OPTIMASI Bahan kajian pada MK. Metode Penelitian Kajian Lingkungan Disarikan oleh :

1. OPTIMASI Bahankajianpada MK. MetodePenelitianKajianLingkungan Disarikanoleh: Prof Dr IrSoemarno MS PMPSLP PPSUB OKTOBER 2010 https://marno.lecture.ub.ac.id

2. DEFINITION OF OPTIMUM The amount or degree of something that is most favorable to some end; especially: the most favorable condition for the growth and reproduction of an organism. Greatest degree attained or attainable under implied or specified conditions .

3. DEFINITION OF OPTIMIZE Optimize : to make as perfect, effective, or functional as possible Examples of OPTIMIZE The new system will optimize the efficiency with which water is used.

4. DEFINITION OF OPTIMIZATION Optimization: an act, process, or methodology of making something (as a design, system, or decision) as fully perfect, functional, or effective as possible; Specifically: the mathematical procedures (as finding the maximum of a function) involved in this process.

5. OPTIMIZATION (Stephen J. Wright) Optimization, also known as mathematical programming,  collection of mathematical principles and methods used for solving quantitative problems in many disciplines, including physics, biology, engineering, economics, and business. The subject grew from a realization that quantitative problems in manifestly different disciplines have important mathematical elements in common. Because of this commonality, many problems can be formulated and solved by using the unified set of ideas and methods that make up the field of optimization.

6. MATHEMATICAL PROGRAMMING The historic term mathematical programming, broadly synonymous with optimization, was coined in the 1940s before programming became equated with computer programming. Mathematical programming includes the study of the mathematical structure of optimization problems, the invention of methods for solving these problems, the study of the mathematical properties of these methods, and the implementation of these methods on computers. Faster computers have greatly expanded the size and complexity of optimization problems that can be solved. The development of optimization techniques has paralleled advances not only in computer science but also in operations research, numerical analysis, game theory, mathematical economics, control theory, and combinatorics.

7. OPTIMIZATION PROBLEMS Optimization problems typically have three fundamental elements. The first is a single numerical quantity, or objective function, that is to be maximized or minimized. The objective may be the expected return on a stock portfolio, a company’s production costs or profits, the time of arrival of a vehicle at a specified destination, or the vote share of a political candidate.

8. OPTIMIZATION PROBLEMS Optimization problems typically have three fundamental elements. The second element is a collection of variables, which are quantities whose values can be manipulated in order to optimize the objective. Examples include the quantities of stock to be bought or sold, the amounts of various resources to be allocated to different production activities, the route to be followed by a vehicle through a traffic network, or the policies to be advocated by a candidate.

9. OPTIMIZATION PROBLEMS Optimization problems typically have three fundamental elements. The third element of an optimization problem is a set of constraints, which are restrictions on the values that the variables can take. For instance, a manufacturing process cannot require more resources than are available, nor can it employ less than zero resources. Within this broad framework, optimization problems can have different mathematical properties. Problems in which the variables are continuous quantities (as in the resource allocation example) require a different approach from problems in which the variables are discrete or combinatorial quantities (as in the selection of a vehicle route from among a predefined set of possibilities).

10. LINEAR PROGRAMMING An important class of optimization is known as linear programming. Linear indicates that no variables are raised to higher powers, such as squares. For this class, the problems involve minimizing (or maximizing) a linear objective function whose variables are real numbers that are constrained to satisfy a system of linear equalities and inequalities.

11. NONLINEAR PROGRAMMING In nonlinear programming the variables are real numbers, and the objective or some of the constraints are nonlinear functions (possibly involving squares, square roots, trigonometric functions, or products of the variables). FUNGSI TUJUAN Dan/atau FUNGSI KENDALANYA…… NON LINEAR

12. MATHEMATICAL OPTIMIZATION Mathematical optimization (optimization or mathematical programming) refers to the selection of a best element from some set of available alternatives. The optimization problem consists of maximizing or minimizing a real function by systematically choosing input values from within an allowed set and computing the value of the function. The generalization of optimization theory and techniques to other formulations comprises a large area of applied mathematics. More generally, optimization includes finding "best available" values of some objective function given a defined domain including a variety of different types of objective functions and different types of domains. http://en.wikipedia.org/wiki/Mathematical_optimization

13. OPTIMIZATION PROBLEM An optimization problem can be represented in the following way Given: a functionf : AR from some setA to the real numbers Sought: an element x0 in A such that f(x0) ≤ f(x) for all x in A ("minimization") or such that f(x0) ≥ f(x) for all x in A ("maximization"). Such a formulation is called an optimization problem or a mathematical programming problem (a term not directly related to computer programming, but still in use for example in linear programming). Many real-world and theoretical problems may be modeled in this general framework. Problems formulated using this technique in the fields of physics and computer vision may refer to the technique as energy minimization, speaking of the value of the function f as representing the energy of the system being modeled.

14. Typically, A is some subset of the Euclidean spaceRn, often specified by a set of constraints, equalities or inequalities that the members of A have to satisfy. The domain A of f is called the search space or the choice set, while the elements of A are called candidate solutions or feasible solutions. The function f is called, variously, an objective function, cost function (minimization), utility function (maximization), or, in certain fields, energy function, or energy functional. A feasible solution that minimizes (or maximizes, if that is the goal) the objective function is called an optimal solution.

15. By convention, the standard form of an optimization problem is stated in terms of minimization. Generally, unless both the objective function and the feasible region are convex in a minimization problem, there may be several local minima, where a local minimum x* is defined as a point for which there exists some δ > 0 so that for all x such that |X - X*| ≤ δ the expression: F(x*) ≤ f(X) holds; that is to say, on some region around x* all of the function values are greater than or equal to the value at that point. Local maxima are defined similarly.

16. Optimization problems are often expressed with special notation. Here are some examples. Minimum and maximum value of a function Consider the following notation: This denotes the minimum value of the objective function x2 + 1, when choosing x from the set of real numbers . The minimum value in this case is 1, occurring at x = 0. Similarly, the notation asks for the maximum value of the objective function 2x, where x may be any real number. In this case, there is no such maximum as the objective function is unbounded, so the answer is "infinity" or "undefined".

17. MULTI-OBJECTIVE OPTIMIZATION Adding more than one objective to an optimization problem adds complexity. For example, to optimize a structural design, one would want a design that is both light and rigid. Because these two objectives conflict, a trade-off exists. There will be one lightest design, one stiffest design, and an infinite number of designs that are some compromise of weight and stiffness. The set of trade-off designs that cannot be improved upon according to one criterion without hurting another criterion is known as the Pareto set. The curve created plotting weight against stiffness of the best designs is known as the Pareto frontier. A design is judged to be "Pareto optimal" (equivalently, "Pareto efficient" or in the Pareto set) if it is not dominated by any other design: If it is worse than another design in some respects and no better in any respect, then it is dominated and is not Pareto optimal.

18. MULTI-MODAL OPTIMIZATION Optimization problems are often multi-modal; that is they possess multiple good solutions. They could all be globally good (same cost function value) or there could be a mix of globally good and locally good solutions. Obtaining all (or at least some of) the multiple solutions is the goal of a multi-modal optimizer. Classical optimization techniques due to their iterative approach do not perform satisfactorily when they are used to obtain multiple solutions, since it is not guaranteed that different solutions will be obtained even with different starting points in multiple runs of the algorithm. Evolutionary Algorithms are however a very popular approach to obtain multiple solutions in a multi-modal optimization task.

19. FEASIBILITY PROBLEM The satisfiability problem, also called the feasibility problem, is just the problem of finding any feasible solution at all without regard to objective value. This can be regarded as the special case of mathematical optimization where the objective value is the same for every solution, and thus any solution is optimal. Many optimization algorithms need to start from a feasible point. One way to obtain such a point is to relax the feasibility conditions using a slack variable; with enough slack, any starting point is feasible. Then, minimize that slack variable until slack is null or negative.

20. Konsep Program Linier : • Merupakan model umum yang dapatdigunakandalampemecahanmasalahpengalokasiansumber-sumber yang terbatas agar bisadigunakansecara optimal • Merupakanteknikmatematiktertentuuntukmendapatkankemungkinanpemecahanmasalahterbaikatassuatupersoalan yang melibatkansumber-sumberorganisasi yang terbatas • Metodematematis yang dapatdigunakansebagaialat bantu pengambilankeputusanbagiseorangmanajerberkaitandenganmasalahmaksimisasiatauminimisasi

21. ProsedurPenyelesaian LP: • Pembuatan Model Matematis (LogikaMatematis), merupakanfaktorkunci/utamadalampermasalahan linier programming • Perhitunganbisadiselesaikandengancara manual (metodegrafik, metode simplex, konsepdualitas) maupundenganKomputer. • Analisishasilhitungan, sebagaisalahsatualatalternatifkeputusandanpengambilankeputusan.

22. TahapanPembuatan Model Matematis • IdentifikasiMasalah : MasalahMaksimisasi (berkaitandenganProfit/Revenue)atauMasalahMinimisasi (berkaitandengandenganCost/biaya) • PenentuanVariabelMasalah : 1) PeubahKeputusan (Variabel yang menyebabkan tujuanmaksimalatau minimal) 2) FungsiTujuan(Objective Function) Z maks. ataumin. 3) FungsiKendala(Constraint Function) Identifikasidanmerumuskanfungsikendala yang ada

23. Program Linear adalahbagianilmumatematikaterapan yang digunakanuntukmemecahkanmasalahoptimasi (pemaksimalanataupeminimalansuatutujuan) yang dapatdigunakanuntukmencarikeuntunganmaksimumsepertidalambidangperdagangan, penjualandsb

24. MULTI-OBJECTIVE OPTIMIZATION Multi-objective optimization (or multi-objective programming), also known as multi-criteria or multi-attribute optimization, is the process of simultaneously optimizing two or more conflicting objectives subject to certain constraints. Multiobjective optimization problems can be found in various fields: product and process design, finance, aircraft design, the oil and gas industry, automobile design, or wherever optimal decisions need to be taken in the presence of trade-offs between two or more conflicting objectives. Maximizing profit and minimizing the cost of a product; maximizing performance and minimizing fuel consumption of a vehicle; and minimizing weight while maximizing the strength of a particular component are examples of multi-objective optimization problems.

25. In mathematical terms, the multiobjective problem can be written as: where μi is the i-th objective function, g and h are the inequality and equality constraints, respectively, and x is the vector of optimization or decision variables. The solution to the above problem is a set of Pareto points. Thus, instead of being a unique solution to the problem, the solution to a multiobjective problem is a possibly infinite set of Pareto points.

26. PARETO EFFICIENCY Pareto efficiency, or Pareto optimality, is a concept in economics with applications in engineering and social sciences. The term is named after Vilfredo Pareto, an Italian economist who used the concept in his studies of economic efficiency and income distribution. Given an initial allocation of goods among a set of individuals, a change to a different allocation that makes at least one individual better off without making any other individual worse off is called a Pareto improvement. An allocation is defined as "Pareto efficient" or "Pareto optimal" when no further Pareto improvements can be made. Pareto efficiency is a minimal notion of efficiency and does not necessarily result in a socially desirable distribution of resources: it makes no statement about equality, or the overall well-being of a society