Simulation-based Scheduling Research Articles

A Semantic Digital Twin for the Dynamic Scheduling of Industry 4.0-based Production of Precast Concrete Elements

Precast concrete construction enhances project efficiency, sustainability, and durability by leveraging a controlled production environment that ensures high-quality outputs. Still, the sequential nature of off-site production, encompassing casting, curing, and storage of the precast elements, is subject to significant uncertainties, including supply chain variability and environmental factors affecting curing times. Dynamic adjustments to the production schedule are essential for aligning with real-time changes, necessitating a robust framework for real-time data acquisition and analysis. Dynamic Scheduling (DS) is a responsive and adaptive approach that accommodates real-time changes, optimizes the production flow, and minimizes downtime or delays. However, the DS approach demands real-time data to quickly and efficiently respond to unforeseen challenges, which requires acquiring and analyzing production-relevant data throughout the production process. The Digital Twin (DT) emerges in Industry 4.0 (I4.0) as a bridge between physical operations and digital capabilities, enabling a seamless flow of information, for which the Asset Administration Shell (AAS) is a reference implementation. This study introduces a DS framework to optimize precast element production, utilizing a DT for real-time data aggregation across the production system. The framework implements a Semantic DT based on the AAS and the Linked Data approach. It employs Resource Description Framework (RDF) serialization of the AAS and an ontological representation of the production system for data integration. The framework leverages a simulation-based scheduler, which exchanges data with the DT in a Service-oriented Architecture (SoA) using SPARQL, a language for querying and updating graph databases. The approach is evaluated through a proof of concept, demonstrating effective uncertainty management in a dynamic production environment.

Simulation-Based Schedule Optimization for Virtual Coupling-Enabled Rail Transit Services with Multiagent Technique

Virtual coupling (VC) is a train-centric next generation signalling system, which can enable multiple trains to operate in a formation just like one train or decouple separately on-the-run or at station flexibly or as planned. With the aim of optimizing the interdeparture train headway time, providing the variable capacity for diverse passenger demand, maximizing the passenger riding comfort degree, and minimizing passenger travel cost and train operation cost, the dynamic schedule for VC-enabled rail transit services is investigated with the multiagent simulation technique on NetLogo platform. Our contribution is mainly fourfold. First, VC-enabled rail transit entity for simulation is represented with the multiagent technique, including representation of train unit, train convoy, passenger attributes and behavior, and mathematical formula for calculation of the train operation cost and passenger travel cost, as well as passengers riding comfort degree are proposed. Second, the operational principles for flexible and self-organisingVC-enabled trains are defined. Third, the VC-enabled train-centric, passenger demand-driven, and agent-based simulation flow and algorithms are developed innovatively, which adopt the ergodic strategy for simulation by traversing each O-D pair demand along each route section over the rail transit network. Finally, we test and discuss the proposed methodology on the designed computational experiment on the NetLogo platform, and the simulation results series validated the effectiveness of the proposed methodology. The provided research can effectively support the VC-enabled platoon operation-oriented train service schedule for future study.

Design and Implementation of Simulation-Based Scheduling System with Reinforcement Learning for Re-Entrant Production Lines

Recently, manufacturing companies have been making efforts to increase resource utilization while ensuring the flexibility of production lines to respond to rapidly changing market environments and customer demand. In the high-tech manufacturing industry, which requires expensive manufacturing facilities and is capital-intensive, re-entrant production lines are used for efficient production with limited resources. In such a production system, a part visits a specific station repeatedly during the production period. However, a re-entrant production line requires an appropriate scheduling system because other parts with different processing requirements are processed at the same station. In this study, a re-entrant production line was modeled as a manufacturing environment via simulation, and an adaptive scheduling system was developed to improve its operational performance by applying deep reinforcement learning (DRL). To achieve this, a software architecture for integrating DRL with the simulation was developed and the states, actions, and rewards of the reinforcement learning (RL) agent were defined. Moreover, a discrete-event simulation control module was designed to collect data from the simulation model and evaluate the policy network trained via DRL. Finally, the applicability and effectiveness of the developed scheduling system were verified by conducting experiments on a hypothetical re-entrant production line.

Kitlesel İmalat Sistemlerinde Dijital İkiz Kullanılarak Gerçek Zamanlı Üretim Çizelgeleme ve Tekstil Sektöründe Bir Uygulama

Üretim ortamının dinamik yapısı planlanmayı güçleştirmektedir. Gelişen teknoloji ile birlikte dijital ikiz, üretim süreçlerine etkin çözümler sunabilmek amacıyla artan bir şekilde kullanılmaya başlanmıştır. Bu çalışmada, kitlesel imalat sistemlerinde üretimin planlanması amacıyla simülasyon temelli dijital ikiz ile bir çizelgeleme modeli önerilmiştir. Böylece çözümü zor olan üretim çizelgeleme problemlerine yenilikçi ve uygulanabilir bir yaklaşım önerilmesi amaçlanmıştır. Üretim hattının bir kopyası olan ve gerçek zamanlı veri ile entegre edilmiş model, hammadde stokları, üretim süreleri, hazırlık süreleri, vardiya değişimleri ve mola zamanları, bakım ve arıza kaynaklı duruşlar gibi birçok üretim parametresini dikkate almaktadır. Önerilen yaklaşımın gerçek hayat uygulamasını göstermek amacıyla, kumaş imalatı yapan bir tekstil işletmesinde üretim hattının dijital ikizi oluşturulmuş ve örgü makineleri için işlerin sıraları belirlenmiştir. Model ile her bir siparişin zamanında tamamlanma olasılığı belirlenerek risk analizi yapılabilmektedir. Böylece yüksek riske sahip siparişler yeniden çizelgelenerek olası gecikmelerden kaçınılabilmektedir.

Framework and deployment of a cloud-based advanced planning and scheduling system

• This paper presented a cloud-based and advanced planning and scheduling (C-APS) system framework, it can be used as a reference for system developers to develop a cloud-based and object-oriented APS with simulation-based scheduling engine that can be applied in practice. • This paper proposed the service structure and approach of deploying the C-APS system in the public cloud infrastructure, platform, and service provider (e.g., AWS TM ) and hybrid cloud platforms. Readers may reference this approach to deploy a server-based enterprise information application system to a cloud-based system. • This paper presented the package diagram to collect the necessary data for building the case company's virtual factory model required by the C-APS system; this feature can significantly improve modeling efficiency and data stability. Besides, the C-APS's objected-oriented, simulation-based scheduling engine has the characteristics of quickly expanding and adjusting to the virtual plant model. • The C-APS system's application in a leading automotive part assembly company's printed circuit board production scheduling shows that the input planning data model is easy to maintain; the scheduling quality is high; the computing time is short and acceptable for practical application. Many small and medium-sized manufacturing enterprises (SMEs) have already implemented enterprise resource planning (ERP) and manufacturing execution system (MES) and began to start the journey of cloud manufacturing; however, the high cost of hardware and software investment, implementation, and maintenance usually hinder SMEs from adopting an advanced planning and scheduling (APS) system. This paper aims to develop a cloud-based APS (C-APS) system framework, the service structure, and approach of deploying the C-APS system in a public cloud infrastructure platform and service provider or hybrid cloud platform. The package diagram is proposed for building the C-APS system's virtual factory model to improve modeling efficiency and data stability. The C-APS system is a cloud-based and object-oriented software; its simulation-based scheduling engine can generate the significant production and operations schedule, and has the characteristics of on-demand self-service, quickly expanding and adjusting to the virtual plant model. The C-APS system's application in a leading automotive part assembly company's printed circuit board production scheduling shows that the input planning data model is easy to maintain. The scheduling quality is high; the computing time is short and acceptable for practical application.

A simulation-based dynamic scheduling and dispatching system with multi-criteria performance evaluation for Industry 3.5 and an empirical study for sustainable TFT-LCD array manufacturing

As the existing manufacturing systems may not be ready to support flexible decisions for smart production with increasing product mix and shortening product life cycle, it is crucial to rapidly respond to dynamic needs to improve bottleneck productivity and ensure the throughput of the whole manufacturing system. Limitations of the existing approaches can be traced in part to the lack of a framework within which different decisions in real settings can be integrated and aligned in light of the changes of manufacturing contexts. To fill the gaps, this study aims to develop a dynamic scheduling and dispatching system with the constructed discrete event simulation model to optimise the scheduling for bottleneck and associated dispatching rules for remaining processes, while considering the manufacturing system as a whole to empower smart manufacturing and reduce waste for sustainable production. A hybrid genetic algorithm is developed to minimise photolithography capacity loss, while considering the waiting time constraints and determining the optimal dispatching rules for non-bottleneck workstations by the design of experiments and multi-criteria decision analysis to integrate related decisions for the manufacturing system as a whole. An empirical study was conducted for validation. The results have shown its practical viability.

An In-Depth Empirical Investigation of State-of-the-Art Scheduling Approaches for Cloud Computing

Recently, Cloud computing has emerged as one of the widely used platforms to provide compute, storage and analytics services to end-users and organizations on a pay-as-you-use basis, with high agility, availability, scalability, and resiliency. This enables individuals and organizations to have access to a large pool of high processing resources without the need for establishing a high-performance computing (HPC) platform. From the past few years, task scheduling in Cloud computing is reckoned as eminent recourse for researchers. However, task scheduling is considered an NP-hard problem. In this research work, we investigate and empirically compare some of the most prominent state-of-the-art scheduling heuristics in terms of Makespan, Average resource utilization (ARUR), Throughput, and Energy consumption. The comparison is then extended by evaluating the approaches in terms of individual VM level load imbalance. After extensive simulation, the comparative analysis has revealed that Task Aware Scheduling Algorithm (TASA) and Proactive Simulation-based Scheduling and Load Balancing (PSSLB) outperformed as compared to the rest of the approaches and seems to be optimal choice keeping in view the trade-of between the complexities involved and the performance achieved concerning Makespan, Throughput, resource utilization, and Energy consumption.

Efficient operating room planning using an ensemble learning approach to predict surgery cancellations

Cancelled surgeries cause insufficient use of operating rooms, surgeons, nurses, equipment and other hospital resources. Operating rooms are one of the most expensive resources in any healthcare system. The average amount of cost for any operating room for a less complex procedure is around $29/min, while the more complex procedures cost up to $80/min. Many studies have been conducted to analyze and understand the reasons behind surgery cancellation; however, a handful of studies aimed to predict which patients have a large risk of cancellation. We used four different traditional data mining techniques—Conditional Inference Tree, C5.0, logistic regression, and Radial Basis Function Kernel Support Vector Machine—to predict surgical cancellations to create efficient surgical patients’ schedules. Then, a stacking generalization ensemble machine was developed and compared to the traditional methods. Three different scheduling scenarios were developed and tested using discrete event simulation (DES). The stacking generalization ensemble machine outperformed all traditional data mining techniques and the benchmark algorithm with an accuracy of 95% and area under the curve (AUC) of 96%. The proposed simulation-based optimization scheduling technique increased the operating room use by 13%. Surgeries cancellation prediction leads to efficient scheduling that ultimately leads to reductions in expenditure.Highlights:Created a robust framework to increase OR utilizationPredicted surgery cancellation using data mining approachesCreated a novel stacking ensemble machineEnhanced the prediction power through ensemble learning that outperformed the other algorithmsDetermined overbooking opportunities through Simulation-based optimization

Investigation of Cloud Scheduling Algorithms for Resource Utilization Using CloudSim

Compute Cloud comprises a distributed set of High-Performance Computing (HPC) machines to stipulate on-demand computing services to remote users over the internet. Clouds are capable enough to provide an optimal solution to address the ever-increasing computation and storage demands of large scientific HPC applications. To attain good computing performances, mapping of Cloud jobs to the compute resources is a very crucial process. Currently we can say that several efficient Cloud scheduling heuristics are available, however, selecting an appropriate scheduler for the given environment (i.e., jobs and machines heterogeneity) and scheduling objectives (such as minimized makespan, higher throughput, increased resource utilization, load balanced mapping, etc.) is still a difficult task. In this paper, we consider ten important scheduling heuristics (i.e., opportunistic load balancing algorithm, proactive simulation-based scheduling and load balancing, proactive simulation-based scheduling and enhanced load balancing, minimum completion time, Min-Min, load balance improved Min-Min, Max-Min, resource-aware scheduling algorithm, task-aware scheduling algorithm, and Sufferage) to perform an extensive empirical study to insight the scheduling mechanisms and the attainment of the major scheduling objectives. This study assumes that the Cloud job pool consists of a collection of independent and compute-intensive tasks that are statically scheduled to minimize the total execution time of a workload. The experiments are performed using two synthetic and one benchmark GoCJ workloads on a renowned Cloud simulator CloudSim. This empirical study presents a detailed analysis and insights into the circumstances requiring a load balanced scheduling mechanism to improve overall execution performance in terms of makespan, throughput, and resource utilization. The outcomes have revealed that the Sufferage and task-aware scheduling algorithm produce minimum makespan for the Cloud jobs. However, these two scheduling heuristics are not efficient enough to exploit the full computing capabilities of Cloud virtual machines.

A Simulation-Based Scheduling Methodology for Construction Projects Considering the Potential Impacts of Delay Risks

This paper tackles the problem of scheduling construction projects considering the influence of delay risks. In the actual body of knowledge, several methods have been proposed to handle this problem, starting from the Project Evaluation and Review Technique to advanced simulation models. However, this investigation proposes a novel integration of one methodology with some approaches already existing in the literature related to Monte Carlo Simulation scheduling techniques as seen from the perspective of a practitioner. The research began with a literature review of both schedule risks and Monte Carlo based scheduling models for construction projects. Based on this, the methodology was designed with the constant participation of experts in the construction industry. As result of this, a comprehensive and practical methodology was constructed. Therefore, a new mathematical structure for the simulation model within the methodology was formulated in which a new concept for each risk defined as “potential impact” was used. Moreover, the simulation model is based on the judgment of experts and methods of the known literature such as the explicit model of the occurrence probability of the risks and the activity-risk factor matrix. Then, to validate the tool, the proposed methodology was applied using the information of an already constructed construction project of a public university of Colombia. The obtained results were a confidence-based forecast of the end date of the project and a quantitative importance measure of the modelled risks. These results were compared against the real history of the project since it was found an excellent performance of the proposed methodology. To sum up, the research process described above supports the validity of the proposed methodology.

Value of Information to Improve Daily Operations in High-Density Logistics

Agro-food logistics is increasingly challenged to ensure that a wide variety of high-quality products are always available at retail stores. This paper discusses high-density logistics issues caused by more frequent and smaller orders from retailers. Through a case study of the distribution process in a Dutch floricultural supply chain, we demonstrate that using inbound and outbound information flows to plan daily warehouse operations improves the logistics performance. A discrete-event simulation and a simulation-based scheduling algorithm are used as decision-support models to assess the value of information. The results indicate that the higher the density of logistics process, the higher the value of the information. Future research will investigate different uses of information as more types of information become available in the agro-food sector supply chains.

Robust simulation-based scheduling methodology to reduce the impact of manual installation tasks on low-volume aerospace production flows

This work focuses on the development, implementation, and evaluation of a robust simulation-based planning and optimization methodology. This methodology attempts to reduce the risk of schedule overruns by identifying plans that are robust to various process delays and disruptions. A representative case study is used to test the methodology's capability to model a real production environment and search for improved scheduling options. The case study involves planning manual sensor installation processes within a provided production schedule to reduce the impact of the sensor installations and the risk of production delays. Properly planning sensor installations is becoming more important as new aerospace vehicles continue to incorporate more sensors to monitor system health, performance, safety, reliability, etc.Traditional scheduling techniques provide a strong framework to plan and optimize, at a medium level of detail, the completion of primary production processes (e.g. structural assembly, system integration, etc.). However, fully defining the interactions and logic required to evaluate the impact resulting from the sensor installation tasks in this scheduling framework is challenging. The discrete-event simulation paradigm simplifies the definition of these production rules and constraints; however, DES models commonly require too much detail, modeling effort, and optimization time/resources to be useful during pre-production planning. The developed methodology addresses this challenge by integrating the process optimization strengths of scheduling with the modeling flexibility of simulation. This enables the fast generation of a limited fidelity simulation that can evaluate the impact of sensor installations to support simulation-based schedule optimization. Throughout this work, the methodology is built up by leveraging various scheduling, simulation, and optimization capabilities. The methodology is then implemented to explore its ability to model and optimize the case study's problem. Finally, results from optimization runs with and without considering schedule robustness are compared to investigate how the methodology utilizes information about schedule risk to improve the resulting plan.

A Two-Stage Optimization Framework for Optimizing Large-Scale Vehicular Evacuation Routing and Scheduling Operation with Uninterrupted Traffic Flow

In many emergency management operations, an efficient evacuation strategy is of great importance because if it is successful, it has the ability to significantly reduce the loss of property and human life. This paper develops a routing and scheduling optimization framework for large-scale vehicular evacuation. To guarantee high optimization efficiency, we consider the routing and scheduling optimization as a two- stage problem instead of optimizing them as a whole (i.e. using time-space network). In the first phase, a multiple-objective mixed integer programming (MIP) model, with the objectives of minimizing the total in-network time and network clearance time is proposed to find an optimal routing plan. In the second phase, a simulation-based scheduling Heuristic is proposed to dynamically generate the time-dependent departure rates. A real-world evacuation scenario in Eastern Shore of Maryland is studied by using the proposed optimization model. The calculation results indicate a good optimization capability and flexibility of the proposed model. Keywords: Vehicular Evacuation, Routing, Scheduling, Optimization, Heuristic Copyright c 2016 SERSC Language: en

A hybrid estimation of distribution algorithm for simulation-based scheduling in a stochastic permutation flowshop

The permutation flowshop scheduling problem (PFSP) is NP-complete and tends to be more complicated when considering stochastic uncertainties in the real-world manufacturing environments. In this paper, a two-stage simulation-based hybrid estimation of distribution algorithm (TSSB-HEDA) is presented to schedule the permutation flowshop under stochastic processing times. To deal with processing time uncertainty, TSSB-HEDA evaluates candidate solutions using a novel two-stage simulation model (TSSM). This model first adopts the regression-based meta-modelling technique to determine a number of promising candidate solutions with less computation cost, and then uses a more accurate but time-consuming simulator to evaluate the performance of these selected ones. In addition, to avoid getting trapped into premature convergence, TSSB-HEDA employs both the probabilistic model of EDA and genetic operators of genetic algorithm (GA) to generate the offspring individuals. Enlightened by the weight training process of neural networks, a self-adaptive learning mechanism (SALM) is employed to dynamically adjust the ratio of offspring individuals generated by the probabilistic model. Computational experiments on Taillard’s benchmarks show that TSSB-HEDA is competitive in terms of both solution quality and computational performance.

Simulation-Based Scheduling of Waterway Projects Using a Parallel Genetic Algorithm

A Parallel Genetic Algorithm (PGA) is used for a simulation-based optimization of waterway project schedules. This PGA is designed to distribute a Genetic Algorithm application over multiple processors in order to speed up the solution search procedure for a very large combinational problem. The proposed PGA is based on a global parallel model, which is also called a master-slave model. A Message-Passing Interface (MPI) is used in developing the parallel computing program. A case study is presented, whose results show how the adaption of a simulation-based optimization algorithm to parallel computing can greatly reduce computation time. Additional techniques which are found to further improve the PGA performance include: (1) choosing an appropriate task distribution method, (2) distributing simulation replications instead of different solutions, (3) avoiding the simulation of duplicate solutions, (4) avoiding running multiple simulations simultaneously in shared-memory processors, and (5) avoiding using multiple processors which belong to different clusters (physical sub-networks).

SIMULATION OF BRIDGE CONSTRUCTION WORKS: AN EXPLORATORY STUDY

Bridge construction projects are associated with uncertainties partly due to sequencing issues, logistics, interactions, and resource constraints. Their construction is mostly cyclic and repetitive, yet complex, requiring innovative approaches to cope with its management. Construction projects planners seek methods to enable them to schedule bridge construction projects in consideration of these constraining factors. The main objective of this research is to develop a concept of a simulation-based modeling approach. It aimed to decide which data in real systems are important for the modeling of an operation, and in which way they must be gathered to help planners approach a proper simulation-based schedule. A case study project based in New Zealand was selected for this paper. It focuses on developing a resource-based model of bridge construction using a launching-girder construction method, taking into account the complex interactions among the project’s components. The discrete elements within the project and steps taken to develop a model are described in this paper. The study enhances knowledge on the applicability of simulation in repetitive construction operation, which in turn can provide more realistic models for scheduling progress.

Model for Evaluating Uncertain Project Duration Considering Construction Interface Problems

Many probabilistic scheduling models have been developed to determine the duration of construction projects. However, these models are not appropriate to capture the effect of the factors that are involved construction interface problems on the durations of multiple activities. This work presents a simulation-based probabilistic scheduling model that includes the impact of construction interface problems on the duration of building projects. The proposed model is established according to an activity duration model and a work-group (WG)-based schedule network. The activity duration model is applied to reflect the effects of general factors on the duration of each activity; the WG-based schedule network is employed to evaluate the effects of construction interface problems on the durations of multiple activities. The results of applying the proposed model to an example project reveal that the duration of a project can be over-optimistically estimated if the effects of construction interface problems are neglected.

조선 선체 생산설계 일정 계획을 위한 상세 이산사건 모델링기반 비즈니스 프로세스 시뮬레이션

For the scheduling and the job assignment of the ship hull production design which is a process-based work, we suggest the simulation-based scheduling using the discrete-event-based business process simulation. First, we analyze the ship hull production design process from the perspective of a job assignment to make it into the simulation model using DEVS (Discrete Event System Specification) which is the representative modeling method for a discrete-event simulation. Based on the APIs of the open-source discrete-event simulation engine, we implement the simulation using the Groovy script. We develop the scenario generator in which the user defines detail information of the construction drawing and its member blocks, and design engineers information, and the various setting for the simulation including the job assignment strategy. We use the XML files from this scenario generator as inputs of simulation so that we can get simulation result in forms of Gantt chart without changes of the simulation model.

The probability distribution of project completion times in simulation-based scheduling

The assumption of the normality of the distribution of Project Completion Times (PCTs) in simulation-based scheduling has been generally accepted as the norm. However, it is well established in the literature that PCTs are not always normally distributed and that their distribution and variability are affected by the distribution and variability of activity durations. This paper presents an automated risk quantification method that determines the best fit Probability Distribution Function (PDF) of PCTs. The algorithm is programmed in MATLAB and generates a set of simulation outputs obtained by systematically changing the probability distribution functions that define activities’ durations in a network and analyzes the effect of different distributions of activity durations on the distribution of the PCTs. The procedure is described and the findings are presented. This easy-to-use computerized tool improves the reliability of simulation-based scheduling by calculating the exact PDFs of activity durations, simulating the network, and calculating the exact PDF of PCTs. It also simplifies the tedious process involved in finding the PDFs of the many activity durations, and is a welcome replacement for the normality assumptions used by most simulation-based scheduling researchers.

A beam search heuristics to solve the parcel hub scheduling problem

In this paper, a beam search scheduling heuristic (BSSH) is presented to solve the parcel hub scheduling problem (PHSP), which is a scheduling problem that is common in the parcel delivery industry (PDI). Companies in the PDI include the United States Postal Service, United Parcel Services, Federal Express, and Deutsche Post. Together, these companies move more than one trillion dollars of the United States’ Gross Domestic Product. The PHSP involves scheduling a set of inbound trailers each containing a set of heterogeneous parcels to a set of unload docks. At the unload docks, the parcels are unloaded, sorted, and moved to the appropriate outbound trailers at the load docks. At the load docks, the parcels are loaded onto the outbound trailers. The objective is to minimize the timespan of the transfer operation at the transshipment terminal. The BSSH is compared to various scheduling approaches: random scheduling algorithm (RSA), genetic-based scheduling algorithm (GBSA), and simulation-based scheduling algorithm (SBSA). While GBSA and SBSA offer solutions that are superior to BSSH for smaller size problems, BSSH outperforms these algorithms on larger size problems requiring much less computational time. The results show that for larger size problems the BSSH is able to produce solutions that are from 4% to 8% of the known optimum solutions. In contrast, GBSA and SBSA, respectively offer solutions from 23% to 38% and from 6% to 47% of the known optimum solutions. The contribution of this paper is a scheduling heuristic to solve the PHSP.