Proposed Simulation Optimization Approach Research Articles

Optimum identification of aquifer parameter zone structures using the SHuffled Ant Lion Optimization approach considering general form of the Voronoi tessellation

A new simulation–optimization approach is proposed for solving the inverse parameter structure identification problems in groundwater systems. In the simulation part of the proposed approach, the numerical groundwater flow process is simulated by using MODFLOW. Parameter structures of the aquifer system are identified by partitioning the flow domain into a finite number of zones employing the general form of the Voronoi tessellation (VT). This MODFLOW and VT-based simulation model is then integrated into an optimization model where the SHuffled Ant Lion Optimization approach (SHALO) is used. This work is the first application of SHALO by considering the general form of the VT zonation approach for solving the inverse groundwater parameter structure identification problems. The applicability of the proposed simulation–optimization approach is evaluated on a hypothetical aquifer model in the literature. This evaluation is conducted by solving the same problem using Ant Lion Optimization (ALO) and its improved version, the self-adaptive ALO (saALO) for the same conditions. Furthermore, the identified results are compared with those obtained using harmony search (HS) and hybrid genetic algorithm (hybrid GA) based solution approaches in the literature. The obtained results indicated that the proposed approach provided better identification results than ALO, saALO, HS, and hybrid GA in terms of different evaluation metrics.

Rice irrigation water efficiency improvement: An AquaCrop-based optimization modeling approach

Rice is the cereal crop that consumes the largest amount of water in the world. To improve the water use efficiency of rice farming and promote the sustainable development of rice production, this study proposes a novel AquaCrop-based optimization modeling approach. First, an AquaCrop-based multiobjective model is constructed to generate dynamic water production functions (WPFs). The NSGA-II algorithm is employed to solve the model, and quadratic dynamic WPFs for rice under different hydrological years (wet, normal and dry) are generated based on the obtained Pareto fronts, which can improve the modeling efficiency and help reflect the complicated reality of water flows. Second, a fuzzy credibility-constrained stochastic multiobjective programming (FCC-SMOP) model is developed for irrigation water allocation based on the acquired WPFs. The model addresses the randomness of precipitation and runoff, the fuzziness of decision-makers' preferences, and conflicting objectives of water savings, benefit increments, and water demand during the critical period of rice growth. The proposed simulation-optimization approach was applied to the Changgang irrigation district in northeast China. Optimal irrigation amounts under different decision-making preference scenarios (including conservative, moderate and positive) and different hydrological years were simultaneously generated. The following conclusions can be drawn: (1) The AquaCrop-based multiobjective method is capable of generating WPFs of different hydrological years with sparse discontinuous field experiments; (2) the decision-making preferences of decision-makers significantly impact the economic benefits, water use efficiency, and practicality of schemes; and (3) the FCC-SMOP model successfully makes tradeoffs among the three objectives under multiple uncertainties and can further improve the system efficiency and increase planting income in the Changgang irrigation district when its irrigation facilities become optimal.

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.

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.

Quantity–quality-based water allocation programming in a reservoir–river system

Abstract Simulation–optimization approaches are useful methods for the assessment of water resource engineering plans and finding the best management policy at the watershed scale. In this study, to find the optimum operation for a reservoir with the purpose of satisfying water demands while meeting the water quantity and quality criteria, a generic reservoir and river basin simulation model (MODSIM) is coupled with the particle swarm optimization (PSO) algorithm leading to construct the PSO–MODSIM model. With the decision variables of the reservoir's monthly releases, the objective function is to maximize the supply for downstream demands while keeping the electrical conductivity (EC) in the river flow lower than a predefined level at the downstream checkpoint, which is a function of the EC in the agricultural return flows. Moreover, a safe flow rate is defined in which the streamflow should not exceed at the checkpoint resulting in mitigation of the submerging lands damage. Results obtained by the PSO–MODSIM model indicate the ability of the proposed simulation–optimization approach for solving the problem of optimal quantity–quality-based water allocation in a reservoir–river system. For instance, the EC at the checkpoint is decreased by 61% in the optimum reservoir operation state comparing the present situation, whereas the municipal and environmental demands are fully met and the agricultural demands are supplied with a desirable reliability satisfaction level.

Minimizing Total Production Cost in a Hybrid Flow Shop: a Simulation-Optimization Approach

To ensure the competitiveness of manufacturing companies in the market, batching and batch scheduling are among the most important tasks. This paper presents a simulation-optimization approach that combines the discrete event simulation (DES) and the genetic algorithm (GA) to solve the batching and batch scheduling problem in a hybrid flow shop (HFS). HFS is widely used for the production of medium and large quantities of different technologically complex products. Based on a real-world manufacturing company, the HFS simulation model was developed using the Tecnomatix Plant Simulation software package. By analysing the influencing factors that represent production costs, a new formulation of the total cost of production was proposed. The purpose of this case study was to ensure timely delivery and minimize production costs by integrating simulation and optimization tools. This research considers sequence-dependent setup times, and availability of manufacturing and transportation equipment. The results of this research showed that the proposed simulation-optimization approach can be applied to solve the problem in many industrial case studies.

Adaptative DNN emulator-enabled multi-objective optimization to manage aquifer−sea flux interactions in a regional coastal aquifer

This study focuses on the analyses of influx and efflux of groundwater at the aquifer−sea interface in response to the total groundwater extraction from a regional coastal aquifer. The groundwater planning and management goal is formulated as a multi-objective optimization problem to optimize the total pumping, groundwater influx and efflux through the coastal boundary. A four-stage optimization strategy is implemented for solving this optimization problem, whereby the first three stages are the iterative optimizations using the proposed Multi-Objective Particle Swarm Optimization algorithm and the analysis of Pareto-optimal solutions, and the last stage is the selection of one bargaining solution using the Kalai-Smorodinsky approach considering compromises among multiple objectives. In order to improve the efficiency of the simulation-optimization model, Deep Neural Networks emulators are fitted to approximate individual optimization objective, and a novel dynamic sampling strategy is applied to adaptively improve the accuracy of the emulators. This study demonstrates that accurate and efficient emulators can be achieved for the regional coastal aquifer with zone-based pumping rate multipliers from eight bands (Band 1 to Band 8) of increasing distance from the coastal boundary as the decision variables. The results from the Pareto-front suggest that the abstractions of Band 2 (close to the sea) can be reduced to the lower boundary of the rate multiplier (0.5) whereas the abstraction of farther bands can be significantly enhanced. The water influx through the coastal boundary was decreased by 15.69% under the slight compromise of the total pumping and water efflux by the selected compromising pumping pattern in the regional model. The final Pareto-optimal solution set and the compromised solution provide valuable information for the groundwater manager to plan sustainable groundwater use. The proposed simulation-optimization approach used in this study can be applied for a wide range of groundwater management problems.

Allocation of unequally-weighted wastewater discharge loads using a simulation-optimization approach

In this study, a new simulation–optimization approach is proposed to solve the multi-pollutant waste load allocation (WLA) problems in surface water systems. The proposed approach simulates the fate and transport of multiple pollutants utilizing the AQUATOX model. Since AQUATOX is an independent model and its integration into the optimization process is computationally not efficient, a multi-pollutant concentration–response matrix (mpCRM) is developed by using the results of the AQUATOX model. This mpCRM is then integrated into an optimization model where a nonlinear generalized reduced gradient (GRG) optimization method is used. Unlike in previously conducted studies, a new optimization formulation is proposed wherein the multiple pollutant loads are allocated among the source locations depending on their pre-assigned load allocation weights. This formulation allows for an equal or variable pollutant load allocation plan among source locations depending on the water management strategy for the watershed. The applicability of the proposed approach is evaluated on a sub-watershed of the Küçük Menderes River Basin (KMRB) in Turkey by considering different load allocation scenarios. Furthermore, a detailed sensitivity analysis is conducted to evaluate the model results for different problem parameters. Identified results suggest that the proposed simulation–optimization approach is an effective way to solve the multi-pollutant WLA problem.

Simulation optimization approach for patient scheduling at destination medical centers

This study presents a simulation optimization approach for scheduling medical tourists who travel to destination medical centers (DMCs). The objective of this research is two-fold: (1) to minimize deviations from a patient's preferred start day and (2) to minimize the flow time of patients at the DMC. The duration of treatment is considered uncertain, and patients are scheduled over a multiday scheduling window. This problem is modeled as a flow-shop scheduling problem (FSP) where each patient should undergo a specific number of stages to complete treatment. The goal is to assign patients to different days within the time horizon such that the minimum deviation from the patient's preferred start day and the minimum average patient flow time are achieved. Due to the complex and stochastic nature of the problem, the simulation-optimization approach is used. This approach includes simulation evaluation at each iteration of the optimization procedure. The optimization procedures used in this research are tabu search (TS) and simulated annealing (SA). Finally, several test problems are used to evaluate the performance of the proposed simulation optimization approach. Based on our results, the total cost of the proposed model is significantly less than that of the problem without any admission planning and patient sequencing.

JIT Production Strategy and Maintenance for Quality Deteriorating Systems

We study the optimal production planning and major maintenance scheduling for an unreliable manufacturing system. We assume that the production unit experiences progressive deterioration that negatively influences product quality. For the production policy, we extend improve traditional threshold policies with a superior alternative, based on a just-in-time (JIT) strategy. The paper brings a new vision on the importance of implementing more effective production strategies based on JIT methods, instead of traditional threshold policies. When a failure occurs, the production unit is minimally repaired, and when the major maintenance is selected, the machine is restored to brand-new conditions. The objective of the model is to determine the simultaneous JIT production and major maintenance strategy that minimizes the total cost. Due to the stochastic features of the system, a simulation-based optimization approach is proposed, which combines the descriptive capabilities of simulation modeling with analytical models, statistical analysis, and optimization techniques. The results verify that the proposed simulation–optimization approach provides new and coherent results that highlight the strong influence of quality deterioration on the determination of the control parameters. A sensitivity analysis and a comparative study are conducted to illustrate that significant cost savings could be obtained with the proposed approach.

A Simulation-Optimization Approach for Two-Way Scheduling/Grouping in a Road-Rail Physical Internet Hub

Physical Internet (PI) was introduced to transform the current logistics systems into a global sustainable logistics network. This paper focuses on two-way scheduling/grouping in both sections of a Road-Rail PI-hub (Road→Rail and Rail→ Road). It consists in scheduling both inbound and outbound trucks as well as grouping of PI-containers in the train’s wagons and outbound trucks. The problem is formulated as a mixed integer programming model (MILP). Then, a simulation-optimization (SO) approach is proposed to provide robust solutions which remain feasible in spite of perturbations. The proposed approach uses a Modified Threshold Accepting meta-heuristic (MTA) as an optimizer combined with an embedded perturbation simulator to ensure the robustness of the solutions. Results show that the proposed SO approach provides robust solutions able to handle perturbations.

Simulation based optimization of a stochastic supply chain considering supplier disruption: An agent-based modeling and reinforcement learning

Many researchers and practitioners in the recent years have been attracted to investigate the role of uncertainties in the supply chain management concept. In this paper a multi-period stochastic supply chain with demand uncertainty and supplier disruption is modeled. In the model, two types of retailers including risk sensitive and risk neutral, with many capacitated suppliers are considered. Autonomous retailers have three choices to satisfy demands: ordering from primary suppliers, reserved suppliers and spot market. The goal is to find the best behavior of the risk sensitive retailer, regarding the forward and option contracts, during several contract periods based on the profit function. Hence, an agent-based simulation approach has been developed to simulate the supply chain and transactions between retailers and unreliable suppliers. In addition, a Q-learning approach (as a method of reinforcement learning) has been developed to optimize the simulation procedure. Furthermore, different configurations for simulation procedure are analyzed. The R-netlogo package is used to implement the algorithm. Also a numerical example has been solved using the proposed simulation-optimization approach. Several sensitivity analyzes are conducted regarding different parameters of the model. Comparison of the numerical results with a genetic algorithm shows a significant efficiency of the proposed Q-leaning approach.

A hybrid simulation–optimization approach for solving the areal groundwater pollution source identification problems

Summary In this study, a new simulation–optimization approach is proposed for solving the areal groundwater pollution source identification problems which is an ill-posed inverse problem. In the simulation part of the proposed approach, groundwater flow and pollution transport processes are simulated by modeling the given aquifer system on MODFLOW and MT3DMS models. The developed simulation model is then integrated to a newly proposed hybrid optimization model where a binary genetic algorithm and a generalized reduced gradient method are mutually used. This is a novel approach and it is employed for the first time in the areal pollution source identification problems. The objective of the proposed hybrid optimization approach is to simultaneously identify the spatial distributions and input concentrations of the unknown areal groundwater pollution sources by using the limited number of pollution concentration time series at the monitoring well locations. The applicability of the proposed simulation–optimization approach is evaluated on a hypothetical aquifer model for different pollution source distributions. Furthermore, model performance is evaluated for measurement error conditions, different genetic algorithm parameter combinations, different numbers and locations of the monitoring wells, and different heterogeneous hydraulic conductivity fields. Identified results indicated that the proposed simulation–optimization approach may be an effective way to solve the areal groundwater pollution source identification problems.

Optimization of a complex urban intersection using discrete event simulation and evolutionary algorithms

Dealing with traffic management for complex crossroads is a challenging problem for traffic control planners. As a contribution to solve this problem, the present paper develops a mesoscopic simulation model for detecting the most suited fire plan for a complex road intersection, using a discrete event simulation tool and an evolutionary algorithm optimization. The modeling goal is to eliminate congestion by choosing an appropriate fire plan which will be adapted to the actual configuration of the intersection, as well as to a future reconfiguration meant to accept a higher inflow of vehicles. The proposed model is applied to a down-town crossroads from Nancy, France. Four different configurations of the input data flow were studied under the proposed simulation-optimization approach, and an optimal fire plan is proposed.

A new statistical modeling and optimization framework for establishing high-resolution PM10 emission inventory – II. Integrated air quality simulation and optimization for performance improvement

An integrated air quality simulation and optimization approach was proposed to improve the accuracy of the high-resolution PM10 emission inventories developed through regression models. A case study of Tangshan region in northern China was presented as an example. A linear programming optimization model was developed to minimize the mean error between simulated and monitored PM10 concentrations in different counties. A transfer coefficient matrix was used to represent source–receptor relationships and was developed through running a MM5-CMAQ air quality model. The results revealed that the proposed simulation–optimization approach could decrease the error of the PM10 emission inventory from 17.0 to 7.9% at the regional level, and from 31.44 to 14.17% at the county level. Accuracy improvement ranged from 0.39 to 61.44% for the study counties in Tangshan. The correlation coefficient between the estimated PM10 emissions and the monitored PM10 concentration in various counties was also increased from 0.82 to 0.91. Together with the regression models, the simulation–optimization method provides a promising and effective framework for developing high-accuracy and high-resolution air pollutant emission inventories.