## 1 Introduction  
We are now facing increasing threats from natural and man-made disasters, which often cause serious damage to lives and property. During disaster rescue operations, the responders often face significant problems for delivering various kinds and huge amounts of relief supplies to disaster-stricken areas in a timely and accurate manner. Therefore, effective planning and scheduling of emergency transportation plays a key role in rescue operations to mitigate the damage. Transportation problems are a class of optimization problems in the domain of operations research. Classical transportation problems are modeled as linear programming problems for minimizing the cost of delivering integral quantities of goods from \(m\) sources to \(n\) targets whilst balancing supply and demand [1]. However, due to the rising complexity of goods and transportation modes, changing customer demands and expanding competition, today's transportation problems involve much more complex sets of objectives and constraints and often have to address variations of uncertainty and randomization. As a consequence, classical mathematical programming methods are often insufficient to effectively handle the problems, and a variety of heuristic and meta-heuristic methods have gained popularity in the research of this topic (e.g., [2; 3; 4; 5; 6; 7]). In comparison with ordinary commercial transportation, transportation of relief supplies in a stressful disaster environment is often characterized by the following additional features [8]:  
* There can be various kinds and a huge number of supplies (such as food, water, medicine, clothes, first-aid kits, lifesaving appliance, _etc._) to be delivered in a timely and efficient manner. * The operations often involve more than one transportation mode, such as air, rail and road. * The transportation is heavily constrained by bottlenecks, such as the availability of vehicles/drivers, the capacity of transportation network and strict time windows. * The available information is often ambiguous, uncertain, incomplete and sometimes even inconsistent and erroneous. * The transportation solutions should be evaluated based on multiple criteria, which may include cost, time responsiveness and utilization efficiency, as well as damage/loss of relief supplies and rescue forces. * Quick response and fast delivery are of vital importance to the success of disaster rescue operations, and thus, the transportation solutions should be generated in a very limited time period. * The environment of the disaster areas is always subject to frequent changes, and the transportation should be flexible enough to cope with the changes. In recent years, bio-inspired meta-heuristic methods, including evolutionary algorithms (EAs) and swarm intelligence (SI)-based algorithms, have received much research interest and have been applied to emergency transportation planning and scheduling in many real-world problems. Inspired by the process of biological evolution, EAs are a class of stochastic search and optimization methods, including the most widely-used genetic algorithms (GAs) and their variants [9]. SI can be viewed as an extended study of evolutionary intelligence inspired by the collective behavior of social insects [10]. A typical SI-based meta-heuristic includes particle swarm optimization (PSO [11]), ant colony optimization (ACO [12]) and the artificial bee colony (ABC) algorithm [13]. Due to their efficiency and robustness in searching in extra-large solution spaces, bio-inspired meta-heuristics have recently received increased interest in the study of transportation planning and scheduling. In this paper, we conduct a survey of bio-inspired algorithms for a variety of transportation problems, discussing related work in four focused classes: general transportation problems, location and routing problems, roadway repair problems and integrated problems.