Dynamic Scheduling Rules Research Articles

Harvester Maintenance Resource Scheduling Optimization, Based on the Combine Harvester Operation and Maintenance Platform

The combine harvester is the main machine for fieldwork during the harvest season. When the harvester fails and cannot continue to work, this indirectly affects the harvest time and the yield in the field. The emergency maintenance service of agricultural machinery can be optimized through the dynamic planning of harvester maintenance tasks, using the operation and maintenance platform. According to the scene, a priority scheme for the operation and maintenance tasks, based on the improved Q-learning algorithm, was proposed. The continuous approximation capability of the model was improved by using the BP neural network algorithm and the Q function value, in iterations, was updated continuously. At the same time, the improved TOPSIS method, based on Mahalanobis distance, was used to calculate the closeness of each harvester maintenance task, so as to determine the priority of the equipment maintenance tasks. An operation and maintenance service platform for combine harvesters was developed based on the B/S architecture, with the goal of minimizing the operation and maintenance costs and improving the tasks’ complete efficiency. In this research process, dynamic scheduling rules were formulated. Operation and maintenance resources were optimized and rationally allocated through dynamic optimization scheduling methods, and feasible solution information was generated from the operation and maintenance service platform. Finally, the actual data from the enterprise were used for verification and analysis. The verification showed the following: through a comparison of algorithm performance, it was seen that the improved BP-Q-Learning algorithm can quickly find the operation and maintenance scheduling scheme in the maintenance scheduling; the priority rules can improve the efficiency of task execution, to a certain extent; the cost of the tasks’ execution can be significantly reduced; and the maintenance distance can be shortened. This research has reference significance for the formulation and optimization of agricultural machinery maintenance for cross-regional operations.

A Reinforcement Learning Approach to Dynamic Scheduling in a Product-Mix Flexibility Environment

Machine bottlenecks, resulting from shifting and unbalanced machine loads caused by resource capacity limitations, impair product-mix flexibility production systems. Thus, the knowledge base (KB) of a dynamic scheduling control system should be dynamic and include a knowledge revision mechanism for monitoring crucial changes that occur in the production system. In this paper, reinforcement learning (RL)-based dynamic scheduling and a selection mechanism for multiple dynamic scheduling rules (MDSRs) are proposed to support the operating characteristics of a flexible manufacturing system (FMS) and semiconductor wafer fabrication (FAB). The proposed RL-based dynamic scheduling MDSR selection mechanism consisted of initial MDSR KB generation and revision phases. According to various performance criteria, the presented approach yields a system performance that is superior to those of the fixed-decision scheduling approach, the machine learning classification approach, and the classical MDSR selection mechanism.

Research on Optimize Scheduling Model and Heuristic Algorithm Based on Capacity Balance

To optimize scheduling pertaining to two production units and many tasks in flexible production, a dynamic scheduling model was proposed considering capacity balance. Each production unit was modeled as a kind of container whose capacity symbolized the container capacity. The capacity was divided as planned and spared parts. Joining spared capacities in two units, the state-to-art dynamic scheduling rules were created according to hydro-mechanical theory. A practical heuristic algorithm was developed for this model. The model was tested as feasible and effective in a case study conducted at an actual business.

Dispatching rules for dynamic scheduling of one-of-a-kind production

This paper presents an extensive performance analysis of dispatching rules for dynamic scheduling in one-of-a-kind production (OKP) where each job has its own due-date and multilevel routing structure. For the analysis, a framework of dynamic dispatching rules is proposed to identify five dispatching rules: job due-date (JDD) rule, normalized JDD rule, latest finish date (LFD) rule, latest start date (LSD) rule, and refined LSD rule. The performance analysis shows that: (i) JDD, LFD and LSD rules perform fairly well, which is compatible with the results of existing studies; (ii) the refined LSD rule outperforms other rules; (iii) the normalized JDD rule performs very poorly. Also presented is a framework of simulation-based dynamic scheduling in which the refined LSD as well as the ‘try-every-rule’ approach can be effectively utilized.

Continuous-time restless bandit and dynamic scheduling for make-to-stock production

We study the "Whittle relaxation" version of the continuous time, discrete, and continuous state space Restless Bandit problem under the discounted cost criterion. Explicit expressions of Whittle's priority indexes, which generalize the Gittins indexes, are derived. This formalism is then used in the context of flexible make-to-stock production to construct dynamic scheduling rules. These analytical results are finally compared with the optimal numerically derived policy, obtained for a server delivering two product types. It is observed that the Whittle relaxation version of the Restless Bandit model nearly yields optimal dynamic scheduling rules.

Dynamic Scheduling Rules for a Multiproduct Make-to-Stock Queue

This paper develops a simple but effective heuristic policy for a stochastic production-inventory system, where several products share a single processor of limited capacity. Our policy combines the best features, and avoids the weaknesses, of prior approaches in the literature. Limited numerical tests suggest that the method performs well.

Modeling, scheduling and control of flexible manufacturing systems by extended high-level evaluation Petri nets

This paper proposes an extended high level evaluation Petri nets (EHLEP-N) for modeling and control of flexible manufacturing systems (FMSs). This new class of models is simple, perceivable and descriptive, having the capability of inference and making decisions. When applied to FMSs, the EHLEP-N can model real-time decisions with which dynamic scheduling rules can be investigated and evaluated. Experimental results show that the throughput for such systems with the new scheduling strategies can increase on an average of 7% by decreasing no-inputs and blockings compared with that of the traditional strategies.

Modeling, scheduling and control of flexible manufacturing systems by extended high-level evaluation Petri nets

This paper proposes an extended high level evaluation Petri nets (EHLEP-N) for modeling and control of flexible manufacturing systems (FMSs). This new class of models is simple, perceivable and descriptive, having the capability of inference and making decisions. When applied to FMSs, the EHLEP-N can model real-time decisions with which dynamic scheduling rules can be investigated and evaluated. Experimental results show that the throughput for such systems with the new scheduling strategies can increase on an average of 7% by decreasing no-inputs and blockings compared with that of the traditional strategies.

Heuristics for operator scheduling in Group Technology cells

We devised heuristic approaches for operator scheduling in manned Grouped Technology (GT) cells. The heuristic approach consists of (1) Cycle Switching Rules (CSR) to deal with production environments covering a longer decision horizon, and (2) Dynamic Scheduling Rules (DSR) to deal with environments covering a shorter decision horizon. They differ in information requirements and decision horizons. In simulation experiments, we show that there is a predictable trade-off between the quality of the resulting schedule and the information content of heuristics used.

Job shop scheduling under resource constraints and varying order priorities

This paper describes the development of a job shop scheduling system, which is based upon dynamic scheduling rules incorporating customer importance and/or order priority under constrained resources. An important part of the system is the development of a forecasting method for determining the estimated completion time for a job by estimating the future work load on the shop. The system has been developed from the analysis of the maintenance operations of a large petrochemical plant. The use of the computer programs which have been developed will be illustrated.

Application of dynamic scheduling rules in maintenance planning and scheduling

A maintenance planning and scheduling system often resembles that of a ‘job shop’ that is, the orders are one of a kind, and is characterized by having to schedule N orders through M or less tasks. The orders are of two types, e.g., (a) emergency—have to be done now, and (b ) non-emergency—can be delayed until later. In this type of ‘job shop’ the schedule becomes immediately out of date as soon as an emergency order is received. Consequently non-emergency orders are continually moved back in the schedule and forecasted completion dates are not met. Further if the orders entering the system exceed the normal available capacity, the backlog will continue to increase causing more disruption of schedules. The research, which is based on a large petrochemical plant, will deal with the above problems by (a) applying dynamic decision rules for day-to-day scheduling to ensure completion dates are met, (b) a method for controlling backlog, and (c) forecasting future load, and completion dates for orders. The resu...