Simulation-optimization Approach Research Articles

Model-based simulation-optimization of irrigation scheduling – A field evaluation with processing tomatoes

Due to climate change, increased regulation of water resources and competition from other beneficial uses, the agricultural sector is under pressure to use water more efficiently. This paper reports the field evaluation of two model-based simulation-optimization approaches for irrigation scheduling: deterministic optimization and stochastic optimization. The field experiments were conducted at the University of California Davis research farm with a processing tomato crop. The crop growth simulation model used in the study was DSSAT-CROPGRO processing tomato. In order to mitigate the impact of weather forecasts inaccuracies, irrigation schedules were updated every 5 to 10 days, depending on operational constraints. These updates were performed via custom graphical user interfaces that enabled the user to visualize the expected outcomes of various decisions or scenarios before choosing which irrigation schedule to implement. An irrigation treatment based on manual monitoring of soil water content with a field-calibrated neutron probe served as benchmark. The model-based treatments achieved yields and water use efficiencies that were not significantly different from those obtained in the neutron probe-based treatment. These results demonstrate the high potential of model-based simulation-optimization approaches for in-season adaptive irrigation scheduling, especially since neutron probes, which are considered one of the most accurate indirect methods of measuring soil water content, are not commonly used by growers.

Hierarchical hybrid simulation optimization of the pharmaceutical supply chain

In this paper, a global simulation optimization approach is developed to imitate and optimize the performance of the Pharmaceutical Supply Chain (PSC). Firstly, a hierarchical hybrid simulation model is developed in which aggregate and detailed data levels are addressed simultaneously. The model consists of two types of interdependent paradigms: the system dynamics paradigm, which depicts the echelons of pharmacies and wholesalers in the PSC, and the discrete event paradigm, which simulates the manufacturers with their detailed production operations, as well as the echelons of suppliers. Secondly, the "As is" scenario analysis and a screening process are performed to extract significant input parameters as well as sensitive outputs of the model. The final step optimizes the performance of PSC. The proposed approach validity is appraised by being applied to the PSC of a leading pharmaceutical company in Jordan. As a result, the opportunity loss cost has considerably decreased for both the manufacturer and wholesalers’ echelons and the service level has improved throughout the PSC.

Scheduling optimization and risk analysis for energy-intensive industries under uncertain electricity market to facilitate financial planning

The planning of energy-intensive processes is intrinsically uncertain due to their dependence on the volatile energy market, with scheduling having a vast impact on the final production cost of these plants. Traditional stochastic methods are mathematically very complex, which translates into a significant computational effort that might prevent a timely response to varying electricity prices. To encounter this uncertainty, we develop a reliable hybrid simulation-optimization approach for optimizing the production plant scheduling, combining scenario analysis with risk analysis. The proposed methodology is demonstrated with real data from a cryogenic air separation plant in Tarragona (Spain). This approach also informs decision-makers about risk or expected shortfall associated with the implied scenario. The generic methodology used here can be easily adapted to schedule facilities in other energy-intensive sectors such as cement, metallurgy or pulp and paper.

Joint pricing and inventory management in a competitive market using reinforcement learning: a combination of the agent-based and simulation-optimization approaches

ABSTRACT One of the most important tools is an appropriate pricing mechanism to attract more customers and increase profits. The retailers’ main question is how to set the prices and inventory policies to maximize profit in a competitive heterogeneous market in presence of non-zero lead time and lost sales. A reinforcement learning algorithm is proposed to create appropriate decision-making mechanisms for pricing. A coordinated inventory policy in a competitive environment reduces logistic costs and leads to a higher profit. We use a reinforcement learning algorithm to investigate the performance of a retailer in a competitive environment. An agent-based modeling experimental environment combined with a simulation-optimization method in which a virtual market has been reproduced is used. The market is not homogeneous with respect to customer behavior. It is assumed that the retailer uses (R, Q) policy where the lead time is a fixed amount (L), and the shortage is permissible. The quality, distance, service level, and price are factors that influence customers’ choices. The simulation results for some randomly generated examples show that the algorithm in the competitive environment can make more profit than other available methods and the combined utilization of simulation-optimization methods has been able to find better solutions for the hybrid model of pricing and inventory management considering customer behavior. The results of simulation for three different categories of customers (more sensitive to price, equally sensitive to price, quality and service level, and more sensitive to quality (indicate that the average profit for the proposed algorithm is higher than that of other examined algorithms.

Selecting the best location of water quality sensors in water distribution networks by considering the importance of nodes and contaminations using NSGA-III (case study: Zahedan water distribution network, Iran).

One of the most effective ways to minimize polluted water consumption is to arrange quality sensors properly in the water distribution networks (WDNs). In this study, the NSGA-III algorithm is developed to improve the optimal locations of sensors by balancing four conflicting objectives: (1) detection likelihood, (2) expected detection time, (3) detection redundancy, and (4) the affected nodes before detection. The research procedure proposed the dynamic variations of chlorine between defined upper and lower bounds, which were determined utilizing the Monte Carlo simulation model. For selecting a contamination matrix with the same characteristics and effects of all possible events, a heuristic method was applied. The coefficients of importance are introduced in this study for the assessment of contamination events and network nodes. The Pareto fronts for each of the two sets of conflicting objectives were computed for benchmark and real water distribution networks using the proposed simulation-optimization approach. Results indicated that sensors should be installed downstream of the network to maximize sensor detection likelihood; however, this increases detection time. For the benchmark network, maximum and minimum detection likelihoods were calculated as 92.8% and 61.1%, respectively, which corresponded to the worst detection time of 11.58min and the best detection time of 5.06min. So, the position of sensors regarding the two objective functions conflicts with each other. Also, the sensitivity analysis related to the number of sensors illustrated that the Pareto fronts became a more efficient tool when the number of sensors increased. The best pollution detection likelihood in the real water network increased by 18.93% and 24.66% by incrementing the number of sensors from 5 to 10 and 5 to 15, respectively. Moreover, adding more than 10 sensors to the benchmark network and more than 15 to the real system will provide little additional detection likelihood.

Simulation optimization for stochastic casualty collection point location and resource allocation problem in a mass casualty incident

After a severe disaster strikes, a large number of casualties in need of urgent treatment, known as a mass casualty incident (MCI) event, arise from multiple disaster areas in a very short period of time and overwhelm available resources of the local healthcare system. This paper focuses on the effective design of casualty collection point (CCP) locations and the efficient allocation of limited emergency medical service (EMS) resources to transport casualties quickly to appropriate hospitals and increase the survival rate of casualties. A hybrid simulation–optimization approach to optimize the CCP location and EMS resource allocation problem over mixed binary and integer feasible domains for the minimization of expected complete delivery time of all casualties from disaster region to hospitals is presented in this work due to the stochastic and dynamic nature of the MCI logistics environment. A high-resolution stochastic discrete event simulation model considering time-varying stochastic casualty arrivals, random triage service times, and stochastic travel times caused by road network vulnerability is first constructed to describe a more detailed modeling of comprehensive MCI humanitarian logistics from the disaster regions to the hospitals. Then, a novel two-stage sequential algorithm, namely a combination of optimal computing budget allocation-based rapid-screening algorithm and adaptive particle global and hyperbox local search (ORSA-APGHLS), is developed to speed up convergence to near-optimal solutions compared to three common existing algorithms (Genetic Algorithm, Tabu Search and Stochastic Nelder-Mead Algorithm) under a limited simulation budget. We collaborate with the National Science and Technology Center for Disaster Reduction (NCDR) in Taiwan to conduct computational experiments to demonstrate the efficiency and efficacy of the proposed two-stage ORSA-APGHLS algorithm according to a potential earthquake scenario occurring in Tainan City, Taiwan. Through sensitivity analysis, the influence of different levels of scarce emergency medical resources and degrees of road damage on the expected complete delivery time of all casualties and the location-allocation decisions are investigated.

A Simulation Optimization Approach to Optimize Dredger Fleet Schedule Plan for Dredging Works in Existing Ports

Making an optimal dredger fleet scheduling (DFS) plan is difficult for dredging works in existing ports, where the dredging activities are interrupted by stochastic vessel traffic. Therefore, a simulation optimization (SO) approach is adopted to solve the DFS problem. In the SO approach, an agent-based simulation model is first developed to simulate the stochastic traffic flow and possible spatiotemporal traffic conflicts between vessels and dredgers. Then, this simulation model is integrated with an optimization model to find the optimal DFS plan with the objective of minimum total dredging equipment usage fees. Finally, the proposed SO approach is applied to a 4-dredging-job dredging work. The results demonstrate its applicability in identifying the bottlenecks of the DFS plan and finding the optimal DFS plan. This research contributes to gaining more insights into using the SO approach to provide decision making for dredging work planning under uncertainty.

Evaluation and Optimization of Heat Extraction Strategies Based on Deep Neural Network in the Enhanced Geothermal System

Production strategies and parameters control the efficiency of geothermal energy extraction related to the thermal stability and economic benefits of a geothermal system. The optimization strategies of geothermal energy extraction play a critical role in engineering and are generally determined through a numerical simulation approach. Considering the correlation among production parameters, numerical simulation requires numerous runs and manual adjustments, resulting in lower calculation efficiency and limited or local optimizations. This study proposes a high-efficiency network based on a three-dimensional heterogeneity model in the Gonghe Basin in China to achieve a high-efficiency and high-precision production strategy. The neural network was successfully established as a surrogate of the numerical model for the repetitive forward simulation. Meanwhile, the neural network is integrated with the Harris Hawks algorithm to optimize extraction strategies for sustainable heat extraction. This paper focuses on the effects of human-controlled operational parameters on geothermal systems. Results indicated that the maximum electrical power can be guaranteed 5.2 MW during a 50-year production period at an injection temperature of 60°C, an injection rate of 39 kg/s, and a well spacing of 380 m. The study provides important operational guidance for sustainable utilization in the Gonghe Basin. This simulation-optimization approach can be applied to other geothermal sites for sustainable energy production.

Simulation optimization for parcel hub scheduling problem in closed-loop sortation system with shortcuts

With the growth of e-commerce, the quantity of delivery orders and parcels is steadily increasing. Reliable and fast parcel delivery services are of utmost importance to customers. To achieve this target, the central parcel consolidation terminals (CPCT) known as parcel sorting hubs are dedicated to improving the operational efficiency especially in the optimal scheduling of inbound trucks. This study aims to determine the optimal schedule and assignment of inbound trucks to the inbound docks, known as the parcel hub scheduling problem with shortcuts (PHSPwS), for minimizing the expected makespan and expected waiting time of inbound trucks in the closed-loop automated sorting conveyor (ASC) system with shortcuts of the parcel sorting hubs. We first construct a mathematical formulation to present the PHSPwS problem based on the traffic control model. However, due to the congestion behavior on the conveyors and stochastic and dynamic nature of the ASC system, the expected objective functions are not analytically available from the mathematical model. Then, a simulation optimization (SO) approach combined with an adaptive genetic-based discrete particle swarm optimization (AGDPSO) algorithm is proposed to tackle the PHSPwS problem in a stochastic and dynamic ASC environment. The closed-loop ASC simulation model is first established to describe the detail operation of the closed-loop ASC sortation system with shortcut. Based on performance evaluation of this simulation model, a novelty algorithm AGDPSO, which hybridized genetic algorithm and particle swarm optimization algorithm with self-tuning parameters is developed to search and find the optimal inbound truck schedule in the large-scale solution space. In particular, the time-varying acceleration coefficients embedded in the AGDPSO are adopted to amplify the effectiveness of the global and local best solutions to obtain a better balance between exploration and exploitation of the searching space. The experimental results reveal that AGDPSO outperforms other algorithms and provides a better and more stable solution. Moreover, the impact of the strategic and operational factors of the closed-loop ASC system for the expected makespan and truck queueing time are also investigated, while the automatic truck unloading mode is confirmed to significantly improve the system efficiency.

A Novel Wastewater Load Allocation Approach for River Basins Using Simulation-Optimization Models

In this study, a new wastewater load allocation approach using a linked simulation-optimization model is proposed to determine the receiving body-based discharge limits by considering the discharge standards used by the European Union Water Framework Directive. By using the proposed approach, wastewater loads of point sources can be determined in such a way that the parameters exceeding the water quality targets (WQT) in receiving water bodiesmeet the relevant WQT. The simulation part is used to determine pollutantconcentrations throughout the river system using the AQUATOX water quality simulation model. However, since AQUATOX is an independent simulation modeland its source code is not publicly available, it is not possible to execute it with the optimization model for the generated load combinations. Therefore, a concentration-response matrix (CRM) is developed as a surrogate water simulation model by using the outputsoftheAQUATOX model. After this process, the developed CRMis integrated into an optimization model where the heuristic differential evolution (DE) optimization approach is used. The performance of the proposed simulation-optimization approach is evaluated on a sub-watershed of the Kucuk Menderes River Basin by considering different wasteload allocation scenarios for the CBOD5 water quality parameter. The results showed that the proposed simulation-optimization approach can effectively allocate the wastewater loads among different point sources by considering the WQT values of the CBOD5 parameter.

Optimization and multi-uncertainty analysis of best management practices at the watershed scale: A reliability-level based bayesian network approach

Best management practices (BMPs) have been widely adopted to mitigate diffuse source pollutants, and the simulated processes of its pollutant reduction effectiveness suffer from manifold uncertainties, such as watershed model parameters and climate change. We presented a novel Bayesian modeling framework for BMPs planning, integrating process-based watershed modeling and Bayesian optimization algorithm to reveal the impact of multiple uncertainties. The proposed framework was applied to a BMPs planning case study in the Erhai watershed, the seventh-largest freshwater lake in China. Firstly, priority management areas (PMAs) were identified for BMPs siting using a simulation-optimization approach. Bayesian networks were subsequently embedded to reveal the multiple uncertainty sources in the optimal planning and the reliability level (RL) is introduced to represent the probability to meet the water quality target with BMPs implementation. The results suggest that ENS of discharge and nutrients concentration simulation by LSPC are both greater than 0.5, which displays satisfactory performance. The identified PMAs account for 0.8% of the total watershed areas while contribute to more than 15% of nutrient loadings reduction. The analysis of multiple uncertainty sources reveals that precipitation is the most influential source of uncertainties in BMP effectiveness. The construction of hedgerows plays an important role in the nutrient reduction. With the improvement of the reliability levels, the cost increases sharply, indicating that the implementation of BMPs has a marginal utility. The study addressed the urgent need for effective and efficient BMPs planning by identifying PMAs and addressing multi-source uncertainties.

Optimal design of multilayer radar absorbing materials: a simulation-optimization approach

Multilayer radar absorbing materials with light weight, strong absorption, and wide absorption bandwidth are urgently demanded with the increase of electromagnetic pollution. However, the current design methods with only simulation operation or optimization strategy are not comprehensive. Here, a simulation-optimization approach including electromagnetic simulation and numerical calculation is proposed based on the interaction between different software, in which the homogeneous medium substitution method is presented to simplify the complicated structures. Besides, return loss and impedance matching of different layer structures are investigated. From the simulated results, it can be found that the structures with better impedance matching have superior absorbing performance. The optimal method shows the advantages of fast and efficient, which has tremendous potential for various applications, such as military stealth and electromagnetic wave elimination.

A reduced order model for triethylene glycol natural gas dehydration system

In natural gas processing plants, glycol dehydration is commonly used to remove water from the gas streams, to avoid pipeline blockage and equipment breakdown due to hydrates formation. This paper proposed a reduced order model developed based on integrated simulation-optimization approach for the glycol dehydration system, with the aim to minimize its operating cost while satisfying pipeline quality specifications. Steady-state process simulation software was used to identify important operating parameters for the glycol dehydration process; these include reboiler temperature and flow ratio of the regeneration column, and solvent flowrate. The identified parameters are built into a non-linear programming model, which was developed as a reduced order model for ease of implementation in the plant. The studied parameters are reboiler duties, hot oil, condenser, and pump, as well as TEG make-up flow rate and CO2 equivalent (CO2-eq) emissions. The Pareto Front is developed to identify the minimum operating cost at different levels of water dew point specification. The work has resulted in the annual savings of more than 34.6%.

An Integrated Simulation-Optimization Approach for Dynamic Design of the Urban Wastewater Collection Systems

In this study, a simulation-optimization approach is proposed for dynamic design of the urban wastewater collection systems. The proposed approach consists of the mutual integration of the Storm Water Management Model (SWMM), developed by the U.S. Environmental Protection Agency (US-EPA) and the heuristic Harmony Search (HS) optimization approach. Unlike the previous approaches developed to solve wastewater collection system design problems, the proposed approach simulates the hydraulic flow process by considering unsteady state flow conditions based on the dynamic wave model. The objective of the HS-based optimization model is to determine the pipe slopes so that the total system cost is minimized. After determining the pipe slopes, the corresponding pipe diameters were determined by developing an internal solution approach. All the physical and managerial constraints that should be considered during the search process have been considered by means of the penalty function approach. The applicability of the proposed approach was evaluated by solving two example problems. Identified results indicated that the proposed approach can effectively solve the dynamic design problems of the wastewater collection systems.

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.

Determining the right time to advertise adopter numbers for a two-sided digital platform: An agent-based simulation optimization approach

Determining the right time to advertise adopter numbers for a two-sided digital platform: An agent-based simulation optimization approach

A simulation-optimization approach to solve the first and last mile of mass rapid transit via feeder services

This paper focuses on the design of optimal feeder bus routes aimed at serving the commuting demand of suburban areas and increasing the ridership of a mass rapid transit system. The optimization problem presents a multi-objective nature. The transit operator is interested in maximizing the number of users served with the lowest vehicle kilometres travelled (i.e., maximizing profits), whereas passengers seek a high quality of service (i.e., minimizing travel times). An Ant Colony Optimization algorithm is here implemented into an agent-based modelling environment to find the optimal set of routes connecting the service area to multiple transfer stations. The potential demand at a feeder bus stop is estimated according to accessibility indicators, derived from GIS-based demographic data. The model is applied to the case study of Catania (a medium-sized city in Italy) to enhance the accessibility of urban railway stations via public transport. The proposed methodology can be used as a decision-making tool for transport operators and public administrations to understand how to design feeder bus routes to improve the accessibility of public transport.

Design of a mathematical model and a simulation-optimisation approach for master surgical scheduling considering uncertainty in length of stay, demands and duration of surgery

Design of a mathematical model and a simulation-optimisation approach for master surgical scheduling considering uncertainty in length of stay, demands and duration of surgery

A novel fully hybrid simulation-optimization approach for enhancing the calibration and verification performance of the TUW hydrological model

In this study, a novel fully hybrid simulation-optimization approach is proposed for enhancing the prediction performance of the conceptual Technische Universität Wien (TUW) hydrological model. In the simulation part of the proposed approach, the TUW model is hybridized with a support vector regression (SVR) model where SVR directly uses the TUW model outputs as input and simulates the runoff process. This hybridized simulation model is then integrated to a hybrid optimization approach where heuristic harmony search (HS) and Nelder-Mead Simplex (NMS) optimization approaches are mutually integrated. The objective of the hybrid HS-NMS optimization approach is to calibrate the associated parameter values of both TUW and SVR models by maximizing the Nash-Sutcliffe efficiency (NSE) calculated between simulated and observed runoff values. The applicability of the proposed approach is evaluated on a Murat sub-basin of the Gediz River Basin (GRB) in Turkey for different simulation and optimization model combinations. Identified results indicated that the proposed fully hybrid simulation–optimization approach not only efficiently calibrates the associated solution parameters, but also improves the prediction performance of the TUW model.

A simulation approach for optimization of optical absorption in amorphous silicon solar cell

One of the major drawbacks of thin-film silicon solar cells is their limited optical absorptance due to the thin absorber layer and the minimal absorption coefficient. The mechanism of using surface textured interface in layered structure is widely accepted to scatter light and enhance the optical path length, which in turn enhances the optical absorption and improves the solar cell efficiency. A textured amorphous silicon solar cell is designed in order to observe the impact of scattering layers on optical absorption and efficiency. A pyramid, cylindrical and micro lenses array structure has been simulated using simulation tool used to obtain the optimum absorption at a particular wave length. A max Power conversion efficiency of 0.0992 is obtained at Voc of 0.9218 V. In this regard, the impact of scattering layers and different geometrical structure on V-I characteristics solar cell were observed. The maximum value obtained for refractive index is 2.0 for optimized a-Si solar cell.