Heuristic Scheduling Rules Research Articles

A novel method for solving dynamic flexible job-shop scheduling problem via DIFFormer and deep reinforcement learning

Due to the dynamic changes of manufacturing environments, heuristic scheduling rules are unstable in dynamic scheduling. Although meta-heuristic methods provide the best scheduling quality, their solution efficiency is limited by the scale of the problem. Therefore, a novel method for solving the dynamic flexible job-shop scheduling problem (DFJSP) via diffusion-based transformer (DIFFormer) and deep reinforcement learning (D-DRL) is proposed. Firstly, the DFJSP is modeled as a Markov decision process, where the state space is constructed in the form of the heterogeneous graph and the reward function is designed to minimize the makespan and maximize the machine utilization rate. Secondly, DIFFormer is used to encode the operation and machine nodes to better capture the complex dependencies between nodes, which can effectively improve the representation ability of the model. Thirdly, a selective rescheduling strategy is designed for dynamic events to enhance the solution quality of DFJSP. Fourthly, the twin delayed deep deterministic policy gradient (TD3) algorithm is adopted for training an efficient scheduling model. Finally, the effectiveness of the proposed D-DRL is validated through a series of experiments. The results indicate that D-DRL achieves better solution quality and higher solution efficiency when solving DFJSP instances.

Flexible job shop scheduling via deep reinforcement learning with meta-path-based heterogeneous graph neural network

The flexible job shop scheduling problem (FJSP) is an important production scheduling problem in intelligent manufacturing. How to model the complex FJSP more accurately and improve the efficiency and generalization of scheduling policies is an urgent challenge to be solved. Therefore, a new end-to-end deep reinforcement learning (DRL) method combined with the meta-path-based heterogeneous graph neural network (MHGNN) is proposed to effectively solve FJSP. The task of solving FJSP is decomposed into two subtasks of operation selection and machine allocation. This dual-task FJSP is represented by introducing a heterogeneous graph and modeled as the dual Markov decision process (DMDP). A MHGNN is proposed to embed the global scheduling states of the dual-task FJSP. A heterogeneous graph neural network (GNN) framework is designed for the dual-task FJSP to efficiently encode the operation nodes and machine nodes, where the extracted embedded feature information is used to represent the operation selection policy and the machine allocation policy. Two policy networks are designed to efficiently predict the operation selection policy and the machine allocation policy, and a soft double-actors critic algorithm is proposed to train these two policy networks, where the trained policies can be used to efficiently solve FJSP instances of different scales. The experiments on three public benchmarks show that the proposed method outperforms the well-known heuristic scheduling rules and some advanced methods for solving FJSP. In particular, when solving large-scale FJSPs, the proposed method performs more prominently in terms of solution quality and solution time.

Deep reinforcement learning for solving steelmaking-continuous casting scheduling problems under time-of-use tariffs

This paper proposes a novel intelligent scheduling method based on deep reinforcement learning (DRL) to solve the multi-objective steelmaking-continuous casting (SCC) scheduling problem, under time-of-use (TOU) tariffs for the first time. The intelligent scheduling system architecture is designed, and a mathematical model is established to minimise the total sojourn time and electricity cost. To effectively reduce production costs by avoiding peak periods of electricity consumption, the ‘start time’ of the system is generated based on the Markov Decision Process (MDP), and heuristic scheduling rules related to power cost are used as the action space, with corresponding reward functions designed according to the characteristics of these two objectives. To satisfy the continuous casting which is a particular SCC constraint, a backward strategy is developed. Additionally, a branching duelling double deep Q-network (BD3QN) is adapted to guide action selection and avoid blind search in the iteration process, and then applied to real-time scheduling. Numerical experiments demonstrate that the proposed method outperforms comparison algorithms in terms of solution quality and CPU times by a large margin.

An actor-critic framework based on deep reinforcement learning for addressing flexible job shop scheduling problems.

With the rise of Industry 4.0, manufacturing is shifting towards customization and flexibility, presenting new challenges to meet rapidly evolving market and customer needs. To address these challenges, this paper suggests a novel approach to address flexible job shop scheduling problems (FJSPs) through reinforcement learning (RL). This method utilizes an actor-critic architecture that merges value-based and policy-based approaches. The actor generates deterministic policies, while the critic evaluates policies and guides the actor to achieve the most optimal policy. To construct the Markov decision process, a comprehensive feature set was utilized to accurately represent the system's state, and eight sets of actions were designed, inspired by traditional scheduling rules. The formulation of rewards indirectly measures the effectiveness of actions, promoting strategies that minimize job completion times and enhance adherence to scheduling constraints. The experimental evaluation conducted a thorough assessment of the proposed reinforcement learning framework through simulations on standard FJSP benchmarks, comparing the proposed method against several well-known heuristic scheduling rules, related RL algorithms and intelligent algorithms. The results indicate that the proposed method consistently outperforms traditional approaches and exhibits exceptional adaptability and efficiency, particularly in large-scale datasets.

A Hyper Heuristic Algorithm Based Genetic Programming for Steel Production Scheduling of Cyber-Physical System-ORIENTED

Intelligent manufacturing is the trend of the steel industry. A cyber-physical system oriented steel production scheduling system framework is proposed. To make up for the difficulty of dynamic scheduling of steel production in a complex environment and provide an idea for developing steel production to intelligent manufacturing. The dynamic steel production scheduling model characteristics are studied, and an ontology-based steel cyber-physical system production scheduling knowledge model and its ontology attribute knowledge representation method are proposed. For the dynamic scheduling, the heuristic scheduling rules were established. With the method, a hyper-heuristic algorithm based on genetic programming is presented. The learning-based high-level selection strategy method was adopted to manage the low-level heuristic. An automatic scheduling rule generation framework based on genetic programming is designed to manage and generate excellent heuristic rules and solve scheduling problems based on different production disturbances. Finally, the performance of the algorithm is verified by a simulation case.

A Programmed Algorithm for Resource Scheduling in Construction.(Dept.C)

If the critical path determined completion time of the construction project is constrained by the lack of resources, which are needed to carry out some of the activities, then the analysis required for scheduling the activities is much more involved. There are two major categories of procedures for solving this problem. The first involves the use of some heuristic rules in determining priorities among activities competing for available resources. The second involves some form of mathematical programming to produce the optimal schedule. Heuristic scheduling rules. computer programmed to give good feasible schedules have been the basis for all practical systems to date. In the same time, the optimal solution of the problem has not been paid enough attention in the construction management literature. The increasing availability of more powerful computer systems should see more emphasis on the development of procedures to give the optimal solution for large complex projects which occur in construction practice. In this paper a Computer program is developed for implementing an implicit enumeration algorithm to give the optimal solution of the problem for construction projects. The algorithm has been chosen to satisfy large projects and its efficiency has been established by specifying a lower bound on the length of the schedule and by sorting project activities and resources in a way such that the computation time is greatly reduced. Experience in applying the algorithm to an actual project is reported.

Infrastructures and services for remote sensing data production management across multiple satellite data centers

With the number of satellite sensors and date centers being increased continuously, it is becoming a trend to manage and process massive remote sensing data from multiple distributed sources. However, the combination of multiple satellite data centers for massive remote sensing (RS) data collaborative processing still faces many challenges. In order to reduce the huge amounts of data migration and improve the efficiency of multi-datacenter collaborative process, this paper presents the infrastructures and services of the data management as well as workflow management for massive remote sensing data production. A dynamic data scheduling strategy was employed to reduce the duplication of data request and data processing. And by combining the remote sensing spatial metadata repositories and Gfarm grid file system, the unified management of the raw data, intermediate products and final products were achieved in the co-processing. In addition, multi-level task order repositories and workflow templates were used to construct the production workflow automatically. With the help of specific heuristic scheduling rules, the production tasks were executed quickly. Ultimately, the Multi-datacenter Collaborative Process System (MDCPS) were implemented for large-scale remote sensing data production based on the effective management of data and workflow. As a consequence, the performance of MDCPS in experiments environment showed that those strategies could significantly enhance the efficiency of co-processing across multiple data centers.

Stochastic Operating Room Scheduling for High-Volume Specialties Under Block Booking

Scheduling elective procedures in an operating suite is a formidable task because of competing performance metrics and uncertain surgery durations. In this paper, we present an optimization framework for batch scheduling within a block booking system that maximizes the expected utilization of operating room resources subject to a set of probabilistic capacity constraints. The algorithm iteratively solves a series of mixed-integer programs that are based on a normal approximation of cumulative surgery durations. This approximation is suitable for high-volume medical specialities but might not be acceptable for the specialties that perform few procedures per block. We test our approach using the data from the ophthalmology department of the Veterans Affairs Pittsburgh Healthcare System. The performance of the schedules obtained by our approach is significantly better than schedules produced by simple heuristic scheduling rules.

Reducing costs of repairable inventory supply systems via dynamic scheduling

We study a system consisting of one repair shop and one stockpoint, where spare parts of multiple critical repairables are kept on stock to serve an installed base of technical systems. Part requests are met from stock if possible, and backordered otherwise. The objective is to minimize aggregate downtime via smart repair job scheduling. We evaluate various relevant dynamic scheduling policies, including two that stem from other application fields. One of them is the myopic allocation rule from the make-to-stock environment. It selects the SKU with the highest expected backorder reduction per invested time unit and has excellent performance on repairable inventory systems. It combines the following three strengths: (i) it selects the SKU with the shortest expected repair time in case of backorders, (ii) it recognizes the benefits of short average repair times even if there are no backorders, and (iii) it takes the stochasticity of the part failure processes into account. We investigate the optimality gaps of the heuristic scheduling rules, compare their performance on a large test bed containing problem instances of real-life size, and illustrate the impact of key problem characteristics on the aggregate downtime. We show that the myopic allocation rule performs well and that it outperforms the other heuristic scheduling rules.

Simulation Studies of Heuristic Approaches for Dynamic Scheduling of Container Terminal Operations

The growth of marine containers transportation along with increasing complex container terminal logistic system (CTLS) has called for more effective approaches for scheduling container terminal resources. This paper discusses the problem of scheduling quay crane, yard crane and container truck for improving the container terminal productivity. One dynamic scheduling method and specific heuristic scheduling rules for dynamically assigning quay crane, yard crane and container trucks are proposed according to stowage plans and storage plans. A simulation model of a container terminal in Shanghai is built by the application of WITNESS2002, a simulation software package, to evaluate the efficiency of the dynamic scheduling method. From simulation results, the production efficiency increased about 8% by the dynamic scheduling method than static one previously discussed in CTLS.

Formal design and analysis of a hybrid supervisory control structure for Virtual Production Systems

Virtual Production Systems (VPSs) is a dynamic paradigm for production resources structure, which was proposed to cope with changing and uncertain manufacturing environment. However, the high performance of VPSs relies on a high-quality control in practice. Motivated by both local quick responses and global optimization, a supervisory control structure based on autonomous and coordination mechanisms is proposed for VPSs in this paper. This hybrid supervisory control structure is formally designed and analysed with timed automata on the platform of an integrated tool named UPPAAL. First, the Discrete Events Dynamic Systems (DEDS) model of VPSs is established. Then autonomous and coordinated supervisory controllers are further designed under the guidance of heuristic scheduling rules, such that the desired characteristics of both performance (production flow and time) and activity (overflow-, conflict- and deadlock-free) can be ensured. Finally, system analysis, including deadlock-free analysis, calculation and simulation of time-optimal scheduling, is made through verification. A case study is made to illustrate that control and scheduling can be well integrated into this hybrid supervisory control structure in practice.

Integrated optimization of production planning and scheduling for a kind of job-shop

This paper addresses an integrated job-shop production planning and scheduling problem with setup time and batches. It not only considers the setup cost, work-in-process inventory, product demand, and the load of equipment, but also the detailed scheduling constraints. That is a way different from the traditional hierarchical production planning method. The hierarchical methods do not consider the detailed scheduling constraints, so it cannot guarantee to obtain a feasible production plan. Here the integrated problem is formulated as a nonlinear mixed integer program model. And in order to simultaneously optimize the production plan and the schedule, an improved hybrid genetic algorithm (HGA) is given. In the model, the detailed scheduling constraints are used to compute the accurate load of a device in order to obtain a feasible production plan. The heuristic scheduling rules such as the shortest processing time (SPT) and the longest processing time (LPT) are used to generate a better initial solution. Also, a subsection coding strategy is offered to convert the planning and scheduling solution into a chromosome. At last, a comparison is made between the hybrid algorithm and a hierarchical production planning and scheduling method, showing that the hybrid algorithm can solve the problem effectively.

An efficient search procedure for the resource-constrained multi-project scheduling problem with discounted cash flows

The main purposes of this study are to incorporate both the project delay penalty and early completion bonus into the objective function of the resource-constrained multi-project scheduling problem with discounted cash flows (RCMPSP-DCF) and to develop an efficient heuristic search scheduling rule. The effectiveness of the proposed heuristic rule is evaluated by comparing it with the optimal solution obtained by the optimal model for 42 small-scale problems. The result indicates that the solution obtained using the proposed heuristic rule is very close to the optimal solution, and that the proposed heuristic rule provides significant savings in computation time. Moreover, the proposed heuristic rule is also compared with four existing heuristic rules based on an experiment involving the single-project and multi-project scheduling problems. The results indicate that the proposed heuristic rule is superior to the four existing rules under the performance criteria of the average total project net present value and the average total project delay. In addition, the number of times that the proposed heuristic rule can obtain the best solution is also far more than that of any other rule considered in this paper under the two aforementioned performance criteria. Furthermore, this study has found that the performance of a heuristic scheduling rule is significantly affected by the number of projects. A rule that can lower the total project delay to the minimum is also able to maxi5 mize the total project net present value.

Workshop scheduling using practical (inaccurate) data Part 1: The performance of heuristic scheduling rules in a dynamic job shop environment using a rolling time horizon approach

This paper is a report on a simulation study to investigate the performance of a number of scheduling rules on the basis of a rolling time horizon approach for a dynamic job shop environment. The performance measure considered is an economic objective which includes the main costs involved in a scheduling decision. The first purpose of the study was to find the best scheduling rule and the second to investigate the effects of the rescheduling interval on performance and examine whether there is a policy that can always improve performance. The simulation study, which is part of a larger project on practical workshop scheduling, has been carried out under widely varying conditions in terms of due date tightness, shop load level, and shop load balance level. The results show that a recently developed scheduling rule, SPT-C/R, is the most appropriate scheduling rule in minimizing overall cost and that the relationship between performance and rescheduling interval can be shown.

Application of Backward Scheduling Rules in JIT/FMS Environment.

Scheduling in flexible manufacturing systems (FMSs) may require some complex solutions due to the complexity of the system. In practice, the application of just-in-time (JIT) theory to an FMS has become an attractive research topic, and heuristic scheduling rules are practical methods for scheduling job orders in real-time scheduling. This article presents a model of an FMS, and then applies the JIT theory with some heuristic scheduling rules to the backward scheduling method in particular. We carry out the simulation test for seeking the performance measures obtained by each rule. It is shown that some proposed heuristic scheduling rules give better results than the others. It is also shown that in JIT, the backward scheduling method can be a useful tool in helping the user to select the most suitable rules for the system at hand.

Heuristics for scheduling projects with resource restrictions and several resource-duration modes

Abstract The problem addressed in this paper is the non-preemptive resource-constrained project scheduling problem in which the duration of each activity depends on the amount of resources allocated to its execution, and the objective is to minimize the overall project duration. It is also assumed that resources are limited but renewable from period to period. Twenty-one heuristic scheduling rules are compared on 240 test problems divided into two main groups containing 50 and 100 activities, respectively. Each group contains one-resource, two-resource and four-resource problems. The results of the comparative study allow us to identify the most efficient heuristics and suggest a combination of five-heuristics which have a high probability (229 over 240) of giving the best (among the 21 solutions obtained) solution.

Near optimal solution for resource-constrained scheduling problems

This paper presents an efficient resource allocation algorithm, and examines its performance against a number of scheduling heuristic rules, aimed at minimizing project durations in a multiple resource-constrained environment. The algorithm has been coded in BASIC and can easily interface with available planning and scheduling software systems. Unlike other heuristic network-based algorithms, resources are allocated simultaneously to sets of activities, rather than to individual activities in a sequential manner. An experiment was conducted to study the performance of the proposed algorithm and compare its results with those generated by four different heuristic scheduling rules, including those found to have the most superior performance such as the widely used least total float rule. Thirty-one network examples adopted from the literature are analysed. The results indicate that the proposed algorithm is superior to the others. The proposed algorithm provides a reasonable trade-off between the best accur...

Multi-project scheduling: a new categorization for heuristic scheduling rules in construction scheduling problems

After more than two decades of applying CPM as a planning and scheduling technique there is growing doubt about some of the advantages initially attributed to it. It is being discarded by both large and small construction companies, and its use limited to cases where it is required by the clients. Some planners and schedulers have begun to depend on their own experience. The author has developed a heuristic planning and scheduling model which uses two new criteria: the tightness degree of completion due-dates and resource tightness degrees. Throughout the scheduling experiments, the hypothetical relationship between the performance of the proposed heuristic scheduling rules and the degrees of completion due-dates and resource tightness proved to be correct and there is a significant difference between the performance of proposed heuristic scheduling rules in the different scheduling classes. The new classification of scheduling problems proved to be practical, effective and helpful. The scheduling experim...

An experimental investigation of FMS scheduling rules under uncertainty

AbstractThis article reports the results of an experimental investigation of scheduling decision rules for a dedicated flexible manufacturing system (FMS) with variable performance times and machine break‐downs. An existing FMS was modeled using the actual performance times obtained from the system's transaction log. The performance of six heuristic scheduling rules were tested under differing uncertainty conditions.The simulation model included three types of uncertainty, namely: minor breakdowns, which averaged two hours and occurred once every two shifts; major breakdowns, which averaged ten hours and occurred once every ten shifts; and operation time deviations, which ranged from 0% to 20% of standard time. The runtime for the simulation approximated the work to be done over a ten‐week period of time, or 7,590 parts.Six different part loading rules were used to determine whether they could produce different performance results. The smallest proportion of jobs launched and the next empty pallet rule worked appreciably better than the rest of the rules in obtaining the highest FMS utilization.

A Comparison of Heuristics for Scheduling Projects with Cash Flows and Resource Restrictions

The problem addressed in this paper is the scheduling of activities in a project to maximize net present value given cash inflows, outflows, and resource restrictions. Although optimization procedures exist for small problems of this type, they generally cannot solve large-scale problems and may not be widely available. Six heuristic scheduling rules are compared on 80 test problems. The test problems include large-scale engineering construction projects as well as 50 resource constrained problems from the literature.