全局信息不全的动态调度问题基于虚拟调度的两级滚动方法

This paper addresses the single-machine scheduling problem with release times mini-mizing the total completion time. Under the circumstance of incomplete global information at each decision time, a two-level rolling scheduling strategy (TRSS) is presented to create the global schedule step by step. The estimated global schedules are established based on a dummy schedule of unknown jobs. The first level is the preliminary scheduling based on the predictive window and the second level is the local scheduling for sub-problems based on the rolling window. Performance analysis demonstrates that TRSS can improve the global schedules. Computational results show that the solution quality of TRSS outperforms that of the existing rolling procedure in most cases.     

On scheduling dag s for volatile computing platforms: Area-maximizing schedules

Many modern computing platforms—notably clouds and desktop grids—exhibit dynamic heterogeneity: the availability and computing power of their constituent resources can change unexpectedly and dynamically, even in the midst of a computation. We introduce a new quality metric, area, for schedules that execute computations having interdependent constituent chores (jobs, tasks, etc.) on such platforms. Area measures the average number of tasks that a schedule renders eligible for execution at each step of a computation. Even though the definition of area does not mention and properties of host platforms (such as volatility), intuition suggests that rendering tasks eligible at a faster rate will have a benign impact on the performance of volatile platforms—and we report on simulation experiments that support this intuition. We derive the basic properties of the area metric and show how to efficiently craft area-maximizing (A-M) schedules for several classes of significant computations. Simulations that compare A-M scheduling against heuristics ranging from lightweight ones (e.g., FIFO) to computationally intensive ones suggest that A-M schedules complete computations on volatile heterogeneous platforms faster than their competition, by percentages that vary with computation structure and platform behavior—but are often in the double digits.     

Greedy randomized adaptive search for dynamic flexible job-shop scheduling

Dynamic flexible job shop scheduling problem is studied under the events such as new order arrivals, changes in due dates, machine breakdowns, order cancellations, and appearance of urgent orders. This paper presents a constructive algorithm which can solve FJSP and DFJSP with machine capacity constraints and sequence-dependent setup times, and employs greedy randomized adaptive search procedure (GRASP). Besides, Order Review Release (ORR) mechanism and order acceptance/rejection decisions are also incorporated into the proposed method in order to adjust capacity execution considering customer due date requirements. The lexicographic method is utilized to assess the objectives: schedule instability, makespan, mean tardiness and mean flow time. A group of experiments is also carried out in order to verify the suitability of the GRASP in solving the flexible job shop scheduling problem. Benchmark problems are formed for different problem scales with dynamic events. The event-driven rescheduling strategy is also compared with periodical rescheduling strategy. Results of the extensive computational experiment presents that proposed approach is very effective and can provide reasonable schedules under event-driven and periodic scheduling scenarios.     

A formal mapping-based approach for distributed schedule coordination on projects

Construction projects involve a large number of participants with overlapping scope of work. Coordination of their activities is usually an iterative manual task undertaken by a general contractor that is often unaware of the detailed constraints of other participants. Project schedules play a key role in this coordination and form the backbone of almost all current approaches to process coordination. However, no single schedule represents the perspective of all participants involved in a project. Rather, each participant keeps in some manner a schedule for its own activities, resulting in multiple schedules that need to be coordinated. The current literature does not support simultaneous reasoning across multiple distributed, overlapping schedules. This paper introduces constructs to formalize the integration of participants’ overlapping schedules that represent the same project tasks, but use a different set breakdown structures and level of detail. Implementation of these constructs allows linking of the master schedule to the other participants’ schedules thereby representing the perspectives of all project participants. This integrated perspective facilitates initial schedule coordination and allows rapid identification of schedule conflicts in response to any schedule changes.     

The effect of work-rest schedules and type of task on the discomfort and performance of VDT users

Consideration of the literature survey indicates that video display terminal (VDT) operators tend to have a high incidence of musculoskeletal problems, visual fatigue, and job stress. Although a number of ergonomic improvements in workstation design and work environment can help to reduce these problems, a proper work-rest schedule deserves consideration since it is easily applicable and inexpensive. The objective of this study was to compare the work-rest schedules for VDT operators considering data entry and mental arithmetic tasks. An experiment was conducted with 10 male college students as participants. The methodology included a discomfort questionnaire and performance measures. The independent variables were the work-rest schedule (60-minute work/10-minute rest, 30-minute work/5-minute rest, and 15-minute work/micro breaks) and the type of task (data entry and a mental arithmetic task). The results were analysed using multiple analysis of variance followed by separate analyses. The 15/micro schedule resulted in significantly lower discomfort in the neck, lower back, and chest than the other schedules for data entry task. The 30/5 schedule followed by 15/micro schedule resulted in the lowest eyestrain and blurred vision. Discomfort in the elbow and arm was the lowest with the 15/micro schedule for the mental arithmetic task. The 15/micro schedule resulted in the highest speed, accuracy, and performance for both of the tasks, compared with the 60/10 and 30/5 schedules. The data entry task resulted in significantly increased speed, accuracy, and performance, and lower shoulder and chest discomfort than the mental arithmetic task.     

The effect of work-rest schedules and type of task on the discomfort and performance of VDT users

Consideration of the literature survey indicates that video display terminal (VDT) operators tend to have a high incidence of musculoskeletal problems, visual fatigue, and job stress. Although a number of ergonomic improvements in workstation design and work environment can help to reduce these problems, a proper work-rest schedule deserves consideration since it is easily applicable and inexpensive. The objective of this study was to compare the work-rest schedules for VDT operators considering data entry and mental arithmetic tasks. An experiment was conducted with 10 male college students as participants. The methodology included a discomfort questionnaire and performance measures. The independent variables were the work-rest schedule (60-minute work/10-minute rest, 30-minute work/5-minute rest, and 15-minute work/micro breaks) and the type of task (data entry and a mental arithmetic task). The results were analysed using multiple analysis of variance followed by separate analyses. The 15/micro schedule resulted in significantly lower discomfort in the neck, lower back, and chest than the other schedules for data entry task. The 30/5 schedule followed by 15/micro schedule resulted in the lowest eyestrain and blurred vision. Discomfort in the elbow and arm was the lowest with the 15/micro schedule for the mental arithmetic task. The 15/micro schedule resulted in the highest speed, accuracy, and performance for both of the tasks, compared with the 60/10 and 30/5 schedules. The data entry task resulted in significantly increased speed, accuracy, and performance, and lower shoulder and chest discomfort than the mental arithmetic task.     

Analyzing Asynchronous Pipeline Schedules

Asynchronous pipelining is a form of parallelism that is useful in both distributed and shared memory systems. We show that asynchronous pipeline schedules are a generalization of both noniterative DAG (directed acyclic graph) schedules as well as simpler pipeline schedules, unifying these two types of scheduling. We generalize previous work on determining if a pipeline schedule will deadlock, and generalize Reiter's well-known formula for determining the iteration interval of a deadlock-free schedule, which is the primary measure of the execution time of a schedule. Our generalizations account for nonzero communication times (easy) and the assignment of multiple tasks to processors (nontrivial). A key component of our generalized approach to pipeline schedule analysis is the use of pipeline scheduling edges with potentially negative data dependence distances. We also discuss implementation of an asynchronous pipeline schedule at runtime; show how to efficiently simulate pipeline execution on a sequential processor; define and derive bounds on the startup time of a schedule, which is a secondary schedule performance measure; and describe a new algorithm for evaluating the iteration interval formula.     

A survey of scheduling metrics and an improved ordering policy for list schedulers operating on workloads with dependencies and a wide variation in execution times

This paper considers the dynamic scheduling of parallel, dependent tasks onto a static, distributed computing platform, with the intention of delivering fairness and quality of service (QoS) to users. The key QoS requirement is that responsiveness is maintained for workloads with a wide range of execution times (minutes to months) even under transient periods of overload. A survey of schedule QoS metrics is presented, classified into those dealing with responsiveness, fairness and utilisation. These metrics are evaluated as to their ability to detect undesirable features of schedules. The Schedule Length Ratio (SLR) metric is shown to be the most helpful for measuring responsiveness in the presence of dependencies. A novel list scheduling policy called Projected-SLR is presented that delivers good responsiveness and fairness by using the SLR metric in its scheduling decisions. Projected-SLR is found to perform equally as well in responsiveness, fairness and utilisation as the best of the other scheduling policies evaluated (Shortest Remaining Time First/SRTF), using synthetic workloads and an industrial trace. However, Projected-SLR does this with a guarantee of starvation-free behaviour, unlike SRTF.     

Intelligent timetable evaluation using fuzzy AHP

There is a substantial body of empirical literature that establishes the benefits of customer satisfaction for enterprises. Among different available options to present our service, selecting the best choice in the customers’ eyes is a vital decision.Developing appropriate passenger train schedules is counted as one of the major managerial concerns in transportation environment. Although different algorithms have been developed to create predictive schedules for a fleet of passenger trains using different performance indicators, selecting the best one embraces some ambiguities and uncertainties. That is because a one-dimensional objective function may not be sufficient for responding customer concerns.The main objective of this paper is to propose an approach within the fuzzy AHP framework for tackling the complexity of multidimensional service evaluations, where “sum of weighted waiting times”, “average of unit waiting time” and “maximum ratio of waiting time to journey time” of a schedule are evaluated and the ultimate judgment on goodness of the schedule is made via the aggregation of the performance measures used. The study is based on the knowledge of certain managers and experts in IRC (Iran Railways Corporation) who are aware of available complexities in train scheduling and have been dealing with customers for several years.     

Rescheduling Manufacturing Systems: A Framework of Strategies, Policies, and Methods

Many manufacturing facilities generate and update production schedules, which are plans that state when certain controllable activities (e.g., processing of jobs by resources) should take place. Production schedules help managers and supervisors coordinate activities to increase productivity and reduce operating costs. Because a manufacturing system is dynamic and unexpected events occur, rescheduling is necessary to update a production schedule when the state of the manufacturing system makes it infeasible. Rescheduling updates an existing production schedule in response to disruptions or other changes. Though many studies discuss rescheduling, there are no standard definitions or classification of the strategies, policies, and methods presented in the rescheduling literature. This paper presents definitions appropriate for most applications of rescheduling manufacturing systems and describes a framework for understanding rescheduling strategies, policies, and methods. This framework is based on a wide variety of experimental and practical approaches that have been described in the rescheduling literature. The paper also discusses studies that show how rescheduling affects the performance of a manufacturing system, and it concludes with a discussion of how understanding rescheduling can bring closer some aspects of scheduling theory and practice.     

Lot sizing and furnace scheduling in small foundries

A lot sizing and scheduling problem prevalent in small market-driven foundries is studied. There are two related decision levels: (1) the furnace scheduling of metal alloy production, and (2) moulding machine planning which specifies the type and size of production lots. A mixed integer programming (MIP) formulation of the problem is proposed, but is impractical to solve in reasonable computing time for non-small instances. As a result, a faster relax-and-fix (RF) approach is developed that can also be used on a rolling horizon basis where only immediate-term schedules are implemented. As well as a MIP method to solve the basic RF approach, three variants of a local search method are also developed and tested using instances based on the literature. Finally, foundry-based tests with a real-order book resulted in a very substantial reduction of delivery delays and finished inventory, better use of capacity, and much faster schedule definition compared to the foundry's own practice.     

基于改进权重自适应GA的冷连轧轧制规程多目标优化

针对聚合多目标优化方法的权重难以确定的问题, 提出了一种改进的权重自适应方法, 并以遗传算法为基础对冷连轧轧制规程进行多目标优化. 首先, 结合某冷轧厂实际的轧制规程优化过程, 选取等功率裕量、轧制能耗及带钢打滑概率作为优化目标, 建立了冷连轧轧制规程的多目标优化模型. 然后将改进的权重自适应遗传算法(GA)应用于不同规格的带钢轧制规程多目标优化中, 结果表明, 与实际应用的轧制规程相比, 该方法有效的降低了3个目标函数的值; 与权重自适应GA相比, 改进的权重自适应GA的针对性更强, 同时重要性高的目标收敛速度更快.     

An anticipative scheduling approach with controllable processing times

In practice, machine schedules are usually subject to disruptions which have to be repaired by reactive scheduling decisions. The most popular predictive approach in project management and machine scheduling literature is to leave idle times (time buffers) in schedules in coping with disruptions, i.e. the resources will be under-utilized. Therefore, preparing initial schedules by considering possible disruption times along with rescheduling objectives is critical for the performance of rescheduling decisions. In this paper, we show that if the processing times are controllable then an anticipative approach can be used to form an initial schedule so that the limited capacity of the production resources are utilized more effectively. To illustrate the anticipative scheduling idea, we consider a non-identical parallel machining environment, where processing times can be controlled at a certain compression cost. When there is a disruption during the execution of the initial schedule, a match-up time strategy is utilized such that a repaired schedule has to catch-up initial schedule at some point in future. This requires changing machine–job assignments and processing times for the rest of the schedule which implies increased manufacturing costs. We show that making anticipative job sequencing decisions, based on failure and repair time distributions and flexibility of jobs, one can repair schedules by incurring less manufacturing cost. Our computational results show that the match-up time strategy is very sensitive to initial schedule and the proposed anticipative scheduling algorithm can be very helpful to reduce rescheduling costs.     

Determining software schedules

Improving software productivity, shortening schedules or time to market, and improving quality are prominent topics in software journals in both contributed articles and advertising copy. Unfortunately, most of these articles and advertisements have dealt with software schedules, productivity, or quality factors in abstract terms. Now we can measure these factors with reasonable accuracy and collect empirical data on both average and best-in-class results. We are particularly interested in the wide performance gaps between laggards, average enterprises, and industry leaders, as well as differences among the various software domains. The function-point metric lets us establish a meaningful database of software performance levels. A simple algorithm raises function points to a total to obtain a useful first-order schedule estimate     

Toward a heuristic optimum design of rolling schedules for tandem cold rolling mills

Scheduling for tandem cold mills refers to the determination of inter-stand gauges, tensions and speeds of a specified product. Optimal schedules should result in maximized throughput and minimized operating cost. This paper presents a genetic algorithm based optimization procedure for the scheduling of tandem cold rolling mills. The optimization procedure initiates searching from a logical staring point — an empirical rolling schedule — and ends with an optimum cost. Cost functions are constructed to heuristically direct the genetic algorithm’s searching, based on the consideration of power distribution, tension, strip flatness and rolling constraints. Numerical experiments have shown that the proposed method is more promising than those based on semi-empirical formulae. The results generated from a case study show that the proposed approach could significantly improve empirically derived settings for the tandem cold rolling mills.     

Optimal Modulo Scheduling Through Enumeration

Resource-constrained software-pipelining has played an increasingly significant role in exploiting instruction-level parallelism and has drawn intensive academic and industrial interest. The challenge is to find a schedule which is optimal : i.e., given the data dependence graph (DDG) for a loop, find the fastest possible schedule under given resource constraints while keeping register usage minimal. This paper proposes a novel enumeration based modulo scheduling approach to solve this problem. The proposed approach does not require any awkward reworking of constraints into linear form and employs a realistic register model. The set of schedules enumerated also allows us to characterize the schedule space and address questions such as whether schedules using a small number of registers tend to require a large number of function units. The proposed approach has been implemented under the MOST testbed at McGill University. Experimental results on more than 1000 loops from popular benchmark programs show that enumeration is generally faster at obtaining optimal schedules than integer linear programming approaches. Compared to Huff's Slack Scheduling , enumeration found a faster schedule for almost 15% of loops, with a mean improvement of 18%. 10% of the remaining loops required fewer registers under enumeration, with a mean reduction of 16%.     

Dynamic non-preemptive single machine scheduling

Considering a dynamic single machine problem in which operations cannot be split, we first develop a decision theory based heuristic called DT-TD (Decision Theory-Tactically Delayed) of computational complexity O(n²). Using a simple look-ahead procedure, it produces, actically delayed (TD) schedules. We then develop a branch-and-bound (BB) algorithm (which uses DT-TD to obtain the initial upper bound) to obtain the optimum schedule. The optimum schedules are examined to identify conditions where TD schedules are necessary. Results based on 540 test problems suggest that TD schedules are important, for job shop scheduling under the range of conditions examined, when due dates are arbitrary and utilization is low. Additional test results indicate that the difference between the optimum schedule and the optimum non-delay schedule could be substantial. Finally, the performance of the DT-TD heuristic is analyzed by comparing its solution to the optimum solution obtained using the BB algorithm. The results indicate that the DT-TD heuristic is effective.     

Robust scheduling on a single machine to minimize total flow time

In a real-world manufacturing environment featuring a variety of uncertainties, production schedules for manufacturing systems often cannot be executed exactly as they are developed. In these environments, schedule robustness that guarantees the best worst-case performance is a more appropriate criterion in developing schedules, although most existing studies have developed optimal schedules with respect to a deterministic or stochastic scheduling model. This study concerns robust single machine scheduling with uncertain job processing times and sequence-dependent family setup times explicitly represented by interval data. The objective is to obtain robust sequences of job families and jobs within each family that minimize the absolute deviation of total flow time from the optimal solution under the worst-case scenario. We prove that the robust single machine scheduling problem of interest is NP-hard. This problem is reformulated as a robust constrained shortest path problem and solved by a simulated annealing-based algorithmic framework that embeds a generalized label correcting method. The results of numerical experiments demonstrate that the proposed heuristic is effective and efficient for determining robust schedules. In addition, we explore the impact of degree of uncertainty on the performance measures and examine the tradeoff between robustness and optimality.     

Impact of forecasting error on the performance of capacitated multi-item production systems

A computer model is built to simulate master production scheduling activities in a capacitated multi-item production system under demand uncertainty and a rolling time horizon. The output from the simulation is analyzed through statistical software. The results of the study show that forecasting errors have significant impacts on total cost, schedule instability and system service level, and the performance of forecasting errors is significantly influenced by some operational factors, such as capacity tightness and cost structure. Furthermore, the selection of the master production schedule freezing parameters is also significantly influenced by forecasting errors. The findings from this study can help managers optimize their production plans by selecting more reasonable forecasting methods and scheduling parameters, thus improving the performance of production systems.     

COMPLEXITY REDUCTION FOR OPTIMIZATION OF DETERMINISTIC TIMED PETRI-NET SCHEDULING BY TRUNCATION

Methods of applying Petri nets to model and analyze scheduling problems, with constraints such as precedence relationships and multiple resource allocation, have been available in the literature. Searching for an optimum schedule can be implemented by combining the branch-and-bound technique with the execution of the timed Petri net. The resulting complexity problem in a large Petri net is handled by a truncation technique such that the original large Petri net is divided into several smaller subnets. The complexity involved in the analysis of each subnet individually is greatly reduced. However, as illustrated in this paper, the schedules for the subnets obtained by treating them separately may not lead to an optimal overall schedule for the original Petri net. To circumvent this problem, algorithms are developed that can be used to search for a proper schedule for each subnet such that the combination of these schedules yields an overall optimum schedule for the original timed Petri net. These algorithms are based on the idea of Petri net execution and branch-and-bound with modification. Finally, the practical application of the timed Petri net truncation technique to scheduling problems in manufacturing systems is illustrated by an example of multirobot task scheduling.