Realistic Objective Function Research Articles

An analytic operating rule for reservoirs under the Budyko “supply–demand” framework

Operating rules play an important role in enhancing the water utilization of reservoirs. Previous studies on hedging investigated the operating rules between water release (R) and available water (AW) based on minimizing the normalized shortage index, and applied the rules to optimization. However, seldom considered a realistic economic objective function to form a more general “supply–demand” relationship. To address this issue, this study formulated a theoretical function by a two-period reservoir operation model with the goal of minimizing the sum of economic loss from the current release and carryover storage targets. The derived function that guides the water supply was numerically validated to obtain the optimal time-varying hedging rules by non-dominated sorting genetic algorithm Ⅱ. The water-supply system of Dongwushi Reservoir in Hebei Province, China was selected as a case study. The results show that (1) The derived function depicts the relationship between “R-AW ratio” and “D-AW ratio”, which is consistent with the Budyko “supply–demand” framework. (2) The optimized results using the derived Budyko hedging function outperform those using two-point hedging rule (TPH) and standard operating policy (SOP). (3) The derived function depicts a general non-linear “supply–demand” relationship rather than the linear one contained in commonly-used TPH. The derived function could be used for varied demands, indicating broader applications than TPH.

Towards gradient-based design optimization of fully-flexible tension-leg platform wind turbines

Novel technologies and design methods are needed to enable cost-competitive development of wind turbines with floating foundations. Tension-leg platforms have an established history in the oil and gas industry, though the complexity in coupled analysis of floating wind turbines generally has limited the exploration of novel concepts. The application of efficient, gradient-based optimization models has shown promise to overcome these difficulties and develop innovative designs. The core of this design optimization approach is an efficient coupled aero-hydro-servo-elastic dynamic model for a generic tension-leg platform wind turbine design, referred to as TLPOpt. The equation of motion is defined based on the generalized elastic mode shapes of the combined main column and tower. TLPOpt is implemented in the OpenMDAO framework for optimization, and analytical derivatives are defined throughout to increase efficiency. Stochastic dynamic analysis in the frequency-domain allows for efficient assessment of fatigue and extreme conditions. Verification steps show good agreement between the linearized analysis and higher-fidelity analysis tools. Early optimization studies suggest the optimization is able to improve upon a reference design, though more realistic constraint and objective functions are needed to draw further conclusions.

A solution for the rational dispatching of concrete transport vehicles

The amount of concrete needed in the construction of projects is enormous. The concrete dispatching in construction is, however, chaotic and causes grievous waste due to the tight construction schedule, heavy tasks, and improper transport vehicle dispatching by the constructor. This paper proposes a more realistic objective function in the vehicle dispatching algorithm and offers a solution to the rational dispatching of concrete transport vehicles. The analysis of the calculation example validates the feasibility of the built model, which provides more appropriate dispatching and more balanced distribution, meets the needs of the worksite and mixing station to the greatest extent and improves the level of project refinement and process management. Therefore, the model is worth promoting in future practice.

A mathematical model and a Clustering Search metaheuristic for planning the helicopter transportation of employees to the production platforms of oil and gas

In several countries, the petroleum is explored offshore and the oil and gas companies use oil drilling facilities and platforms far away from the coast. Thus, the oil companies transport its employees by helicopters between onshore basins, airports and platforms. This problem is known as the Capacitated Helicopter Routing Problem (CHRP). In this paper, it is proposed a formal mathematical model to the CHRP with several specific constraints and a more realistic objective function. Besides a Clustering Search (CS) metaheuristic is proposed to solve it. The mathematical model and the CS metaheuristic were tested with real data from a Brazilian oil and gas company. A set of instances with up to 1000 transportation requests and 100 helicopters was generated. Computational experiments indicate that CPLEX is not able to solve optimally even small instances of the problem, but the CS metaheuristic is capable of solving the proposed model generating good, stable and fast solutions.

Heuristics for Mixed Model Assembly Line Balancing Problem with Sequencing

The growing global competition compels organizations to use many productivity improvement techniques. In this direction, assembly line balancing helps an organization to design its assembly line such that its balancing efficiency is maximized. If the organization assembles more than one model in the same line, then the objective is to maximize the average balancing efficiency of the models of the mixed model assembly line balancing problem. Maximization of average balancing efficiency of the models along with minimization of makespan of sequencing models forms a multi-objective function. This is a realistic objective function which combines the balancing efficiency and makespan. This assembly line balancing problem with multi-objective comes under combinatorial category. Hence, development of meta-heuristic is inevitable. In this paper, an attempt has been made to develop three genetic algorithms for the mixed model assembly line balancing problem such that the average balancing efficiency of the model is maximized and the makespan of sequencing the models is minimized. Finally, these three algorithms and another algorithm in literature modified to solve the mixed-model assembly line balancing problem are compared in terms of the stated multi-objective function using a randomly generated set of problems through a complete factorial experiment.

An ILS-based algorithm to solve a large-scale real heterogeneous fleet VRP with multi-trips and docking constraints

Distribution planning is crucial for most companies since goods are rarely produced and consumed at the same place. Distribution costs, in addition, can be an important component of the final cost of the products. In this paper, we study a VRP variant inspired on a real case of a large distribution company. In particular, we consider a VRP with a heterogeneous fleet of vehicles that are allowed to perform multiple trips. The problem also includes docking constraints in which some vehicles are unable to serve some particular customers, and a realistic objective function with vehicles’ fixed and distance-based costs and a cost per customer visited. We design a trajectory search heuristic called GILS-VND that combines Iterated Local Search (ILS), Greedy Randomized Adaptive Search Procedure (GRASP) and Variable Neighborhood Descent (VND) procedures. This method obtains competitive solutions and improves the company solutions leading to significant savings in transportation costs.

Optimized Quasi-Interpolators for Image Reconstruction.

We propose new quasi-interpolators for the continuous reconstruction of sampled images, combining a narrowly supported piecewise-polynomial kernel and an efficient digital filter. In other words, our quasi-interpolators fit within the generalized sampling framework and are straightforward to use. We go against standard practice and optimize for approximation quality over the entire Nyquist range, rather than focusing exclusively on the asymptotic behavior as the sample spacing goes to zero. In contrast to previous work, we jointly optimize with respect to all degrees of freedom available in both the kernel and the digital filter. We consider linear, quadratic, and cubic schemes, offering different tradeoffs between quality and computational cost. Experiments with compounded rotations and translations over a range of input images confirm that, due to the additional degrees of freedom and the more realistic objective function, our new quasi-interpolators perform better than the state of the art, at a similar computational cost.

A Utility-Theoretic Approach to Privacy in Online Services

Online offerings such as web search, news portals, and e-commerce applications face the challenge of providing high-quality service to a large, heterogeneous user base. Recent efforts have highlighted the potential to improve performance by introducing methods to personalize services based on special knowledge about users and their context. For example, a user's demographics, location, and past search and browsing may be useful in enhancing the results offered in response to web search queries. However, reasonable concerns about privacy by both users, providers, and government agencies acting on behalf of citizens, may limit access by services to such information. We introduce and explore an economics of privacy in personalization, where people can opt to share personal information, in a standing or on-demand manner, in return for expected enhancements in the quality of an online service. We focus on the example of web search and formulate realistic objective functions for search efficacy and privacy. We demonstrate how we can find a provably near-optimal optimization of the utility-privacy tradeoff in an efficient manner. We evaluate our methodology on data drawn from a log of the search activity of volunteer participants. We separately assess users’ preferences about privacy and utility via a large-scale survey, aimed at eliciting preferences about peoples’ willingness to trade the sharing of personal data in returns for gains in search efficiency. We show that a significant level of personalization can be achieved using a relatively small amount of information about users.

Optimal Fixed Bed Reactor Network Configuration for the Efficient Recycling of CO2 into Methanol

An optimal design strategy of a network of fixed bed reactors for Methanol Production (MP) is proposed in this study. Both methanol production and profit spanning a production period of eight years have been set as objective functions to find the optimal production network. The conservation of mass and energy laws on a heterogeneous model of a single industrial methanol reactor was first developed. The model was solved numerically and was validated with industrial plant data. Different reactor network arrangements were then simulated in order to find an optimal superstructure. It was found that a structure of four reactors (two in series in parallel with another two in series) provide maximum production rate. The application of the more realistic objective function of profit showed that a configuration of two parallel reactors is the best configuration. This optimal structure produces 92 tons/day more methanol than a single reactor.

Efficient Sensor Placement Optimization for Securing Large Water Distribution Networks

The problem of deploying sensors in a large water distribution network is considered, in order to detect the malicious introduction of contaminants. It is shown that a large class of realistic objective functions—such as reduction of detection time and the population protected from consuming contaminated water—exhibits an important diminishing returns effect called submodularity. The submodularity of these objectives is exploited in order to design efficient placement algorithms with provable performance guarantees. The algorithms presented in this paper do not rely on mixed integer programming, and scale well to networks of arbitrary size. The problem instances considered in the approach presented in this paper are orders of magnitude (a factor of 72) larger than the largest problems solved in the literature. It is shown how the method presented here can be extended to multicriteria optimization, selecting placements robust to sensor failures and optimizing minimax criteria. Extensive empirical evidence on the effectiveness of the method presented in this paper on two benchmark distribution networks, and an actual drinking water distribution system of greater than 21,000 nodes, is presented.

Optimal Resource Allocation for Defense of Targets Based on Differing Measures of Attractiveness

This article describes the results of applying a rigorous computational model to the problem of the optimal defensive resource allocation among potential terrorist targets. In particular, our study explores how the optimal budget allocation depends on the cost effectiveness of security investments, the defender's valuations of the various targets, and the extent of the defender's uncertainty about the attacker's target valuations. We use expected property damage, expected fatalities, and two metrics of critical infrastructure (airports and bridges) as our measures of target attractiveness. Our results show that the cost effectiveness of security investment has a large impact on the optimal budget allocation. Also, different measures of target attractiveness yield different optimal budget allocations, emphasizing the importance of developing more realistic terrorist objective functions for use in budget allocation decisions for homeland security.

Economic optimization of non-sharp separation sequences by means of evolutionary algorithms

The general distillation sequence synthesis problem featuring the separation of multicomponent feed streams into multicomponent products is addressed. Potential flowsheets include stream bypassing and mixing and use sharp separations as well as non-sharp splits where key component distribution is allowed. Compared to conventional sharp distillation sequence synthesis, this leads to a mixed-integer non-linear programming problem of increased complexity, including non-convexities as well as multi-modalities. Product specifications create additional constraints while simultaneously call for a rigorous modeling of the non-key distribution. A synthesis method is proposed that models the various flowsheet configurations with a new and flexible superstructure concept and connects the gradient-free optimization technique of application-orientedly developed Evolutionary Algorithms (EAs) to the rigorous modeling capabilities of the Aspen Plus ™ simulation system, thus enabling realistic process design and cost objective function calculation. The re-examination of two published examples illustrates the applicability and the potential of the approach.

A guided simulated annealing search for solving the pick-up and delivery problem with time windows and capacity constraints

Routing and scheduling requests with pick-ups and deliveries is still one of the greatest operative challenges in inter-company logistics. Pick-up and delivery activities have to be bundled into efficient routes and their sequence has to be optimised within the routes without violating time and capacity constraints. The objective is to find a schedule of routes with minimal total costs. These costs result from the arising travelling costs, costs due to waiting and service times, and due to dispatching vehicles. This paper presents a guided local search method based on simulated annealing for solving this kind of routing and scheduling problem. Additionally, a new more realistic objective function that covers the total decision-relevant costs is introduced. The computational results show that the algorithm presented clearly outperforms standard implementations of simulated annealing and hill climber search.

Applying Optimization Technology in Reservoir Management

Distinguished Author Series articles are general, descriptive representations that summarize the state of the art in an area of technology by describing recent developments for readers who are not specialists in the topics discussed. Written by individuals recognized as experts in the area, these articles provide key references to more definitive work and present specific details only to illustrate the technology. Purpose:to inform the general readership of recent advances in various areas of petroleum engineering. Abstract This paper presents the application of optimization techniques in reservoir management to determine the best development plan that maximizes the economic performance of an asset. A distinguishing characteristic of this approach is that it can evaluate all feasible scenarios for a specific financial objective within a set of well-defined constraints. The output of this integrated workflow is a complete set of decisions that drive capital investment, development scheduling, production, hydrocarbon recovery, and, ultimately, economic performance of the asset. The conventional approach to reservoir management often considers a limited number of development options because of the complexity of the events within an investment plan and the time required for the evaluation. Input into the decision-making process traditionally comes in the form of sensitivity analyses from reservoir simulation, cost engineering, wellbore planning, pipeline networking, and project economic-analysis tools. These technologies are used to simulate performance by examining a specific development scenario, calculating future production, and performing a discounted cash-flow analysis to assess the return on the required investment. The exact scheduling of new infrastructure and production facilities as well as the drilling and stimulation of additional wells is critical to maximizing economic performance, especially if remaining reserves are material in size. These discrete events sometimes receive little attention. While the industry's ability to predict asset performance and assimilate several diverse perspectives together has significantly advanced, many reservoir-management decisions still are based on heuristics, rules of thumb, analogs to other assets, and ease of execution. Use of optimization techniques is not new in the energy business. However, in reservoir management, the technology must cope with more nonlinear phenomena within the physical system, complex and realistic objective functions and constraints, and discrete decisions that occur at distinct points over the asset life. The use of mixed-integer nonlinear programming algorithms can be useful in these situations. These algorithms implicitly construct a tree of all possible development plans and then efficiently prune the branches to eliminate candidates that can be shown to be nonoptimal. The effectiveness of optimization technology is illustrated by applying it to the ongoing expansion of a regional complex of gas reservoirs. The approach is used to determine the development schedule of the different reservoirs and the timing of their wells, the timing and staging of compression, and the development of satellites to meet a contract specification.

Simulation Optimization of the Crossdock Door Assignment Problem

The purpose of this report is to present the Crossdock Door Assignment Problem, which involves assigning destinations to outbound dock doors of Crossdock centres such that travel distance by material handling equipment is minimized. We propose a two fold solution; simulation and optimization of the simulation model – simulation optimization. The novel aspect of our solution approach is that we intend to use simulation to derive a more realistic objective function and use Memetic algorithms to find an optimal solution. The main advantage of using Memetic algorithms is that it combines a local search with Genetic Algorithms. The Crossdock Door Assignment Problem is a new domain application to Memetic Algorithms and it is yet unknown how it will perform.

Optimization of batch and fed-batch bioreactors using simulated annealing.

In order for biobased industrial products to compete economically with petroleum-derived products, significant reduction in their processing cost is necessary. Since most bioprocesses are operated in batch or fed-batch mode, their optimization involves theoretical and computational challenges. Simulated annealing (SA), a stochastic optimization algorithm, is used in this study to solve a number of challenging optimization problems related to the design and operation of bioreactors. Two well-known case studies are considered in which the robustness and efficiency of the SA algorithm is demonstrated. More specifically, in the first case study it is shown that the global optimal solution located by SA achieves significant improved productivity when compared with the results of previous investigations. In the second case study a realistic objective function is considered where the economic performance of a bioprocess is optimized. SA exhibits impeccable performance and robustness and was able to locate the global optimal solution irrespective of the initial point selected.

Fundamental issues, recent advances, and future directions in myodynamics

A state-of-the-art report is presented on recent progress in selected areas of myodynamics, but also on problems that severely hamper the further development of the discipline. Significant advances have been made in elucidating the force-producing interaction between actin and the myosin-S1-subunit, including the localization of the most probable molecular site of power stroke initiation. Concerning the architecture of the myostructures, strong experimental evidence has accumulated for numerous intra-, inter-, and extramuscular pathways for lateral force transmission in addition to the serial sarcomere-to-sarcomere myotendinous path. It is shown that contemporary muscle models are inadequate in most respects and lag far behind the requirements an appropriate myodynamic model should fulfil. A similar comment applies to the current approaches designed to solve the myoskeletal indeterminacy problem. These formulations neglect myodynamic properties and do not allow for the implementation of biologically realistic objective functions. The solutions currently obtained are highly unsatisfactory. New research directions to rectify these situations are suggested, also with regard to the identification of subject-specific myodynamic parameters.

Optimal design of the inspection buffer for an unreliable process

Placing an inspection buffer immediately after an unreliable process subject to failure protects the process customers from receiving non-conforming products. Such protection is achieved by screening the buffer contents whenever a shift in the process is detected. Previous studies have suggested that, if properly designed, such buffers can reduce overall costs. In this paper we make two types of contributions to the study of inspection buffers. First, we refine the model proposed by Klastorin et al. (1993), by incorporating a more efficient way of sampling, a more realistic objective function and more accurate cost expressions. Second, we provide analytical results to assist in the calculation of the economically optimal buffer size. Specifically, these include conditions for determining whether an inspection buffer is at all justified and a theorem for bounding the search for the optimal buffer size. The performance of the search bound and the sensitivity of the model are examined computationally.

Application of genetic algorithm for reliability allocation in nuclear power plants

Abstract Reliability allocation is an optimization process of minimizing the total plant costs subject to the overall plant safety goal constraints. Reliability allocation was applied to determine the reliability characteristics of reactor systems, subsystems, major components and plant procedures that are consistent with a set of top-level performance goals; the core melt frequency, acute fatalities and latent fatalities. Reliability allocation can be performed to improve the design, operation and safety of new and/or existing nuclear power plants. Reliability allocation is a kind of a difficult multi-objective optimization problem as well as a global optimization problem. The genetic algorithm, known as one of the most powerful tools for most optimization problems, is applied to the reliability allocation problem of a typical pressurized water reactor in this article. One of the main problems of reliability allocation is defining realistic objective functions. Hence, in order to optimize the reliability of the system, the cost for improving and/or degrading the reliability of the system should be included in the reliability allocation process. We used techniques derived from the value impact analysis to define the realistic objective function in this article.

Stochastic Design Optimization for a Simple 2-D Aerofoil

This paper describes the results of a study, which considers the aerodynamic optimization of a 2-D aerofoil, given a number of realistic but simple constraints. It is shown that this simple problem, with a realistic objective function, has a number of local optima. It is demonstrated that stochastic optimisation methods provide a distinct advantage over the more conventional methods and it is further shown that the optimal results are obtained when these methods are combined with conventional methods.