Agent based simulation of the dial-a-flight problem

A dissertation submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in ful lment of the requirements for the degree of Master of Science in Engineering. Johannesburg, May 2018Agent based simulation and modelling (ABSM) has been noted as a novel method in solving complex problems. This dissertation makes use of the ABSM method in conjunction with a Genetic Algorithm to find good solutions to the dial-a-flight problem. The task is to generate a schedule for a heterogeneous fleet of aircraft, with the objective to reduce operational cost but maintain customer satisfaction. By making use of booking list data from an air taxi business, operating in the Okavango Delta, two agent based models were designed, the first makes use of multi-criteria decision analysis (MCDA) and the other a method proposed by Campbell [7], to test their effectiveness against either upper bound or manual solutions. The solution quality varied between tests, with booking list sizes between 10 and 200 requests producing improvements to the upper bound and manual results with a mean improvement from the benchmarks of 1.61\%. The method could also be refined further by adopting improvement mechanisms to final schedules or by making use of retrospective decision making aided by self learning techniques.MT 201

US Army Aviation air movement operations assignment, utilization and routing

Purpose – The purpose of this study was to create an air movement operations planning model to rapidly generate air mission request (AMR) assignment and routing courses of action (COA) in order to minimize unsupported AMRs, aircraft utilization and routing cost. Design/methodology/approach – In this paper, the US Army Aviation air movement operations planning problem is modeled as a mixed integer linear program (MILP) as an extension of the dial-a-ride problem (DARP). The paper also introduces a heuristic as an extension of a single-vehicle DARP demand insertion algorithm to generate feasible solutions in a tactically useful time period. Findings – The MILP model generates optimal solutions for small problems (low numbers of AMRs and small helicopter fleets). The heuristic generates near-optimal feasible solutions for problems of various sizes (up to 100 AMRs and 10 helicopter team fleet size) in near real time. Research limitations/implications – Due to the inability of the MILP to produce optimal solutions for mid- and large-sized problems, this research is limited in commenting on the heuristic solution quality beyond the numerical experimentation. Additionally, the authors make several simplifying assumptions to generalize the average performance and capabilities of aircraft throughout a flight. Originality/value – This research is the first to solve the US Army Aviation air movement operations planning problem via a single formulation that incorporates multiple refuel nodes, minimization of unsupported demand by priority level, demand time windows, aircraft team utilization penalties, aircraft team time windows and maximum duration and passenger ride time limits

Disruption Management of ASAE's Inspection Routes

The Rapid development and the emergence of technologies capable of producing real-time data opened new horizons to both planning and optimization of vehicle routes [4]. In this dissertation, the Autoridade de Segurança Alimentar e Económica (ASAE) operation's scenario will be explored and analyzed as a case study to the problem. ASAE is a Portuguese administrative authority specialized in food security and economic auditing and is responsible to regulate thousands of economic entities in the Portuguese territory. ASAE inspections are usually done by brigades using vehicles to inspect economic operators, taking into account their timetables. Previous work on this topic led to the implementation of an inspection route optimization module capable of defining and assigning routes to inspect economic operators, seeking to maximize a utility function. Using optimization algorithms, inspection routes are calculated for each brigade, with information regarding specific map paths and inspection schedules. The approach used does not take into consideration the dynamic properties of real-life scenarios, as the precalculated operation plan is not reviewed in real-time. This work aims to study the dynamic properties of ASAE's operational environment and proposes a solution to efficiently review the precalculated inspection routes and apply the required changes in an appropriate time frame. Vehicle routing problems (VRP) are optimization problems where the aim is to calculate the set of optimized routes for a vehicle fleet, from a starting point to several interesting locations. Dynamic vehicle routing problem (DVRP) is a variant of VRP that makes use of real-time information to calculate the most optimized set of routes at a certain moment [39]. DVRP is a challenging problem because its scope is real-time, meaning that decisions sometimes must be made in short time windows, preventing the use of complex algorithms that require long computational times [10]. The typical approach to this problem is to initially calculate the routes for the whole fleet and dynamically revise the defined operations plan in real-time, once a disruption occurs. This work will model the problem as a DVRP and will compare the performance of heuristics and other modern optimization techniques, proposing a solution that will reduce the impact of disruptions on inspection routes. An optimized operations plan will reduce the time required for inspections, allowing massive economic savings, while reducing a company's ecological footstep. The work can eventually be scaled and used in other institutions, such as GNR or PSP in Portugal, that operate similarly

Applications of biased-randomized algorithms and simheuristics in integrated logistics

Transportation and logistics (T&L) activities play a vital role in the development of many businesses from different industries. With the increasing number of people living in urban areas, the expansion of on-demand economy and e-commerce activities, the number of services from transportation and delivery has considerably increased. Consequently, several urban problems have been potentialized, such as traffic congestion and pollution. Several related problems can be formulated as a combinatorial optimization problem (COP). Since most of them are NP-Hard, the finding of optimal solutions through exact solution methods is often impractical in a reasonable amount of time. In realistic settings, the increasing need for 'instant' decision-making further refutes their use in real life. Under these circumstances, this thesis aims at: (i) identifying realistic COPs from different industries; (ii) developing different classes of approximate solution approaches to solve the identified T&L problems; (iii) conducting a series of computational experiments to validate and measure the performance of the developed approaches. The novel concept of 'agile optimization' is introduced, which refers to the combination of biased-randomized heuristics with parallel computing to deal with real-time decision-making.Las actividades de transporte y logística (T&L) juegan un papel vital en el desarrollo de muchas empresas de diferentes industrias. Con el creciente número de personas que viven en áreas urbanas, la expansión de la economía a lacarta y las actividades de comercio electrónico, el número de servicios de transporte y entrega ha aumentado considerablemente. En consecuencia, se han potencializado varios problemas urbanos, como la congestión del tráfico y la contaminación. Varios problemas relacionados pueden formularse como un problema de optimización combinatoria (COP). Dado que la mayoría de ellos son NP-Hard, la búsqueda de soluciones óptimas a través de métodos de solución exactos a menudo no es práctico en un período de tiempo razonable. En entornos realistas, la creciente necesidad de una toma de decisiones "instantánea" refuta aún más su uso en la vida real. En estas circunstancias, esta tesis tiene como objetivo: (i) identificar COP realistas de diferentes industrias; (ii) desarrollar diferentes clases de enfoques de solución aproximada para resolver los problemas de T&L identificados; (iii) realizar una serie de experimentos computacionales para validar y medir el desempeño de los enfoques desarrollados. Se introduce el nuevo concepto de optimización ágil, que se refiere a la combinación de heurísticas aleatorias sesgadas con computación paralela para hacer frente a la toma de decisiones en tiempo real.Les activitats de transport i logística (T&L) tenen un paper vital en el desenvolupament de moltes empreses de diferents indústries. Amb l'augment del nombre de persones que viuen a les zones urbanes, l'expansió de l'economia a la carta i les activitats de comerç electrònic, el nombre de serveis del transport i el lliurament ha augmentat considerablement. En conseqüència, s'han potencialitzat diversos problemes urbans, com ara la congestió del trànsit i la contaminació. Es poden formular diversos problemes relacionats com a problema d'optimització combinatòria (COP). Com que la majoria són NP-Hard, la recerca de solucions òptimes mitjançant mètodes de solució exactes sovint no és pràctica en un temps raonable. En entorns realistes, la creixent necessitat de prendre decisions "instantànies" refuta encara més el seu ús a la vida real. En aquestes circumstàncies, aquesta tesi té com a objectiu: (i) identificar COP realistes de diferents indústries; (ii) desenvolupar diferents classes d'aproximacions aproximades a la solució per resoldre els problemes identificats de T&L; (iii) la realització d'una sèrie d'experiments computacionals per validar i mesurar el rendiment dels enfocaments desenvolupats. S'introdueix el nou concepte d'optimització àgil, que fa referència a la combinació d'heurístiques esbiaixades i aleatòries amb informàtica paral·lela per fer front a la presa de decisions en temps real.Tecnologies de la informació i de xarxe

Metaheuristics for designing efficient routes & schedules for urban transportation networks

This thesis tackles the Urban Transit Network Design Problem (UTNDP) which involves determining an efficient set of routes and schedules for public transit networks. The UTNDP can be divided into five subproblems as identified by Ceder and Wilson [24]: i) network design, ii) frequency setting, iii) timetable development, iv) bus scheduling, and v) driver scheduling, with each problem requiring the output of the previous. In this thesis we focus on the first two stages, network design and frequency setting. We identify that evaluation is a major bottleneck for the network design problem and propose alternative approaches with the aim of decreasing the computation time. A multi-objective evolutionary algorithm (MOEA) for the network design problem is then presented that trades-off the passenger and operator costs. A passenger wishes to travel from their origin to destination in the shortest possible time, whereas the network operator must provide an adequate level of service whilst balancing the operational costs i.e. number of drivers and vehicles. The proposed MOEA combines a heuristically seeded population, using a novel construction algorithm, with several genetic operators to produce improved results compared with the state of the art from the literature. We provide an evaluation of the effectiveness of the genetic operators showing that improved performance, in terms of the number of dominating and nondominating solutions, is achieved as the size of the problem instance increases. Four surrogate models are proposed and an empirical evaluation is performed to assess the solution quality versus run trade-off in each case. It is found that surrogate models perform well on large problem instances producing improved Pareto sets compared with the original algorithm due to the increased amount of evolution that is allowed to occur under fixed time limits. Finally we empirically evaluate three multi-objective approaches for the frequency setting problem utilising the route networks produced during our network design procedure. It is shown that a MOEA based on the NSGAII framework provides the best quality solutions due to the cost of evaluation when using a neighbourhood based approach such as multi-objective tabu search. Constraints on vehicle capacity and fleet size are then introduced. It is shown that such constraints vastly reduce the number of solutions from network design that can successfully undergo frequency setting. A discussion is then presented highlighting the limitations of conducting network design and frequency setting separately along with alternative approaches that could be used in the future. We conclude this thesis by summarising our findings and presenting topics for future works

The Pickup and Multiple Delivery Problem

This thesis presents my work on the pickup and multiple delivery problem, a real-world vehicle routing and scheduling problem with soft time windows, working time and last-in-first-out constraints, developed in collaboration with Transfaction Ltd., who conduct logistics analysis for several large retailers in the UK. A summary of relevant background literature is presented highlighting where my research fits into and contributes to the broader academic landscape. I present a detailed model of the problem and thoroughly analyse a case-study data set, obtaining distributions used for further research. A new variable neighbourhood descent with memory hyper-heuristic is presented and shown to be an effective technique for solving instances of the real-world problem. I analyse strategies for cooperation and competition amongst haulage companies and quantify their effectiveness. The value of time and timely information for planning pickup and delivery requests is investigated. The insights gained are of real industrial relevance, highlighting how a variety of business decisions can produce significant cost savings

Hyper-heuristics for two complex vehicle routing problems: the urban transit routing problem, and a delivery and installation problem

Hyper-heuristics have emerged as general purpose search techniques that explore the space of low-level heuristics to improve a given solution under an iterative framework. They were introduced to raise the level of generality of search techniques representing self-configuring and automated reusable heuristic approaches for solving combinatorial problems. There are two classes of hyper-heuristics identified in the literatire: generation and selection hyper-heuristics. In this thesis, we focus on the class of selection hyperheuristics and their efficient design and application on complex routing problems. We specifically focus on two routing problems: the Urban Transit Network design Problem (UTRP), and a rich vehicle routing problem for the delivery and installation of equipment which was the subject of the VeRoLog solver challenge 2019. The urban transit routing problem (UTRP) aims to find efficient travelling routes for vehicles in public transportation systems. It is one of the most significant problems faced by transit planners and city authorities throughout the world. This problem belongs to the class of combinatorial problems whose optimal solution is hard to find with the complexity that arises from the large search space, and the multiple constraints imposed in constructing the solution. Furthermore, realistic benchmark data sets are lacking, making it difficult for researchers to compare their problem solving techniques with those of other researchers. We evaluate and compare the performance of a set of selection hyperheuristics on the UTRP, with the goal of minimising the passengers’ travel time and the operators’ costs. Each selection hyper-heuristic is empirically tested on a set of known benchmark instances and statistically compared against all the other hyper-heuristics to determine the best approach. A sequence-based selection method utilising a hidden markov model achieved the best performance between the tested selection methods, and better solutions than the current known best solutions are achieved on benchmark instances. Then, we propose a hyper-heuristic algorithm specifically designed to solve the UTRP with defined terminal nodes that determine the start and end points of bus journeys. The algorithm is applied to a novel set of benchmark instances with real world size and characteristics representing the extended urban area of Nottingham city. We compare the hyper-heuristic performance on the data set with the NSGAII algorithm and real world bus routes, and prove that better solutions are found by hyper-heuristics. Due to the clear gap in research between the application of optimisation algorithms in public routes network optimisation and the real world planning processes, we implemented a hyper-heuristic algorithm that interactively work with interface procedures to optimise the public transport lines in Visum transportation modelling software. We adopt Selection Hyper-heuristics for two optimisation problems and the optimisation objectives include the passengers’ average travel time and operators’ costs. The results demonstrate the successful implementation of the applied optimisation methods for multi-modal public transport networks. Finally we introduce a population based hyperheuristic algorithm and apply it on a complex vehicle routing problem consisting of two stages: a Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) for the delivery of equipment, and the Service Technician Routing and Scheduling Problem (STRSP) for the installation of the delivered equipment. This problem was the subject of the VeRoLog solver challenge 2019. We apply the hyper-heuristic population-based algorithm on a small and large size data sets, and show that our approach performed better in terms of results and run time on small instances compared to the results of mathematical model implemented for this problem. We perform analysis of the new proposed algorithm and show that it finds better quality solutions compared to its constituent selection hyper-heuristics when applied individually. Finally we conclude the thesis with a summary of the work and future plans

A simulation study of cane transport system improvements in the Sezela Mill area.

Thesis (M.Sc.)-University of KwaZulu-Natal, Pietermaritzburg, 2009.The South African sugar industry is of significant local and international importance and covers an area in excess of 450 000 hectares. This area yields approximately 21 million tons of sugarcane per annum which is transported almost exclusively by road, from farms to the sugar mills. The industry is under increasing economic pressures to improve its productivity and competitiveness and sugarcane transport in the sugarcane supply chain has been identified as one area where large improvements and associated cost reductions can be made. This is mainly due to the excess in number of vehicles in the inbound transport system, the high relative cost of transport compared to other production costs in producing sugarcane, and the high fixed costs associated with truck fleet operations. A simulation case study of the transport system was completed in 2005 in the Sezela Mill area in which approximately 2.2 million tons of sugarcane is transported per annum over an average distance of 29 km by approximately 120 independently managed vehicles owned by a wide range of hauliers and individual growers. This amounts to an estimated cost of R58 million per annum. This study investigated the potential savings that could occur as a result of a central fleet control system with integrated vehicle scheduling. A scheduling software package named ASICAM, which resulted in significant savings in the timber industry (Weintraub et al, 1996), was applied within the Sezela region. Results suggested that the number of trucks in the fleet could theoretically be reduced by at least 50%, providing that a central office controls vehicle movements and that all hauliers serve all growers in an equitable fashion. In addition, investigations towards decreasing loading times, decreasing offloading times, changing vehicle speeds and increasing payloads by reducing trailer tare mass showed further reductions in the number of trucks required