Simulation-based Optimization Framework Research Articles

Simulation-based optimization framework for reuse of agricultural drainage water in irrigation

A simulation-based optimization framework for agricultural drainage water (ADW) reuse has been developed through the integration of a water quality model (QUAL2Kw) and a genetic algorithm. This framework was applied to the Gharbia drain in the Nile Delta, Egypt, in summer and winter 2012. First, the water quantity and quality of the drain was simulated using the QUAL2Kw model. Second, uncertainty analysis and sensitivity analysis based on Monte Carlo simulation were performed to assess QUAL2Kw's performance and to identify the most critical variables for determination of water quality, respectively. Finally, a genetic algorithm was applied to maximize the total reuse quantity from seven reuse locations with the condition not to violate the standards for using mixed water in irrigation. The water quality simulations showed that organic matter concentrations are critical management variables in the Gharbia drain. The uncertainty analysis showed the reliability of QUAL2Kw to simulate water quality and quantity along the drain. Furthermore, the sensitivity analysis showed that the 5-day biochemical oxygen demand, chemical oxygen demand, total dissolved solids, total nitrogen and total phosphorous are highly sensitive to point source flow and quality. Additionally, the optimization results revealed that the reuse quantities of ADW can reach 36.3% and 40.4% of the available ADW in the drain during summer and winter, respectively. These quantities meet 30.8% and 29.1% of the drainage basin requirements for fresh irrigation water in the respective seasons.

Simulation-Based Optimization for Yard Design at Mega Container Terminal under Uncertainty

The conventional approach of designing a container yard should be reexamined in the context of sustainable port development. Considering the uncertain future throughput, a simulation-based optimization framework is proposed to obtain a cost-effective and reliable design solution to the physical layout and equipment deployment strategy of the yard at a mega container terminal. In this framework, a two-stage stochastic programming model is presented aided with a simulation procedure of terminal operations. Finally, an application is given and the results show that the proposed integrated decision framework is effective and helpful for optimizing container yard design in the context of sustainable development of container terminals.

Combining system dynamics and multi-objective optimization with design space reduction

– The purpose of this paper is to introduce an effective methodology of obtaining Perot-optimal solutions when combining system dynamics (SD) and multi-objective optimization (MOO) for supply chain problems. , – This paper proposes a new approach that combines SD and MOO within a simulation-based optimization framework for generating the efficient frontier for supporting decision making in supply chain management (SCM). It also addresses the issue of the curse of dimensionality, commonly found in practical optimization problems, through design space reduction. , – The integrated MOO and SD approach has been shown to be very useful for revealing how the decision variables in the Beer Game (BG) affect the optimality of the three common SCM objectives, namely, the minimization of inventory, backlog, and the bullwhip effect (BWE). The results from the in-depth BG study clearly show that these three optimization objectives are in conflict with each other, in the sense that a supply chain manager cannot minimize the BWE without increasing the total inventory and total backlog levels. , – Having a methodology that enables effective generation of optimal trade-off solutions, in terms of computational cost, time as well as solution diversity and intensification, assist decision makers in not only making decision in time but also present a diverse and intense solution set to choose from. , – This paper presents a novel supply chain MOO methodology to assist in finding Pareto-optimal solutions in a more effective manner. In order to do so the methodology tackles the so-called curse of dimensionality by reducing the design space and focussing the search of the optimization to regions of inters. Together with design space reduction, it is believed that the integrated SD and MOO approach can provide an innovative and efficient approach for the design and analysis of manufacturing supply chain systems in general.

Time-of-day vehicle mileage fees for congestion mitigation and revenue generation: A simulation-based optimization method and its real-world application

Congestion pricing of a large-scale network is characterized by expensive-to-evaluate objective functions without closed forms. This paper further enhances a computationally efficient simulation-based optimization (SBO) framework to solve the problem within tight computational budget. This paper applies surrogate models to solve the optimization problem with computationally expensive objective functions based on simulation-based dynamic traffic assignment (DTA). DIRECT (a deterministic search algorithm with modification to Lipschitzian optimization) is used for metamodel parameter tuning. A trade-off of different objectives (i.e. the average travel time minimization, expected network throughput maximization, and toll revenue maximization) are converted into a single desirability function. To demonstrate the SBO framework with an application to the vehicle mileage traveled (VMT) based pricing for a real-world freeway network, this paper utilizes a calibrated simulation-based DTA model to evaluate system performance. A stochastic mesoscopic simulator is applied. We investigate the existence of an invariant macroscopic fundamental diagram (MFD) for the network, and compare simulated MFDs with measurements of fixed detectors and probe data. The proposed SBO framework is generic and can be used to solve other congestion pricing problems.

A simulation-based genetic algorithm approach for remanufacturing process planning and scheduling

Remanufacturing has attracted growing attention in recent years because of its energy-saving and emission-reduction potential. Process planning and scheduling play important roles in the organization of remanufacturing activities and directly affect the overall performance of a remanufacturing system. However, the existing research on remanufacturing process planning and scheduling is very limited due to the difficulty and complexity brought about by various uncertainties in remanufacturing processes. We address the problem by adopting a simulation-based optimization framework. In the proposed genetic algorithm, a solution represents the selected process routes for the jobs to be remanufactured, and the quality of a solution is evaluated through Monte Carlo simulation, in which a production schedule is generated following the specified process routes. The studied problem includes two objective functions to be optimized simultaneously (one concerned with process planning and the other concerned with scheduling), and therefore, Pareto-based optimization principles are applied. The proposed solution approach is comprehensively tested and is shown to outperform a standard multi-objective optimization algorithm.

Population-Specific Models of Glycemic Control in Intensive Care: Towards a Simulation-Based Methodology for Protocol Optimization.

Stress-induced hyperglycemia is common in critically ill patients, where elevated blood glucose and glycemic variability have been found to contribute to infection, slow wound healing, and short-term mortality. Early clinical studies demonstrated improvement in mortality and morbidity resulting from intensive insulin therapy targeting euglycemia. Follow-up clinical studies have shown mixed results suggesting that the risk of hypoglycemia may outweigh the benefits of aggressive glycemic control. None of the prior studies clarify whether euglycemic targets are in themselves harmful, or if the danger lies in the inadequacy of the available methods for achieving desired glycemic outcomes. In this paper, we use a recently developed simulation model of stress hyperglycemia to demonstrate that given an insulin protocol glycemic outcomes are specific to the patient population under consideration, and that there is a need to optimize insulin therapy at the population level. Next, we use the simulator to demonstrate that the performance of Adaptive Proportional Feedback (APF), a popular format for computerized insulin therapy, is sensitive to its parameters, especially to the parameters that govern the aggressiveness of adaptation. Finally, we propose a framework for simulation-based protocol optimization using an objective function that penalizes below-range deviations more heavily than comparable deviations above.

Use of Post-consumer Scrap in Aluminium Wrought Alloy Structural Components for the Transportation Sector

Wrought alloy aluminium such as AA 6082 is traditionally based on virgin aluminium and some small amount of recycled internal scrap. The “sink” of recycled aluminium is mostly cast alloys. The papers describes how changes to the material tolerances, reverse logistics and novel sustainable business models are key elements for a sustainable use of post-consumer scrap in high-end structural components based on wrought alloy aluminium. The paper describes the results from the EU FP7 Project “SuPLight” and demonstrates why a holistic view and a cross- disciplinary approach are needed. A generic framework for simulation-based optimization is described as well as novel aluminium technology preparing for post consumer scrap recycling of aluminium to high-end wrought alloys. An alternative manufacturing process route for wrought aluminium forging directly on casted billets, omitting the extrusion process step, has been developed.

Simulation-based optimization framework for multi-echelon inventory systems under uncertainty

Inventory optimization is critical in supply chain management. The complexity of real-world multi-echelon inventory systems under uncertainties results in a challenging optimization problem, too complicated to solve by conventional mathematical programing methods. We propose a novel simulation-based optimization framework for optimizing distribution inventory systems where each facility is operated with the (r, Q) inventory policy. The objective is to minimize the inventory cost while maintaining acceptable service levels quantified by the fill rates. The inventory system is modeled and simulated by an agent-based system, which returns the performance functions. The expectations of these functions are then estimated by the Monte-Carlo method. Then the optimization problem is solved by a cutting plane algorithm. As the black-box functions returned by the Monte-Carlo method contain noises, statistical hypothesis tests are conducted in the iteration. A local optimal solution is obtained if it passes the test on the optimality conditions. The framework is demonstrated by two case studies.

Designing of an intelligent self-adaptive model for supply chain ordering management system

One of the challenging issues in supply chain management is the coordination of ordering processes, especially in dynamic situations. In recent years, reinforcement learning algorithms are considered to be efficient techniques for solving such problems. In this paper, an agent-based simulation technique has been integrated with a reinforcement learning algorithm and has been applied to model a four-echelon supply chain that faces non-stationary customer demands. This approach leads to the development of a novel and intelligent simulation-based optimization framework, which includes a detailed simulation modeling of supply chain behavior. Finally statistical methods, including the Var technique, are used for the risk evaluation and sensitivity analysis have been provided to support the decision making process under uncertainty.

Optimizing the number and location of time point stops

Public transport service is subject to multiple sources of uncertainty that impact its reliability. Holding control strategies are a common method to prevent the deterioration of service reliability along the route. This paper expands on previous studies by considering the general case of determining both the optimal number and optimal location of the time point stops (TPS) where holding takes place, and assessing their impacts on transit performance using simulation. Holding times are determined based on a real-time headway-based holding strategy designed to improve service regularity by seeking uniform headways along the route. The evaluation of the performance of alternative TPS layouts is simulation-based, using BusMezzo, a transit operations simulation model which models the dynamic performance of bus transit systems. The proposed framework also considers the multiple objectives incorporating passenger and operator points of view. The simulation-based optimization framework was applied in a case study with one of the premium bus lines in Stockholm, Sweden, using two solution methods—greedy and genetic algorithms. A multi-objective evaluation was conducted considering both passenger and operator perspectives. The results demonstrate that transit performance varies considerably with alternative TPS layouts. The best solution obtained by the proposed methodology reduces total weighted passenger journey times and cycle times compared to both the current layout and the case of no holding control. The proposed method could assist transit agencies and operators in evaluating and recommending alternative time point layouts.

Simulation-based optimization for generating the dimensions of a dredged coastal entrance channel

An entrance channel is dredged when its natural dimensions are not adequate to accumulate more and larger ships. Considering the reconciliation among costs, safety and efficiency and the complex relationship among channel type, navigable water level and sailing speed, a simulation-based optimization framework is proposed to optimize channel dimensions with limited dredging budget constraints in an integrated way. In this framework, the optimization program generates channel dimensions acceptable for safe navigation with budget constraints, and seeks an optimal balance between the dredging costs and navigation efficiency, including vessel waiting time and berth utilization. The simulation model evaluates the values of navigation efficiency measures considering the randomness and dynamics in ship operation. Finally, an application is given and the results show that the proposed framework is effective and helpful for optimizing the dimensions of the entrance channel. Moreover, the proposed modeling and solution methods are general and can be applied to the port basin, anchorage area as well.

Simulation-Based Optimization Methodology for Offshore Natural Gas Liquefaction Process Design

A simulation-based optimization framework is introduced. First, a base case design is conducted using the commercial simulator. Then, design variables are decided. Next, minimum and maximum ranges of the design variable are determined. We will call this the design space. A comprehensive simulation of the design space is executed. Next, empirical modeling of this design space is performed. This is called the process mapping step. After verification of the model using a test data set, the optimization problem is solved using the developed data-driven model. The methodology is applied to the optimal design of a natural gas liquefaction process for an offshore liquefied natural gas (LNG) plant.

Hybrid Simulation Based Optimization Framework for Centralized and Decentralized Supply Chains

A chemical supply chain is a network of suppliers, production facilities, warehouses, and markets designed to acquire raw materials and manufacture, store, and distribute products among the markets. The individual entities in such a network have their own goals and objectives. Usually the different units belong to different organizations and are parts of more than just one supply chain. The different processes of the supply chain are distributed depending on the structure of the network and the integration of these units. In this work, we investigate how the supply chains behave under different network structures and decision-making policies. Hybrid simulation based optimization frameworks have been used to predict the optimal operation under the different scenarios.

Social Welfare Maximization of Multimodal Transportation

Multimodal urban transportation systems exhibit complex inter actions between components, including users, multimodal transportation facilities, supply side agencies, and operators. Although these interactions are obvious, rigorous quantitative methods for optimizing control variables across modes of transportation on real-world networks are deficient. A social welfare maximization model was established for joint optimization of bus fare, rail transit fare, and congestion tolls for private cars. The authors determined the optimality conditions and the second-order partial derivatives for this optimization problem. Because of the complexity of the multimodal urban transportation system, the objective function of social welfare has no closed form and is extremely expensive to evaluate. The authors therefore proposed a simulation-based framework for evaluation of the objective function and optimization of three decision variables across multiple travel modes. Several metamodels (i.e., mathematical functions that approximate the true shape of an unknown, nonlinear, and complex objective function) were adopted to approximate the highly nonlinear input–output mappings in the urban system. This is the first study to develop a simulation-based method for joint optimization of transit and road network operations. The case study applied the simulation-based optimization framework to the Sino–Singapore ecocity in Tianjin, China, by using VISSUM as the urban systems simulator. An “ecocity” is defined by the authors as “a thriving city that is socially harmonious, environmentally friendly, resource-efficient, and a model for sustainable development.” Results show that metamodels can accurately approximate the real objective function and produce good suboptimal and near-optimal solutions. The optimal combination of transit fares and congestion tolls significantly outperform those under two baseline scenarios. The optimal solutions also suggest that extreme transit fares (too high or too low) or congestion tolls are contrary to welfare-maximizing objectives. The proposed method can be applied for joint optimization of other multimodal planning and operational strategies, such as investment and operational decisions across various modes of transportation.

A Simulation-Based Optimization Framework for Urban Transportation Problems

This paper proposes a simulation-based optimization (SO) method that enables the efficient use of complex stochastic urban traffic simulators to address various transportation problems. It presents a metamodel that integrates information from a simulator with an analytical queueing network model. The proposed metamodel combines a general-purpose component (a quadratic polynomial), which provides a detailed local approximation, with a physical component (the analytical queueing network model), which provides tractable analytical and global information. This combination leads to an SO framework that is computationally efficient and suitable for complex problems with very tight computational budgets. We integrate this metamodel within a derivative-free trust region algorithm. We evaluate the performance of this method considering a traffic signal control problem for the Swiss city of Lausanne, different demand scenarios, and tight computational budgets. The method leads to well-performing signal plans. It leads to reduced, as well as more reliable, average travel times.

A framework for parallelized efficient global optimization with application to vehicle crashworthiness optimization

This article presents a framework for simulation-based design optimization of computationally expensive problems, where economizing the generation of sample designs is highly desirable. One popular approach for such problems is efficient global optimization (EGO), where an initial set of design samples is used to construct a kriging model, which is then used to generate new ‘infill’ sample designs at regions of the search space where there is high expectancy of improvement. This article attempts to address one of the limitations of EGO, where generation of infill samples can become a difficult optimization problem in its own right, as well as allow the generation of multiple samples at a time in order to take advantage of parallel computing in the evaluation of the new samples. The proposed approach is tested on analytical functions, and then applied to the vehicle crashworthiness design of a full Geo Metro model undergoing frontal crash conditions.

Integrated analysis of high-penetration PV and PHEV with energy storage and demand response

The increasing utilization of Plug-in Hybrid Electric Vehicles (PHEVs) in future will have significant impact on utility companies and customers depending on their charging characteristics. Uncontrolled or unscheduled vehicle charging may increase the residential peak and the risk of electric distribution network failure. In order to reduce the peak load utility companies encourage customers who have such high electricity consuming devices to participate in various demand response (DR) programs. Additionally, considering increasing Photovoltaic (PV) generation and storage facility installation on customer side, the integration of PHEVs needs more attention as system’s performance becomes highly fluctuating and unpredictable. This paper presents a simulation based optimization framework for integrated analysis of demand response programs, with high penetration of PHEV and PV and storage systems from residential customer’s perspective as well as utility company’s perspective. An agent-based simulation model is built in AnyLogic to mimic the household load, PHEV charging, PV generation and storage together with various demand response programs such as time-of-use (TOU), Real-time Pricing (RTP), and curtailment. In the considered scenarios, customers schedule their PHEV charging time and duration to minimize the electricity bill considering the price at the time of use, while the utility company aims to minimize the peak due to the PHEV charging by changing prices. The constructed simulation helps the utility company identify the best policies, and helps customers make better response under different DR programs. Three demand response programs are simulated with and without PV and storage. Results show that the simulation model successfully represents the cases and appropriate scheduling has benefits for both customers and utility companies.

Continuous-discrete simulation-based decision making framework for solid waste management and recycling programs

Solid waste produced as a by-product of our daily activities poses a major threat to societies as populations grow and economic development advances. Consequently, the effective management of solid waste has become a matter of critical importance for communities. However, solid waste management systems are inherently large-scale, diverse, and subject to many uncertainties, and must serve numerous stakeholders with divergent objectives. In this study, we propose a simulation-based decision-making and optimization framework for the analysis and development of effective solid waste management and recycling programs. The proposed solution includes a database and two main modules: an assessment module and a resource allocation optimization module. The assessment module identifies the sources of uncertainties in the system, which are then parameterized and incorporated into the resource allocation optimization module. The resource allocation optimization module involves a novel discrete–continuous model of the system under consideration, in which the continuous nature of decision variables is maintained while inherently discrete processing and transfer operations are accurately captured. The model operates with respect to the waste types and characteristics, costs, environmental impacts, types, location and capacities of processing facilities, and their technological capabilities. Then, an optimization mechanism embedded in the resource allocation optimization module solves the multi-criteria problem of the allocation of limited resources by simultaneously optimizing all relevant decision variables, evaluating performance in real-time via the model. Here, the optimum solution is considered as the combination of parameters that will lead to the highest recycling rate with minimum cost. The proposed framework has been successfully demonstrated for the Miami-Dade County Solid Waste Management System in the State of Florida.

A Three-Stage Optimization Algorithm for the Stochastic Parallel Machine Scheduling Problem with Adjustable Production Rates

We consider a parallel machine scheduling problem with random processing/setup times and adjustable production rates. The objective functions to be minimized consist of two parts; the first part is related with the due date performance (i.e., the tardiness of the jobs), while the second part is related with the setting of machine speeds. Therefore, the decision variables include both the production schedule (sequences of jobs) and the production rate of each machine. The optimization process, however, is significantly complicated by the stochastic factors in the manufacturing system. To address the difficulty, a simulation-based three-stage optimization framework is presented in this paper for high-quality robust solutions to the integrated scheduling problem. The first stage (crude optimization) is featured by the ordinal optimization theory, the second stage (finer optimization) is implemented with a metaheuristic called differential evolution, and the third stage (fine-tuning) is characterized by a perturbation-based local search. Finally, computational experiments are conducted to verify the effectiveness of the proposed approach. Sensitivity analysis and practical implications are also discussed.

Optimization of replenishment policies for decentralized and centralized capacitated supply chains under various demands

In this paper, the optimal replenishment policies of capacitated supply chains (SC) operating under two different control strategies (decentralized vs. centralized) and various demands are determined and insights useful to management are discussed. The details of the system used, including the underlying supply chain inventory model and the simulation-based optimization framework, are presented. The effectiveness and efficiency of the proposed optimization framework is illustrated by comparing it with brute force estimation of the true optimum on three selected scenarios. The entire study was carried out by following a design of experiment, covering ten demand patterns, four levels of capacity constraints, and two control strategies. The main and two-factor interaction effects were analyzed via ANOVA. Detailed analyses of the ordering patterns, cost distribution over the SC and internal service level were carried out to provide useful insights. For demand with high variation, capacity constraint may lead to significant changes in ordering patterns. Failing to detect those changes may lead to unnecessary cost increase. Overall it is beneficial to adopt centralized control and cost savings realized are dependent upon the demand patterns. An incentive mechanism is proposed to coordinate the decentralized system so that each player in the SC will be better off. Additional experiments were also carried out to investigate the effect of using different allocation rules by the distributor.