A two-stage heuristic for single machine capacitated lot-sizing and scheduling with sequence-dependent setup costs

This paper considers a single machine capacitated lot-sizing and scheduling problem. The problem is to determine the lot sizes and the sequence of lots while satisfying the demand requirements and the machine capacity in each period of a planning horizon. In particular, we consider sequence-dependent setup costs that depend on the type of the lot just completed and on the lot to be processed. The setup state preservation, i.e., the setup state at the end of a period is carried over to the next period, is also considered. The objective is to minimize the sum of setup and inventory holding costs over the planning horizon. Due to the complexity of the problem, we suggest a two-stage heuristic in which an initial solution is obtained and then it is improved using a backward and forward improvement method that incorporates various priority rules to select the items to be moved. Computational tests were done on randomly generated test instances and the results show that the two-stage heuristic outperforms the best existing algorithm significantly. Also, the heuristics with better priority rule combinations were used to solve case instances and much improvement is reported over the conventional method as well as the best existing algorithm.     

A survey of dynamic scheduling in manufacturing systems

In most real-world environments, scheduling is an ongoing reactive process where the presence of a variety of unexpected disruptions is usually inevitable, and continually forces reconsideration and revision of pre-established schedules. Many of the approaches developed to solve the problem of static scheduling are often impractical in real-world environments, and the near-optimal schedules with respect to the estimated data may become obsolete when they are released to the shop floor. This paper outlines the limitations of the static approaches to scheduling in the presence of real-time information and presents a number of issues that have come up in recent years on dynamic scheduling. The paper defines the problem of dynamic scheduling and provides a review of the state-of-the-art of currently developing research on dynamic scheduling. The principles of several dynamic scheduling techniques, namely, heuristics, meta-heuristics, multi-agent systems, and other artificial intelligence techniques are described in detail, followed by a discussion and comparison of their potential.     

Using scheduling in flexible manufacturing systems

In this paper the concept of recursive Allen temporal algebra is fully described, along with its relationship to the smart scheduler program and the concept of smart scheduling in general. Flexible manufacturing systems (FMS) are described, and their relationship with smart scheduling is explored. A case study that shows the utility of SSP in an FMS environment is presented in detail. Conclusions about the concept of smart scheduling are presented in closing.     

基于遗传算法的拉动式浸染生产动态排产策略

为解决印染企业中不合理的人工排产使其高耗能、污染大的行业弊端更加明显的问题,提出了一种基于遗传算法的拉动式浸染生产动态排产策略,实现对实际生产中多约束条件的求解,根据染缸的运行情况,在企业资源规划(ERP)系统中实时获取订单进行排产.仿真结果对比人工排产结果表明,该策略在提高生产效率的同时也达到了节能减排的目的.     

A numerical method in optimal production and setup scheduling ofstochastic manufacturing systems

In this paper, we consider optimal production and setup scheduling in a failure-prone manufacturing system consisting of a single machine. The system can produce several types-of products, but at any given time it can only produce one type of product. A setup is required if production is to be switched from one type of product to another. The decision variables are a sequence of setups and a production plan. The objective of the problem is to minimize the cost of setup, production, and surplus. An approximate optimality condition is given together with a computational algorithm for solving the optimal control problem     

Reactive and energy-aware scheduling of flexible manufacturing systems using potential fields

This paper presents a reactive scheduling approach for flexible manufacturing systems, which integrates the overall energy consumption of the production. This work is justified by the growing needs of manufacturers for energy-aware control, due to new important environmental criteria, which holds true in the context of high reactivity. It makes production hard to predict. The proposed reactive scheduling model is based on potential fields. In this model, resources that sense the intentions from products are able to switch to standby mode to avoid useless energy consumption and emit fields to attract products. Simulations are provided, featuring three indicators: makespan, overall energy consumption and the number of resource switches. Real experiments were carried out to illustrate the feasibility of the approach on a real system and validate the simulation results.     

Robotic material handler scheduling in flexible manufacturing systems for mass customization

The scheduling problem of robotic material handlers in flexible manufacturing systems (FMSs) is NP-hard. This paper proposes a state-dependent algorithm for the FMS robot scheduling problem in make-to-order (MTO) environments for mass customization (MC). A mathematical model of the problem is formulated. A computational study of the proposed algorithm is performed. The algorithm is compared to an effective FMS robot scheduling rule, the shortest remaining processing time first (SRPF) rule. The results reveal the effectiveness of the algorithm in increasing the productivity-based measures of the FMS. Practical application insights are discussed. Further research is also provided.     

Dynamic scheduling of manufacturing job shops using genetic algorithms

Most job shop scheduling methods reported in the literature usually address the static scheduling problem. These methods do not consider multiple criteria, nor do they accommodate alternate resources to process a job operation. In this paper, a scheduling method based on genetic algorithms is developed and it addresses all the shortcomings mentioned above. The genetic algorithms approach is a schedule permutation approach that systematically permutes an initial pool of randomly generated schedules to return the best schedule found to date.A dynamic scheduling problem was designed to closely reflect a real job shop scheduling environment. Two performance measures, namely mean job tardiness and mean job cost, were used to demonstrate multiple criteria scheduling. To span a varied job shop environment, three factors were identified and varied between two levels each. The results of this extensive simulation study indicate that the genetic algorithms scheduling approach produces better scheduling performance in comparison to several common dispatching rules.     

实时协同的调度算法研究

研究了目前流行的实时调度技术,归纳总结了不同调度技术下的典型调度算法,介绍了实时调度算法的调度规则、调度特点、适用场合以及需要解决的问题,分析了典型商业实时操作系统中的调度技术,提出了增强操作系统实时性能需要解决的技术问题,为将优秀的实时调度算法应用在实时操作系统中奠定了理论基础。     

Dynamic priority scheduling of periodic and aperiodic tasks in hard real-time systems

This paper addresses the problem of scheduling aperiodic tasks in real-time systems. The proposed scheme combines the Earliest-Deadline-First algorithm for scheduling periodic tasks with the Deferrable Server approach for servicing aperiodic tasks. Necessary and sufficient conditions are derived for guaranteeing feasibility of a given periodic task set when a deferrable server is present. An analytic model is proposed for selecting the best feasible period and computation time of the server to minimize the mean response time of aperiodic tasks. An evaluation of the proposed model using a simulator indicates that the server parameters selected by the model result in mean response times that are close to the best mean response time determined by the simulator.     

A holonic approach to dynamic manufacturing scheduling

Manufacturing scheduling is a complex combinatorial problem, particularly in distributed and dynamic environments. This paper presents a holonic approach to manufacturing scheduling, where the scheduling functions are distributed by several entities, combining their calculation power and local optimization capability. In this scheduling and control approach, the objective is to achieve fast and dynamic re-scheduling using a scheduling mechanism that evolves dynamically to combine centralized and distributed strategies, improving its responsiveness to emergence, instead of the complex and optimized scheduling algorithms found in traditional approaches.     

Reactive control of overall power consumption in flexible manufacturing systems scheduling: A Potential Fields model

In recent years, designing “energy-aware manufacturing scheduling and control systems” has become more and more complex due to the increasing volatility and unpredictability of energy availability, supply and cost, and thus requires the integration of highly reactive behavior in control laws. The aim of this paper is to propose a Potential Fields-based flexible manufacturing control system that can dynamically allocate and route products to production resources to minimize the total production time. This control system simultaneously optimizes resource energy consumption by limiting energy wastage through the real-time control of resource states, and by dynamically controlling the overall power consumption taking the limited availability of energy into consideration. The Potential Fields-based control model was proposed in two stages. First, a mechanism was proposed to switch resources on/off reactively depending on the situation of the flexible manufacturing system (FMS) to reduce energy wastage. Second, while minimizing wastage, overall power consumption control was introduced in order to remain under a dynamically determined energy threshold. The effectiveness of the control model was studied in simulation with several scenarios for reducing energy wastage and controlling overall consumption. Experiments were then performed in a real FMS to prove the feasibility of the model. The superiority of the proposition is its high reactivity to manage production in real-time despite unexpected restrictions in the amount of energy available. After providing the limitations of the work, the conclusions and prospects are presented.     

Production scheduling and inventory control for multi-stage manufacturing systems

In this paper, singular perturbation methods are used to synthesize high-gain error-actuated production control policies for a class of multi-stage manufacturing systems in which each manufacturing sub-system comprises either one or several production facilities. It is shown that when the manufacturing sub-systems are connected in cascade, each sub-system can be controlled in isolation, but that production control policies based on this approach lead to undesirable transient behaviour if the value of the feedback gain parameter is small. An alternative approach whereby production control policies are synthesized for the composite system leads to improved transient behaviour. However, if the value of the feedback gain parameter is large, both approaches are shown to yield good transient behaviour. In addition, it is demonstrated that trade-off between rapid adjustments in production rates and large inventory-level deviation can be achieved by selecting an appropriate value for the feedback gain parameter.     

Dynamic instabilities and stabilization methods in distributedreal-time scheduling of manufacturing systems

The paper concerns policies for sequencing material through a flexible manufacturing system to meet desired production goals for each part type. The authors demonstrate by examples that cyclic material flow and certain distributed scheduling policies can lead to instability in the sense that the required buffer levels are unbounded. This can be the case even when the set-up times for changing part types are zero. Sufficient conditions are then derived under which a class of distributed policies is stable. Finally, a general supervisory mechanism is presented which will stabilize any scheduling policy (i.e. maintain bounded buffer sizes at all machines) while satisfying the desired production rates     

Integrated scheduling of resource-constrained flexible manufacturing systems using constraint programming

This contribution presents a novel approach to address the scheduling of resource-constrained flexible manufacturing systems (FMSs). It deals with several critical features that are present in many FMS environments in an integrated way. The proposal consists in a constraint programming (CP) formulation that simultaneously takes into account the following sub-problems: (i) machine loading, (ii) manufacturing activities scheduling, (iii) part routing, (iv) machine buffer scheduling, (v) tool planning and allocation, and (vi) AGV scheduling, considering both the loaded and the empty movements of the device. Before introducing the model, this work points out the problems that might appear when all these issues are not concurrently taken into account. Then, the FMS scheduling model is presented and later assessed through several case-studies. The proposed CP approach has been tested by resorting to problems that consider dissimilar number of parts, operations per part, and tool copies, as well as different AGV speeds. The various examples demonstrate the importance of having an integrated formulation and show the important errors that can occur when critical issues such as AGV empty movements are neglected.     

Hierarchical production and setup scheduling in stochasticmanufacturing systems

This paper is concerned with an asymptotic analysis of hierarchical production and setup scheduling in a stochastic manufacturing system consisting of a single failure-prone machine and facing constant demands for a number of products. At any given time the system can only produce one type of product, and the system requires a setup if production is to be switched from one type of product to another. A setup may involve setup time or setup cost or both. The objective of the problem is to minimize the total costs of setup, production, and surplus. The control variables are a sequence of setups and a production plan. An asymptotic analysis with respect to increasing rates of change in machine states gives rise to a deterministic limiting optimal control problem in which there is a control variable associated with each of the machine states and the production rate is obtained by weighting these controls with the stationary probabilities of the corresponding states. Asymptotic optimal controls for the original problem from optimal or near-optimal controls for the limiting problem are constructed     

Petri net based process scheduling: A model of the control system of flexible manufacturing systems

In this paper, we propose a class of algorithms for the sub-optimal solution of a particular class of problems of process scheduling, particularly focusing on a case study in the area of flexible manufacturing systems (FMSs). The general class of problems we face in our approach is characterized as follows: there is a set of concurrent processes, each formed by a number of temporally related tasks (segments). Tasks are executable by alternate resource sets, different both in performance and costs. Processes and tasks are characterized by release times, due dates, and deadlines. Time constraints are also present in the availability of each resource in resource sets. It has been proven that such a problem does not admit an algorithm for an optimal solution in polynomial time. Our proposed algorithm finds a sub-optimal schedule according to a set of optimization criteria, based on task and process times (earliness, tardiness), and/or time independent costs of resources. Our approach to process scheduling is based on Timed Coloured Petri Nets. We describe the structure of the coordination and scheduling algorithms, concentrating on (i) the general-purpose component, and (ii) the application-dependent component. In particular, the paper focuses on the following issues: (i) theautomatic synthesis of Petri net models of the coordination subsystem, starting from the problem knowledge base; (ii) the dynamic behavior of the coordination subsystem, whose kernel is a High Level Petri net executor, a coordination process based on an original, general purpose algorithm; (iii) the structure of the real-time scheduling subsystem, based on particular heuristic sub-optimal multi-criteria algorithms. Furthermore, the paper defines the interaction mechanisms between the coordination and scheduling subsystems. Our approach clearly distinguishes the mechanism of the net execution from the decision support system. Two conceptually distinct levels, which correspond to two different, interacting implementation modules in the prototype CASE tool, have been defined: theexecutor and thescheduler levels. One of the outstanding differences between these levels is that the executor is conceived as a fast, efficient coordination process, without special-purpose problem-solving capabilities in case of conflicts. The scheduler, on the other hand, is the adaptive, distributed component, whose behavior may heavily depend on the problem class. If the scheduler fails, the executor is, in any case, able to proceed with a general-purpose conflict resolution strategy. Experimental results on the real-time performance of the kernel of the implemented system are finally shown in the paper. The approach described in this paper is at the basis of a joint project with industrial partners for the development of a CASE tool for the simulation of blast furnaces.     

The robustness of scheduling policies in multi-product manufacturing systems with sequence-dependent setup times and finite buffers

In this paper, a continuous time Markov chain model is introduced to study multi-product manufacturing systems with sequence-dependent setup times and finite buffers under seven scheduling policies, i.e., cyclic, shortest queue, shortest processing time, shortest overall time (including setup time and processing times), longest queue, longest processing time, and longest overall time. In manufacturing environments, optimal solution may not be applicable due to uncertainty and variation in system parameters. Therefore, in this paper, in addition to comparing the system throughput under different policies, we introduce the notion of robustness of scheduling policies. Specifically, a policy that can deliver good and stable performance resilient to variations in system parameters (such as buffer sizes, processing rates, and setup times) is viewed as a “robust” policy. Numerical studies indicate that the cyclic and longest queue policies exhibit robustness in subject to parameter changes. This could provide production engineers a guideline in operation management.     

Erratum to: Dynamic task scheduling modeling in unstructured heterogeneous multiprocessor systems

正The original version of this article unfortunately contained a mistake. The affiliation of the first author was incorrect. The correct affiliation is: Department of Computer Engineering, Quchan Branch, Islamic Azad University, Quchan, Iran.