Heuristic algorithms for dynamic capacitated arc routing

This thesis concerns the capacitated arc routing problem (CARP), which can be used as a model of various real-life scenarios such as rubbish collection, snow ploughing, and other situations where an emphasis is placed on providing a certain service along streets. The goal of the CARP is to find a minimum-cost set of routes such that (i) each route starts and ends at the depot, (ii) each task is serviced in one of the routes, and (iii) the total demand in each route does not exceed the capacity. Until recently, the study of the CARP is concentrated on its "static" version, that is, it is assumed that the problem remains unchanged after vehicles start their journeys. However, with today's communication technology, a route planner and drivers can communicate with each other in real time, hence the possibility of amending vehicle routes if deemed necessary or appropriate for changes that may occur in the problem. This motivates the study of a dynamic CARP. This thesis focusses on one type of change in the dynamic CARP, namely the appearance of new tasks. To ensure that a service can be performed smoothly, the ability to update a solution quickly is often preferable to achieving optimality with an excessive amount of computational effort. For this reason, we opt to develop a dynamic CARP solver based on heuristic algorithms. An investigation is conducted to gain more insights about what makes an algorithm improve a solution quickly. Furthermore, factors in the dynamic CARP beyond a solution-seeking algorithm are investigated. This includes the frequency of updating the solution and the idea of instructing vehicles to wait for additional tasks at certain locations. Efforts are focussed on reducing the total distance at the end of the service while ensuring that the service completion time is not excessive

Investigating edge-reordering procedures in a tabu search algorithm for the capacitated arc routing problem

This paper presents two ideas to guide a tabu search algorithm for the Capacitated Arc Routing Problem to a promising region of the solution space. Both ideas involve edge-reordering, although they work in different ways. One of them aims to directly tackle deadheading cycles, and the other tries to reorder edges with the aim of extending a scope of solutions that can be reached from a given solution. Experiments were performed on 134 benchmark instances of various sizes, and the two ideas were shown to have an ability to guide the search to good solutions. Possible issues that may arise when implementing these ideas are also discussed

Effects of update frequencies in a dynamic capacitated arc routing problem

The capacitated arc routing problem (CARP) concerns a minimum‐cost set of routes for vehicles that provide service on edges in a given graph while ensuring that the total demand in each route does not exceed the vehicle's capacity. This paper concerns a dynamic variant of the CARP. In particular, it focuses on a problem in which new tasks appear over time. We find that simply increasing the number of iterations of a tabu search algorithm does not always lead to a better solution for a dynamic CARP. This paper investigates how the solution quality can be affected by changing the frequency of updating solutions. Furthermore, we investigate whether or not such effect varies with a method of integrating new tasks into the solution at each update

Effects of update frequencies in a dynamic capacitated arc routing problem

The capacitated arc routing problem (CARP) concerns a minimum‐cost set of routes for vehicles that provide service on edges in a given graph while ensuring that the total demand in each route does not exceed the vehicle's capacity. This paper concerns a dynamic variant of the CARP. In particular, it focuses on a problem in which new tasks appear over time. We find that simply increasing the number of iterations of a tabu search algorithm does not always lead to a better solution for a dynamic CARP. This paper investigates how the solution quality can be affected by changing the frequency of updating solutions. Furthermore, we investigate whether or not such effect varies with a method of integrating new tasks into the solution at each update