Simulation-based Optimization Approach Research Articles

Developing a Flexible Manufacturing Control System Considering Mixed Uncertain Predictive Maintenance Model: a Simulation-Based Optimization Approach

Developing a Flexible Manufacturing Control System Considering Mixed Uncertain Predictive Maintenance Model: a Simulation-Based Optimization Approach

Meta-Prediction Models for Bullwhip Effect Prediction of a Supply Chain Using Regression Analysis

In this study, the main factors that can cause the bullwhip effect and stock amplification are investigated using a simulation-based optimization approach and regression analysis. A two-echelon supply chain with uncertain customer demand and delivery lead time operating with the periodic-review reorder cycle policy is studied. The parameters of smoothing inventory replenishment and forecasting methods are required. These parameters are optimized in terms of minimizing the Total Stage Variance Ratios (TSVRs) of both echelons. The results show that even though all factors of interest have an impact on the bullwhip effect, using smoothing proportional controllers can reduce TSVRs (the sum of the order varaince ratio and net stock amplification). The meta-prediction models can effectively help predict the amount of the bullwhip effect of a chain under various situations with an average MAPE of less than 11%. The results can assist decision makers in the management of a supply chain to realize, benchmark with the optimal results, and reduce the TSVRs under an uncertain environment.

Joint Scheduling of Yard Crane, Yard Truck, and Quay Crane for Container Terminal Considering Vessel Stowage Plan: An Integrated Simulation-Based Optimization Approach

The joint scheduling of quay cranes (QCs), yard cranes (YCs), and yard trucks (YTs) is critical to achieving good overall performance for a container terminal. However, there are only a few such integrated studies. Especially, those who have taken the vessel stowage plan (VSP) into consideration are very rare. The VSP is a plan assigning each container a stowage position in a vessel. It affects the QC operations directly and considerably. Neglecting this plan will cause problems when loading/unloading containers into/from a ship or even congest the YT and YC operations in the upstream. In this research, a framework of simulation-based optimization methods have been proposed firstly. Then, four kinds of heuristics/metaheuristics has been employed in this framework, such as sort-by-bay (SBB), genetic algorithm (GA), particle swarm optimization (PSO), and multiple groups particle swarm optimization (MGPSO), to deal with the yard crane scheduling problem (YCSP), yard truck scheduling problem (YTSP), and quay crane scheduling problem (QCSP) simultaneously for export containers, taking operational constraints into consideration. The objective aims to minimize makespan. Each of the simulation-based optimization methods includes three components, load-balancing heuristic, sequencing method, and simulation model. Experiments have been conducted to investigate the effectiveness of different simulation-based optimization methods. The results show that the MGPSO outperforms the others.

Simulation-based design optimization of heat pump systems considering building variations

Heat pump systems (HPS) are a promising technology to reduce greenhouse gas emissions in the building sector by low-emission heat supply. However, their market penetration is inhibited by lower costs of conventional technologies. Thus, heat pumps must become cheaper to purchase and operate. Investment costs are related to the design, while operating costs are related to operational efficiency. Although design and operation are mutually interdependent, they are considered separately in conventional design procedures. This disconnect prevents the full potential of the technology from being realized.In this contribution, we present a framework to simultaneously consider design and operation in one optimization step using a simulation-based optimization approach called HPS eOD Design. Based on a building heat demand, the framework optimizes annualized costs and emissions of heat pump systems considering the operational behaviour already in the design. By coupling the optimization program with a software for automatic generation of building models called TEASER, HPS eOD Design can be applied to a wide variety of building types and locations.Optimization results reveal that optimizing heat pump system sizing improves annualized costs and emissions for six examined buildings by up to 8.2% in costs and 12.7% in emissions compared to sizings derived by the conventional design procedure VDI4645. To exploit even further potential, the integration of operational optimization within the design is highly recommended. In addition, a more detailed modeling approach of the building should be implemented in HPS eOD Design. Both measures lead to an acceleration of the building sector defossilization.

Hybrid Simulation-Based Optimization for Production Planning of a Dedicated Remanufacturing System

This paper presents a comparative simulation study of a dedicated remanufacturing system. The production line of a dedicated remanufacturing system producing multiple products under uncertain environment is improved through the simulation-based optimization approach. Appropriate inventory capacity of buffers, a proper switching rule, and a suitable run size of each product should be optimally set to yield the highest system's profit. Then, hybrid simulation-based optimization algorithms with two hybrid optimization forms using a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) as complementary to each other in relation to their standard algorithms are proposed and compared. A case study is used to demonstrate and compare the performances among the algorithms to show the advantages of the proposed algorithms. This approach can assist in decision making for the planning and management of dedicated remanufacturing systems that are required to operate with various decision variables under uncertainties.

A Simulation-based Optimization Approach to Logistic and Supply Chain Network Design

The Department of Defense (DoD) operates a world-wide supply chain, which in 2017 contained nearly 5 million items collectively valued at over $90 billion. Since at least 1990, designing and operating this supply chain, and adapting it to ever-changing military requirements, are highly complex and tightly coupled problems, which the highest levels of DoD recognize as weaknesses. Military supply chains face a wide range of challenges. Decisions made at the operational and tactical levels of logistics can alter the effectiveness of decisions made at the strategic level. Decisions must be made with incomplete information. As a result, practical solutions must simultaneously incorporate decisions made at all levels as well as take into account the uncertainty faced by the logistician. The design of modern military supply chains, particularly for large networks where many values are not known precisely, is recognized as too complex for many techniques found in the academic literature. Much of the literature in supply chain network design makes simplifying assumptions, such as constant per-unit transportation costs regardless of the size of the shipment, the shipping mode selected, the time available for the delivery, or the route taken. This article avoids these assumptions to provide an approach the practitioner can use when designing and adapting supply chain networks. This research proposes a simulation-based optimization approach to find a near-optimal solution to a large supply chain network design problem of the scale faced by a theater commander, while recognizing the complexity and uncertainty that the practicing military logistician must deal with.

Designing Pareto optimal electricity retail rates when utility customers are prosumers

Electric retail rate design is relevant to utilities, customers, and regulators as retail rates impact the utility's revenue as well as the customers' electricity bills. In California, regulators approve rate proposals by privately owned vertical integrated utilities. Approval, however, is subject to compliance with multiple, potentially conflicting objectives such as economic or environmental objectives. Additionally, retail rates are price signals that affect how customers use electricity services. When utility customers change their usage, they also impact the ratemaking objectives to which rates have been designed. This suggests a feedback loop, which is particularly pronounced with prosumers, as they can systematically optimize their interactions with the electricity system. Prevalent ratemaking methods may not deliver retail rates that are optimal for multiple objectives when customers are prosumers. We propose a novel ratemaking method that formalizes the problem of designing retail rates as a multi-criteria optimization problem and accounts for prosumer reactions through a simulation-based optimization approach. Through a fictive case study, we found that the resulting Pareto frontiers are useful in recognizing and balancing tradeoffs among conflicting ratemaking objectives. Additionally, our results indicate that prevailing retail rates in California are not Pareto optimal.

Design of Comminution Plants in the Ceramic Industry Using a Simulation-based Optimization Approach

In many cases, the design process of a structural ceramic comminution plant typically consists of an ‘expert designer’ who makes decisions using intuitive criteria to select commercial equipment. This paper proposes a simulationbased optimization approach to help decisionmaking. The complexity of the problem lies in selecting the model and amount of equipment for each stage at the lowest cost while simultaneously satisfying a previously fixed production and granulometry. The proposed approach is based on a genetic algorithm to generate solutions and facilitate the optimization process, together with discrete simulation to evaluate the performance of the comminution process according to its service level. To evaluate the algorithm, different problems, whose parameters are based on the requirements of the ceramic industry, are solved and analyzed.

Performance-based Bi-objective optimization of structural systems subject to stochastic wind excitation

This paper outlines the development of a stochastic simulation-based design optimization approach for dynamic wind excited structures in which correlations between component damages and losses are explicitly treated. The proposed approach integrates a bi-objective design optimization scheme with a probabilistic performance-based wind engineering methodology which systematically accounts for the various sources of uncertainties involved in system loss estimation. Through the ∊-constraint technique, the bi-objective optimization problem is transformed into a series of single-objective stochastic optimization problems. To solve each ∊-constraint optimization problem, a pseudo-simulation scheme is proposed that allows for the formulation of an approximate sub-problem that can be solved sequentially to identify solutions that define a set of Pareto optimal designs. In the proposed scheme, samples of engineering demands are approximated in terms of auxiliary variable vectors, which are by-products of an augmented simulation carried out in a fixed design point. Analytical expressions are derived that relate the engineering demand samples to the second-order statistics of wind-induced losses based on the concept of fragility. Potential correlations between the component capacities and component losses are explicitly treated. The effectiveness of the proposed approach and its scalability to high-dimensional problems are illustrated through optimal designs of moment-resisting frames subject to stochastic wind loads.

A Bi-Objective Simulation-Based Optimization Approach for Optimizing Price, Warranty, and Spare Part Production Decisions Under Imperfect Repair

This paper proposes an efficient methodology based on the Monte-Carlo simulation-based bi-objective optimization, to determine base-warranty (BW) and extended warranty (EW) parameters based on the product lifecycle. The first objective, which is from the manufacturer’s perspective, maximizes the profit while the second objective minimizes the expected number of failures that occurred during the out-of-warranty period. The manufacturer can rectify failed products via minimal repair, imperfect repair and perfect repair. The optimization model has decision variables including the product price, BW length, EW length, EW price, product failure rate, imperfect repair level, and spare part production rate in each time interval. The structure of the model admits the design of a hybrid method based on the multi-objective optimization search algorithm, Monte-Carlo simulation and an exact Out-Of-Kilter (OOK) algorithm. The nondominated sorting genetic algorithm (NSGA-II) and the multi-objective particle swarm optimization (MOPSO) algorithm are used as search algorithms. The proposed approach consists of three stages, where in the first stage, product price, BW length, EW length, EW price, product failure rate, imperfect repair level are set by NSGA-II/MOPSO. In the second stage, the number of failed products is calculated by the Monte-Carlo simulation and in the third stage, we show that the spare part inventory control sub problem can be transformed to a minimum cost network flow problem which is optimized by the OOK algorithm to attain a unified solution. A Taguchi approach is used to find the optimum level of parameters. The performance of algorithms is compared based on three different metrics. Results on a real-world problem demonstrate that the NSGA-II-OOK algorithm is more effective than the MOPSO-OOK algorithm. Through a sensitivity analysis, we analyze how various levels of planning horizon can affect Pareto-set which indicates valuable managerial insight.

Solving the buffer allocation problem using simulation-based optimisation

In production lines, buffers function as a means to decouple stations, which reduce the effect that station failures and varying process times have on the complete line’s throughput. However, adding larger buffers can be costly, for example, in the automotive industry where it results in increased working capital. This manuscript addresses the buffer allocation problem (BAP), seeking the smallest total buffer size while meeting a prescribed throughput by employing a simulation-based optimisation approach. A Tabu Search algorithm searches the solution space for the optimal buffer configuration while a discrete event simulation model evaluates each configuration, accounting for the machine (un)reliability. Since the multiple simulations add a sizeable computational burden, our approach introduces a novel neighbourhood search mechanism, which borrows from the Theory of Constrains. Solving test sets available in the literature suggest that this approach is 18 times faster than prior Adaptive Tabu Search approaches for small problems, and more than five times faster for medium-sized problems.

Scenarios for the integration of renewable gases into the German natural gas market – A simulation-based optimisation approach

Natural gas plays an essential role in Germany's energy system, both in the heating and electricity sectors. In order to achieve climate goals, numerous technologies aim at substituting fossil with renewable gas. Those technologies start reaching technical maturity, yet they are mostly not economically competitive under the current market conditions. To find transition paths to resolve this issue this paper introduces the simulation-based optimisation model MIREG (Model for the Integration of Renewable Gases). Combining a system-dynamic simulation of the various renewable gas options with an optimisation model of the gas market, the model puts strong focus on the reproduction of gas market's mechanisms and the implications of a rising penetration of renewable gases. The model is then applied to analyse the effects of different developments of market conditions and funding strategies on the possible share of renewable gases in the German gas mix until 2050. Results show that if renewable gases are supposed to account for a significant share of gas consumed in Germany they either need to be funded substantially or market conditions have to change. For a share of 23% renewable gas in the gas mix CO2 prices for example would have to reach a level of 150 €/tCO2 by 2050 (300 €/tCO2 for 54%). The scenarios also indicate that in order to meet significant amounts of the German gas consumption with renewable gases, international solutions need to be aimed at e.g. by importing renewable gas from locations with high potential of renewable energy or by importing biomass.

Incentive Design for Flexibility Provisions From Residential Energy Hubs in Smart Grid

Transforming residential loads to residential energy hubs (REHs) and hence creating a flexibility in their energy consumption, can offer significant benefits to the power grid, such as peak reduction, congestion relief and capacity deferral. In this article a novel framework is proposed to simultaneously determine the optimal incentive paid by the local distribution company (LDC) to residential customers and their optimal level of participation in transforming to REHs, considering the economic benefits of both parties. The proposed framework models the relationship between customers’ participation and incentives offered by the LDC. A Monte Carlo simulation based optimization approach is employed to create a large dataset of the incentives ( $\omega $ , $\rho ^{Inc}$ ) payable by the LDC, and a desired internal rate of return (IRR) by the residential customer, so as to cover the wide range of possible IRRs the customers may have, and the impact of the incentives ( $\omega $ , $\rho ^{Inc}$ ) on each other. Numerical simulations considering a 33-bus distribution system show that the proposed framework is effective and can economically benefit the LDC and residential customers, while also achieving peak load reduction and improving distribution system operation.

Optimizing Reconfigurable Manufacturing Systems for Fluctuating Production Volumes: A Simulation-Based Multi-Objective Approach

In today’s global and volatile market, manufacturing enterprises are subjected to intense global competition, increasingly shortened product lifecycles and increased product customization and tailoring while being pressured to maintain a high degree of cost-efficiency. As a consequence, production organizations are required to introduce more new product models and variants into existing production setups, leading to more frequent ramp-up and ramp-down scenarios when transitioning from an outgoing product to a new one. In order to cope with such as challenge, the setup of the production systems needs to shift towards reconfigurable manufacturing systems (RMS), making production capable of changing its function and capacity according to the product and customer demand. Consequently, this study presents a simulation-based multi-objective optimization approach for system re-configuration of multi-part flow lines subjected to scalable capacities, which addresses the assignment of the tasks to workstations and buffer allocation for simultaneously maximizing throughput and minimizing total buffer capacity to cope with fluctuating production volumes. To this extent, the results from the study demonstrate the benefits that decision-makers could gain, particularly when they face trade-off decisions inherent in today’s manufacturing industry by adopting a Simulation-Based Multi-Objective Optimization (SMO) approach.

Optimizing reconfigurable manufacturing systems: A Simulation-based Multi-objective Optimization approach

Application of reconfigurable manufacturing systems (RMS) plays a significant role in manufacturing companies’ success in the current fiercely competitive market. Despite the RMS’s advantages, designing these systems to achieve a high-efficiency level is a complex and challenging task that requires the use of optimization techniques. This study proposes a simulation-based optimization approach for optimal allocation of work tasks and resources (i.e., machines) to workstations. Three conflictive objectives, namely maximizing the throughput, minimizing the buffers’ capacity, and minimizing the number of machines, are optimized simultaneously while considering the system’s stochastic behavior to achieve the desired system’s configuration.

Optimization of Agent-Based Models Through Coarse-Graining: A Case Study in Microbial Ecology.

Optimization and control are important objectives across biology and biomedicine, and mathematical models are a key enabling technology. This paper reports a computational study of model-based multi-objective optimization in the setting of microbial ecology, using agent-based models. This modeling framework is well-suited to the field, but is not amenable to standard control-theoretic approaches. Furthermore, due to computational complexity, simulation-based optimization approaches are often challenging to implement. This paper presents the results of an approach that combines control-dependent coarse-graining with Pareto optimization, applied to two models of multi-species bacterial biofilms. It shows that this approach can be successful for models whose computational complexity prevents effective simulation-based optimization.

An Algorithm for Efficient Generation of Customized Priority Rules for Production Control in Project Manufacturing with Stochastic Job Processing Times

Project Planning and Control (PPC) problems with stochastic job processing times belong to the problem class of Stochastic Resource-Constrained Multi-Project Scheduling Problems (SRCMPSP). A practical example of this problem class is the industrial domain of customer-specific assembly of complex products. PPC approaches have to compensate stochastic influences and achieve high objective fulfillment. This paper presents an efficient simulation-based optimization approach to generate Combined Priority Rules (CPRs) for determining the next job in short-term production control. The objective is to minimize project-specific objectives such as average and standard deviation of project delay or makespan. For this, we generate project-specific CPRs and evaluate the results with the Pareto dominance concept. However, generating CPRs considering stochastic influences is computationally intensive. To tackle this problem, we developed a 2-phase algorithm by first learning the algorithm with deterministic data and by generating promising starting solutions for the more computationally intensive stochastic phase. Since a good deterministic solution does not always lead to a good stochastic solution, we introduced the parameter Initial Copy Rate (ICR) to generate an initial population of copied and randomized individuals. Evaluating this approach, we conducted various computer-based experiments. Compared to Standard Priority Rules (SPRs) used in practice, the approach shows a higher objective fulfilment. The 2-phase algorithm can reduce the computation effort and increases the efficiency of generating CPRs.

A continuous review policy for two complementary products with interrelated demand

We consider a retailer selling two complementary products. The products are demanded individually or jointly. The retailer uses a continuous review (Q,r) policy to manage the inventory of each product. The customers arrive according to a Poisson process and unmet demands are lost. We aim to calculate the optimal policy parameters which maximize the expected profit rate. A shortage of one product might result in a lost sale of its complementary product, even if this product is in stock. This fact complicates the analysis. We rely on a simulation-based optimization approach to derive the optimal parameters of the (Q,r) policy for each product. Our results reveal that the maximum profit rate is attained when all customers demand both products, i.e., the bundle of complementary products. In addition, we observe that when the demand rate is low, the interaction between product demands has significant impact on the expected profit rate, and this effect becomes even more prominent when the unit lost sale costs are low and the unit holding costs are high. We derive the analytical properties of a single-product inventory system based on which an efficient approximation algorithm is developed.

Simulation-based optimization for modeling and mitigating tunnel-induced damages

This research develops a simulation-based optimization approach that is capable of modeling and mitigating tunnel-induced damages. Two fuzzy cognitive maps (FCMs) (i.e., one with self-feedback and the other without self-feedback) are learned from historical datasets by using the real-coded genetic algorithm (RCGA) on a data-driven modeling manner. Then, the optimal variable value set is searched in the input space. Two new measures, namely “maximum response” and “average response”, are proposed to search the optimal variable value set in the input space in the FCM dynamic simulation process. A realistic tunnel case in the Wuhan metro system in China is extensively investigated to demonstrate the applicability and effectiveness of the developed approach. Results indicate that (1) The FCM with self-feedback is more stable than the FCM without self-feedback considering its higher coefficient of determination in the testing samples, where less modification of input variables realizes comparable improvement in the objective in the FCM with self-feedback. (2) The measure “maximum response” shows a larger change in the objective than the measure “average response”, where modifications in input space are similar. (3) It is revealed that the ground settlement is more sensitive to TBM operational parameters than tunnel geometry and geological conditions in the two learned FCMs. The developed approach provides insights into a better understanding of causal relationships among factors in tunnel-induced damages, enabling the planning of proactive control strategies for mitigating tunnel-induced damages.

Aggregation–Decomposition-Based Multi-Agent Reinforcement Learning for Multi-Reservoir Operations Optimization

Stochastic dynamic programming (SDP) is a widely-used method for reservoir operations optimization under uncertainty but suffers from the dual curses of dimensionality and modeling. Reinforcement learning (RL), a simulation-based stochastic optimization approach, can nullify the curse of modeling that arises from the need for calculating a very large transition probability matrix. RL mitigates the curse of the dimensionality problem, but cannot solve it completely as it remains computationally intensive in complex multi-reservoir systems. This paper presents a multi-agent RL approach combined with an aggregation/decomposition (AD-RL) method for reducing the curse of dimensionality in multi-reservoir operation optimization problems. In this model, each reservoir is individually managed by a specific operator (agent) while co-operating with other agents systematically on finding a near-optimal operating policy for the whole system. Each agent makes a decision (release) based on its current state and the feedback it receives from the states of all upstream and downstream reservoirs. The method, along with an efficient artificial neural network-based robust procedure for the task of tuning Q-learning parameters, has been applied to a real-world five-reservoir problem, i.e., the Parambikulam–Aliyar Project (PAP) in India. We demonstrate that the proposed AD-RL approach helps to derive operating policies that are better than or comparable with the policies obtained by other stochastic optimization methods with less computational burden.