Application of Multi-objective Simulation-optimization Techniques to Inventory Management Problems

2006 ◽  
Author(s):  
Loo Hay Lee ◽  
Suyan Teng ◽  
Ek Peng Chew ◽  
Kong Wei Lye ◽  
P. Lendermann ◽  
...  
Keyword(s):  
Inventory Management ◽  
Simulation Optimization ◽  
Optimization Techniques ◽  
Multi Objective ◽  
Management Problems

scholarly journals Swarm intelligence for groundwater management optimization

2010 ◽  
Vol 13 (3) ◽  
pp. 520-532 ◽  
Author(s):  
A. Sedki ◽  
D. Ouazar
Keyword(s):  
Swarm Intelligence ◽  
Groundwater Management ◽  
Simulation Optimization ◽  
Groundwater Resources ◽  
Flow Simulation ◽  
Optimization Techniques ◽  
Optimization Models ◽  
Simulation Code ◽  
Management Problems ◽  
Management Optimization

This paper presents some simulation–optimization models for groundwater resources management. These models couple two of the most successful global optimization techniques inspired by swarm intelligence, namely particle swarm optimization (PSO) and ant colony optimization (ACO), with one of the most commonly used groundwater flow simulation code, MODFLOW. The coupled simulation–optimization models are formulated and applied to three different groundwater management problems: (i) maximization of total pumping problem, (ii) minimization of total pumping to contain contaminated water within a capture zone and (iii) minimization of the pumping cost to satisfy the given demand for multiple management periods. The results of PSO- and ACO-based models are compared with those produced by other methods previously presented in the literature for the three case studies considered. It is found that PSO and ACO are promising methods for solving groundwater management problems, as is their ability to find optimal or near-optimal solutions.

scholarly journals Multi-Objective Optimization of Switched Reluctance Machine Design Using Jaya Algorithm (MO-Jaya)

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1107
Author(s):  
Mohamed Afifi ◽  
Hegazy Rezk ◽  
Mohamed Ibrahim ◽  
Mohamed El-Nemr
Keyword(s):  
Optimization Techniques ◽  
Design Parameters ◽  
Jaya Algorithm ◽  
Switched Reluctance Machine ◽  
Nsga Ii ◽  
Switched Reluctance ◽  
Multi Objective ◽  
Reluctance Machine ◽  
Linear Relationships ◽  
Forward Equations

The switched reluctance machine (SRM) design is different from the design of most of other machines. SRM has many design parameters that have non-linear relationships with the performance indices (i.e., average torque, efficiency, and so forth). Hence, it is difficult to design SRM using straight forward equations with iterative methods, which is common for other machines. Optimization techniques are used to overcome this challenge by searching for the best variables values within the search area. In this paper, the optimization of SRM design is achieved using multi-objective Jaya algorithm (MO-Jaya). In the Jaya algorithm, solutions are moved closer to the best solution and away from the worst solution. Hence, a good intensification of the search process is achieved. Moreover, the randomly changed parameters achieve good search diversity. In this paper, it is suggested to also randomly change best and worst solutions. Hence, better diversity is achieved, as indicated from results. The optimization with the MO-Jaya algorithm was made for 8/6 and 6/4 SRM. Objectives used are the average torque, efficiency, and iron weight. The results of MO-Jaya are compared with the results of the non-dominated sorting genetic algorithm (NSGA-II) for the same conditions and constraints. The optimization program is made in Lua programming language and executed by FEMM4.2 software. The results show the success of the approach to achieve better objective values, a broad search, and to introduce a variety of optimal solutions.

Efficient Computation of Probabilistic Dominance in Multi-objective Optimization

2021 ◽  
Vol 1 (4) ◽  
pp. 1-26
Author(s):  
Faramarz Khosravi ◽  
Alexander Rass ◽  
Jürgen Teich
Keyword(s):  
Optimization Problems ◽  
Statistical Tests ◽  
Optimization Techniques ◽  
Simultaneous Optimization ◽  
Sampled Data ◽  
Efficient Computation ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Comparison Operator

Real-world problems typically require the simultaneous optimization of multiple, often conflicting objectives. Many of these multi-objective optimization problems are characterized by wide ranges of uncertainties in their decision variables or objective functions. To cope with such uncertainties, stochastic and robust optimization techniques are widely studied aiming to distinguish candidate solutions with uncertain objectives specified by confidence intervals, probability distributions, sampled data, or uncertainty sets. In this scope, this article first introduces a novel empirical approach for the comparison of candidate solutions with uncertain objectives that can follow arbitrary distributions. The comparison is performed through accurate and efficient calculations of the probability that one solution dominates the other in terms of each uncertain objective. Second, such an operator can be flexibly used and combined with many existing multi-objective optimization frameworks and techniques by just substituting their standard comparison operator, thus easily enabling the Pareto front optimization of problems with multiple uncertain objectives. Third, a new benchmark for evaluating uncertainty-aware optimization techniques is introduced by incorporating different types of uncertainties into a well-known benchmark for multi-objective optimization problems. Fourth, the new comparison operator and benchmark suite are integrated into an existing multi-objective optimization framework that features a selection of multi-objective optimization problems and algorithms. Fifth, the efficiency in terms of performance and execution time of the proposed comparison operator is evaluated on the introduced uncertainty benchmark. Finally, statistical tests are applied giving evidence of the superiority of the new comparison operator in terms of \epsilon -dominance and attainment surfaces in comparison to previously proposed approaches.

Multi-Objective Hydro-Thermal Scheduling Problem Using Two Novel Optimization Techniques

2021 ◽  
Vol 12 (3) ◽  
pp. 1-36
Author(s):  
Provas Kumar Roy ◽  
Moumita Pradhan ◽  
Tandra Pal
Keyword(s):  
Power Systems ◽  
Test System ◽  
Optimization Techniques ◽  
Computational Time ◽  
Environmental Aspects ◽  
Grey Wolf Optimization ◽  
Multi Objective ◽  
Efficiency And Effectiveness ◽  
Loading Effects ◽  
Solution Accuracy

This article describes an efficient and reliable strategy for the scheduling of nonlinear multi-objective hydrothermal power systems using the grey wolf optimization (GWO) technique. Moreover, the theory of oppositional-based learning (OBL) is integrated with original GWO for further enhancing its convergence rate and solution accuracy. The constraints related to hydro and thermal plants and environmental aspects are also considered in this paper. To show its efficiency and effectiveness, the proposed GWO and OGWO algorithms are authenticated for the test system consisting of a multi-chain cascade of 4 hydro and 3 thermal units whose valve-point loading effects are also taken into account. Furthermore, statistical outcomes of the conventional heuristic approaches available in the literature are compared with the proposed GWO and OGWO approaches, and these methods give moderately better operational fuel cost and emission in less computational time.

scholarly journals Notes on Using Simulation-Optimization Techniques in Traffic Simulation

2017 ◽  
Vol 27 ◽  
pp. 881-888 ◽  
Author(s):  
Xavier Ros-Roca ◽  
Lídia Montero ◽  
Jaume Barceló
Keyword(s):  
Traffic Simulation ◽  
Simulation Optimization ◽  
Optimization Techniques

A Strategy for Multi-Point Compressor Blading Using Multi-Objective Optimization

2012 ◽  
Author(s):  
Alireza Fathi ◽  
Abdollah Shadaram ◽  
Mohammad Alizadeh
Keyword(s):  
Incidence Angle ◽  
Optimization Algorithms ◽  
Optimization Techniques ◽  
Design System ◽  
Design Calculation ◽  
Blade Design ◽  
Objective Functions ◽  
Multi Objective ◽  
First Case ◽  
Through Flow

This paper introduces a framework to perform a multi-objective multipoint aerodynamic optimization for an axial compressor blade. This framework considers through-flow design requirements and mechanical and manufacturing constraints. Typically, components of a blade design system include geometry generation tools, optimization algorithms, flow solvers, and objective functions. In particular, optimization algorithms and objective functions are tuned to reduce blade design calculation cost and to match designed blade performance to the through flow design criteria and mechanical and manufacturing constrains. In the present study, geometry parameters of blade are classified to three categories. For each category, a distinct optimization loop is applied. In outer loop, Gradient-based optimization techniques are used to optimize parameters of the second category and a two-dimensional compressible viscous flow code is used to simulate the cascade fluid flow. Surface curvature optimization is carried out in inner loop, and its objective function is defined by integrating the normalized curvature and curvature slope. The genetic algorithm is used to optimize the parameters in the interior loop. To highlight the capabilities of the design method and to develop design know-how, an initial profile is optimized with three different design philosophies. The highest performance improvement in the first case is 15% reduction in loss at design incidence angle. In the second case, 16.5% increase in allowable incidence angle range, improves blade’s performance at off design conditions.

scholarly journals Application of Urban Scale Energy Modelling and Multi-Objective Optimization Techniques for Building Energy Renovation at District Scale

2021 ◽  
Vol 13 (20) ◽  
pp. 11554
Author(s):  
Fahad Haneef ◽  
Giovanni Pernigotto ◽  
Andrea Gasparella ◽  
Jérôme Henri Kämpf
Keyword(s):  
Energy Efficiency ◽  
Building Energy ◽  
Energy Performance ◽  
Building Envelope ◽  
Optimization Techniques ◽  
Multi Objective Optimization ◽  
Building Stock ◽  
Energy Efficiency Measures ◽  
Multi Objective ◽  
Efficiency Measures

Nearly-zero energy buildings are now a standard for new constructions. However, the real challenge for a decarbonized society relies in the renovation of the existing building stock, selecting energy efficiency measures considering not only the energy performance but also the economic and sustainability ones. Even if the literature is full of examples coupling building energy simulation with multi-objective optimization for the identification of the best measures, the adoption of such approaches is still limited for district and urban scale simulation, often because of lack of complete data inputs and high computational requirements. In this research, a new methodology is proposed, combining the detailed geometric characterization of urban simulation tools with the simplification provided by “building archetype” modeling, in order to ensure the development of robust models for the multi-objective optimization of retrofit interventions at district scale. Using CitySim as an urban scale energy modeling tool, a residential district built in the 1990s in Bolzano, Italy, was studied. Different sets of renovation measures for the building envelope and three objectives —i.e., energy, economic and sustainability performances, were compared. Despite energy savings from 29 to 46%, energy efficiency measures applied just to the building envelope were found insufficient to meet the carbon neutrality goals without interventions to the system, in particular considering mechanical ventilation with heat recovery. Furthermore, public subsidization has been revealed to be necessary, since none of the proposed measures is able to pay back the initial investment for this case study.

UAS Mission Path Planning System (MPPS) Using Hybrid-Game Coupled to Multi-Objective Optimiser

2009 ◽  
Author(s):  
DongSeop Lee ◽  
Jacques Periaux ◽  
Luis Felipe Gonzalez
Keyword(s):  
Path Planning ◽  
Computational Cost ◽  
Optimization Techniques ◽  
Planning System ◽  
Unmanned Aerial Systems ◽  
Three Dimension ◽  
Design Quality ◽  
Nsga Ii ◽  
Start Position ◽  
Multi Objective

This paper presents the application of advanced optimization techniques to Unmanned Aerial Systems (UAS) Mission Path Planning System (MPPS) using Multi-Objective Evolutionary Algorithms (MOEAs). Two types of multi-objective optimizers are compared; the MOEA Non-dominated Sorting Genetic Algorithms II (NSGA-II) and a Hybrid Game strategy are implemented to produce a set of optimal collision-free trajectories in three-dimensional environment. The resulting trajectories on a three-dimension terrain are collision-free and are represented by using Be´zier spline curves from start position to target and then target to start position or different position with altitude constraints. The efficiency of the two optimization methods is compared in terms of computational cost and design quality. Numerical results show the benefits of adding a Hybrid-Game strategy to a MOEA and for a MPPS.

Dynamic Programs with Shared Resources and Signals: Dynamic Fluid Policies and Asymptotic Optimality

2021 ◽  
Author(s):  
David B. Brown ◽  
Jingwei Zhang
Keyword(s):  
Inventory Management ◽  
Fluid Model ◽  
Asymptotic Optimality ◽  
Capital Budgeting ◽  
Stochastic Dynamic ◽  
Performance Bounds ◽  
Sequential Decision ◽  
Shared Resources ◽  
Management Problems ◽  
Dynamic Programs

Allocating Resources Across Systems Coupled by Shared Information Many sequential decision problems involve repeatedly allocating a limited resource across subsystems that are jointly affected by randomly evolving exogenous factors. For example, in adaptive clinical trials, a decision maker needs to allocate patients to treatments in an effort to learn about the efficacy of treatments, but the number of available patients may vary randomly over time. In capital budgeting problems, firms may allocate resources to conduct R&D on new products, but funding budgets may evolve randomly. In many inventory management problems, firms need to allocate limited production capacity to satisfy uncertain demands at multiple locations, and these demands may be correlated due to vagaries in shared market conditions. In this paper, we develop a model involving “shared resources and signals” that captures these and potentially many other applications. The framework is naturally described as a stochastic dynamic program, but this problem is quite difficult to solve. We develop an approximation method based on a “dynamic fluid relaxation”: in this approximation, the subsystem state evolution is approximated by a deterministic fluid model, but the exogenous states (the signals) retain their stochastic evolution. We develop an algorithm for solving the dynamic fluid relaxation. We analyze the corresponding feasible policies and performance bounds from the dynamic fluid relaxation and show that these are asymptotically optimal as the number of subsystems grows large. We show that competing state-of-the-art approaches used in the literature on weakly coupled dynamic programs in general fail to provide asymptotic optimality. Finally, we illustrate the approach on the aforementioned dynamic capital budgeting and multilocation inventory management problems.

Sign in / Sign up

Export Citation Format

Share Document