Discrete Event Simulation Research Articles

Integration of MILP and discrete-event simulation for flow shop scheduling using Benders cuts

Optimization-based scheduling in the chemical industry is highly beneficial but also highly difficult due to its combinatorial complexity. Different modeling and optimization techniques exist, each with individual strengths. We propose Benders decomposition to integrate mixed-integer linear programming (MILP) and discrete-event simulation (DES) to solve flow shop scheduling problems with makespan minimization objective. The basic idea is to generate valid Benders cuts based on sensitivity information of the DES sub problem, which can be found in the critical paths of DES solutions. For scaled literature flow shops, our approach requires at least an order of magnitude fewer iterations than a genetic algorithm and provides optimality gap information. For a real-world case study, our approach finds good solutions very quickly, making it a powerful alternative to established methods. We conclude that the Benders-DES algorithm is a promising approach to combine rigorous MILP optimization capabilities with high-fidelity DES modeling capabilities.

A Combined Discrete Event Simulation and Factorial Design Experiment for the Scheduling Problem in a Hybrid Flow Shop

Production plants have always been confronted with the problem of scheduling, as this has a direct impact on production time and therefore on production costs. This is especially true in today's world, where it is necessary to produce a quality product at a low price and in a short time, while at the same time responding flexibly to customer demands. Due to the complexity and for economic reasons, testing different variants in a real production environment is insufficient and ineffective. For this reason, this paper proposes a new method that combines discrete event simulation and factorial design experiment to find the optimal schedule in hybrid flow shop. It was tested with the goal of achieving the minimum makespan. The results show that this method makes it possible to find improved solution very quickly. Compared to the original production schedule, the makespan could be reduced by 3 hours and 31 minutes which is reduction of 16.3%. The proposed methodology can be used in many discrete and process production plants.

A hybrid simulation approach to predicting noise in restaurants

High levels of noise are a well-known source of occupant discomfort in dining environments, and noise ranks highly among the most common customer complaints in restaurant reviews. Prior work has focused on measuring existing restaurant soundscapes and attempting to predict acoustic performance; however, current techniques are still limited in accuracy for restaurants with time-varying occupancy and complex layouts. This presentation demonstrates a novel approach to predicting ambient noise in restaurants via a hybrid simulation framework. Acoustical computations for a given restaurant space are performed in a room acoustics model, while a discrete event simulation (DES) model is used to simulate the arrival patterns of patrons and their conversational behaviors. Predictions from the room acoustics model inform the DES model, allowing for noise level predictions at each seat position. Results from the prediction model are validated against measurements in a case study restaurant. Further development of this framework may support the practical assessment of design interventions in any restaurant while also accounting for time-varying patterns of occupancy.

Enhancing operation efficiency of leather manufacturing industry through hybrid of value stream mapping and discrete event simulation

Enhancing operation efficiency of leather manufacturing industry through hybrid of value stream mapping and discrete event simulation

Combining balancing, sequencing and buffer allocation decisions to improve the efficiency of mixed-model asynchronous assembly lines

When the production of a variety of models is considered in assembly lines, the more suitable control system is asynchronous: the pace of the line is not imposed but depends on the actual time needed in each work centre to complete a product. In asynchronous lines blocking and starvation phenomena are responsible of performances deterioration. Three popular techniques able to limit these phenomena are: balancing the workload among stations by tasks assignment, allocating buffers between stations, optimizing of the sequence of models entering the line. These three techniques correspond to the Mixed-model Assembly Line Balancing Problem (MALBP), the buffer allocation problem (BAP) and the sequencing problem (SP). The presented approach is able to simultaneously solve these three problems, which are strictly connected, and is based on a Genetic Algorithm procedure that uses discrete event simulation to evaluate the fitness function of individuals. Tests conducted on a set of benchmark instances allow quantifying the effect on line efficiency of sequencing and buffer allocation decisions separately, with the aim to assess which of the two techniques is more effective and to investigate whether if combining sequencing optimization and buffer allocation gives additional advantages in terms of increasing line efficiency.

Predicting the Population Health Economic Impact of Current and New Cancer Treatments for Colorectal Cancer: A Data-Driven Whole Disease Simulation Model for Predicting the Number of Patients with Colorectal Cancer by Stage and Treatment Line in Australia

ObjectivesEffective healthcare planning, resource allocation, and budgeting require accurate predictions of the number of patients needing treatment at specific cancer stages and treatment lines. The Predicting the Population Health Economic Impact of Current and New Cancer Treatments (PRIMCAT) for Colorectal Cancer (CRC) simulation model (PRIMCAT-CRC) was developed to meet this requirement for all CRC stages and relevant molecular profiles in Australia. MethodsReal-world data were used to estimate treatment utilization and time-to-event distributions. This populated a discrete-event simulation, projecting the number of patients receiving treatment across all disease stages and treatment lines for CRC and forecasting the number of patients likely to utilize future treatments. Illustrative analyses were undertaken, estimating treatments across disease stages and treatment lines over a 5-year period (2022-2026). We demonstrated the model’s applicability through a case study introducing pembrolizumab as a first-line treatment for mismatch-repair-deficient stage IV. ResultsClinical registry data from 7163 patients informed the model. The model forecasts 15 738 incident and 2821 prevalent cases requiring treatment in 2022, rising to 15 921 and 2871, respectively, by 2026. Projections show that over 2022 to 2026, there will be a total of 116 752 treatments initiated, with 43% intended for stage IV disease. The introduction of pembrolizumab is projected for 706 patients annually, totaling 3530 individuals starting treatment with pembrolizumab over the forecasted period, without significantly altering downstream utilization of subsequent treatments. ConclusionsPRIMCAT-CRC is a versatile tool that can be used to estimate the eligible patient populations for novel cancer therapies, thereby reducing uncertainty for policymakers in decisions to publicly reimburse new treatments.

Implementation of Industry 4.0 and Digital Manufacturing Practices in Indian Manufacturing Industries Using Discrete Event Simulation (DES)

Industry 4.0 and digital manufacturing have emerged as significant areas of research in recent years. According to several reputed research papers, Industry 4.0 is a new industrial revolution that involves the integration of cutting-edge technologies such as the Simulation (Discrete Event Simulation), Internet of Things (IoT), artificial intelligence (AI), and big data analytics into the manufacturing process. Smart manufacturing, on the other hand, is the implementation of these technologies in the manufacturing sector to increase efficiency, reduce waste, and improve product quality. The research suggests that Industry 4.0 and smart manufacturing have the potential to revolutionize the manufacturing industry by enabling companies to achieve higher levels of automation, productivity, and agility. The Indian manufacturing industry has witnessed substantial growth in recent years, becoming a significant contributor to the country's economic development. To maintain its competitive edge in the global market and meet the increasing demand for innovative and cost-effective products, the industry has been actively adopting digital manufacturing practices. In this context, Industry 4.0, characterized by the integration of cyber-physical systems, Simulation, the Internet of Things, and data analytics, offers new opportunities for enhancing efficiency and productivity. This research paper presents a comprehensive study on the successful implementation of digital manufacturing practices in the Indian automobile industry through the use of simulation approaches, in alignment with Industry 4.0 principles. The study aims to identify the key challenges faced by the industry and proposes viable solutions to harness the full potential of digitalization. The paper delves into the critical aspects of simulation techniques, such as discrete event simulation and virtual manufacturing, and their role in optimizing manufacturing processes, reducing lead times, and enhancing product quality. The results of this study indicate that the integration of digital manufacturing practices with Industry 4.0 principles has resulted in significant improvements across the entire value chain of the Indian automobile industry. Reduced production costs, increased production flexibility, and enhanced product quality have been observed as the key outcomes of this strategic alignment.

Simulation-Based Assessment of Hyperconnected Megacity Parcel Logistics

Background: The concept of Hyperconnected Megacity Parcel Logistics (HMPL) was introduced in 2018 and aims to enhance the efficiency, responsiveness, resilience, and sustainability of parcel movements in megacities. However, evaluating such fundamental solutions presents challenges and requires a comprehensive understanding of all stakeholders and decisions involved. Methods: This study introduces a discrete-event agent-based simulation platform that encompasses critical stakeholders and addresses various levels of decision-making. This platform provides an opportunity to evaluate key decisions within an HMPL structure. Results: To demonstrate the capability of the simulator, we assess the impact of package routing and consolidation strategies facilitated by HMPL compared to traditional practices. Preliminary findings suggest that increased interconnection among nodes in HMPL reduces transit times, thereby enabling tighter customer delivery services. However, examining different consolidation heuristics reveals potential trade-offs between handling and shipping costs under fixed shipment schedules, prompting further investigation into dynamic shipment services. Conclusions: The findings of this study suggest that the benefits of innovative approaches in a complex environment, such as parcel logistics, cannot be evaluated in isolation from other decisions. Accurate assessment of the ultimate outcomes and underlying trade-offs requires multi-faceted models that incorporate all key variables.

COVID-19 Bed Management Using a Two-Step Process Mining and Discrete-Event Simulation Approach

The sudden admission of many patients with similar needs caused by the COVID-19 (SARS-CoV-2) pandemic forced health care centers to temporarily transform units to respond to the crisis. This process greatly impacted the daily activities of the hospitals. In this paper, we propose a two-step approach based on process mining and discrete-event simulation for sizing a recovery unit dedicated to COVID-19 patients inside a hospital. A decision aid framework is proposed to help hospital managers make crucial decisions, such as hospitalization cancellation and resource sizing, taking into account all units of the hospital. Three sources of patients are considered: (i) planned admissions, (ii) emergent admissions representing day-to-day activities, and (iii) COVID-19 admissions. Hospitalization pathways have been modeled using process mining based on synthetic medico-administrative data, and a generic model of bed transfers between units is proposed as a basis to evaluate the impact of those moves using discrete-event simulation. A practical case study in collaboration with a local hospital is presented to assess the robustness of the approach. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In this paper we develop and test a new decision-aid tool dedicated to bed management, taking into account exceptional hospitalization pathways such as COVID-19 patients. The tool enables the creation of a dedicated COVID-19 intensive care unit with specific management rules that are fine-tuned by considering the characteristics of the pandemic. Health practitioners can automatically use medico-administrative data extracted from the information system of the hospital to feed the model. Two execution modes are proposed: (i) fine-tuning of the staffed beds assignment policies through a design of experiment and (ii) simulation of user-defined scenarios. A practical case study in collaboration with a local hospital is presented. The results show that our model was able to find the strategy to minimize the number of transfers and the number of cancellations while maximizing the number of COVID-19 patients taken into care was to transfer beds to the COVID-19 ICU in batches of 12 and to cancel appointed patients using ICU when the department hit a 90% occupation rate.

Discrete Event Simulation in Healthcare: A Comprehensive Review

Discrete Event Simulation in Healthcare: A Comprehensive Review

Sizing requirements of the photovoltaic charging station for small electrical vehicles

Electric vehicles (EVs) are being introduced in Rwanda and becoming attractive for different reasons. For instance, these types of vehicles can help decrease air pollution and noise emissions. In addition, it presents an alternative to combustion engines, given the increased price of fuel resources in Rwanda and around the world. This paper presents a tool tailored to optimize the design of an electrical charging station serving small-sized electric vehicles, utilizing the algorithm to assist in sizing stand-alone mopped charging stations. The developed tool is based on the toolbox EventSim from MathWorks, which permits the combination of the simulation of discrete events (such as the arrival of customers at the station) with continuous states (such as the simulation of the charging process). The required PV power was estimated by utilizing solar resources, for the location, from renewables. Ninja. The number of customers arriving at the existing oil station is normalized to estimate the energy requirements of the mopped fleet. A Poisson distribution was proposed to model the battery discharge upon arrival, and different related parameters were evaluated through a sensitivity analysis to identify their effects on the performance of photovoltaic charging station. For the testing values, the station parameters were changed by ±25% to determine the impact of key design parameters on station performance, as well as other satisfaction measures such as average waiting time and average queue length. With a 25% increase in photovoltaic panels, the blackout period decreases by 2.12%, while a 25% decrease in photovoltaic panels causes an increase of 2.18% in the blackout period. Utilizing the energy management system (EMS), the waiting time was reduced by 8%.

Deep Inspiration Breath Hold in Left-Sided Breast Radiotherapy: A Balance Between Side Effects and Costs

BackgroundDeep inspiration breath hold (DIBH) is an effective technique for reducing heart exposure during radiotherapy for left-sided breast cancer. Despite its benefits, cost considerations and its impact on workflow remain significant barriers to widespread adoption. ObjectivesThis study aimed to assess the cost-effectiveness of DIBH and compare its operational, financial, and clinical outcomes with free breathing (FB) in breast cancer treatment. MethodsTreatment plans for 100 patients with left-sided breast cancer were generated using both DIBH and FB techniques. Dosimetric data, including the average mean heart dose, were calculated for each technique and used to estimate the cardiotoxicity of radiotherapy. A state-transition microsimulation model based on SCORE2 (Systematic Coronary Risk Evaluation) algorithms projected the effects of DIBH on cardiovascular outcomes and quality-adjusted life-years (QALYs). Costs were calculated from a provider perspective using time-driven activity-based costing, applying a willingness-to-pay threshold of €40,000 for cost-effectiveness assessment. A discrete event simulation model assessed the impacts of DIBH vs FB on throughput and waiting times in the radiotherapy workflow. ResultsIn the base case scenario, DIBH was associated with an absolute risk reduction of 1.72% (95% CI: 1.67%-1.76%) in total cardiovascular events and 0.69% (95% CI: 0.67%-0.72%) in fatal cardiovascular events over 20 years. Additionally, DIBH was estimated to provide an incremental 0.04 QALYs (95% CI: 0.05-0.05) per left-sided breast cancer patient over the same time period. However, DIBH increased treatment times, reducing maximum achievable throughput by 12.48% (95% CI: 12.36%-12.75%) and increasing costs by €617 per left-sided breast cancer patient (95% CI: €615-€619). The incremental cost-effectiveness ratio was €14,023 per QALY. ConclusionsDespite time investments, DIBH is cost-effective in the Belgian population. The growing adoption of DIBH may benefit long-term cardiovascular health in breast cancer survivors.

Cost-effectiveness of large-panel next-generation sequencing in guiding first-line treatment decisions for patients with nonsquamous advanced non-small cell lung cancer.

Clinical practice guidelines recommend broad-panel genomic profiling to identify actionable genomic alterations for patients with advanced non-small cell lung cancer (aNSCLC). To assess the cost-effectiveness of large-panel next-generation sequencing (LP-NGS) compared with current empirical single-gene test (SGT) patterns to inform first-line treatment decisions for patients with aNSCLC from a US commercial payer perspective, accounting for the effect of testing turnaround time and time to treatment initiation. We developed a discrete-event simulation model to estimate the impact of LP-NGS vs SGT for patients with nonsquamous aNSCLC. Discrete events and timing included testing patterns, receipt of the initial test result, treatment initiation (targeted vs nontargeted therapies), switching, retesting, rebiopsies, clinical trial participation, progression on therapy, and death. LP-NGS and SGT cohorts each comprised 100,000 adults with aNSCLC simulated over a 5-year postdiagnosis period, assumed to have the same distribution of genomic alterations. The model predicted the proportion of patients receiving appropriate first-line therapy according to clinical practice guidelines. Economic outcomes included expected life-years gained, quality-adjusted life-years, and the total costs of care over 5 years. Sensitivity and scenario analyses explored the robustness of the base-case model results. In the base-case model, LP-NGS was likely to identify more alterations than SGT. Total 5-year costs per patient were $539,658 for LP-NGS and $544,550 for SGT (net difference, $4,892 lower costs per patient for LP-NGS), which is likely to be cost-effective 95.1% of the time. The most influential model parameters on the 5-year total costs of care were preprogression nondrug medical costs on nontargeted therapy, NGS turnaround time, and clinical trial participation. This study suggests that LP-NGS to guide first-line treatment decisions is clinically more appropriate (more likely to identify alterations and subsequently allocate patients to clinically appropriate therapy) and provides a dominant cost-effectiveness treatment strategy over 5 years for patients with newly diagnosed aNSCLC in the United States.

Decision support for traffic safety: case of rear-end collision modelling

ABSTRACT This paper presents the decision problem of selecting a collision-warning activation algorithm for Rear-End Collision Alert Signal (RECAS) systems installed in motorcycles and other powered two-wheelers. Using Agent-Based Modelling, Discrete Event Simulation and System Dynamics, we have developed a hybrid simulation model of three scenarios of rear-end collisions to measure the performance of several algorithms found in the literature. The best-performing algorithm, by Hirst & Graham, has reduced the probability of collisions on average by 31.21% in comparison with the baseline scenario of using a standard brake light. Using this algorithm also resulted in up to 11.20% reduction in the estimated severity of injuries when a rear-end collision was not averted. As the Hirst & Graham algorithm was the most effective in all the tested emergency braking scenarios and across all the tested speed limits, we recommend this algorithm to be used in RECAS systems in powered two-wheelers.

Analytical examination of shuttle-based storage and retrieval systems with multiple-capacity lifts

The unbroken trend towards e-commerce and the ever-increasing demands on storage technology present the providers of storage systems with ever greater challenges. One way to meet these requirements is to significantly enhance warehouses’ handling systems. Multiple capacity lifts in tier-captive single-aisle shuttle-based storage and retrieval systems (SBS/RS) are a method to raise the throughput performance of such systems. Therefore, a straightforward calculation approach is presented based on a cycle time model combined with a time-continuous single open Markovian queue with limited capacity. A comparison of the invented approach to a discrete-event simulation (DES) is made to validate it. Finally, a numerical example of the influences of the different handling capacities and their usage in the design process of SBS/RS is depicted. The result is that with double the handling capacity, the throughput increases by about 50%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$50\\%$$\\end{document}. Another result is sorting the stops within one handling cycle, which leads to a significantly higher throughput.

Planning of multi-production line maintenance

Efficiency of production lines is significantly impacted by maintenance activities, which are often overshadowed by the focus on production planning. The symbiotic relationship between maintenance and production activities is a critical yet overlooked factor. We examine the importance of maintenance planning, specifically within the dynamic context of production line operations. In this maintenance planning setting, scarce resources, particularly technicians, are commonly disregarded. Our research addresses the challenge of real-time maintenance planning across multiple production lines by developing a state-of-the-art heuristic, which takes into account machine production states and resource availability and is augmented by re-enactment procedures. We validate the heuristic by employing Markov Decision Processes and digital twin Discrete Event Simulations, which leverage real-world production data. This modeling framework is new for this specific problem. In our experiments, we compare the heuristic against optimal policies and practitioner-based policies. Our heuristic matches the performance of optimal policies in the Markov Decision Processes and significantly outperforms FCFS and practitioner-based policies in the Discrete Event Simulations. Notably, our heuristic is not only effective against standard policies, realizing improvement of 1% to 3%, but it is also the first to be generally applicable to production lines involving multiple machines and resources in real-time.

Adaptive Randomization Method to Prevent Extreme Instances of Group Size and Covariate Imbalance in Stroke Trials.

A recent review of randomization methods used in large multicenter clinical trials within the National Institutes of Health Stroke Trials Network identified preservation of treatment allocation randomness, achievement of the desired group size balance between treatment groups, achievement of baseline covariate balance, and ease of implementation in practice as critical properties required for optimal randomization designs. Common-scale minimal sufficient balance (CS-MSB) adaptive randomization effectively controls for covariate imbalance between treatment groups while preserving allocation randomness but does not balance group sizes. This study extends the CS-MSB adaptive randomization method to achieve both group size and covariate balance while preserving allocation randomness in hyperacute stroke trials. A full factorial in silico simulation study evaluated the performance of the proposed new CSSize-MSB adaptive randomization method in achieving group size balance, covariate balance, and allocation randomness compared with the original CS-MSB method. Data from 4 existing hyperacute stroke trials were used to investigate the performance of CSSize-MSB for a range of sample sizes and covariate numbers and types. A discrete-event simulation model created with AnyLogic was used to dynamically visualize the decision logic of the CSSize-MSB randomization process for communication with clinicians. The proposed new CSSize-MSB algorithm uniformly outperformed the CS-MSB algorithm in controlling for group size imbalance while maintaining comparable levels of covariate balance and allocation randomness in hyperacute stroke trials. This improvement was consistent across a distribution of simulated trials with varying levels of imbalance but was increasingly pronounced for trials with extreme cases of imbalance. The results were consistent across a range of trial data sets of different sizes and covariate numbers and types. The proposed adaptive CSSize-MSB algorithm successfully controls for group size imbalance in hyperacute stroke trials under various settings, and its logic can be readily explained to clinicians using dynamic visualization.

An Approach to Enhancing Manufacturing Throughput Capability through System Design

&lt;div&gt;This article presents a new approach that integrates and balances cycle time and reliability considerations during system design to enhance manufacturing system capability, with direct applicability to automotive and high-volume manufacturing. The method addresses system throughput capability in three steps without complex mathematical modeling. The steps are: (1) managing workload cycle times, (2) considering real-world operational availability, and (3) achieving capability balance. Through analytical estimation and discrete event simulation of an example manufacturing system, this approach’s application shows a substantial increase in throughput capacity compared to a traditional workload-based design. This throughput-centric design approach, serving as a valuable tool for manufacturing system synthesis aimed at maximizing throughput, can apply to volume production systems to enhance throughput capacity through system design.&lt;/div&gt;

Reducing police response times: Optimization and simulation of everyday police patrol

AbstractPolice forces around the world are adapting to optimize their current practices through intelligence‐led and evidence‐based policing. This trend towards increasingly data‐driven policing also affects daily police routines. Police patrol is a complex routing problem because of the combination of reactive and proactive tasks. Moreover, a trade‐off exists between these two patrol tasks. In this article, a police patrol algorithm that combines both policing strategies into one strategy and is applicable to everyday policing, is developed. To this end, a discrete event simulation model is built that compares a p‐median redeployment strategy with several benchmark strategies, that is, p‐median deployment, hotspot (re)deployment, and random redeployment. This p‐median redeployment strategy considers the continuous alternation of idle and non‐idle vehicles. The mean response time was lowest for the p‐median deployment strategy, but the redeployment strategy results in better coverage of the area and low mean response times.

Queuing System Simulation to Optimize Waiting Time using the Quality Function Deployment Approach at KFC Pontianak

The aim of this research is to find the best scenario to optimize the queuing system so that it can reduce waiting time during customer service at KFC Pontianak. Queue systems are prevalent in public services. The research employs Quality Function Deployment (QFD) and Discrete Event Simulation (DES) methodologies. Alternatives generated are simulated using ARENA Simulation. 70 respondents participated in the KFC Pontianak customer questionnaire, comprising 48.6% females and 51.4% males aged between 18-45 years. The results of the questionnaire showed that 51.4% of respondents were dissatisfied with the speed of service at KFC Pontianak. Customer needs identified through QFD include service speed, order accuracy, ease of use, customer satisfaction, queue comfort, food quality, and payment integration. Analysis reveals three queue system scenarios, two of which propose improvements. The second scenario involves 2 cashiers and 2 self-service cashiers. Self-service allows customers to order by scanning a QR code integrated with the service provider's application. Arrival type employs an exponential distribution with an average of 61 seconds, unlimited max arrival, and first creation with a value of 0. In self-service, there is a value difference of 1 and a standard deviation of 0.2. Simulation results indicate that 51 customers choose cashier 1, 41 choose cashier 2, 39 opt for self-service cashier 1, and 53 prefer self-service cashier 2. The total successfully served customers are 94, with a success rate of 51%. In the third scenario involved 4 self-service cashiers only, the values of 1 and standard deviation of 0.2. The total number of successfully served customers is 173, with a success rate of 80%. In conclusion, this research offers valuable insights for optimizing KFC Pontianak's queue system, addressing customer dissatisfaction with service speed. The proposed scenarios, especially those involving self-service options, demonstrate potential improvements in customer satisfaction and operational efficiency.