Simulation-based Optimization Model Research Articles

Self-calibrated transit service monitoring using automated collected data

This paper proposes a self-calibrated transit service monitoring framework that aims to obtain the performance of a transit system using automated collected data. We first introduce an event-based transit simulation model, which allows the detailed simulation of passenger travel behavior in a transit system, including boarding, alighting, and transfer walking. To estimate passenger path choices, we assume the path choices can be modeled using a C-logit model, and propose a simulation-based optimization model to estimate the path choice parameters based on automated fare collection and automated vehicle location data. The path choices can be estimated on a daily basis, which enables the simulation model to adapt to dynamic passenger behavior changes, and output more accurate network performance indicators for regular service monitoring such as train load, passenger travel time, and crowding at platforms. The proposed system eliminates the need for conventional monitoring equipment such as cameras at platforms and scaling/weighing systems on trains. The Hong Kong Mass Transit Railway (MTR) system is used as the case study. Results show that the model can well estimate the path choice behavior of passengers in the system. The output passenger exit flows are closer to the actual one compared to the two benchmark models (shortest path and uniform path choice).

A simulation-based optimization model for balancing economic profitability and working capital efficiency using system dynamics and genetic algorithms

Economic uncertainty has been increasing, as evidenced by recent fluctuations in global markets and unpredictable economic indicators such as volatile demand, stock market fluctuations, and unpredictable interest rates. Economic profitability and working capital efficiency are pivotal indicators of a business’s financial health, both of which are adversely impacted by economic uncertainty. However, these metrics may diverge as distinct objectives drive them. There exists a gap in the literature regarding effective strategies for managing the trade-off between these metrics under economic uncertainty. This study addresses this gap by introducing a simulation-based optimization model that integrates system dynamics simulation and genetic algorithms. The proposed model aims to balance economic profitability and working capital efficiency within inventory management under partial trade credit. A recent real case study demonstrates the model’s applicability and reveals its superiority over conventional system dynamics simulation modeling. With its capacity to inform strategic and tactical decision-making, this model emerges as a valuable tool for supply chain and financial managers seeking to ensure financial stability amidst economic volatility.

A simulation-based optimization model for quality control in solid waste collection process

A simulation-based optimization model for quality control in solid waste collection process

Pre-auction optimization for the selection of shared customers in the last-mile delivery

AbstractCompanies are constantly looking for new strategies to improve their logistics performance and ensure their competitiveness in the global market. This article provides a new scheme for managing the selection of shared customers for a logistics company. The new mechanism proposes the use of the auction as a tool to manage the selection of shared clients through the coalition pool. Thus, all unprofitable shared customers will be pushed to the pool for outsourcing by the other collaborating carriers. Then, some profitable auctioned ones will be selected. The selection system is designed based on solving a vehicle routing problem that aims to maximize the carrier's profit in a decentralized context. At first, a mixed integer linear programing model is derived to solve the deterministic version of the problem. Then in order to efficiently address the stochastic version of the problem, a simulation-based optimization model is developed. This model is employed to solve a real case study of a parcel delivery company, considering the travel times as a bimodal distribution. A comparative study is conducted to demonstrate the effectiveness of the auction approach in managing shared customers. The results of our study reveal that the proposed auction approach efficiently manages the shared customers which leads to the substantial increase of 22.65% in profits for the delivery company. These findings have significant implications for logistics companies seeking to improve their performance and competitiveness in the global market.

Optimisation-in-the-loop simulation of multi products single vendor-multi buyers supply chain systems with reactive lateral transhipment

Considering that batik is one of the most popular products in Indonesia, it is important to analyse the supply chain system for batik products. In reality, the supply chain system for batik products enables orders between buyers to receive products more rapidly, allowing them to anticipate stock outs and obtain lower ordering costs than when ordering from vendors. It is referred to as reactive lateral transshipment. This paper discusses the development of a simulation-based stochastic optimisation model for a batik product supply chain system with multiproducts and single vendor-multi buyers. The utilised solution searching algorithm is a modified Genetic Algorithms (GA) executed in-loop with the developed simulation-based stochastic model. The results demonstrate that the proposed modified GA is able to provide a global optimum solution, allowing the proposed simulation-based stochastic model to reduce the joint total cost (JTC) of the investigated supply chain system by up to 19% when compared to the local optimisation model in each supply chain party.

An Overview of Non-Point Source Pollution Modeling: Current Status and Future Prospect

Non-point source (NPS) pollution control is an increasingly important topic in aquatic environment protection due to its significant adverse effect on water quality. Characterizing NPS pollution loads is crucial for preparing BMPs to improve water quality and protect the aquatic ecosystem. Modeling tools are commonly used for simulating NPS pollution in watersheds; however, the modeling process contains uncertainties and complications, which makes the prediction of NPS pollution loads challenging and complicated. To deal with this issue, significant progress has been made to address NPS pollution modeling problems in the past few decades. The current study reviews different approaches being used for NPS pollution modeling. In this context, the main methods in NPS modeling are described and classified into three categories. 1. Empirical models, 2. Physically based models, and 3. Simulation based optimization models. The present study contributes to fulfilling the gaps in the classification of NPS pollution modeling approaches and highlights the advantages and drawbacks of each approach in order to set a standard for choosing a proper simulation tool for estimating NPS pollution loads based on the limitations and requirements of the understudy circumstance

Simulation-Based Headway Optimization for the Bangkok Airport Railway System under Uncertainty

The ever-increasing demand for intercity travel, as well as competition among all modes of transportation, is an unavoidable reality that today’s urban rail transit system must deal with. To meet this problem, urban railway companies must try to make better use of their existing plans and resources. Analytical approaches or simulation modeling can be used to develop or change a rail schedule to reflect the appropriate passenger demand. However, in the case of complex railway networks with several interlocking zones, analytical methods frequently have drawbacks. The goal of this article is to create a new simulation-based optimization model for the Bangkok railway system that takes into account the real assumptions and requirements in the railway system, such as uncertainty. The common particle swarm optimization (PSO) technique is combined with the developed simulation model to optimize the headways for each period in each day. Two different objective functions are incorporated into the models to consider both customer satisfaction by reducing the average waiting time and railway management satisfaction by reducing needed energy usage (e.g., reducing operating trains). The results obtained using a real dataset from the Bangkok railway system demonstrate that the simulation-based optimization approach for robust train service timetable scheduling, which incorporates both passenger waiting times and the number of operating trains as equally important objectives, successfully achieved an average waiting time of 11.02 min (with a standard deviation of 1.65 min) across all time intervals.

A simulation-based multi-objective two-level optimization decision-making approach for supporting balanced irrigation water management

In this study, a simulation-based multi-objective two-level optimization decision-making approach is developed for optimal irrigation water allocation, improving irrigation water productivity and controlling regional accumulated salts. Techniques of multi-objective programming, two-level programming and simulation model of water and salt physical movement process are incorporated into the modeling framework. The simulated processes are parameterized and calibrated with field experimental data while the optimization model is used to generate optimal solutions though predefined objectives and the associated constraints. This model is applied to a case study on irrigation water allocation in the Jiefangzha Irrigation Subarea in Hetao Irrigation District, Northwest China. Firstly, the study area is delineated into several homogeneous irrigation decision-making units (IDMUs) for better characterizing their spatial variability because it’s spatially heterogeneous. Afterwards, decomposition-coordination algorithm is introduced to solve such an integrated simulation-based multi-objective two-level optimization model. Finally, optimal solutions of irrigation water allocation for different crops during crop growth periods in different IDMUs can be obtained for supporting sustainable strategies of irrigation. The results can achieve balanced tradeoffs between different stakeholders (i.e., the upper-level decision-makers and the lower-level farmers) and between conflicting economic objectives and environmental objectives. Moreover, optimal solutions have a slight increase in economic returns over the baseline scenario (i.e., status quo), but the irrigation water productivity is increased by nearly 60% due to less irrigation water used. Regional accumulated salts can be controlled because the soil salinity is constrained within the predetermined salt accumulation constraint. Therefore, these findings can provide evidence for efficient use of irrigation water resources and further support of sustainable irrigated agriculture.

Traffic Signal Timing Optimization Model Based on Video Surveillance Data and Snake Optimization Algorithm.

With the continued rapid growth of urban areas, problems such as traffic congestion and environmental pollution have become increasingly common. Alleviating these problems involves addressing signal timing optimization and control, which are critical components of urban traffic management. In this paper, a VISSIM simulation-based traffic signal timing optimization model is proposed with the aim of addressing these urban traffic congestion issues. The proposed model uses the YOLO-X model to obtain road information from video surveillance data and predicts future traffic flow using the long short-term memory (LSTM) model. The model was optimized using the snake optimization (SO) algorithm. The effectiveness of the model was verified by applying this method through an empirical example, which shows that the model can provide an improved signal timing scheme compared to the fixed timing scheme, with a decrease of 23.34% in the current period. This study provides a feasible approach for the research of signal timing optimization processes.

Urban traffic signal control robust optimization against Risk-averse and Worst-case cyberattacks

Ensuring the cybersecurity of urban traffic signal control systems has become increasingly important in the digital age. This study proposes a passive countermeasure by designing robust signal control plans to mitigate the risks of worst-case and risk-averse cyberattacks. To achieve this objective, the study develops two bi-level simulation-based optimization (SO) models and solves them using an improved biobjective robust simulation-based optimization (IBORSO) algorithm. The attack-defense process is formulated as a Stackelberg game, where the lower level attacker aims to find optimal attack plans to degrade traffic efficiency and safety indices, while the upper level defender adjusts the signal control plan to compensate for the effects of the attack on traffic efficiency and safety indices. The proposed approach is evaluated on an urban road network in Changsha, China. The results show that the developed robust signal control plans can withstand risk-averse and worst-case attacks and perform better than the counterpart ones in terms of biobjective performance. The proposed bi-level modeling framework and solution algorithm can be used to build robust traffic signal control systems that are resilient to cyberattacks.

Development of an optimization model for decision-making in building retrofit projects using RETROSIM

ABSTRACT Nearly-zero energy buildings are standard for new constructions in the European Union. The challenge for decarbonized cities is retrofitting the existing buildings. Retrofitting the existing buildings provides considerable opportunities for improving occupants’ comfort and reducing global energy consumption and greenhouse gas emissions. Building retrofit is being considered one of the main approaches to achieve sustainability in the built environment at relatively low-cost and high-uptake rates. Although a broad range of retrofit technologies are readily available, methods to ascertain the most suitable set of retrofit actions for specific projects are still a practical challenge. This article first aims to develop and present a simulation-based multi-objective optimization model, named RETROSIM, to quantitatively assess technology choices in a building retrofit project. RETROSIM is a combination of a building energy simulation model and RETROSIM proprietary optimization engine. Secondly, in this study, RETROSIM is used to evaluate a building retrofit project as a case study to show the functionality of the proposed method. The study initiates with the single optimization of objective functions focusing on the building’s characteristics and performance: primary energy consumption, global costs and thermal discomfort hours. Then this methodology is used to study the interaction between these conflicting objectives and evaluate their trade-offs.

Energy-water nexus in low-carbon electric power systems: A simulation-based inexact optimization model

Energy and water resources are closely linked in electric power systems, and the application of low-carbon technologies further affects electricity generation and water consumption in those systems. The holistic optimization of electric power systems, including generation and decarbonization processes, is necessary. Few studies have considered the uncertainty associated with the application of low-carbon technologies in electric power systems optimization from an energy-water nexus perspective. To fill such a gap, this study developed a simulation-based low-carbon energy structure optimization model to address the uncertainty in power systems with low-carbon technologies and generate electricity generation plans. Specifically, LMDI, STIRPAT and grey model were integrated to simulate the carbon emissions from the electric power systems under different socio-economic development levels. Furthermore, a copula-based chance-constrained interval mixed-integer programming model was proposed to quantify the energy-water nexus as the joint violation risk and generate risk-based low-carbon generation schemes. The model was applied to support the management of electric power systems in the Pearl River Delta of China. Results indicate that, the optimized plans could mitigate CO2 emission by up to 37.93% over 15 years. Under all scenarios, more low-carbon power conversion facilities would be established. The application of carbon capture and storage would increase energy and water consumption by up to [0.24, 7.35] × 106 tce and [0.16, 1.12] × 108 m3, respectively. The optimization of the energy structure based on energy-water joint violation risk could reduce the water utilization rate and the carbon emission rate by up to 0.38 m3/104 kWh and 0.04 ton-CO2/104 kWh, respectively.

A Simulation-Based Optimization Model for Control of Soil Salinization in the Hetao Irrigation District, Northwest China

The average annual water diversion of the Hetao Irrigation District (HID) from the Yellow River is 4.5 billion cubic meters, mainly used for surface irrigation. Because the groundwater depth is shallow, strong evaporation conditions and unmatched irrigation conditions lead to serious soil salinization in the area; thus, the irrigation area’s ecological environment is fragile. Based on the current situation of the Yellow River irrigation project in the area, an interval two-stage robust stochastic optimization model is proposed to address the problem. In 2015, the Shuguang Experimental Station in the middle of the HID, Inner Mongolia, discussed the impact of different degrees of water–salt coordinated regulation on water consumption, yield and price of wheat, maize and sunflower under drip irrigation conditions. The obtained results provide the water shortage and water distribution targets of multiple water sources and multiple water levels in five irrigation areas of the HID. Those water distribution targets were used as the main input parameter and entered into the SALTMOD model based on the principle of water and salt balance. The output included data on groundwater mineralization and groundwater depth. It was observed that (1) integrated interval two-stage robust stochastic programming and the SALTMOD Model to couple optimization model under uncertainty can simulate a model together; (2) systemic risk issues were considered; and (3) the proposed method can be applied to the HID in northwest China to solve the soil salinization control problem. This approach is applicable to arid and semiarid regions that face similar problems.

A simulation-based optimization approach for designing transit networks

Public transport network design deals with finding efficient network solution(s) from a set of alternatives that best satisfies the often-conflicting objectives of stakeholders like passengers and operators. This work presents a simulation-based optimization (SBO) model for designing public transport networks. The work’s novelty is in developing such a network design model that fully accounts for the stochastic behavior of commuters on the transit network. The SBO discipline solves decision-based problems like the transit network design problem (TNDP) by combining simulation and optimization models. The proposed model integrates a disaggregated activity-based travel demand simulation with a multi-objective network optimization algorithm. Trip-based travel demand models are commonly used to represent traveler behavior in the literature. The approach limits its ability to accommodate the stochastic realities of traveler behavior in a transit network design solution. Using activity-based simulation instead makes it possible to account for a more realistic traveler behavior, especially real-time decisions made in response to changing network dynamics which ultimately affect the distribution of demand over time on the network. The proposed model is applied to the improved design of the integrated public transport network in the City of Cape Town, South Africa. The results show SBO can design efficient network solutions that reflect the objectives of network stakeholders.

Joint Optimization of Facility Layout and Spatially Differential Parking Pricing for Parking Lots

The parking cruising problem can be attributed to the unbalanced spatial parking space occupancy in the parking lot with multiple trip purposes and high traffic volumes, and it has already become an obstacle to improving travel efficiency. This study aims to investigate the reasons for the unbalanced occupancy of parking spaces and develop strategies to achieve spatial equilibrium by reducing parking cruising time. A field survey is first conducted to analyze the factors influencing parking space choice behavior, such as searching distances, walking distances, parking price, and parkers’ attributes. Based on the survey, a mixed logit model is calibrated to reveal parking space choice patterns of different parkers. Afterwards, the parking lot is divided into three zones based on searching distances and walking distances, using the K-means algorithm. According to the results, a simulation-based optimization model is then established, for minimizing the variance of average occupancy. An optimal pricing strategy is obtained at last. A case study in Nanjing, China, is conducted to verify the feasibility and rationality of the proposed method. The difference between weekdays and weekends is explored. The results show that, with the optimal strategies, the standard deviation of average occupancy in each zone decreases by 39.52% during weekends and 42.29% during weekdays. Finally, political implications and recommendations on the layout design of facilities and the adoption of differential parking pricing strategies are provided.

A simulation-optimization approach for integrating physical and financial flows in a supply chain under economic uncertainty

In the last decade, increasing costs and organizational concerns regarding the funding and allocation of financial resources have led to significant attention being given to financial flow and its effects on planning decisions throughout supply chain networks. This study aims to develop a simulation-optimization model to integrate the financial and physical flows in a supply chain planning problem under economic uncertainty. The simulation-optimization model includes a mixed-integer linear programming model and a simulation-based optimization model that are connected through an iterative process. The economic value added (EVA) index is used to measure the financial performance of the supply chain. This study extends the literature on two research domains namely supply chain planning and finance and simulation-optimization modelling for supply chain management. The proposed model applies a scenario approach to cope with economic uncertainty in the supply chain. To demonstrate the efficiency of the proposed model, the performance of the proposed model in solving a test problem from the recent literature is compared with the performance of a conventional simulation-based optimization and mixed-integer linear programming approaches. The results of the study show a minimum of 6% improvement in the EVA obtained from the proposed simulation-optimization model compared to the EVA obtained from the simulation-based optimization model in all the studied scenarios. Moreover, the standard deviation of the EVA obtained from the proposed simulation-optimization model is at least 69% lower than the EVA obtained from the mixed integer programming model in all the studied scenarios. This shows that the proposed simulation-optimisation approach is more robust to economic uncertainty than the mixed-integer linear programming approach.

Data-driven simulation-based planning for electric airport shuttle systems: A real-world case study

Many airports are adopting battery electric buses in their shuttle fleets due to concerns over air quality and regulations. This study proposes a simulation-based optimization modeling framework to help airport shuttle operators effectively deploy electric buses. We evaluated a planned airport electric shuttle system with an event-driven simulator. Empirical data collected from existing systems were used to drive the simulations. We then proposed a simulation-based optimization model to determine the battery capacity, charging power, and number of chargers so that predefined objective(s) (e.g., minimizing total capital cost, minimizing emissions) are optimized. Compared to existing studies, the primary contribution of the proposed method is that it can model the real-world stochastic nature of operations in an electric bus system with much higher fidelity. To demonstrate the proposed modeling framework, we study a real-world shuttle system at the Dallas-Fort Worth International Airport, and present extensive numerical studies. When considering partial fleet electrification, the model can provide a set of Pareto optimal solutions. When considering full fleet electrification, the optimal solution requires a 50-kWh battery capacity and four 210-kW chargers, resulting in a total capital cost of $26,744,000. The results demonstrate that the proposed modeling framework can effectively optimize the planning of electric airport shuttle systems with partial or full fleet electrification.

A discrete simulation-based optimization approach for multi-period redeployment in emergency medical services

Emergency Medical Service (EMS) managers continuously strive to improve the coverage performance, i.e., the percentage of calls responded to within a specific target time, to save lives in case of life-threatening emergencies. This goal can be achieved by dynamically adjusting the location of rescue teams during a day in response to some temporal or geographical fluctuations such as demand patterns, traffic conditions, or the number of teams on duty. This relocation is known as the multi-period redeployment problem. In this study, we propose a discrete simulation-based optimization model to adress the multi-period redeployment problem in the French EMS of the Val-de-Marne department (France), named SAMU 94. The proposed model uses an iterative method that combines the use of a mathematical model to find the optimal locations of rescue teams with the use of the SAMU 94 simulation model implemented in Arena software, to evaluate the busy fraction parameters required to solve the mathematical model. The model performance was compared with that of the simulation-based optimization software, OptQuest. The experimental results demonstrated that the iterative method could produce solutions with better coverage performance, 20 times faster than OptQuest.

Clustering and Stochastic Simulation Optimization for Outpatient Chemotherapy Appointment Planning and Scheduling.

Outpatient Chemotherapy Appointment (OCA) planning and scheduling is a process of distributing appointments to available days and times to be handled by various resources through a multi-stage process. Proper OCAs planning and scheduling results in minimizing the length of stay of patients and staff overtime. The integrated consideration of the available capacity, resources planning, scheduling policy, drug preparation requirements, and resources-to-patients assignment can improve the Outpatient Chemotherapy Process's (OCP's) overall performance due to interdependencies. However, developing a comprehensive and stochastic decision support system in the OCP environment is complex. Thus, the multi-stages of OCP, stochastic durations, probability of uncertain events occurrence, patterns of patient arrivals, acuity levels of nurses, demand variety, and complex patient pathways are rarely addressed together. Therefore, this paper proposes a clustering and stochastic optimization methodology to handle the various challenges of OCA planning and scheduling. A Stochastic Discrete Simulation-Based Multi-Objective Optimization (SDSMO) model is developed and linked to clustering algorithms using an iterative sequential approach. The experimental results indicate the positive effect of clustering similar appointments on the performance measures and the computational time. The developed cluster-based stochastic optimization approaches showed superior performance compared with baseline and sequencing heuristics using data from a real Outpatient Chemotherapy Center (OCC).

Time-of-day pricing for toll roads under traffic demand uncertainties: A distributionally robust simulation-based optimization method

Road pricing has received increasing attention for traffic congestion mitigation, but traffic demand uncertainties cause a profound impact on its effectiveness. This study proposes a link-based time-of-day pricing distributionally robust simulation-based optimization (DRSO) model to minimize the worst-case expected total travel time (TTT). This model aims to answer where and how much to charge under origin–destination (OD) traffic demand uncertainties that are described by an ambiguity set of probability distributions, and is solved by a DRSO method with two stages. The first stage searches an infill sample after building two stochastic kriging models (i.e., SKG-I and SKG-II) to map the relation between the traffic demand uncertainty parameter and the average TTT for each decision sample, and that between the decision variable and the worst-case average TTT, respectively. The second stage introduces the optimal computational budget allocation (OCBA) method to distribute more simulation replications to more promising combination samples, which helps to improve the prediction accuracy of SKGs in promising regions. The DRSO method is first tested with an M/M/1 queuing problem, and numerical results show its outperformance compared with one counterpart algorithm. After that, it is applied to address the link-based time-of-day pricing DRSO problem on Anaheim network and two solution algorithms are also added for comparison. Numerical results show that the robust toll plan solved by the DRSO method outperforms no toll plan and two other toll plans solved by the comparison algorithms in handling traffic dynamics and different levels of traffic demand uncertainties. In conclusion, the DRSO method is promising to address the costly simulation-based optimization (SO) problems under uncertainties characterized by an ambiguity set of probability distributions.