Simulation-optimization Approach Research Articles

Harvesting fragmented boreal forest: system selection using a simulation-optimization approach

ABSTRACT Nordic forests, such as those found in Canada were known to offer opportunities for large and relatively homogeneous harvesting blocks. Increased fragmentation of forests makes operation planning more difficult and affects costs of road building and machinery relocation. Currently, the diversity of systems used for forest operations in eastern Canada represents only a fraction of existing alternatives. It might be that at least one alternative outperforms the systems currently used in the fragmented boreal forest. Hence, a subset of potential systems was identified in a preliminary evaluation of harvest systems in fragmented operations. From this sample, the objective is to develop a mathematical model to identify the harvest system with the lowest wood procurement cost in fragmented boreal forests. The candidate systems were simulated and optimized using a static deterministic approach. According to our results, the best systems in fragmented forests involve fewer machines, hence a lower relocation cost. Two CTL systems outperformed all others. The system using removable crane self-loading trucks for transport and the one using the forwarder for loading both resulted in 4 $/m3 (USD) advantage over the third least expensive system in the most fragmented harvest sites. The proposed model can be directly applied to assess harvest systems in other parts of the world with similar forest fragmentation challenges.

A simulation optimization approach for weight valuation in analytic hierarchy process

The analytic hierarchy process (AHP) is a structured technique used to analyze complex decision-making situations such as resource allocation, benchmarking, and quality management. In the weight valuation step of using AHP to select the best design, pairwise comparison matrices are used to calculate the local priorities for designs that have contentious and unresolved criticisms. In this study, we propose a Bayesian approach using a Dirichlet-multinomial model to estimate local priorities during weight valuation. Experts are only asked to select the best design with respect to predetermined criterion. Subsequently, local priorities are estimated without pairwise comparison matrices. To improve the efficiency of the AHP, we propose two expert allocation policies (AHP-KG and AHP-AKG) based on the ranking and selection procedures. Our numerical results show that the proposed AHP-KG and AHP-AKG policies outperform pure exploration and proportional allocation policies.

Dynamic soft-kill weapon-target assignment in naval environments

One of the most significant threats faced by ships is anti-ship missiles. Nowadays, these missiles, equipped with diverse guidance systems, can locate their trajectory and attack the ship. Consequently, ships need to utilize their weapons to attempt to neutralize these threats. This article aims to develop dynamic assignment algorithms to assign a ship’s defensive soft-kill weapons to a set of incoming missiles, to minimize the average damage inflicted on the ship. To this end, initially, a binary linear programming model is developed to solve the static weapon-target assignment problem. Subsequently, a simulation–optimization algorithm and a reinforcement learning-based approach, grounded in the value iteration algorithm, are developed to solve the dynamic weapon-target assignment problem. To compare and evaluate the performance of the developed solution methods, we employ a set of randomly generated test instances. Computational results indicate that the reinforcement learning approach, due to its inherent foresight, outperforms the simulation–optimization approach in reducing the inflicted damages. However, in terms of CPU run time, the simulation–optimization approach is more efficient.

Simulation Optimization of Station-Level Control of Large-Scale Passenger Flow Based on Queueing Network and Surrogate Model

Urban rail transit encounters supply–demand contradictions during peak hours, seriously affecting passenger experience. Therefore, it is necessary to explore and optimize passenger-flow control strategies for urban rail transit stations during peak hours. However, current research mostly focuses on passenger-flow control at the network level, and there is insufficient exploration of specific operational strategies at the station level. At the same time, the microscopic simulation model for passenger-flow control at the station level faces the challenge of balancing efficiency and accuracy. This paper presents a simulation optimization approach to optimize the station-level passenger-flow controlling measures, based on a queueing network and surrogate model, aiming to improve throughput, minimize congestion, and enhance passenger experience. The first stage of the method modeled the urban railway station using queueing network theory and multi-agent theory, and then built a mesoscale simulation model that was based on an urban railway station. In the second stage, a passenger flow management and control model for ingress flow was established by combining the Kriging model with a queuing network model, and the particle swarm optimization algorithm was used to solve the model. On this basis, a simulation optimization method for station passenger-flow control was established. Finally, we conducted an example analysis of Zhongguancun Station on the Beijing subway. By comparing the simulation results before and after control, as well as comparing the optimal control scheme obtained by this method with the results of other control schemes, the results showed that the simulation optimization method proposed in this paper can propose an optimal passenger-flow control scheme. By using this method, stations can significantly enhance sustainability. For example, the method not only saves human resources but also effectively avoids or reduces congestion, boosting passenger travel efficiency and safety. By minimizing wait times, these methods lower energy consumption and support the sustainable development of public transportation systems, contributing to more sustainable urban environments.

Shelter location–allocation problem for disaster evacuation planning: A simulation optimization approach

Immediately after a major earthquake strikes, a segment of the people in the disaster region must evacuate to relief shelters. If the evacuation proceeds in an efficient and safe manner, many lives can potentially be saved as the effects of secondary factors, e.g., secondary collapses, stampedes, and aftershocks, can largely be mitigated, while at the same time the people taking refuge can quickly gain access to vital relief goods. Formulating the best possible protocols for post-earthquake evacuation is a crucial, yet highly challenging problem considering the profound uncertainty which is present in the post-disaster environment, including the location and degree of infrastructural damage, the number and location of people who need to evacuate, the behavior and psychology of evacuees, etc. In this research, we formulate a two-stage stochastic evacuation simulation model capable of credibly accounting for these uncertainties. We develop a solution methodology to solve the shelter location–allocation problem represented by the two-stage model, finding the optimal location of shelters to be open as the first stage solution and the optimal assignment of evacuees to shelters as the second stage solution to minimize the total expected cost. The cost-based objective function consists of the cost of opening shelters, a deprivation cost component, the cost of trapped evacuees, and the cost of shelters exceeding capacity. In developing this framework, we collaborate with the National Science and Technology Center for Disaster Reduction (NCDR) in utilizing GIS-based probabilistic models of road damage. We apply the proposed framework to run an extensive numerical study and present various valuable observations and insights which can be gained.

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.

Close-to-nature management of tropical timber plantations is economically viable and provides biodiversity benefits

Abstract Reforestation of tropical forests is crucial to mitigate the climate crisis and restore ecosystems. However, past efforts have been criticized for establishing monoculture timber plantations with exotic tree species. Close-to-nature (CTN) practices aim to minimize negative forest management impacts on forests ecosystems by mimicking natural dynamics. So far, CTN management practices are rarely applied in tropical plantation forestry. This study evaluates the economic, carbon sequestration, and biodiversity potential of CTN management in tropical mixed-species plantations in Central America using a simulation-optimization approach. To our knowledge, this study is the first to assess the potential of tropical CTN-managed plantations on the basis of detailed process-based forest growth simulations. CTN practices such as selective harvesting, retention forestry, and shelterwood cutting of mixed-species stands were compared to even-aged mixtures and conventional monoculture practices. Results showed that CTN management was economically viable for certain species mixtures and management practices at an 8 % discount rate and had the potential to increase carbon storage and biodiversity in the modeled plantations. At current carbon prices, CTN-managed plantations may only become financially competitive with monocultures, if monocultures are excluded from carbon certification schemes that increasingly aim at co-producing non-carbon benefits like biodiversity conservation. If carbon prices increase, the sale of carbon credits could finance the transformation of monocultures to CTN-managed mixed-species stands. The competitiveness of CTN management could also be improved through performance-based biodiversity payments, such as the sale of biodiversity credits.

Optimizing equity in intermittent water supply systems: A volume-driven demand and flow control approach

In many developing countries, intermittent water supply (IWS) systems are prevalent, resulting in limited access to water for consumers who do not have continuous 24/7 supply. Consumers are compelled to utilize private storage tanks to adapt to IWS conditions and overcome periods with no water access. While achieving greater water availability, the use of storage tanks exacerbates existing supply inequity. We propose a simulation–optimization approach that models consumers using a volume-driven demand technique to account for IWS consumer behavior, and employs a Bayesian optimization to determine the flow control valve schedule to improve local supply and global equity. The results demonstrate the existence of a hierarchy in supply in which consumers with favorable hydraulic conditions are able to fill their tanks any time supply is available, leaving consumers with less-favorable hydraulic conditions to fill their tanks only when an excess in supply exists. With limited control, only some global disparities can be offset. Additionally, intermittency in supply exacerbates the inequity and limits the efficacy of controls, reinforcing the importance of improving the continuity in supply. The proposed approach provides insights into the mechanisms behind inequitable supply in IWS systems, where further research is needed to maximize consumer welfare.

Development of a multi-objective reservoir operation model for water quality-quantity management

This study aims to develop a multi-objective quantitative-qualitative reservoir operation model (MOQQROM) by a simulation-optimization approach. However, the main challenge of these models is their computational complexity. The simulation-optimization method used in this study consists of CE-QUAL-W2 as a hydrodynamic and water quality simulation model and a multi-objective firefly algorithm-k nearest neighbor (MOFA-KNN) as an optimization algorithm which is an efficient algorithm to overcome the computational burden in simulation-optimization approaches by decreasing simulation model calls. MOFA-KNN was expanded for this study, and its performance was evaluated in the MOQQROM. Three objectives were considered in this study, including (1) the sum of the squared mass of total dissolved solids (TDS), (2) the sum of the squared temperature difference between reservoir inflow and outflow as water quality objectives, and (3) the vulnerability index as a water quantity objective. Aidoghmoush reservoir was employed as a case study, and the model was investigated under three scenarios, including the normal, wet, and dry years. Results showed the expanded MOFA-KNN reduced the number of original simulation model calls compared to the total number of simulations in MOQQROM by more than 99%, indicating its efficacy in significantly reducing execution time. The three most desired operating policies for meeting each objective were selected for investigation. Results showed that the operation policy with the best value for the second objective could be chosen as a compromise policy to balance the two conflicting goals of improving quality and supplying the demand in normal and wet scenarios. In terms of contamination mass, this policy was, on average, 16% worse than the first policy and 40% better than the third policy in the normal scenario. In the wet scenario, it was, on average, 55% worse than the first policy and 16% better than the third policy. The outflow temperature of this policy was, on average, only 8.35% different from the inflow temperature in the normal scenario and 0.93% different in the wet scenario. The performance of the developed model is satisfactory.

A simulation-optimization approach for economic assessment of an imperfect serial production management under uncertainty

ABSTRACT One of the critical quandaries in real-world production management systems is uncertainty in the product demand, which is closely associated to the product price. This uncertain behavior of demand affects production decisions in a system. In this context, this study develops a decision support framework for an imperfect serial production management system under uncertainty. The proposed framework is analyzed considering price-dependent uncertain demand and random defective rate in the system, thus considering both internal and external uncertainties of the system. Three distinct production models are formulated considering different types of uncertain relations among the product demand and market price. Closed form solutions of the decisions are obtained through hybrid of simulation and analytical optimization and are tested through an experimental case analysis. @RISK of Palisade Suite is utilized for simulation and to capture the uncertainty in defective proportion and demand elasticity parameters by creating several possibilities.

Hybrid simulation-optimization approach for planning relief-aid distribution with a real-world case study

PurposeAuthorities have set up numerous security checkpoints during times of armed conflict to control the flow of commercial and humanitarian trucks into and out of areas of conflict. These security checkpoints have become highly utilized because of the complex security procedures and increased truck traffic, which significantly slow the delivery of relief aid. This paper aims to improve the process at security checkpoints by redesigning the current process to reduce processing time and relieve congestion at checkpoint entrance gates.Design/methodology/approachA decision-support tool (clearing function distribution model [CFDM]) is used to minimize the effects of security checkpoint congestion on the entire humanitarian supply network using a hybrid simulation-optimization approach. By using a business process simulation, the current and reengineered processes are both simulated, and the simulation output was used to estimate the clearing function (capacity as a function of the workload). For both the AS-IS and TO-BE models, key performance indicators such as distribution costs, backordering and process cycle time were used to compare the results of the CFDM tool. For this, the Kerem Abu Salem security checkpoint south of Gaza was used as a case study.FindingsThe comparison results demonstrate that the CFDM tool performs better when the output of the TO-BE clearing function is used.Originality/valueThe efforts will contribute to improving the planning of any humanitarian network experiencing congestion at security checkpoints by minimizing the impact of congestion on the delivery lead time of relief aid to the final destination.

Spatial allocation of bioretention cells considering interaction with shallow groundwater: A simulation-optimization approach

Green infrastructure (GI), as one type of ecological stormwater management practices, can potentially alleviate water problems and deliver a wide range of environmental benefits in urban areas. GIs are often planned and designed to reduce runoff and mitigate pollution. However, the influence of GI on groundwater hydrology and that of shallow groundwater on GI performance was seldom considered. This study utilized a calibrated surface-subsurface hydrological model, i.e., Storm Water Management Model coupled with USGS's modular hydrologic model (SWMM-MODFLOW) to consider the interaction between GI and groundwater into the process of GI planning. The optimal implementation ratio, aggregation level and upstream-downstream location of bioretention cells (BC, one type of GI) under different planning objectives and hydrogeologic conditions was explored. The consideration of groundwater management exerted a significant impact on the optimal spatial allocation of BCs. The results showed that when groundwater management was more concerned than runoff control, BCs were recommended to be allocated more apart from each other and more upstream in the catchment because more-distributed and upstream BCs can result in lower groundwater table rise which is beneficial. BCs were overall recommended to be allocated in areas of deeper groundwater tables, coarser soils, and flatter topographies. However, the spatial features of BCs are related to each other, the choice of them are affected by various hydrogeologic factors simultaneously. The exact location of BCs should be determined by considering the trade-off between runoff control efficiency and groundwater impact. The findings obtained in this study can provide guidance on GI planning in shallow groundwater areas.

Stochastic ambulance dispatching and routing in mass casualty incident under road vulnerability

This paper investigates the stochastic ambulance dispatching and routing (SADR) problem with multiple casualty collection points in the mass casualty incidents (MCI) under uncertainty of road vulnerability and traffic congestion caused by an earthquake. A two-stage stochastic mixed-integer nonlinear programming model is formulated to derive a reliable and high-quality ambulance scheduling, dispatching and routing solution for maximizing expected number of survivors of all casualties. An integrated simulation optimization approach combining a data-driven travel time scenario generation algorithm, sample average approximation, and a column-generation-based heuristic method is developed to efficiently solve this complicated two-stage SADR model with a nonlinear objective function and continuous travel time distributions. Collaborating with the National Science and Technology Center for Disaster Reduction (NCDR), an empirical study using a potential real-world earthquake scenario occurring in Taiwan is conducted to demonstrate the usefulness and effectiveness of our proposed model and solution approach compared to the deterministic model and two current-practice heuristics.

Ternary Organic Solar Cells—Simulation–Optimization Approach

Ternary Organic Solar Cells—Simulation–Optimization Approach

A discrete event simulation model assessing the impact of using new packaging in an agri-food supply chain

Fresh product loss is a global issue having negative impact on both the economy and the environment. The perishability of fruits and vegetables can lead to post-harvest losses in the supply chain as well as at consumer’s homes. Packaging plays an important role in preventing such losses. This paper focuses on the impact of new packaging on reducing product waste and other logistics costs. The objective is to evaluate the performance of an inventory control policy that uses new packaging to extend the shelf life of strawberries while maintaining their quality. We have developed a discrete event simulation model, that represents store inventory, by considering random consumer demand and random product shelf life, to assess various packaging types leading to different post-harvest lives of strawberries. We then use a simulation-optimisation approach to determine optimal inventory control parameters and conduct extensive numerical experimentations to evaluate the impact of the new packaging, under three distribution strategies (FIFO, FEFO, and LEFO). Results indicate that the new packaging has a significant impact: extending the shelf life of strawberries can reduce the total logistics cost by 0.66% (when using LEFO strategy) to 6.37% (FIFO strategy) and improve sales by 0.67% (FEFO) to 10.07% (FIFO).

Sustainable and integrated rail operations planning for grain exports

ABSTRACT The grain export in Brazil is operated by several players and regulated by a few governmental agencies. Although players have particular interests, sustainability is an increasing concern that poses a common challenge to rail operations: reducing CO2 emissions. This work proposes a simulation-optimisation approach for assigning trains to routes in a complex queuing system, minimising CO2 emissions of the country-wide rail operations without compromising the delivery of planned volume exports. The model considers a multi-modal logistic system, i.e., terminals, railways, and ports integrated into a closed circuit. The computational experiments using real-world data of 2021 reveal that the CO2 emissions could be reduced by 5.32 kilo tons and still increase the grain export by 2.20% per year.

Part transformation-based spare parts inventory control model for the high-tech industries

Timely and cost-effective supply of spare parts is the main purpose of spare parts inventory management and substitution is an effective way to fulfill demand on time. However, direct substitution of spare parts is not suitable for the high-tech industries due to the ever-changing nature of the product structures. Hence, parts should be transformed to be used as substitutes. This paper provides a novel spare parts inventory control model for the high-tech industries. In the proposed model, part transformation-based substitution is considered and the near-optimal values of spare part inventory levels (s, S) that minimize total cost are determined by using a simulated annealing-based simulation optimization approach. Computational analyses are performed for a hypothetical inventory system by considering transformation and no-transformation cases. The results reveal that transformation is very useful for the companies who endure long production lead times and high penalty costs associated with backorders.

Novel Simulation Optimization Approach for Supply Chain Coordination and Management

The importance of supply chain management is paramount nowadays and this research seeks ways for better supply chain coordination. We present a roadmap for implementing true digital replicas of physical supply chains based on a disruptive simulation modeling approach. These digital replicas can enable the comparative evaluation of existing Just-In-Time (JIT) control policies for the first time without the unrealistic assumptions of existing studies in the literature. These realistic supply chain simulation models have the prospect of giving us better understanding of their behavior and of their complex dynamics. We provide guidelines for using the digital replicas and advanced optimization approaches to derive novel supply chain coordination policies pushing the field of JIT control beyond the state-of-the-art. An equally important contribution of this research is that it advances the field of hybrid discrete event-system dynamics (DES-SD) simulation with the prospect of pushing Systems Engineering to the next level. This research discusses theoretical foundations for mixing DES with SD simulation, and explores technical aspects and implementation details. The implications of this extent well beyond the supply chain field since both DES and SD have dozens of application areas including manufacturing, service systems and economics, social sciences, respectively. The paper is concluded with a discussion on the potential impact of the proposed approach including insights for practitioners, applicability of scientific results in industry and prerequisites for business process re-engineering.

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).

Simulation–optimisation approach for sustainable planning of intermodal logistics in the Brazilian grain export industry

Planning nationwide sustainable logistics for grain exports at a tactical level assumes flexibility in handling uncertainties. Despite the dynamic operations, the solution’s approach focuses mainly on optimisation, leaving out the queuing requirements. This paper presents a simulation–optimisation approach for planning the Brazilian intermodal grain export chain by integrating trucks, railways, and port terminal operations. We evaluate a base scenario, aiming to minimise CO2 emissions in rail transport, meeting demand requirements, and three alternative planning scenarios concerning demand retraction, trend, and expansion. The sustainable grain transportation plan of the base scenario shows a 66.8 kilo tons CO2/year emission reduction while increasing the exports by 2.51% (approximately 146 kilo tons grains/year) and a contribution to profit of $43.82 million in the short term. Scenario analyses open discussions about sustainable investments in intermodal transport for the medium and long term. The proposed approach is suitable for other commodity chains, representing a step towards consistently applying environmentally oriented supply chain planning.