This paper describes and analyzes, in terms of content, the types of TSP, methods and fields of inspiration - the most relevant "literary space" on TSP. It is built based on the top most cited on the TSP history and the Top 10 cited from 2006 to 2010. The study of TSP still prevails in Research, focusing on the original problem and its variants: Multiple TSP (m-TSP) and Probabilistic TSP (PTSP). Evidence shows that there has been progress in the development of TSP solving methods, highlighted by various inspiration fields: biological evolution, behavior of real ants, thermodynamics, systematic strategies for combining decision rules, and neighborhood search. There is a tendency to develop hybrid methods, in particular by integrating global approaches to local search. There is need to introduce new fields of inspiration.

Se describe y se analiza un espacio literario relevante sobre el Problema del Vendedor Viajero (TSP) en términos de contenido, clases de TSP, métodos y campos de inspiración. Los datos empleados provinieron de los trabajos más citados en Scopus sobre el TSP, tanto a través de la historia como en el período 2006-2010. Se encontró que el TSP prevalece en las investigaciones, con enfoques tanto en el problema original como en sus variantes, entre las cuales se identificaron el TSP Múltiple y el TSP Probabilístico. Entre los principales campos de inspiración para resolver el TSP están la evolución biológica y su base genético-molecular, el comportamiento de hormigas reales, la termodinámica, las estrategias sistemáticas para combinar reglas de decisión y la búsqueda de vecindades. Hoy día se tiende a desarrollar métodos híbridos, especialmente integrando enfoques globales con búsquedas locales, y se identifica la necesidad de introducir nuevos campos de inspiración.

In this paper, a decision problem involving a car parts manufacturing company is modeled in order to prepare the company for an increase in demand. Mixed-integer linear programming was used with the following decision variables: creating a second shift, purchasing additional equipment, determining the required work force, and other alternatives involving new manners of work distribution that make it possible to separate certain operations from some workplaces and integrate them into others to minimize production costs. The model was solved using GAMS. The solution consisted of programming 19 workers under a configuration that merges two workplaces and separates some operations from some workplaces. The solution did not involve purchasing additional machinery or creating a second shift. As a result, the manufacturing paradigms that had been valid in the company for over 14 years were broken. This study allowed the company to increase its productivity and obtain significant savings. It also shows the benefits of joint work between academia and companies, and provides useful information for professors, students and engineers regarding production and continuous improvement.

This paper presents a novel method to solve instances of the TSP. This method is comparable in effectiveness and efficiency with "nearest neighbour", "cheapest insertion", "two-way exchange improvement" and "branch and bound". The first section provides a literature review of the combinatorial optimization, the second provides a reference frame, the third the methodology used and the fourth contains, inter alia, system thinking, AxB factorial design and management tool CAP-DO. The fourth section presents the design "new" method called systemic tetraheuristic, followed by ANOVA and Duncan ranges for each factor: method and number of cities; this section concludes with an analysis of the proportion of "failures" of the proposed algorithm with increasing complexity of the TSP. As a result of this study a method is presented for solving the TSP. This method consists of: 1- "nearest neighbour", 2- "short-term sacrifice" 3- "LIFO transfer" and a support heuristic "right search 4P4". For the design of the "sacrifice short-term" procedure, it was necessary to analyze the "Nearest Neighbour" heuristic from a systems perspective. This heuristic is related to the "quick solutions that fail" archetype, which can be applied to day-to-day. The proposed method has proved to be more efficient than others, in what concerns quality of the solution and the computation time, especially when the complexity of the TSP increases.

Este artículo presenta un novedoso método, basado en elementos del pensamiento sistémico, para solucionar instancias del problema del vendedor viajero (TSP), el cual es comparado en términos de eficacia y eficiencia con "nearest neighbour", "cheapest insertion", "two-wwy exchange improvement" y "branch and bound". El primer apartado introduce la optimización combinatoria, el segundo ofrece un marco de referencia, el tercero presenta la metodología empleada, el cuarto apartado presenta el desarrollo de la tetraheurística sistémica, seguido del análisis de varianza y de rangos de Duncan para los factores: método y cantidad de ciudades; este apartado finaliza con el análisis del comportamiento de la proporción de "fracasos" del algoritmo propuesto a medida que aumenta la complejidad del TSP. Como resultado se obtiene un método para resolver instancias del TSP, conformado por tres heurísticas misionales: 1."vecino más cercano", 2. "sacrificio cortoplacista" y 3. "traslado LIFO", y una de apoyo llamada "búsqueda derecha 4P4". El diseño de la heurística denominada "sacrificio cortoplacista" es inspirado en el análisis sistémico del "vecino más cercano", al cual se le identifica el arquetipo de "soluciones rápidas que fallan", con aplicación a decisiones cotidianas. La tetraheurística sistémica se destaca, respecto a las demás, en solución arrojada y en tiempo computacional consumido, especialmente cuando incrementa la complejidad del TSP.