Optimisation of transportation service network using κ-node large neighbourhood search

The Service Network Design Problem (SNDP) is generally considered as a fundamental problem in transportation logistics and involves the determination of an efficient transportation network and corresponding schedules. The problem is extremely challenging due to the complexity of the constraints and the scale of real-world applications. Therefore, efficient solution methods for this problem are one of the most important research issues in this field. However, current research has mainly focused on various sophisticated high-level search strategies in the form of different local search metaheuristics and their hybrids. Little attention has been paid to novel neighbourhood structures which also play a crucial role in the performance of the algorithm. In this research, we propose a new efficient neighbourhood structure that uses the SNDP constraints to its advantage and more importantly appears to have better reachability than the current ones. The effectiveness of this new neighbourhood is evaluated in a basic Tabu Search (TS) metaheuristic and a basic Guided Local Search (GLS) method. Experimental results based on a set of well-known benchmark instances show that the new neighbourhood performs better than the previous arc-flipping neighbourhood. The performance of the TS metaheuristic based on the proposed neighbourhood is further enhanced through fast neighbourhood search heuristics and hybridisation with other approaches

The Incremental Cooperative Design of Preventive Healthcare Networks

This document is the Accepted Manuscript version of the following article: Soheil Davari, 'The incremental cooperative design of preventive healthcare networks', Annals of Operations Research, first published online 27 June 2017. Under embargo. Embargo end date: 27 June 2018. The final publication is available at Springer via http://dx.doi.org/10.1007/s10479-017-2569-1.In the Preventive Healthcare Network Design Problem (PHNDP), one seeks to locate facilities in a way that the uptake of services is maximised given certain constraints such as congestion considerations. We introduce the incremental and cooperative version of the problem, IC-PHNDP for short, in which facilities are added incrementally to the network (one at a time), contributing to the service levels. We first develop a general non-linear model of this problem and then present a method to make it linear. As the problem is of a combinatorial nature, an efficient Variable Neighbourhood Search (VNS) algorithm is proposed to solve it. In order to gain insight into the problem, the computational studies were performed with randomly generated instances of different settings. Results clearly show that VNS performs well in solving IC-PHNDP with errors not more than 1.54%.Peer reviewe

The Elderly Centre Location Problem

© The Operational Research Society 2020. This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of the Operational Research Society on 12 Feb 2020, available online: https://doi.org/10.1080/01605682.2020.1718020.Increased human life expectancy combined with declining birth rates around the globe has led to ageing populations, particularly in the developed world. This phenomenon brings about increased dependency ratios and calls for setting new policies for the elderly citizens. This comprises the provision of a set of life-enhancing services in an accessible and equitable way. In this paper, we consider a multi-period problem of locating senior centres offering these services to the elderly population against budget constraints and capacity limitations. We assume that the attractiveness of facilities to elderlies is inversely proportional with the travel time to access these facilities. Both consistent and inconsistent versions of the problem are considered, aiming at identifying the set of facilities to operate in each region at each period, the service type(s) to be offered and the allocation of budget in each period to location and operation of facilities. A mixed integer mathematical programming model is presented, an efficient iterated local search procedure is proposed and managerial insights are provided.Peer reviewedFinal Accepted Versio

Multi-fidelity modelling approach for airline disruption management using simulation

Disruption to airline schedules is a key issue for the industry. There are various causes for disruption, ranging from weather events through to technical problems grounding aircraft. Delays can quickly propagate through a schedule, leading to high financial and reputational costs. Mitigating the impact of a disruption by adjusting the schedule is a high priority for the airlines. The problem involves rearranging aircraft, crew and passengers, often with large fleets and many uncertain elements. The multiple objectives, cost, delay and minimising schedule alterations, create a trade-off. In addition, the new schedule should be achievable without over-promising. This thesis considers the rescheduling of aircraft, the Aircraft Recovery Problem. The Aircraft Recovery Problem is well studied, though the literature mostly focusses on deterministic approaches, capable of modelling the complexity of the industry but with limited ability to capture the inherent uncertainty. Simulation offers a natural modelling framework, handling both the complexity and variability. However, the combinatorial aircraft allocation constraints are difficult for many simulation optimisation approaches, suggesting that a more tailored approach is required. This thesis proposes a two-stage multi-fidelity modelling approach, combining a low-fidelity Integer Program and a simulation. The deterministic Integer Program allocates aircraft to flights and gives an initial estimate of the delay of each flight. By solving in a multi-objective manner, it can quickly produce a set of promising solutions representing different trade-offs between disruption costs, total delay and the number of schedule alterations. The simulation is used to evaluate the candidate solutions and look for further local improvement. The aircraft allocation is fixed whilst a local search is performed over the flight delays, a continuous valued problem, aiming reduce costs. This is done by developing an adapted version of STRONG, a stochastic trust-region approach. The extension incorporates experimental design principles and projected gradient steps into STRONG to enable it to handle bound constraints. This method is demonstrated and evaluated with computational experiments on a set of disruptions with different fleet sizes and different numbers of disrupted aircraft. The results suggest that this multi-fidelity combination can produce good solutions to the Aircraft Recovery Problem. A more theoretical treatment of the extended trust-region simulation optimisation is also presented. The conditions under which a guarantee of the algorithm's asymptotic performance may be possible and a framework for proving these guarantees is presented. Some of the work towards this is discussed and we highlight where further work is required. This multi-fidelity approach could be used to implement a simulation-based decision support system for real-time disruption handling. The use of simulation for operational decisions raises the issue of how to evaluate a simulation-based tool and its predictions. It is argued that this is not a straightforward question of the real-world result being good or bad, as natural system variability can mask the results. This problem is formalised and a method is proposed for detecting systematic errors that could lead to poor decision making. The method is based on the Probability Integral Transformation using the simulation Empirical Cumulative Distribution Function and goodness of fit hypothesis tests for uniformity. This method is tested by applying it to the airline disruption problem previously discussed. Another simulation acts as a proxy real world, which deviates from the simulation in the runway service times. The results suggest that the method has high power when the deviations have a high impact on the performance measure of interest (more than 20%), but low power when the impact is less than 5%

An architectural framework for self-configuration and self-improvement at runtime

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Optimisation approaches for an orienteering problem with applications to wildfire management

During uncontrollable wildfires, Incident Management Teams (ITMs) dispatch vehicles for tasks aimed at reducing the hazard to key assets. The deployment plan is complicated by the need for vehicle capabilities to match asset requirements within time-windows determined by the progression of the fire. Assignment of the response vehicles to undertake protection activities at different assets is known as the asset protection problem. The asset protection problem is one of the real-life applications of the Cooperative Orienteering Problem with Time Windows (COPTW). The COPTW is a class of problems with some important applications and yet has received relatively little attention. In the COPTW, a certain number of team members are required to collect the associated reward from each node simultaneously and cooperatively. This requirement to have one or more team members simultaneously available at a vertex to collect the reward, poses a challenging task. It means that while multiple paths need to be determined as in the team orienteering problem with time-windows (TOPTW), there is the additional requirement that certain paths must meet at some of the vertices. Exact methods are too slow for operational purposes and they are not able to handle large scale instances of the COPTW. This thesis addresses the problem of finding solutions to COPTW in times that make the approaches suitable for use in certain emergency response situations. Computation of exact solutions within a reasonable time is impossible due to the nature of the COPTW. Thus, the thesis introduces an efficient heuristic approach to achieve reliable solutions in short computation times. Thereafter, a new set of algorithms are developed to work together as components of an adaptive large neighbourhood search algorithm. The proposed solution approaches in this work are the first algorithms that can achieve promising solutions for realistic sizes of the COPTW in a time efficient manner. In addition to the COPTW, this thesis presents an algorithmic approach to solve the asset protection problem. The complexities involved in the asset protection problem are handled by a metaheuristic algorithm. The asset protection problem is often further complicated by a wind change that is expected but with uncertainty in its timing. For this situation a two-stage stochastic model is introduced for the optimal deployment of response vehicles. The model addresses uncertainty in the timing of changes in the problem conditions for the first time in the literature. It is shown that deployment plans, which improve on current practices, can be generated in operational times thus providing useful decision support in time-pressured environments. The performance of the proposed approaches are validated through extensive computational studies. The computational results show that the proposed methods are effective in obtaining good quality solutions in times that are suitable for operational purposes. This is particularly useful for increasing the tools available to IMT's faced with making deployment decisions crucial to savings lives and critical assets

Macroscopic Traffic Model Validation of Large Networks and the Introduction of a Gradient Based Solver

Traffic models are important for the evaluation of various Intelligent Transport Systems and the development of new traffic infrastructure. In order for this to be done accurately and with confidence the correct parameter values of the model must be identified. The focus of this thesis is the identification and confirmation of these parameters, which is model validation. Validation is performed on two different models; the first-order CTM and the second-order METANET model. The CTM is validated for two UK sites of 7.8 and 21.9 km and METANET for the same two sites using a variety of meta-heuristic algorithms. This is done using a newly developed method to allow for the optimisation method to determine the number of parameters to be used and the spatial extent of their application. This allows for the removal of expert engineering knowledge and ad-hoc decomposition of networks. This thesis also develops a methodology by use of Automatic Differentiation to allow gradient based optimisation to be used. This approach successfully validated the METANET model for the 21.9 km site and also a large network surrounding the city of Manchester of 186.9 km. This proves that gradient based optimisation can be used for the macroscopic traffic model validation problem. In fact the performance of the developed gradient method is superior to the meta-heuristics tested for the same sites. The methodology defined also allows for more data to be obtained from the model such as its Jacobian and the sensitivity of the objective function being used relative to the individual parameters. Space-Time contour plots of this newly acquired data show structures and shock waves that are not visible in the mean speed contour diagrams