Multi-objective Simulation Optimization Research Articles

A Multi-Objective Simulation-Optimization framework for water resources management in canal-well conjunctive irrigation area based on nexus perspective

Water scarcity drives the nexus of water-food-energy-ecosystem (WFEE) in arid and semi-arid agricultural irrigation areas, while the impacts of different irrigation strategies can propagate from water sector to other sectors through groundwater system. In this work, a Multi-Objective Simulation Optimization (MOSO) framework that couples the numerical groundwater model (MODFLOW) with the multi-objective optimization model (NSGA-III) is proposed to coordinate multiple resource systems with conflicting interests based on nexus perspective by reasonably allocating irrigation water resources from surface water diversion and groundwater abstraction. The management objectives include maximization of water use efficiency and food economic efficiency, as well as minimization of energy consumption efficiency and the impact of irrigation on ecosystem subject to groundwater levels, surface water supply and irrigation water demand. To demonstrate the feasibility of the MOSO framework, a water resources management problem in typical canal-well irrigation area − Baojixia Irrigation Area (BIA), located in Shaanxi province in northwest China, has been solved. The major findings are: (a) for every 10 % increase in the proportion of groundwater usage across the total irrigation area, water use efficiency decreases by 0.05 kg/m3, energy consumption efficiency increases by 900 CNY/ha, the ecosystem impact index decreases by 0.1, and food economic efficiency remains unchanged; (b) the four-dimensional Pareto-optimal solutions comprehensively consider all the impacts of the WFEE nexus system, avoiding decision shortsightedness caused by a low-dimensional perspective; (c) spatial differences in irrigation strategies across different zones are primarily influenced by crop structure, hydrogeological parameters and surface elevation, while temporal variations are mainly concentrated during the maize irrigation period. The framework based on water resources management and groundwater dynamics can efficiently evaluate trade-offs and synergies among nexus sectors, providing far-sighted suggestions for policy makers to improve resource-use efficiency, while mitigating the impact of irrigation on ecosystem.

Multi-objective simulation–optimization for water resources management and uses in multi-dam systems in low-water regions

One of the challenging issues in surface water resources management is the optimal operation of multi-dam systems. In addition to the applicability in determining the optimal pattern of operation of existing dams, this approach can be implemented in specifying the optimal capacity of under design dams. In this research, the simulation–optimization technique is used along with the reservoir zoning method in order to specify the optimal role curve of each of the dams in a multi-dam system (i.e., the Marun and Jarreh dams) in monthly intervals. The objective of this study is to develop a multi-objective algorithm on the basis of the zoning of reservoirs for the optimal operation of multi-dam systems. In this technique, instead of increasing the reliability of supplying demands in the whole period regardless the dry months, some of the inflow of rainy periods is stored in dam reservoirs to be consumed in dry months in order to mitigate the severity of deficiency. To examine the efficiency of the proposed model, the results obtained from the system operation in the current conditions are compared under two scenarios including optimal and reference. In the reference scenario, during 30-years, in some dry and low-water years, especially in the last six years of planning, the percentage of the demand supply is zero or less than five percent in several consecutive months. After optimizing the system, the minimum supply percentage in critical and dry months reaches 30 to 60 percent. Also, in the optimization scenario, the percentage of downstream ecological demands is improved. This research indicates that using the solution of this research leads to the better management of reservoirs in multi-dam systems and reduces the severity of deficiency in supplying various uses in low-water months.

A simulation-based Digital Twin for smart warehouse: Towards standardization

Cyber–physical systems are developed to meet the need to improve process flexibility, optimality, and transparency through Digital Twin (DT). Unfortunately, the application of DT is still limited in practice, and there is no standard way to achieve integration. An Asset Administration Shell (AAS) appears as a promising concept for realizing DT in a standard manner. A literature review shows that most studies only reached static DT and only considered a few specific assets, especially unmovable ones. This study contributes to DT development by proposing 3D-based computer simulation technology as dynamic DT based on the AAS Framework. The proposed concept enables DT to conduct dynamic monitoring, optimization, and direct controlling. A smart warehouse in Taiwan is used to verify the proposed concept. Assets considered include Automated Guided Vehicles (AGVs), Operator, Conveyor, Forklift, and Storage Rack. AAS structure, simulation model built in FlexSim software, and system integration architecture have been constructed. Two advantages of using simulation for DT are demonstrated: multi-objective simulation–optimization for AGV capacity planning and creating dynamic dashboards. Based on the proposed concept, industry 4.0 scenarios can be integrated comprehensively.

Integration of deep learning and improved multi-objective algorithm to optimize reservoir operation for balancing human and downstream ecological needs

Dam (reservoir)-induced alterations of flow and water temperature regimes can threaten downstream fish habitats and native aquatic ecosystems. Alleviating the negative environmental impacts of dam-reservoir and balancing the multiple purposes of reservoir operation have attracted wide attention. While previous studies have incorporated ecological flow requirements in reservoir operation strategies, a comprehensive analysis of trade-offs among hydropower benefits, ecological flow, and ecological water temperature demands is lacking. Hence, this study develops a multi-objective ecological scheduling model, considering total power generation, ecological flow guarantee index, and ecological water temperature guarantee index simultaneously. The model is based on an integrated multi-objective simulation-optimization (MOSO) framework which is applied to Three Gorges Reservoir. To that end, first, a hybrid long short-term memory and one-dimensional convolutional neural network (LSTM_1DCNN) model is utilized to simulate the dam discharge temperature. Then, an improved epsilon multi-objective ant colony optimization for continuous domain algorithm (ε-MOACOR) is proposed to investigate the trade-offs among the competing objectives. Results show that LSTM _1DCNN outperforms other competing models in predicting dam discharge temperature. The conflicts among economic and ecological objectives are often prominent. The proposed ε-MOACOR has potential in resolving such conflicts and has high efficiency in solving multi-objective benchmark tests as well as reservoir optimization problem. More realistic and pragmatic Pareto-optimal solutions for typical dry, normal and wet years can be generated by the MOSO framework. The ecological water temperature guarantee index objective, which should be considered in reservoir operation, can be improved as inflow discharge increases or the temporal distribution of dam discharge volume becomes more uneven.

Multi-objective simulation-optimization for integrated automated storage and retrieval systems planning considering energy consumption

An Automated Storage and Retrieval System (AS/RS) is one of modern technologies in warehouse operation. Despite many advantages offered by AS/RS such as improving accuracy, efficiency, and safety, AS/RS operation is very complex started from strategical, tactical, to operational level. Reaching optimal combination for all decisions become important. However, due to the dynamic and combinatorial complexity as well as uncertainty in supply – demand, it cannot be solved through the general mathematical optimization. Therefore, this study introduces simulation–optimization (SO) framework for integrated AS/RS planning considering 7 decisions at a time. Furthermore, a comprehensive mathematical model for measuring AS/RS energy consumption is formulated. The proposed framework is implemented in the China’s warehouse company for optimizing multi-objective namely energy consumption and travel time per unit. Non-dominated Sorting Genetic Algorithm II (NSGA-II) is developed as metaheuristics algorithm and discrete-event simulation is modeled based on FlexSim. The results produce non-dominated solutions that are further summarized through clustering algorithm resulting in 4 different clusters with significantly different impacts. This study provides insightful analysis and managerial implications for reaching near-global optimum in AS/RS planning towards green operation.

River water quality management using an integrated multi-objective optimization-simulation approach based on bankruptcy rules.

The aim of this research is to allocate the river's self-purification (acceptance capacity of pollution) fairly between the beneficiaries (pollutant sources) using bankruptcy theory. For this purpose, four bankruptcy rules (CAE, CEL, P, and TAL) were called using the link of the water quality simulation model (QULA2Kw) to an evolutionary optimization algorithm (multi-objective imperialist competition algorithm (MOICA)). The objective functions were reducing polluters' wastewater treatment costs and preventing biochemical oxygen demand (BOD) violations of the standard level along the river. The applicability of the approach is demonstrated by the case study that was carried out on the Dez River in Iran. According to the results, the CEL scenario is the most effective method for the Dez River when taking into account the most optimal state for both objective functions (selecting the best compromise solution from the Pareto front). This is because it has the lowest violation value of the standard level for BOD along the river when compared to other scenarios. Alternatively, when considering Solution 20, which focuses on the maximum cost of treating the polluters while staying within the acceptable level of pollution in the river, the results indicated that the CEA rule emerged as the most favorable option. This is due to its lower treatment cost (156.9 (1000$)) and higher pollution discharge to the river (681.91 g/s).

Developing a multi-objective simulation-optimization model for ecological water conveyance in arid inland river basins

Study regionMiddle and lower reaches of Tarim River, southwestern of Xinjiang Province, China. Study focusEcological water conveyance is an important measure to protect the fragile ecosystem of inland rivers in arid areas. This paper attempted to present a integrated model for optimal ecological water conveyance scheme (EWCS). In this model, the impact of the duration of water conveyance and the water allocation of each channel on the groundwater level was considered. The optimized ecological water conveyance scheme can simultaneously maximize groundwater level rise value (GLRV) and minimize ecological water conveyance volume (EWCV). New hydrological insights for the regionAn simulation-optimization model was proposed based on MODFLOW model, Kriging surrogate model and NSGA-II. The study obtained the Pareto frontiers of EWCS in three typical years (dry, normal and wet), and made decisions using the TOPISIS-Entropy method. The scenario comparison results indicated that the optimized EWCS requires less EWCV than the existing one when achieving the same GLRV, proving that the optimized EWCS has higher water resource utilization efficiency. This simulation-optimization model fills the research gap of EWCS in arid inland river basins and provides scientific guidance for government management.

An application of multi-objective simulation optimization to medical resource allocation for the emergency department in Taiwan

An application of multi-objective simulation optimization to medical resource allocation for the emergency department in Taiwan

Multi-Fidelity Simulation Modeling for Discrete Event Simulation: An Optimization Perspective

Multi-fidelity simulation is an effective approach to balancing speed and accuracy in expensive simulation, and its performance is affected by the quality of multi-fidelity simulation models. Building high-quality simulation models is non-trivial, especially for complex systems, because current manual modeling methods require sufficient domain knowledge and experience, increasing the labor and time costs. Motivated by the issues, this paper focuses on one of the most crucial simulation types, discrete event simulation, and develops a computer-aid multi-fidelity simulation modeling method called Optimization-based Multi-fidelity Simulation Modeling (OMFSM). OMFSM formulates multi-fidelity simulation modeling as a bi-objective simulation optimization problem to optimize speed and accuracy. An efficient optimization algorithm called Multi-objective Simulation Optimization based on Hypervolume (MOSO-HV) is tailored to select a set of high-quality models. Experimental results in a digital twin emergency department demonstrate that the computer-aid modeling method builds more and better multi-fidelity simulation models than manual modeling and reveal the effectiveness of MOSO-HV for OMFSM. The utility of OMFSM in multi-fidelity simulation is also justified by a real-world optimization problem. Note to Practitioners—Multi-fidelity simulation is an essential technique to fulfill the demand for accuracy analysis and quick decision-making in Industrial 4.0, such as digital twins and virtual reality. The quality of multi-fidelity simulation models significantly influences the performance of multi-fidelity simulation. Developers currently build multi-fidelity simulation models manually, and their experience determines the model’s quality. To reduce the labor and time costs in constructing high-quality multi-fidelity simulation models, we propose a computer-aid method named OMFSM from optimization for the first time. Experiments on a real case prove that OMFSM lightens the burden of manual modeling and provides more and better models for multi-fidelity simulation.

Reconciling crop production and ecological conservation under uncertainty: A fuzzy credibility-based multi-objective simulation-optimization model

To cope with the problems of agricultural water conflicts and secondary soil salinization in arid regions, a fuzzy credibility-based multi-objective simulation-optimization model is proposed for optimizing irrigation water allocation and crop area planning under uncertainty. This model combines simulation module of enabling to quantify daily physical process of water-salt movement among soil water, crop root zone and groundwater aquifers, optimization module of managing water resources and fuzzy credibility-constrained programming into a general framework. It's applied to a case study in the Jiefangzha Irrigation Subarea in Hetao Irrigation District, Northwest China. Three objectives encompassing maximizing net economic benefits, maximizing nutritional water productivity and minimizing carbon emissions from agricultural system are interconnected through decision variables. Four credibility scenarios of fuzzy constrains including β = 0.6 to 0.9 are presented for obtaining decision-making solutions. Through NSGA-III, such a high-dimensional multi-objective problem is solved. This study uses the multi-objective constraint-handling strategy to handle constraints, which exploits the effective information within infeasible solutions. Moreover, it emphasizes the importance of soil water-salt movement processes in determining optimal solutions and helps decision makers weigh the system outputs and risk level of violating constraints. Results illustrate that when β increases from 0.6 to 0.9, net economic benefit decreases from 1.742 × 109 Yuan to 1.706 × 109 Yuan, nutritional water productivity decreases from 9136.0 kcal/m3 to 8819.6 kcal/m3, and carbon emissions increase from 439.6 × 106 kg. C to 441.6 × 106 kg.C, which shows that an increasing credibility level leads to lower system benefits and conservative system outputs. The results can provide valuable information for managing irrigation water resources and controlling salinity accumulation. Furthermore, dynamic decisions related to water-salt movement processes can be readily generated. These findings show that the developed approach is globally applicable for managing irrigation water in arid and semiarid regions that face similar problems.

Hybrid MCDM and simulation-optimization for strategic supplier selection

Supplier selection for strategic items requires a comprehensive framework dealing with qualitative and quantitative aspects of a company’s competitive priorities and supply risk, decision scope, and uncertainty. In order to address these aspects, this study aims to tackle supplier selection for strategic items with a multi-sourcing, taking into account multi-criteria, incorporating uncertainty of decision-makers judgment and supplier–buyer parameters, and integrating with inventory management which the past studies have not addressed well. We develop a novel two-phase solution approach based on integrated multi-criteria decision-making (MCDM) and multi-objective simulation-optimization (S-O). First, MCDM methods, including fuzzy AHP and interval TOPSIS, are applied to calculate suppliers’ scores, incorporating uncertain decision makers’ judgment. S-O then combines the (quantitative) cost-related criteria and considers supply disruptions and uncertain supplier–buyer parameters. By running this approach on data generated based on previous studies, we evaluate the impact of the decision maker’s and the objective’s weight, which are considered important in supplier selection.

Evaluating the effect of aquifer heterogeneity on multiobjective optimization of in-situ groundwater bioremediation

In-situ bioremediation is a cost-effective technique to eliminate petroleum hydrocarbons from groundwater. In previous studies, this has been mostly applied under the approximation of a homogeneous porous medium, even though in reality aquifers are often characterized by significant heterogeneities. Here, different heterogeneous and equivalent homogeneous hydraulic conductivity fields are considered to study the effect of aquifer heterogeneity on optimal in-situ bioremediation. For this, the multiobjective simulation-optimization (S/O) model referred to as BIOEFGM-NSGA II is proposed — which uses the element-free Galerkin method (EFGM) for discretization of the governing equations, and the non-dominated sorting genetic algorithm II (NSGA II) for multiobjective optimization. This model is then applied to different heterogeneous conductivity fields generated through a pseudo-random correlated field generator for different combinations of variance and correlation lengths with constant mean. Results show that the optimal pumping policy for an equivalent homogeneous conductivity field violates the constraint of maximum allowable concentration in all the studied heterogeneous fields. To satisfy this constraint, in-situ bioremediation cost increases by 8.47% to 56.83% to that of a homogeneous field. It shows the significance of aquifer heterogeneity in designing an optimized in-situ bioremediation system and hence, should be incorporated in the S/O model for in-situ groundwater bioremediation.

Multi objective simulation-optimization operation of dam reservoir in low water regions based on hedging principles.

The high level of reliability of water resources is always an advantage for consumers, but in arid and semi-arid regions where the inflow to the reservoir is faced with severe fluctuations, it makes sense to decrease the percentage of reliability of the system and allocate less water to consumption zones to prevent critical conditions such as emptying of the reservoir. In this research, the employed operation model is based on the simulation-optimization combination by considering the objectives of minimizing the violation of the allowed capacity of the reservoir and maximizing the percentage of supplying the demands. The optimal hedging variables are specified by linking the WEAP (Water Evaluation and Planning System) to the MOPSO multi-objective optimization algorithm. According to the available data, the duration of the simulation and optimization period in the model is 360months. After 1000 iterations, the optimal reservoir volume values are obtained at the hedging level and hedging coefficient in different months. Finally, the model results are compared with the results obtained from the standard operation policy (SOP). The results show that the proposed model is able to manage the allocation to needs in the dry months and prevent the reservoir from emptying. Also, by storing a part of the flow in the reservoir in watery months and consuming it in low water months, it increases the supply of needs by 20 to 35% and reduces the failure rate in dry months.

Determination of the cost-benefit efficient interval for sponge city construction by a multi-objective optimization model

The cost-benefit is a key factor when selecting an appropriate sponge city construction scheme. The research of applying intelligent technology to find cost-benefit efficient planning and construction of sponge city is urgently required. This paper established a multi-objective simulation optimization framework of sponge city construction which considered minimization of runoff control rate, pollutant control rate and life-cycle cost Non-dominated sorting genetic algorithm (NSGA-II) was successfully coupled to Storm water management model to complete the simulation-optimization process. A case study in Xining, China, was conducted to demonstrate the proposed framework. The results of this research suggested that 1) different sponge city construction schemes lead to different runoff control rates and pollutant control rates although under the same investment; 2) the runoff control rate and pollutant control rate total suspended solids decreased with the increase of the rainfall return period, while the cost of sponge city construction increased with the increase of rainfall return period. Furthermore, for T = 2-year, the sponge facility exhibited the most stable control effect on runoff and pollutants among the three different return periods (T = 2-year, 5-year, 10-year); 3) sponge city construction exhibited a “cost-benefit” efficient interval. For T = 2-year, the cost-benefit high efficiency interval of sponge city construction is calculated between 1.2 billion and 1.8 billion; for T = 5-year, the interval is between 1.2 billion and 1.8 billion, while for T = 10-year, the interval is between 1.3 billion and 2.1 billion. The above observations provide reference for reasonable and effective sponge city construction in Xining, China.

Design of a biomass-heating network with an integrated heat pump: A simulation-based multi-objective optimization framework

The integration of compression heat pumps is a promising technology to recover waste heat from exhaust gases of a biomass-based heating network. This requires, however, the consumption of additional electricity, which has a high emission factor or a high price in many countries. In this context, the integration of small-scale gasifier cogeneration is supposed to be superior technology to provide the necessary power for heat pumps. Nevertheless, the multiple possibilities of integrating these components imply a high degree of system complexity and, therefore, higher design requirements. To maximize the benefits of the integrated system, development of an optimization approach at the system level is necessary, so that the connection variants of the heat pump, the installation of gasifier cogeneration, and their optimal design can be adequately planned. This work introduces a multi-objective simulation–optimization framework for the design of the proposed integrated system based on the genetic algorithm, taking into account the complex thermodynamic processes as well as the techno-economic performances and environmental impacts of the concepts. As a case study, an existing biomass heat network located in Germany is investigated to test the capabilities of the proposed approach. The analysis of the optimization results demonstrates that it is possible to ensure the effective utilization of biomass resources while simultaneously achieving the economic and environmental compatibility of the system through an appropriate optimization design. The proposed simulation–optimization framework allows decision-makers to achieve an optimal system design under the given constraints and the chosen objectives.

Multi-objective simulation–optimization via kriging surrogate models applied to natural gas liquefaction process design

A surrogate-based multi-objective optimization framework is employed in the design of natural gas liquefaction processes using reliable, black-box process simulation. The conflicting objectives are minimizing both power consumption and heat exchanger area utilization. The Pareto solutions of the single-mixed refrigerant (SMR) and propane-precooled mixed refrigerant (C3MR) processes are compared to determine the suitability of each process in terms of energy consumption and heat exchanger area. Kriging models and the ɛ-constraint methodology are used to sequentially provide simple surrogate optimization subproblems, whose minimizers are promising feasible and non-dominated solutions to the original black-box problem. The surrogate-based ɛ-constrained optimization subproblems are solved in GAMS using CONOPT. The Pareto Fronts achieved with the surrogate-based framework dominate the results from the NSGA-II, a well-established meta-heuristics of multi-objective optimization. The objective functions of non-dominated solutions go as low as 1045 and 980.3 kJ/kg-LNG and specific UA values of 212.2 and 266.9 kJ/(°C kg-LNG) for SMR and C3MR, respectively. The trade-off solutions that present the minimum sum of relative objectives are analyzed as well as the dominance of C3MR over SMR at low power consumption values and conversely at low heat exchanger area utilization.

Hybrid rebalancing with dynamic hubbing for free-floating bike sharing systems

For managing the supply-demand imbalance in free-floating bike sharing systems, we propose dynamic hubbing (i.e., geofencing areas varying from one day to another) and hybrid rebalancing (combining user-based and operator-based strategies) and solve the problem with a novel multi-objective simulation optimization approach. Given historical usage data and real-time bike GPS location information, the basic concept is that dynamic hubs are determined to encourage users to return bikes to desired areas towards the end of the day through a user incentive program. Then, for the remaining unbalanced bikes, an operator-based rebalancing operation will be scheduled. The proposed modeling and optimization solution algorithm determines the number of hubs, their locations, the start time for initiating the user incentive program, and the amount of incentive by considering two conflicting objectives, i.e., level of service and rebalancing cost. In this study, for free-floating bike sharing, the level of service is represented by the walking distance of users for locating a usable bike, which is different from level of service metrics commonly used by station-based bike sharing, and the rebalancing cost is weighted incentive credits plus operator-based rebalancing cost. We implemented the proposed method on the Share-A-Bull free-floating bike sharing system at the University of South Florida. Results show that a hybrid rebalancing and dynamic hubbing strategy can significantly reduce the total rebalancing cost and improve the level of service. Moreover, taking the advantage of crowd-sourcing (or job-sharing) reduces negative impacts—energy consumption and greenhouse gas emissions—of the operation of rebalancing vehicles and makes bike sharing a more promising environmentally friendly sharing transportation mode.

Satellite data-driven multi-objective simulation-optimization modeling for water-environment-agriculture nexus in an arid endorheic lake basin

In the arid endorheic lake basin with highly developed agriculture, water scarcity drives the nexus of water-environment-agriculture (WEA) while potential evapotranspiration (ETp) is a critical element linking basin-scale irrigation water demand and agro-economic development. The study proposes a satellite remote sensing data-driven multi-objective simulation-optimization (SO) framework to implement the basin-scale WEA nexus management. The hydrological model (MODFLOW-NWT) combined with the MODIS (Moderate Resolution Imaging Spectroradiometer) based ETp estimation with a 500-m spatial resolution simulates the monthly surface water (SW) and groundwater (GW) irrigation practices. Then, the multi-objective evolutionary algorithm (ε-MOMA) is applied to solve management model which includes maximization of agro-economic benefit and environmental flow and minimization of environmental risk of total nitrogen loading subject to irrigation water demand, land availability and food supply. The SO framework is validated in an arid endorheic lake basin with intensive agricultural irrigation in northwestern China. The four-dimensional Pareto-optimal solutions are achieved to optimize crop planting structure and irrigation water allocation in order to avoid cognitive myopia triggered by low-dimensional or single objective optimization. The results highlight the trade-off among the high value crop planting, SW-GW irrigation and environmental flows, and indicating that lowering nitrogen loading is more sensitive to agro-economic benefit. Historical maximum and minimum ETp scenarios are tailored to quantify the impact of ETp change on WEA nexus management. Results show the increasing ETp has a great negative effect on the environmental flow objectives. Therefore, how multi-sectoral interests change depending on the basin-wide development goals may be difficult to regulate due to complex WEA nexus and associated deep uncertainty in the future.

Multi-Objective Simulation Optimization Integrated With Analytic Hierarchy Process and Technique for Order Preference by Similarity to Ideal Solution for Pollution Routing Problem

Routing is one of the most important components of logistics and has a vital role in economic growth. Inefficient routing has also contributed to troubling pollutants that have adverse environmental effects worldwide. Over the years, operations research practitioners have therefore drawn considerable attention to the routing problem. In this paper, a pollution routing problem (PRP) with time window constraints and capacitated vehicles is considered to integrate environmental issues in routing for pollution reduction. This work introduces a simulation optimization methodology that includes the realization of important parameters in real-world applications to minimize the total lateness cost and fuel consumption. The proposed method was developed with Simio simulation software and non-dominated sorting genetic algorithm-II (NSGA-II). It evaluates the dynamic system operations over time and performs a comprehensive analysis. In addition, a two-step hierarchical multi-criteria decision-making (MCDM) method is employed to choose the best strategy for PRP. Results of the two-step hierarchical MCDM method demonstrated that the strategy considering the total cost of supply chain is the best alternative for the proposed system. The use of MCDM methods after simulation can be a more robust and effective way to find solutions to such complex problems.

Multi-Objective Design Optimization of Flexible Manufacturing Systems Using Design of Simulation Experiments: A Comparative Study

One of the basic components of Industry 4.0 is the design of a flexible manufacturing system (FMS), which involves the choice of parameters to optimize its performance. Discrete event simulation (DES) models allow the user to understand the operation of dynamic and stochastic system performance and to support FMS diagnostics and design. In combination with DES models, optimization methods are often used to search for the optimal designs, which, above all, involve more than one objective function to be optimized simultaneously. These methods are called the multi-objective simulation–optimization (MOSO) method. Numerous MOSO methods have been developed in the literature, which spawned many proposed MOSO methods classifications. However, the performance of these methods is not guaranteed because there is an absence of comparative studies. Moreover, previous classifications have been focused on general MOSO methods and rarely related to the specific area of manufacturing design. For this reason, a new conceptual classification of MOSO used in FMS design is proposed. After that, four MOSO methods are selected, according to this classification, and compared through a detailed case study related to the FMS design problem. All of these methods studied are based on Design of Experiments (DoE). Two of them are metamodel-based approaches that integrate Goal Programming (GP) and Desirability Function (DF), respectively. The other two methods are not metamodel-based approaches, which integrate Gray Relational Analysis (GRA) and the VIKOR method, respectively. The comparative results show that the GP and VIKOR methods can result in better optimization than DF and GRA methods. Thus, the use of the simulation metamodel cannot prove its superiority in all situations.