Optimal Decision Variables Research Articles

A simulation-based optimization approach evaluating maintenance and spare parts demand interaction effects

Industrial facilities frequently experience significant production losses due to unanticipated failures, sub-optimal maintenance, operational and spare parts logistics challenges. These among other factors directly affect the plant's performance measures such as availability, repair time and costs. Consequently, optimization addresses such challenges. However, a fundamental problem presented here relates to the need for a framework that assists in the determination of critical system to be optimized, variables that significantly impact the performance of such systems, and subsequently undertake optimization. To realistically model such complexities, a framework that applies the discrete simulation model of critical repairable subsystems, undergoing deterioration is proposed. The study utilises empirical maintenance data, where Pareto analysis is employed to identify critical subsystems, while expert input is incorporated to derive model variables. A full factorial Design of Experiment (DOE), is employed to establish the variables with significant main and interaction effects on the total repair time and subsequently employed as decision variables for a simulation-based optimization. The proposed framework is demonstrated in a case study of a thermal power plant. Simulation results highlight the turbocharger as the critical subsystem, while spares availability, the time between overhaul (TBO) and reliance on different maintenance strategies exhibit most significant main and interaction effects. The optimization results obtained demonstrate that TBO, spares availability and reliance on various maintenance strategies, provide a significant impact on the reduction of the repair time. The framework enhances maintenance decision making by optimizing the plants' operational and maintenance related factors identified.

Optimal management of bio-based energy supply chains under parametric uncertainty through a data-driven decision-support framework

This paper addresses the optimal management of a multi-objective bio-based energy supply chain network subjected to multiple sources of uncertainty. The complexity to obtain an optimal solution using traditional uncertainty management methods dramatically increases with the number of uncertain factors considered. Such a complexity produces that, if tractable, the problem is solved after a large computational effort. Therefore, in this work a data-driven decision-making framework is proposed to address this issue. Such a framework exploits machine learning techniques to efficiently approximate the optimal management decisions considering a set of uncertain parameters that continuously influence the process behavior as an input. A design of computer experiments technique is used in order to combine these parameters and produce a matrix of representative information. These data are used to optimize the deterministic multi-objective bio-based energy network problem through conventional optimization methods, leading to a detailed (but elementary) map of the optimal management decisions based on the uncertain parameters. Afterwards, the detailed data-driven relations are described/identified using an Ordinary Kriging meta-model. The result exhibits a very high accuracy of the parametric meta-models for predicting the optimal decision variables in comparison with the traditional stochastic approach. Besides, and more importantly, a dramatic reduction of the computational effort required to obtain these optimal values in response to the change of the uncertain parameters is achieved. Thus the use of the proposed data-driven decision tool promotes a time-effective optimal decision making, which represents a step forward to use data-driven strategy in large-scale/complex industrial problems.

A New Distributed Model Predictive Control for Unconstrained Double-Integrator Multiagent Systems

In this paper, a distributed model predictive control (DMPC) is proposed for static formation of unconstrained double-integrator multiagent systems. The formation problem is formulated in the leader-follower framework, where the leaders have access to both their own and relative neighboring information, and the followers only have access to relative neighboring information. In the process of optimization, only current relative information is communicated between neighboring agents. For each agent, its predicted control vector and neighboring control vectors are regarded as decision variables for optimization, but only the own optimized current control of the agent is implemented. An analytical solution to the DMPC is obtained, and new sufficient conditions are given for achieving the static formation.

Penggunaan Metode Goal Programming Untuk Optimasi Perancangan Proses Produksi Produk Wingko Babat

One form of actual problems in the manufacturing industry is how to maximize the value of profit with various constraints or constraints. The problem is not only experienced by big industry but also small industry like in UD. Wingko trip Mr. Moel who is trying to maximize the value of profit by estimating the amount of wingko trips that must be produced because there is a problem of the difference in the amount of production with one of the limits associated with the production capacity. The resulting production difference causes the amount of wingko trip or excess production in certain month. The method of Goal Programming is the one method of optimization with more than one objective that are interconnected with each other or conflicting to solve the problem. This research uses the Goal Programming method to optimize the production amount and maximize the profit of the product, and to forecast the number of forward demand by using the number of the past request to know how many products must be produced. Goal Programming Formulations use forecasting as decision variables for optimization of results in compliance with criteria and constraints. The results show the optimal amount of production in January - December 2017 are 29860 bag wingko original, 24000 bag wingko chocolate chocolate, 26004 bag wingko tripe jackfruit and 18084 bag wingko tripe durian with total profit in 2017 is Rp. 429.736.600.Â

Optimal sizing and energy management of stand-alone hybrid photovoltaic/wind system based on hydrogen storage considering LOEE and LOLE reliability indices using flower pollination algorithm

In this paper, the optimal design and energy management of the hybrid systems including the photovoltaic (PV) panels, wind turbine (WT) and fuel cell (FC) based on hydrogen storage (HS) (PWFHS) are presented to minimize the total net present cost (TNPC) of northwest region of Iran using intelligent flower pollination algorithm (FPA). The reliability indices that are considered simultaneously as technical constraints are the loss of energy expected (LOEE) and the loss of load expected (LOLE). The FPA performance is compared with well-known optimization methods such as teaching-learning based optimization (TLBO), particle swarm optimization (PSO) and also last researches in hybrid renewable energy designing. The simulation results are presented including decision variables, TNPC, LOEE, LOLE, energy management of generation units in different LOLEmax and LOEEmax and different combination of PWFHS. The results show that the proposed methodology finds the optimal decision variables easily with fast convergence, lower cost and better reliability values in different reliability indices and different PWFHS in comparison to TLBO and PSO.

Coordinated contracts in a two-echelon green supply chain considering pricing strategy

Abstract Three possibilities (consumer environmental awareness, cap and trade regulation, and production of low-carbon products) are usually employed by a supply chain during production to reduce the carbon emission and its devastating effects on human life. The high costs of reducing the emission raise the price of product, which can be in apposite of customer’s affordability, and to solve this problem the cooperation and coordination contract as the solutions used to provide low-carbon products with suitable price. Therefore, in this research, we expand a two-echelon supply chain consisting of one manufacturer and one retailer, which sells one kind of product with lower carbon emission. The market demand depends on price of product and the rate of carbon reduction. Supply chain analyzed in both cooperative and non-cooperative forms. In non-cooperative form, game theory approach, including Nash and Stackelberg games are applied to find optimal decision variables and profits of supply chain and its members; furthermore, three coordination contracts, including wholesale price (WP), cost sharing (CS), buyback (BB) contract to enhance the supply chain’ performance are used. The results show that supply chain’s profit in the cooperative form is higher than non-cooperative one. Moreover, a comparison between contracts indicates that the supply chain in WP contract in both Nash and Stackelberg games has the highest profit and can coordinate the chain.

Game theoretic analysis of horizontal carrier coordination with revenue sharing in E-commerce logistics

This paper proposes comparisons among two different competitive power structures with and without horizontal coordination. We conduct a game theoretic analysis on the situation where two logistics service providers compete in an e-commerce logistics market with respect to the order quantity and service level decision on a particular logistics service product. Under a consumer utility-based demand, we acquire the equilibrium order quantity decisions and optimal profits under the centralised setting and decentralised setting. An analytical model is established to examine the existence of the Nash equilibrium in the service game. We then prove that the logistics service demand depends on different competitive power structures with the establishment of Cournot game and Stackelberg game model. The analytical results and the equilibrium analysis of the e-commerce logistics system are investigated. After that, we compare the equilibrium results among the centralised system, Cournot game, and Stackelberg game. To empower the horizontal carrier coordination, we design a revenue sharing contract for the decentralised model. A numerical study illustrates the impact of parameters on the optimal decision variables. In this paper, we prove the effectiveness of horizontal carrier collaboration and explicate the reason for the lower marginal profit in logistics theoretically.

Integrated maintenance policy optimization with finite spares

This paper proposes an integrated maintenance policy optimization with finite spares. A system with one working unit and N spare parts is considered. The system is replaced periodically by spare parts until no spare part is feasible. Once the working unit is faulty, the system fails. The decision objective is minimizing the expected cost per unit time by optimizing the replacement interval and inventory quantity of spare parts. A numerical example is presented to demonstrate the efficiency of the proposed model. The optimal decision variables are derived by the proposed optimization algorithms. Finally, the case with infinite spare parts is also presented.

Optimizing Column Length and Particle Size in Preparative Batch Chromatography Using Enantiomeric Separations of Omeprazole and Etiracetam as Models: Feasibility of Taguchi Empirical Optimization

The overreaching purpose of this study is to evaluate new approaches for determining the optimal operational and column conditions in chromatography laboratories, i.e., how best to select a packing material of proper particle size and how to determine the proper length of the column bed after selecting particle size. As model compounds, we chose two chiral drugs for preparative separation: omeprazole and etiracetam. In each case, two maximum allowed pressure drops were assumed: 80 and 200 bar. The processes were numerically optimized (mechanistic modeling) with a general rate model using a global optimization method. The numerical predictions were experimentally verified at both analytical and pilot scales. The lower allowed pressure drop represents the use of standard equipment, while the higher allowed drop represents more modern equipment. For both compounds, maximum productivity was achieved using short columns packed with small-particle size packing materials. Increasing the allowed backpressure in the separation leads to an increased productivity and reduced solvent consumption. As advanced numerical calculations might not be available in the laboratory, we also investigated a statistically based approach, i.e., the Taguchi method (empirical modeling), for finding the optimal decision variables and compared it with advanced mechanistic modeling. The Taguchi method predicted that shorter columns packed with smaller particles would be preferred over longer columns packed with larger particles. We conclude that the simpler optimization tool, i.e., the Taguchi method, can be used to obtain “good enough” preparative separations, though for accurate processes, optimization, and to determine optimal operational conditions, classical numerical optimization is still necessary.

Optimal design of Raman fibre amplifier based on terminal value optimization strategy and shuffled frog leaping algorithm

This paper introduces an evolutionary algorithm, Shuffled Frog Leaping Algorithm (SFLA), to solve the optimization problem in designing the multi-pumped Raman Fibre Amplifier (RFA). SFLA is a powerful optimizer tool because of its efficient mathematical expressions and global search capability. We utilize SFLA to determine the optimal pump wavelengths and pump powers by minimizing the gain ripple of RFA. To accelerate calculations, a terminal value optimization strategy (TVOS) is incorporated into the evolution of SFLA. This proposed strategy takes the terminal power values of pumps as the decision variables in optimization. Then, the optimal original power values of the pumps are obtained by solving the Power Coupled Equations once, without using the traditional method of repetitive guesses.The combination of SFLA and TVOS enhances the efficiency of optimization and accelerates calculation, while satisfying the design requirements of RFA.The simulation results show that nearly 65% of computational time has been saved compared with the traditional average power analysis. The 4-pumped C+L band of backward multi-pumped RFA with the average net gain of 0 dB, 1 dB and 2 dB are designed individually, where the gain ripple is less than 0.64 dB. The combination of SFLA and TVOS enhance the optimization efficiency and improve the performance of RFA with good gain profile.

Determination of Economic Lot Size between Suppliers and Manufacturers for Imperfect Production System with Probabilistic Demand

This study aims to develop a combined economical lot size model between supplier and manufacturer for imperfect production processes with probabilistic demand patterns and constant lead times. The supplier side produces the product within a certain time interval then sent to the manufacturer with a certain amount of lot size. Imperfect supplier production systems are characterized by the probability of defective product (γ). The model decision variables are the lot size of the manufacturer's ordering, supplier lot size, and the reorder point of the manufacturer. The optimal decision variables are obtained by minimizing the total expected cost of the combined costs between the suppliers and the manufacturers borne by both parties. The model is built compared to the transactional partnership model, in which the supplier does not participate in the efficiency of its inventory system. A numerical example is given as an illustration of the JELS model and the transactional partnership model. Sensitivity analysis of the model is done by changing the parameters aimed at analyzing the behavior of the developed model.

Towards robust OPF solution strategy for the future AC/DC grids: case of VSC‐HVDC‐connected offshore wind farms

This study jointly addresses two major challenges in power system operations: (i) sustained growth of intermittent offshore wind farms (OWFs) connected to AC grid via multi-terminal voltage source converter (VSC)-based high-voltage DC (HVDC) grid that brings new challenges to the power system operation, and (ii) dealing with non-linearity of the AC power flow equations with the multi-terminal VSC-based HVDC grid model. To overcome these challenges, firstly, to deal with the uncertainties caused by the high penetration of the intermittent OWFs, this study introduces a robust optimisation approach. The proposed framework is computationally efficient and does not require the probability density function of the wind speed. The proposed decision-making framework finds the optimal decision variables in a way that they remain robust against the set of uncertainties. Secondly, the mathematical representation of the full AC optimal power flow (OPF) problem, with the added modelling of multi-terminal VSC-based HVDC grid in a day-ahead scheduling problem, is a mixed-integer non-linear programming (MINLP) optimisation problem, which is computationally burdensome for large-scale systems. Accordingly, this paper proposes a computationally efficient method for adjustment of solutions set points, which is also compatible with existing customary solvers with minimal modification efforts.

Conceptual design optimization of an integrated membrane bioreactor system for wastewater treatment

This paper suggests an efficient optimization approach for the design of an integrated membrane bioreactor (MBR) system. Therefore, conceptual design studies to find optimal design conditions were performed with different constraints for the wastewater treatment using a mathematical modeling method. The integrated MBR system is composed of two anoxic tanks, two aerobic tanks, and one immersed membrane tank (Janus, 2013; Janus and Ulanicki, 2016). For the design of the system, the optimal process configurations, reactor volumes, and flow rates of each stream must be decided reasonably. Especially for the decision of the optimal process configurations, binary variables are generally employed as optimization decision variables for the mathematical modeling. Hence, a mixed integer nonlinear programming (MINLP) technique can be generally used to simultaneously determine the optimal design and operating conditions including process configurations. But if the MINLP problems are formulated as highly nonlinear dynamic equations, the optimal values of binary variables may sometimes be difficult to be properly decided. Thus, this paper introduces (1) the efficient optimization approach to solve the MINLP problems by parameterizing the binary variables leading to the reduction of the nonlinearity of the model, and (2) its applications to the conceptual designs of the integrated MBR system.

Multiobjective optimization of asphalt pavement design and maintenance decisions based on sustainability principles and mechanistic-empirical pavement analysis

ABSTRACTHighway pavement as an important component of transport infrastructure has significant impacts on economy, society, and environment. The management of highway pavement has been traditionally focused on economy. In this study, the impacts of management decisions are examined in three dimensions, including life-cycle cost (LCC), energy consumption, and greenhouse gas (GHG) emissions. Quantitative models to predict the three dimensions are developed from mechanistic-empirical pavement analysis results. Two decision variables, pavement thickness and threshold roughness level for pavement resurfacing, are found to be significant in affecting the three dimensions. These two variables are subsequently used as decision variables in multiobjective optimization. The ranges of decisions that result in minimum LCC, energy consumption, and GHG emissions are identified through multiobjective optimization. Although the analysis is illustrated in the context of pavement design and management in Hong Kong, the analysis techniques and procedures can be easily applied in other regions.

Optimal design of PCM thermal storage tank and its application for winter available open-air swimming pool

This paper presents a general procedure to optimize the design of a PCM storage tank, including the specification of design objectives, the identification of decision variables (for optimization), the construction of computer simulation platform and the final decision making. The general procedure was demonstrated by applying it to an open-air swimming pool, where the design objective was to minimize the volume of the PCM storage tank when it was required to enable heat storing to maintain the water temperature of an open-air swimming pool inside the thermal comfortable range during its open period in winter season. After identifying decision variables, a computer simulation platform was developed using TRNSYS and MATLAB to investigate the effects of the decision variables on the volume of the PCM storage tank. Case studies show that the proposed optimization is able to efficiently reduce the volume of the PCM storage tank without sacrificing the thermal storage capacity. Therefore, this study provides useful guidelines to optimize the design of a PCM storage tank.

Aging based optimal scheduling framework for power plants using equivalent operating hour approach

In this paper a scheduling optimization framework is developed to enhance power plants operational decision making process. The proposed framework optimizes plant schedule including operating conditions and maintenance intervals simultaneously and on an hourly basis. In a long term operation plant performance deteriorates due to components aging. This study employs equivalent operating hour (EOH) approach to describe components aging impact on the plant performance deterioration and consequently plant long term profit. Modeling of components aging increases system simulation accuracy in long term operation and the optimum decision variables would be more reliable and realistic.Validity and usefulness of the proposed methodology are demonstrated by optimizing the operating conditions and maintenance intervals of a gas turbine power plant, under different seasonal ambient conditions and energy prices. The case study results effectively meet all the positive expectation that are placed on the proposed aging based optimal scheduling framework. Results show that optimal operation schedule depends on the maintenance intervals. Therefore, operating conditions and maintenance intervals should be optimized simultaneously. In addition, derived optimal schedule increases the system’s long term profit approximately 4% annually in comparison with a case that operates at optimal schedule without considering the aging effects in the optimization procedure.

Constrained multi-objective optimization of radial expanders in organic Rankine cycles by firefly algorithm

Organic Rankine Cycles are viable energy conversion systems in sustainable energy systems due to their compatibility with low-temperature heat sources. In the present study, one dimensional model of radial expanders in conjunction with a thermodynamic model of organic Rankine cycles is prepared. After verification, by defining thermal efficiency of the cycle and size parameter of a radial turbine as the objective functions, a multi-objective optimization was conducted regarding tension and vibration constraints for 4 different organic working fluids (R22, R245fa, R236fa and N-Pentane). In addition to mass flow rate, evaporator temperature, maximum pressure of cycle and turbo-machinery design parameters are selected as the decision variables. Regarding Pareto fronts, by a little increase in size of radial expanders, it is feasible to reach high efficiency. Moreover, by assessing the distribution of decision variables, the variables that play a major role in trending between the objective functions are found. Effects of mechanical and vibration constrains on optimum decision variables are investigated. The results of optimization can be considered as an initial values for design of radial turbines for Organic Rankine Cycles.

Optimal accelerated life tests under a cost constraint with non-uniform stress durations

ABSTRACTTo collect the information about the lifetime distribution of a product, a standard life testing method at normal working conditions is impractical when the product has a substantially long lifespan. Accelerated life testing solves this problem by subjecting the test units at higher stress levels for quicker and more failure data. Due to constrained resources in practice, several decision variables such as the allocation proportions and stress durations must be determined carefully at the design stage in order to run an accelerated life test efficiently. These decision variables directly affect the experimental cost as well as the estimate precision of the parameters of interest. This article investigates these optimal decision variables based on several well-known optimality criteria under the constraint that the total experimental cost does not exceed a pre-specified budget. A general scale family of distributions is considered for the underlying lifetimes to accommodate different lifetime models at different stress levels for flexible modeling. The constant-stress and step-stress accelerated life tests are then studied in detail with linearly decreasing stress durations as the stress level progresses. Under the identical budget constraint, the efficiencies of these two stress loading schemes are compared using two case studies.

Socially optimal replacement of conventional with electric vehicles for the US household fleet

ABSTRACTIn this study, a framework is proposed for minimizing the societal cost of replacing gas-powered household passenger cars with battery electric ones (BEVs). The societal cost consists of operational costs of heterogeneous driving patterns' cars, government investments for charging deployment, and monetized environmental externalities. The optimization framework determines the timeframe needed for conventional vehicles to be replaced with BEVs. It also determines the BEVs driving range during the planning timeframe, as well as the density of public chargers deployed on a linear transportation network over time. We leverage data sets that represent US household driving patterns, as well as the automobile and the energy markets, to apply the model. Results indicate that it takes 8 years for 80% of our conventional vehicle sample to be replaced with electric vehicles, under the base case scenario. The socially optimal all-electric driving range is 204 miles, with chargers placed every 172 miles on a linear corridor. All public chargers should be deployed at the beginning of the planning horizon to achieve greater savings over the years. Sensitivity analysis reveals that the timeframe for the socially optimal conversion of 80% of the sample varies from 6 to 12 years. The optimal decision variables are sensitive to battery pack and vehicle body cost, gasoline cost, the discount rate, and conventional vehicles' fuel economy. Faster conventional vehicle replacement is achieved when the gasoline cost increases, electricity cost decreases, and battery packs become cheaper over the years.

Determining the Optimum Decision Variables and Rescheduling the Irrigation System of Tendaho Sugar Estate, Ethiopia

Irrigation scheduling, flow rate and cut-off time are the three field irrigation variables currently affecting irrigation performance of Tendaho Sugar estate (Northeast of Ethiopia). As result, the study was initiated with the objective of improving existing irrigation scheduling and determining significant decision variables (flow rate and cut-off time). The long year’s climatic data were collected and analyzed by CROPWAT8.0 software to calculation of the right amount of water needed for the irrigation of crop fields, and soil samples were collected from representative cane fields to analyze soil characteristics. Parshall flumes were used to measure inflow rates at each field. The results of irrigation scheduling have showed slighter irrigation intervals than existing intervals. Therefore, to improvement of irrigation scheduling have proposed based on soil, crop ages and climatic casus of the area. Besides, the inflow rate and cut-off time of two major soils of study area were proposed to be 5 liters per second and 45 minute for 100 meter length of furrows as best decision variables of the sugar estate. Finally, the author trusts the right combination of these critical variables will help the sugar estate to overcome its existing irrigation problems, and result in better field water application and field conditions.