Parallelization of Dial-a-Ride Using Tabu Search

Dial-A-Ride is a transport system heavily constrained by following fleet size, vehicle capacity, and a fixed number of requests (pickup and drop-off points) with time windows. It is often modelled as Integer Programming, various solutions are proposed using heuristics. One such heuristic is Tabu Search . Tabu Search is very CPU intensive with its process of search, therefore many modern computing techniques like using GPUs have been employed to make it efficient. As with many other greedy algorithms, the local optima is not always the same as the global optima, so it is not possible to go past the local optima using greedy techniques for this problem. It is often modelled as Integer Programming, with the search space being very big, there are proven to not be so efficient. So, many heuristics have been proposed in the past, one such heuristic is Tabu Search . The local search of this heuristic uses memory to keep track of recent moves made and tries to avoid them for specified iterations (marks as Tabu) and also continues to explore the neighbourhood search space even with the degradation optimization function value, thus helping the algorithm to go past the local optima towards global optima. This thesis focuses on limitations of parallelizing DARP-TS for multi-core CPU, discussing major factors like (i) number of good moves in the neighbourhood and how we can estimate a value for N\_SIZE (number of parallel moves to make in each iteration), (ii) difference between a CPU core and a GPU core in the context of thread scheduling, memory layout and memory limitations, (iii) proposes few data-structures to keep the memory allocations low thus reducing the time for garbage collection and (iv) proposes an incremental way of calculating the value of optimization function in the local search phase which helps in mapping the execution and evaluation of N\_SIZE moves in each iteration onto the multiple CPU cores

Optimization of vehicular networks in smart cities: from agile optimization to learnheuristics and simheuristics

Vehicular ad hoc networks (VANETs) are a fundamental component of intelligent transportation systems in smart cities. With the support of open and real-time data, these networks of inter-connected vehicles constitute an ‘Internet of vehicles’ with the potential to significantly enhance citizens’ mobility and last-mile delivery in urban, peri-urban, and metropolitan areas. However, the proper coordination and logistics of VANETs raise a number of optimization challenges that need to be solved. After reviewing the state of the art on the concepts of VANET optimization and open data in smart cities, this paper discusses some of the most relevant optimization challenges in this area. Since most of the optimization problems are related to the need for real-time solutions or to the consideration of uncertainty and dynamic environments, the paper also discusses how some VANET challenges can be addressed with the use of agile optimization algorithms and the combination of metaheuristics with simulation and machine learning methods. The paper also offers a numerical analysis that measures the impact of using these optimization techniques in some related problems. Our numerical analysis, based on real data from Open Data Barcelona, demonstrates that the constructive heuristic outperforms the random scenario in the CDP combined with vehicular networks, resulting in maximizing the minimum distance between facilities while meeting capacity requirements with the fewest facilities.Peer ReviewedPostprint (published version

Investigation into heuristic methods of solving time variant Vehicle Routing Problems

Traditionally, Vehicle Routing Problems (VRPs) are modelled with fixed traversal times. The amount of time it takes to drive from one end of a road to the other is unchanged throughout the day. Nearly always, the reality of the situation that is being modelled is very different, with road speeds varying heavily, especially with “rush hour" traffic. Modelling VRPs with time varying congestion means that even slight changes early in a vehicle tour can have major knock-on effects that are hard to predict. Recalculating the total traversal time of vehicles whenever their tours are changed drastically increases metaheuristic calculation times compared to non-time varying models. In this thesis we use a simple technique of calculating the localised change and inferring the global effects resulting from neighbourhood moves. Only if the localised change suggests that the global result is satisfactory do we then calculate the actual global result. Inevitably using these estimates does not give as accurate results as always calculating the changes, but we aim to show that the loss of solution quality is overshadowed by the significant savings in calculation time. We present a series of experiments comparing simple metaheuristics with and without using estimates and show consistent savings in calculation time whenever estimates are used compared to when they are not. These savings shown to increase as the size of the problem (in terms of the number of customers) increases. In addition to synthetic problems, we also present a problem based on real world vehicle traversal times and show that our estimates prove just as accurate, if not more so, at retaining solution quality as the synthetic methods. Lastly, we briefly discuss further methods of solving VRPs that could also benefit from our work here

A Polyhedral Study of Mixed 0-1 Set

We consider a variant of the well-known single node fixed charge network flow set with constant capacities. This set arises from the relaxation of more general mixed integer sets such as lot-sizing problems with multiple suppliers. We provide a complete polyhedral characterization of the convex hull of the given set

A system dynamics & emergency logistics model for post-disaster relief operations

Emergency teams’ efficiency in responding to disasters is critical in saving lives, reducing suffering, and for damage control. Quality standards for emergency response systems are based on government policies, resources, training, and team readiness and flexibility. This research investigates these matters in regards to Saudi emergency responses to floods in Jeddah in 2009 and again in 2011. The study is relevant to countries who are building emergency response capacity for their populations: analysing the effects of the disaster, communications and data flows for stakeholders, achieving and securing access, finding and rescuing victims, setting up field triage sites, evacuation, and refuges. The research problem in this case was to develop a dynamic systems model capable of managing real time data to allow a team or a decision-maker to optimise their particular response within a rapidly changing situation. The Emergency Logistics Centre capability model responds to this problem by providing a set of nodes relevant to each responsibility centre (Civil Defence, regional/local authority including rescue teams, police and clean-up teams, Red Crescent). These nodes facilitate information on resource use and replenishment, and barriers such as access and weather can be controlled for in the model. The dynamic systems approach builds model capacity and transparency, allowing emergency response decision-makers access to updated instructions and decisions that may affect their capacities. After the event, coordinators and researchers can review data and actions for policy change, resource control, training and communications. In this way, knowledge from the experiences of members of the network is not lost for future position occupants in the emergency response network. The conclusion for this research is that the Saudi emergency response framework is now sufficiently robust to respond to a large scale crisis, such as may occur during the hajj with its three million pilgrims. Researchers are recommended to test their emergency response systems using the Emergency Logistics Centre model, if only to encourage rethinking and flexibility of perhaps stale or formulaic responses from staff. This may lead to benefits in identification of policy change, training, or more appropriate pathways for response teams