This chapter, which concludes the third part of the book, focuses on local search algorithms for finding suboptimal schedules in time-dependent scheduling problems. The chapter is divided into four sections: Sect. 11.1 introduces basic definitions and concepts related to local search algorithms, Sect. 11.2 reviews different types of local search algorithms, Sect. 11.3 discusses specific algorithms for time-dependent scheduling problems, and Sect. 11.4 provides conclusions and a table. In Sect. 11.1, the chapter defines an optimization problem \( P \) as a collection of instances, each specified by a pair \( (\mathfrak{F}, f) \), where \( \mathfrak{F} \) is the set of feasible solutions and \( f \) is a criterion function. A solution \( s^* \) is optimal if \( f(s^*) \leq f(s) \) for all \( s \in \mathfrak{F} \). The chapter also introduces the concept of a neighborhood function \( \mathcal{N} \), which defines the set of neighbors for each solution \( s \). A solution \( s^\circ \) is a local minimum if \( f(s^\circ) \leq f(s) \) for all \( s \in \mathcal{N}(s^\circ) \). The chapter further explains the general approach of local search, which involves starting from an initial solution, constructing its neighborhood, and searching for better solutions within that neighborhood. The pseudo-code for a general local search algorithm is provided, along with remarks on its components and modifications.This chapter, which concludes the third part of the book, focuses on local search algorithms for finding suboptimal schedules in time-dependent scheduling problems. The chapter is divided into four sections: Sect. 11.1 introduces basic definitions and concepts related to local search algorithms, Sect. 11.2 reviews different types of local search algorithms, Sect. 11.3 discusses specific algorithms for time-dependent scheduling problems, and Sect. 11.4 provides conclusions and a table. In Sect. 11.1, the chapter defines an optimization problem \( P \) as a collection of instances, each specified by a pair \( (\mathfrak{F}, f) \), where \( \mathfrak{F} \) is the set of feasible solutions and \( f \) is a criterion function. A solution \( s^* \) is optimal if \( f(s^*) \leq f(s) \) for all \( s \in \mathfrak{F} \). The chapter also introduces the concept of a neighborhood function \( \mathcal{N} \), which defines the set of neighbors for each solution \( s \). A solution \( s^\circ \) is a local minimum if \( f(s^\circ) \leq f(s) \) for all \( s \in \mathcal{N}(s^\circ) \). The chapter further explains the general approach of local search, which involves starting from an initial solution, constructing its neighborhood, and searching for better solutions within that neighborhood. The pseudo-code for a general local search algorithm is provided, along with remarks on its components and modifications.