## 1 Introduction  
Corridor is an indispensable requirement to build linear installations such as pipelines, electric transmission lines, communications ways, etc. [1, 2]. The corridor planning process must consider legal, social, political, environmental, and economic factors interfering and making the corridor design complex [3]. However, proper corridor planning involves identifying alternative routes and selecting the best one based on design specifications, environmental laws, and good practices [4]. The corridor planning problem is generally addressed by one or more selection criteria such as distance, environmental impact, and safety, and it is modeled as an optimization problem subject to several topographical constraints [2, 5]. According to [6], locating an optimal corridor is analogous to identifying the shortest or least expensive path between two points. Traditional approaches generate a set of alternative routes usingenumerative methods with penalty techniques or by specifying one or multiple intermediate points where the path must pass [7; 8; 9]. The running time of these methods tends to grow exponentially, depending on the size of the geographical study area. In addition, the generation of near-optimal alternative routes in reasonable times using exact methods is hard since they find only one solution in their searching process [10]. Nowadays, the creation of new corridors to provide different services through linear facilities is growing for the emergence and expansion of urban areas. However, their construction can be delayed due to poor planning, so generating alternative routes for evaluating and comparing candidate solutions is crucial in the corridor planning process. The problem of making alternative routes has led researchers to develop diverse solution methods using different problem representations and applying several deterministic shortest-path-search algorithms such as the Dijkstra algorithm [11] and its variants. However, generating alternative routes requires exploring the geographical study area, and exact methods only get one route at a time. Thus, an efficient method must include mechanisms allowing the examination of alternative routes spatially dispersed, in reasonable computing time. On the other hand, non-deterministic approaches such as swarm and evolutionary algorithms, and the simulated annealing can find near-optimal solutions in a reasonable time due to their exploration and exploitation skills. This paper presents a simulated-annealing-based optimization method applying a variable-neighborhood structure to generate and evaluate alternative corridor routes and select the best one. The proposed algorithm starts its search process with a feasible solution obtained by a greedy uninformed search strategy, the breadth-first-search (BFS) algorithm, which does not use some cost function for exploring possible routes, and not necessarily find the optimal global path. Then, the variable neighborhood mechanism randomly selects two points in the corridor for exploring alternative routes through prohibited and guided movements. The results obtained with the simulated annealing method are compared with the BFS algorithm, to show the effectiveness in improving the solutions obtained by the proposed algorithm. The experimental analysis compares the variable-neighborhood mechanism on three real problem scenarios. It suggests that the proposed mechanism explores and improves the initial solution. Likewise, the neighborhood structure permits creating and evaluating alternative corridor routes and selecting the best one, based on the objective value. Improvements are reported above 18% on the greedy algorithm in the solution quality. The main contribution of this work is the implementation of a variable-search mechanism generating alternative routes inside a corridor. The proposed algorithm dynamically selects the best route considering topographic information collected from three Mexican regions located in the Veracruz Basin.