machines

This paper studies a problem in the knitting process of the textile industry. In such a production system, each job has a number of attributes and each attribute has one or more levels. Because there is at least one different attribute level between two adjacent jobs, it is necessary to make a set-up adjustment whenever there is a switch to a different job. The problem can be formulated as a scheduling problem with multi-attribute set-up times on unrelated parallel machines. The objective of the problem is to assign jobs to different machines to minimise the makespan. A constructive heuristic is developed to obtain a qualified solution. To improve the solution further, a meta-heuristic that uses a genetic algorithm with a new crossover operator and three local searches are proposed. The computational experiments show that the proposed constructive heuristic outperforms two existed heuristics and the current scheduling method used by the case textile plant.

Graphical Modelling Classification for Construction Project Scheduling

The main goals of graphical modelling and data visualization are to communicate information clearly and effectively through graphical means. Little research has been undertaken in this area and current scheduling methods seem to be unable, individually, to meet all of the planner's needs, to be understood visually, and to be efficient in terms of displaying as much information as possible. We have analyzed the graphical modelling visual parameters and variables, and their associated values (tabular, graphical, colors, shapes, fills, borders, patterns, text, fonts, etc.), defined the graphical approach of existing scheduling methods, and developed a new classification for construction projects’ scheduling. Scheduling methods are classified into seven (7) categories: Table Series, Cross-tabulation Series, Pure Logic Series, Time-Scaled Series, Chrono Series, CAD Series, and Chart Series. This classification illustrates how the information can be presented using different strategies resulting in the presentation of the same project schedule through different compatible approaches. The planner has the ability to switch from one approach to another by changing the graphical parameters. In this way, graphic representation becomes a living, transformable image, thus assisting planners in solving problems of a variable nature, and simplifying site management while simultaneously utilizing the visual space as efficiently as possible.

Integrated Optimization of Finished Product Logistics in Iron and Steel Industry Using a Multi-objective Variable Neighborhood Search

This paper is concerned with the integrated optimization of finished product logistics that consists of two sub-problems: consolidation planning and transportation scheduling. In this problem, three kinds of decisions should be made simultaneously, namely the selection of candidate finished products in each warehouse (i.e., consolidation planning) and the vehicle allocation and transportation sequence of selected finished products in each warehouse (i.e., transportation scheduling). Since the loading times of products to vehicles are sequence-dependent, a tradeoff should be made between two conflicting objectives: maximization of loadage of ships and maximization of efficiency of the whole logistics. In practical industry the schedulers usually handle the two sub-problems separately, which often cause much lower efficiency of the whole logistics and higher transportation cost. Therefore, in this paper the two sub-problems are integrated and formulated as a multi-objective mixed-integer programming model, and then a two-layer multi-objective variable neighborhood search (TLMOVNS) algorithm is developed to solve it. Computational results on simulated instances show that the proposed TLMOVNS is very efficient for this problem and much superior to the current scheduling method generally used in practice.

High reliable real-time bandwidth scheduling for virtual machines with hidden Markov predicting in telehealth platform

Reliable and high-performance resource scheduling for Virtual Machines (VMs) in cloud can guarantee the efficiency of remote rescue with telehealth system. When a local disaster, e.g. earthquake and tsunami, happens in a densely populated area, the surging health care demand leads to the increasing workload in Data Centers (DCs) by storing and delivering a mass of patients’ information and real-time physiology signals. However, the current self-adaptive scheduling methods cannot provide a high-accuracy recognizing of the two conditions: urgency or normal, which would procrastinate the system into a high-performance status, while the best rescue time is lost. In this paper, we propose a Primary Node-based architecture for typical telehealth service on cloud, which takes into account both storage and delivery efficiency. We also design a novel algorithm to predicting and allocating the future bandwidth of all VMs in the telehealth service context. This method is able to dynamically adjust each parameter of a Hidden Markov Model (HMM) through collecting the historical information of the bandwidth workload. After we predict the future bandwidth consumption of VMs, a high-performance scheduling method is used to adjust the bandwidth to each VM for health care service. The simulation results prove that this algorithm provides a high-accurate prediction, which guides the allocating module to make decision before the request burst comes. Nevertheless, our algorithm improves the reliability of telehealth services for storing and delivering patients’ information among DCs.

Unrelated parallel machine scheduling with dedicated machines and common deadline

This paper addresses a scheduling problem in the manufacturing of Polyvinyl Chloride pipes. There are two main attributes of PVC pipes: diameter and color. Each attribute has a corresponding attribute setup time and usually has several different levels. Each extruder produces different PVC pipe products based on the diameters as large, middle and small. The alternatives exist between these extruders, where the large and the middle type extruders can be used to produce the PVC pipes with the other diameters; the small type extruders can be used to produce the PVC pipes with middle diameters but cannot produce those with large diameters. The processing times are longer in all of the alternatives among different types of extruders. The objective is to minimize the total completion time for the unrelated parallel machine problem.Three dedicated machine heuristics are proposed herein for the problem and have been evaluated by comparing with the current scheduling method used in the case plant. The computational results show that the proposed constructive heuristics outperform the current scheduling method with significant improvements and can be used to solve large-size problems in reasonable computational times.

Scheduling with multi-attribute setup times on two identical parallel machines

This paper considers a real-life identical parallel scheduling problem originating from the manufacturing plant producing polyvinyl chloride leather products. In the considered scheduling problem, each job has some attributes and each attribute has several different levels. Because there is at least one different level of attribute between two adjacent jobs, it is necessary to make a setup adjustment whenever there is a switch from processing a job to a different job on each machine. The problem can be classified as a scheduling problem to minimize the makespan on two identical parallel machines with multi-attribute setup times. A constructive heuristic, named COIT, is first proposed for the problem and evaluated by comparing with the current scheduling method used by the case plant. To further improve the solution, a variable neighborhood search (VNS) metaheuristic is presented and compared with a mixed integer programming model. The computational results show that the COIT heuristic outperforms the current scheduling method with a significant improvement, and the VNS can further improve the solution.

Rapid Vehicle Scheduling for Emergency Personnel Evacuation in Disaster Rescue

T Vehicle scheduling for personnel evacuation plays a significant role in disaster rescue. However, current vehicle scheduling methods do not meet the needs of emergency response in disaster rescue. This paper proposes a rapid vehicle scheduling method to evacuate the crowd and reduce losses in an emergent situation. We construct a specific model for vehicle scheduling problem in disaster rescue firstly, and then put forward a heuristic algorithm to solve this problem. An empirical example is used to evaluate the effectiveness of the method. Scheduling and evacuation data are computed in real-time and considerations about dangerous zones of different dangerous levels are also included in this method. The characteristics of the method make it more suitable in actual emergency rescue.

The chronographical approach for construction project modelling

Graphical modelling is considered a suitable approach for displaying project data because of its ability effectively to communicate information. The current scheduling methods seem to be unable, individually, to meet all of the planner’s needs, to be understood visually and to be efficient in terms of displaying as much information as possible. The main purpose of this paper is to present the chronographical approach for planning and monitoring construction projects. The chronographical approach is a more complete communication method, having the ability to alternate from one visual approach to another by manipulation of graphics by way of a set of defined graphical parameters. Each individual approach can help to schedule a certain project type or speciality, show valuable information in a clear and comprehensible manner and facilitate the management of construction site problems visually. Visual communication can also be improved through layering, sheeting, juxtaposition, alterations and permutations, allowing for groupings, hierarchies and classification of project information. In this way, graphical representation becomes a living, transformable image, thus assisting planners in solving problems of a variable nature, and simplifying site management while simultaneously using the visual space as efficiently as possible.

USING SOFT PRECEDENCE RELATIONS FOR REDUCTION OF THE CONSTRUCTION PROJECT DURATION

Current network techniques used for project scheduling presume that there is only one logical sequence of activities. However, in reality, the precedence logic of activities is not necessarily unchangeable. There are two types of relations between activities: fixed (hard) and soft. Current scheduling methods do not differentiate between these two types of relations and require a planner to choose one particular logical sequence based on some assumptions. The paper presents a model that allows for “weakening” the links between activities, i.e. that permits concurrent execution of activities or reversing their order, but this may require additional resource-consuming works or expenditures. The model involves two subtypes of soft logic: REVERSED and CANCELED to represent the soft relations between activities. Its mathematical form is a mixed linear integer programming model of minimizing the project duration without exceeding the allowed budget. The paper presents an example of practical application of the method to scheduling interior finishing works. The results support the argument that using soft logic relations between construction activities provides more flexibility in reducing project duration, activities timing and resource allocation.

Scheduling with multi-attribute setup times

In this paper we address a scheduling problem with multi-attribute setup times originated from the manufacturing plant of a company producing PVC sheets. In the considered scheduling problem, each job has a number of attributes and each attribute has one or more levels. Because there is at least one different level of attribute between two adjacent jobs, it is necessary to make a setup adjustment whenever there is a switch to a different job. The objective of the problem is to determine a processing sequence so as to minimize the total setup time on a single machine.We develop a constructive heuristic based on several theorems for the problem. The heuristic has been evaluated by comparing with an existing constructive heuristic, a tabu search heuristic and a dynamic programming approach, and its efficiency and effectiveness have been demonstrated. The computational results show that the proposed heuristic outperforms the current scheduling method used by the case plant with a significant improvement.

Two Hybrid Metaheuristic Algorithms for Hot Rolling Scheduling

This paper presents a model for the hot rolling scheduling problem (HRSP), which is derived from the actual steel production. The model is characterized by some new features, such as the rolling length of the consecutive slabs with the same width, temperature jump between adjacent slabs. The warm up part is a minor part of a turn and is usually ignored, but it affects directly the product quality. Therefore, besides the slab sequence in the staple material section, we also consider the slab sequence in the warm up material section. These features make the solution methodology more difficult. Therefore, two hybrid strategies are proposed to determine good approximate solutions for this complicated problem. The first one (CT_ACO) is a hybrid strategy based on the solution construction mechanism of ant colony optimization (ACO) with cyclic transfers (CT). The second one (CT_SS) is to hybridize scatter search (SS) and CT neighborhood search. Moreover, we design a decision support system in which two algorithms have been embedded for the HRSP. The most popular feature of the system is the architecture of component management, which allows us to modify easily some components according to the practice situation. The computational experiments show that CT_ACO is superior to general ACO, and CT_SS is also better than SS in terms of solution quality. The CT_ACO method and CT_SS have more potential for improvement to solve the HRSP compared with the current scheduling method, and the CT_ACO generates slightly better quality solutions than CT_SS algorithm.

Application of line‐of‐balance and 4D CAD for lean planning

PurposeThe purpose of this paper is to suggest the application of the line‐of‐balance (LoB) scheduling technique in combination with a 4D CAD workspace model as a method to improve the management of the flow of resources through locations in construction projects, defined as work flow. Current scheduling methods fail to consistently manage work flow, which can disrupt the construction process, leading to waste such as conflicts in time and space by construction crews.Design/methodology/approachLoB and 4D CAD are applied to a case study of multi‐story timber housing project involving the construction of 95 apartments in five six‐storey buildings. Based on the case study results, the benefits and limitations of the combined use of both methods are discussed.FindingsThe majority of the problems experienced during the actual construction process quickly become evident from an analysis of a relatively simple LoB diagram. Furthermore, the 4D CAD workspace model provides additional insights in the scheduling of construction activities, such as workspace availability, the spatial context of workspaces and partial overlap of workspaces.Practical implicationsVirtual design and construction methods based on principles from lean construction can contribute significantly to the value of the product and the elimination of waste in any construction project.Originality/valueThe paper refers to the guiding principles from lean construction in relation to virtual design and construction methods, such as simulations with 4D CAD. Additional research and studies of practical applications are suggested to facilitate the combination of principles from lean construction with virtual design and construction methods.

Integration of batching and scheduling for hot rolling production in the steel industry

This paper investigates the hot rolling production scheduling problem in the steel industry and proposes a new mixed integer programming model for this problem based on the monolithic modeling strategy that integrates batching and scheduling. Using this strategy, the new model can simultaneously schedule multiple turns and simultaneously determine the production timetable of these turns in a global optimal view. A long-term tabu search heuristic using frequency-based memory is developed to obtain near-optimal solutions for this problem. Three kinds of speed-up strategies are developed to accelerate the search procedure of the proposed tabu search. A practical scheduling system combining the proposed mathematical model and the tabu search heuristic has been developed and tested on instances collected from practical production data. The experimental results show that the proposed mathematical model and the tabu search heuristic outperform both the current manual scheduling method and the traditional serial method.