Development of a new dioid algebraic model for manufacturing with the scheduling decision making capability

The dioid algebraic model, which is developed in this paper, captures the discontinuous nature of Discrete-Event Dynamic Systems and has widespread applicational capabilities. As a further improvement over existing dioid algebraic models, decision making capability is introduced to the developed dioid algebraic model. With this new capability, sequencing decisions at each machine can be represented in the model. Furthermore, the developed algebraic model is capable of representing job and resource unavailability, technological constraints, alternative process plans, and is easily modifiable to include new jobs and exclude finished jobs. In a possible application, the model can be used in representing dynamic scheduling problems and can be used as an important part of a real-time control and scheduling scheme of a manufacturing system.     

A fuzzy hierarchical reinforcement learning based scheduling method for semiconductor wafer manufacturing systems

Scheduling semiconductor wafer manufacturing systems has been viewed as one of the most challenging optimization problems owing to the complicated constraints, and dynamic system environment. This paper proposes a fuzzy hierarchical reinforcement learning (FHRL) approach to schedule a SWFS, which controls the cycle time (CT) of each wafer lot to improve on-time delivery by adjusting the priority of each wafer lot. To cope with the layer correlation and wafer correlation of CT due to the re-entrant process constraint, a hierarchical model is presented with a recurrent reinforcement learning (RL) unit in each layer to control the corresponding sub-CT of each integrated circuit layer. In each RL unit, a fuzzy reward calculator is designed to reduce the impact of uncertainty of expected finishing time caused by the rematching of a lot to a delivery batch. The results demonstrate that the mean deviation (MD) between the actual and expected completion time of wafer lots under the scheduling of the FHRL approach is only about 30 % of the compared methods in the whole SWFS.     

Parallel machine scheduling problems in green manufacturing industry

Manufacturing companies are now more conscious about the environment. As such, there are more concerns in reducing the consumption of energy and the production of pollutants. Reduced consumption of energy will save cost, while reduction of pollutants will decrease the cost of cleaning up the environment. This paper considers scheduling problems that arise in green manufacturing companies. Suppose the manufacturing company has a set of parallel machines. Each machine has a cost per unit time that differs from machine to machine. The cost here is the sum of the energy cost and the clean up cost. A set of jobs is to be processed by these machines. Our goal is to find a schedule that minimizes the makespan (schedule length) or the total completion time, subject to the constraint that the total cost is not more than a given threshold value. We propose efficient heuristics and show, by computational experiments, that they perform very well in practice.     

Hybrid genetic algorithm and augmented neural network application for solving the online advertisement scheduling problem with contextual targeting

Worldwide growth of the online community continues to push the popularity of internet marketing. Fueled by this trend, the online advertising industry is experiencing unprecedented revenue growth. One of the most important drivers of this revenue is banner advertising, which has long been a staple of the online advertising industry. Previous research has introduced quantitative models and solution approaches for the challenging basic scheduling optimization problem. We extend this work by incorporating the most common and popular trend in the in the industry, online advertisement targeting. In addition, motivated by the NP-hard nature of the resulting problem, we propose and test several heuristic and metaheuristic based solution techniques for the proposed problem.     

半导体制造设备预维修调度的知识进化算法研究*

半导体制造设备预维修调度问题是半导体企业迫切需要解决的问题之一。本文设计了求解该问题的知识进化算法方案,详细地阐述了方案的具体实现过程,并计算了其他文献中的仿真实例,仿真试验的结果表明了本文提出的知识进化算法方案优于其他文献中的粒子群算法方案和先来先服务方案。     

Flow shop scheduling with blocking using modified harmony search algorithm with neighboring heuristics methods

The flow shop scheduling with blocking is considered an important scheduling problem which has many real-world applications. This paper proposes a new algorithm which applies heuristic techniques in harmony search algorithm (HSA) to minimize the total flow time. The proposed method is called modified harmony search algorithm with neighboring heuristics methods (MHSNH). To improve the initial harmony memory, we apply two heuristic techniques: nearest neighbor (NN) and constructive modified NEH (MNEH). A modified version of harmony search algorithm evolves to explore and generates a new solution. The newly generated solution is then enhanced by using neighboring heuristics. Lastly, another neighboring heuristic is applied to improve the obtained solution. The proposed algorithm is evaluated using 12 real-world problem instances each with 10 samples. The experimental evaluation is accomplished using two factors: CPU computational time and the number of iterations. For the first factor, comparative evaluation against six well-established methods shows that the proposed method achieves almost the best overall results in six problem instances out of the twelve and yields fruitful results for others. For the second factor, comparative evaluation against twelve well-regarded methods shows that the proposed method achieves the best overall results in three problem instances and obtains very good results in other instances. In a nutshell, the proposed MHSNH is an effective strategy for solving the job shop scheduling problem.     

Intelligent scheduling in the manufacturing environment

This article is intended to present a model for intelligent scheduling in the manufacturing environment. The model combines an object oriented scheduler with a visual event recognizer to create an interactive visual-event intelligent scheduling tool.     

Integrating scheduling and control functions in computer integrated manufacturing using artificial intelligence

Proper integration of scheduling and control in Flexible Manufacturing Systems will make available the required level of decision-making capacity to provide a flexibly-automated, efficient, and quality manufacturing process. To achieve this level of integration, the developments in computer technology and sophisticated techniques of artificial intelligence (AI) should be applied to such FMS functions as scheduling. In this paper, we present an Intelligent Scheduling System for FMS under development that makes use of the integration of two AI technologies. These two AI technologies — Neural Networks and Expert Systems — provide the intelligence that the scheduling function requires in order to generate goodschedules within the restrictions imposed by real-time problems. Because the system has the ability to plan ahead and learn, it has a higher probability of success than conventional approaches. The adaptive behavior that will be achieved contribute to the integration of scheduling and control in FMS.     

On insertion tie-breaking rules in heuristics for the permutation flowshop scheduling problem

The most efficient approximate procedures so far for the flowshop scheduling problem with makespan objective – i.e. the NEH heuristic and the iterated greedy algorithm – are based on constructing a sequence by iteratively inserting, one by one, the non-scheduled jobs into all positions of an existing subsequence, and then, among the so obtained subsequences, selecting the one yielding the lowest (partial) makespan. This procedure usually causes a high number of ties (different subsequences with the same best partial makespan) that must be broken via a tie-breaking mechanism. The particular tie-breaking mechanism employed is known to have a great influence in the performance of the NEH, therefore different procedures have been proposed in the literature. However, to the best of our knowledge, no tie-breaking mechanism has been proposed for the iterated greedy. In our paper, we present a new tie-breaking mechanism based on an estimation of the idle times of the different subsequences in order to pick the one with the lowest value of the estimation. The computational experiments carried out show that this mechanism outperforms the existing ones both for the NEH and the iterated greedy for different CPU times. Furthermore, embedding the proposed tie-breaking mechanism into the iterated greedy provides the most efficient heuristic for the problem so far.     

A simulation-based scheduling system for real-time optimization and decision making support

This paper presents an industrial application of simulation-based optimization (SBO) in the scheduling and real-time rescheduling of a complex machining line in an automotive manufacturer in Sweden. Apart from generating schedules that are robust and adaptive, the scheduler must be able to carry out rescheduling in real time in order to cope with the system uncertainty effectively. A real-time scheduling system is therefore needed to support not only the work of the production planner but also the operators on the shop floor by re-generating feasible schedules when required. This paper describes such a real-time scheduling system, which is in essence a SBO system integrated with the shop floor database system. The scheduling system, called OPTIMISE scheduling system (OSS), uses real-time data from the production line and sends back expert suggestions directly to the operators through Personal Digital Assistants (PDAs). The user interface helps in generating new schedules and enables the users to easily monitor the production progress through visualization of production status and allows them to forecast and display target performance measures. Initial results from this industrial application have shown that such a novel scheduling system can help both in improving the line throughput efficiently and simultaneously supporting real-time decision making.     

Application of a continuous supervisory fuzzy control on a discrete scheduling of manufacturing systems

This paper considers the design and the practical implementation of a stable multiple objective real-time scheduling problem for a complex production system. In this paper, a complex production system is viewed as a kind of systems producing a variety of products (multiple-part-type) under constraints and multiple production objectives often conflicting. Previously, fuzzy control theory and fuzzy intervals arithmetic have been used to develop a distributed and supervised continuous-flow control architecture. In this framework, the objective of the distributed control structure is to balance the production process by adjusting the continuous production rates of the machines on the basis of the average local behavior. The supervisory control methodology aims at maintaining the overall performances within acceptable limits. In the new proposed approach, the problem of a stable real-time scheduling of jobs is considered at the shop-floor level. In this context, as the stability of the control structure is ensured, the actual dispatching times are determined from the continuous production rates through a discretization procedure. To deal with conflicts between jobs at a shared machine, a decision is made. It concerns the actual part to be processed and uses some criterions representing a measure of the job's priority. The simulation results show the validity of the proposed approach in terms of production cost, robustness and system stability.     

A distributed scheduling and shop floor control method

We suggest a market-like framework for scheduling and shop floor control in computer-controlled manufacturing systems where each resource agent and part agent acts like an independent profit maker. A part-resource negotiation procedure is suggested including price-based bid construction and price revising mechanism. Alternative routings for each production order are considered in scheduling and shop floor control. A simulation-based scheduling method is suggested to estimate the start time and the completion time of each task. We develop a prototype soffivare which utilizes the object-oriented concept.     

Computational performances of a simple interchange heuristic for a scheduling problem with an availability constraint

This paper deals with a scheduling problem on a single machine with an availability constraint. The problem is known to be NP-complete and admits several approximation algorithms. In this paper we study the approximation scheme described in He et al. [Y. He, W. Zhong, H. Gu, Improved algorithms for two single machine scheduling problems, Theoretical Computer Science 363 (2006) 257–265]. We provide the computation of an improved relative error of this heuristic, as well as a proof that this new bound is tight. We also present some computational experiments to test this heuristic on random instances. These experiments include an implementation of the fully-polynomial time approximation scheme given in Kacem and Ridha Mahjoub [I. Kacem, A. Ridha Mahjoub, Fully polynomial time approximation scheme for the weighted flow-time minimization on a single machine with a fixed non-availability interval, Computers and Industrial Engineering 56 (2009) 1708–1712].     

Loading flexible manufacturing systems: a heuristic approach

In this study, heuristic algorithms are developed for loading Flexible Manufacturing Systems. The heuristic approach is an efficient way of planning the FMS with multiple nonlinear loading objectives.     

Smart manufacturing scheduling: A literature review

Within the scheduling framework, the potential of digital twin (DT) technology, based on virtualisation and intelligent algorithms to simulate and optimise manufacturing, enables an interaction with processes and modifies their course of action in time synchrony in the event of disruptive events. This is a valuable capability for automating scheduling and confers it autonomy. Automatic and autonomous scheduling management can be encouraged by promoting the elimination of disruptions due to the appearance of defects, regardless of their origin. Hence the zero-defect manufacturing (ZDM) management model oriented towards zero-disturbance and zero-disruption objectives has barely been studied. Both strategies combine the optimisation of production processes by implementing DTs and promoting ZDM objectives to facilitate the modelling of automatic and autonomous scheduling systems. In this context, this particular vision of the scheduling process is called smart manufacturing scheduling (SMS). The aim of this paper is to review the existing scientific literature on the scheduling problem that considers the DT technology approach and the ZDM model to achieve self-management and reduce or eliminate the need for human intervention. Specifically, 68 research articles were identified and analysed. The main results of this paper are to: (i) find methodological trends to approach SMS models, where three trends were identified; i.e. using DT technology and the ZDM model, utilising other enabling digital technologies and incorporating inherent SMS capabilities into scheduling; (ii) present the main SMS alignment axes of each methodological trend; (iii) provide a map to classify the literature that comes the closest to the SMS concept; (iv) discuss the main findings and research gaps identified by this study. Finally, managerial implications and opportunities for further research are identified.     

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.     

A cutting and scheduling problem in float glass manufacturing

This paper considers a cutting and scheduling problem of minimizing scrap motivated by float glass manufacturing and introduces the float glass scheduling problem. We relate it to classical problems in the scheduling literature such as no-wait hybrid flow shops and cyclic scheduling. We show that the problem is NP-hard, and identify when each of the problem’s components are polynomially solvable and when they induce hardness. In addition, we propose a simple heuristic algorithm, provide its worst-case performance bounds, and demonstrate that the bounds are tight. When the number of machines is two, the worst-case performance is 5/3.     

Complex scheduling of a printing process

The intricate nature of scheduling at a printing company is described. Many of its subtle operational complexities and sophisticated work order selection rules are explained in depth to emphasize the highly refined nature of a strongly sequence-dependent process. A simulation-based scheduling tool integrated with an enterprise resource planning (ERP) system is used to capture the required level of process detail. The results are comprehensive schedules that reflect the collective needs of the entire production process, improved understanding of interdepartmental effects, increased productivity, better visibility of future production requirements, and faster schedule turn around times.     

A decision analysis based system for formulating manufacturing strategy

Although a substantial number of decision and management science models of production management decisions have been developed, these models have not generally addressed the strategic framework of manufacturing, considered as an integrated pattern of decisions. They have usually been concerned with only one aspect of manufacturing strategy such as capacity or technology choice and have framed that choice using a single dimension of value such as cost. This study develops a comprehensive decision analysis model base which can be implemented as a DSS, to gain insight about the broader manufacturing strategy set. Decision trees, influence diagrams, Monte Carlo risk analysis and multiple criteria utility functions can contribute to a better understanding of and to decision support for manufacturing strategy formulation.     

A model, a heuristic and a decision support system to solve the scheduling problem of an earth observing satellite constellation

China plans to launch four small optical satellites and four small SAR satellites to form a natural disaster monitoring constellation. Data can be obtained by the constellation in all weather conditions for disaster alert and environmental damage analysis. The scheduling problem for the constellation consists of selecting and timetabling the observation activities to acquire the requested images of the earth surface and scheduling the download activities to transmit the image files to a set of ground stations. The scheduling problem is required to be solved every day in a typical 1-day horizon and it must respect complex satellite operational constraints as well as request preferences, such as visibility time windows, transition time between consecutive observations or downloads, memory capacity, energy capacity, polygon target requests and priorities. The objective is to maximize the rewards of the images taken and transmitted. We present a nonlinear model of the scheduling problem, develop a priority-based heuristic with conflict-avoided, limited backtracking and download-as-needed features, which produces satisfactory feasible plans in a very short time. A decision support system based on the model and the heuristic is also provided. The system performance shows a significant improvement with respect to faster and better scheduling of an earth observing satellite constellation.