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Today, the editor brings you the optimization theory of "Research on Profit Maximization Strategies of Fresh Agricultural Products Supply Chain Under the Dominance of Different Subjects".
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This issue of tweets shows the optimization theory of the master's thesis "Research on Profit Maximization Strategy of Fresh Agricultural Product Supply Chain under the Dominance of Different Subjects" through three sections: mind map, intensive reading content, and knowledge supplementation.
This tweet shows the optimization theory of the master's thesis "Research on Profit Maximization Strategies of Fresh Agricultural Products Supply Chain under the Dominance of Different Subjects" through three sections: Mind Map, Intensive Reading Content, and Knowledge Supplement. Readers are invited to follow the editor to learn!
01
Mind mapping
The optimization theory of the doctoral dissertation "Research on Profit Maximization Strategy of Fresh Agricultural Product Supply Chain under the Dominance of Different Subjects", the mind map is as follows:
Doctoral dissertation "Research on Profit Maximization Strategies of Fresh Agricultural Products Supply Chain under the Dominance of Different Subjects" Optimization Theory, mind map is as follows:
02
Intensive reading content
In the chapter on concept definition, the optimization theory is introduced. Firstly, the optimization theory is commonly used in the following problems: 1. Manufacturers arrange production plans in order to maximize their own profits. 2. Retailers develop purchasing and pricing plans in order to maximize their profits. 3. The government formulates the optimal subsidy policy in order to achieve the goal of maximizing social welfare.
In the concept definition chapter, the optimization theory is introduced. Firstly, it is explained that the optimization theory is often used in the following problems: 1. Manufacturers arrange production plans to maximize their own profits. 2. Retailers formulate procurement and pricing plans to maximize their own profits. 3. The government formulates the optimal subsidy policy to maximize social welfare.
Then, it is explained that the core content of the optimization theory is to study the mathematical model, and its general formula is 1. objective function. 2. The decision variable satisfies the constraints.
Then it is explained that the core content of optimization theory is to study mathematical models, and its general formula is 1. Objective function. 2. Decision variables satisfy constraints.
The main problems involved in profit optimization are divided into two parts: 1. Constraint optimization problems. As the name suggests, it is to solve the optimal solution where the objective function Z reaches the maximum or minimum value within the range determined by the constraints. 2. Unconstrained optimization problem, that is, there are no constraints, and the feasible domain is the full set.
The main problems involved in the optimization of profit are divided into two parts: 1. Constrained optimization problem. As the name implies, it is to solve the optimal solution where the objective function Z reaches the maximum or minimum value within the range determined by the constraints. 2. Unconstrained optimization problem, that is, there are no constraints, and the feasible region is the complete set.
Constraint programming is divided into linear programming and nonlinear programming, and the feasible domain is a subset of the finite dimensional space and the constraint is a linear function, which is called linear optimization. The constraints are not linear functions, i.e., nonlinear programming.
Constrained programming is divided into linear programming and nonlinear programming. The feasible region is a subset of the finite dimensional space and the constraints are linear functions, which is called linear optimization. The constraints are not linear functions, that is, nonlinear programming.
Nonlinear programming is the most common optimization problem, and the solution methods are the fastest descent method, the quasi-Newtonian method, the nonlinear least squares method and the optimality condition directly.
Nonlinear programming is the most common optimization problem, and the solution methods are steepest descent method, quasi-Newton method, nonlinear least squares method and direct solution of optimality conditions.
In the direct solution of optimality conditions, when only the equality constraints are included, the Lagrangian function method is used to solve the problem. When the constraint is an inequality and the objective function is a strictly concave function or a quasi-concave function, the solution is based on the Karush-kuhn-Tucker condition and the relationship between the local extreme point and the global maximum point.
In the direct solution of optimality conditions, when only equality constraints are included, the Lagrangian function method is used to solve them. When the constraints are inequalities, when the objective function is strictly concave or quasi-concave, it is solved based on the Karush-kuhn-Tucker condition and the relationship between the local extreme point and the global maximum point.
At the end of the chapter, the author introduces the optimization theory used in this paper, which is a nonlinear programming model to study the maximization of profits, social welfare, and utility of supply chain enterprises. The solution method is to solve the optimality condition of unconstrained optimization and constrained optimization.
At the end of the chapter, the author introduces the optimization theory used in this paper, and the research content is the nonlinear programming model to study the profit maximization, social welfare maximization, and utility maximization of supply chain enterprises. The solution methods are unconstrained optimization and constrained optimization.
03
Knowledge supplementation
The optimization theory is mentioned in the article, what are the application scope of the optimization theory in real life?
1. Engineering: In fields such as architecture, mechanics, electronics, and aerospace, engineers use optimization theory to design optimal structures, circuits, systems, and processes.
2. Production management: In manufacturing, optimization theory is used in production scheduling, inventory control, supply chain management, and resource allocation to improve efficiency and reduce costs.
3. Transportation: Optimization theory is used in traffic flow management, route planning, logistics distribution, and vehicle scheduling to reduce congestion and transportation costs.
4. Financial Investment: In the financial field, optimization theory is used in portfolio management, risk assessment, option pricing, and trading strategy optimization.
5. Energy Management: In the power system, oil and gas industry, optimization theory is used for the optimization of energy production and distribution, as well as the integration of renewable energy systems.
6. Healthcare: In the medical field, optimization theory is used for patient scheduling, resource allocation, treatment planning, and drug dosage optimization.
7. Environmental management: In the field of environmental protection and sustainable development, optimization theory is used for pollution control, waste management, and ecosystem protection.
8. Information Technology: In the field of computer science and information technology, optimization theory is used in algorithm design, network optimization, data storage, and processing.
9. Marketing: In market research, optimization theory is used for advertising, product pricing, and sales strategy optimization.
10. Public Policy: In government decision-making, optimization theory is used for budget allocation, policy making, and social welfare maximization.
The article mentions optimization theory. What are the applications of optimization theory in real life?
1. Engineering design: In the fields of architecture, machinery, electronics, and aerospace, engineers use optimization theory to design optimal structures, circuits, systems, and processes.
2. Production management: In manufacturing, optimization theory is used for production scheduling, inventory control, supply chain management, and quotas to improve efficiency and reduce costs.
3. Transportation: Optimization theory is used in traffic flow management, route planning, logistics distribution, and vehicle scheduling to reduce congestion and transportation costs.
4. Financial investment: In finance, optimization theory is used in portfolio management, threat and risk assessment, option pricing, and trading strategy optimization.
5. Energy management: In the power system, oil and gas industries, optimization theory is applied to the optimization of energy production and distribution, as well as the integration of renewable energy systems.
6. Healthcare: In the medical field, optimization theory is used for patient scheduling, quotas, treatment planning, and drug dose optimization.
7. Environmental management: In the field of environmental protection and sustainable development, optimization theory is applied to pollution control, waste management, and ecosystem protection.
8. Information technology: In the fields of computer science and information technology, optimization theory is used in algorithm design, network optimization, data storage, and processing.
9. Marketing: In market research, optimization theory is used to optimize advertising placement, product pricing, and sales strategies.
10. Public policy: In government decision-making, optimization theory is used for budget allocation, policy making, and maximizing social welfare.
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Reference: Wen Hui. Research on profit maximization strategy of fresh agricultural product supply chain under the dominance of different subjects [D]. Wuhan:Huazhong Agricultural University, 2020.
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