The thesis focuses on how to optimize vehicle routes for distributing logistics. This vehicle route optimization is known as a vehicle routing problem (VRP). The VRP has been extended in numerous directions for instance by some variations that can be combined. One of the extension forms of VRP is a capacitated VRP with stochastics demands (CVRPSD), where the vehicle capacity limit has a non-zero probability of being violated on any route. So, a failure to satisfy the amount of demand can appear. A strategy is required for updating the routes in case of such an event. This strategy is called as recourse action in the thesis. The main objective of the research is how to design the model of CVRPSD and find the optimal solution. The EEV (Expected Effective Value) and FCM (Fuzzy C-Means) – TSP (Travelling Salesman Problem) approaches are described and used to solve CVRPSD. Results have confirmed that the EEV approach has given a better performance than FCM-TSP for solving CVRPSD in small instances. But EEV has disadvantage, that the EEV is not capable to solve big instances in an acceptable running time because of complexity of the problem. In the real situation, the FCM –TSP approach is more suitable for implementations than the EEV because the FCM – TSP can find the solution in a shorter time. The disadvantage of this algorithm is that the computational time depends on the number of customers in a cluster.

The following work concerns the study of the evolutionary algorithm, which optimizes freight transport planning. The demand for freight transport is constantly increasing nowadays and with creating, implementing and using proper route planning we are able to significantly reduce transportation costs. However, it is preferably to implement it in companies with large numbers of served customers and with a sufficiently large fleet of vehicles.   The study starts by defining what fright transport planning problem is and by characterizing its existing specifications and variants. My work proceeds to give a background of the possible solutions to the multifaceted aspects of the problem. The specific subproblem I choose to focus on is the Vehicle routing problem with Pickup and Delivery for which I apply the optimization solution. In the main body of my thesis, I will elaborate on the chosen optimization solution which encompasses the genetic algorithm and evolutionary strategy. The aim of the study is to measure the suitability of the algorithms and techniques used, for which reason the final part of my work will deal with the analysis and evaluation of the experiments.

This thesis deals with portation of the OptaPlanner tool to the Android operating system. The OptaPlanner is used for solving planning problems and it is completely written in the Java programming language which is also used for application development of the Android operating system. However, Android does not contain all of the Java Standard Edition Application Programming Interface libraries and porting of OptaPlanner to Android thus causes dependency problems. The result of the thesis is solution design and implementation of the problems mentioned above and model Android Vehicle Routing Problem application which uses ported OptaPlanner tool.

Context. The purpose of this research paper is to describe a new approach for solving the disruption in the vehicle routing problem (DVRP) which deals with the disturbance that will occur unexpectedly within the distribution area when executing the original VRP plan. The paper then focuses further on the foremost common and usual problem in real-time scenarios i.e., vehicle-breakdown part. Therefore, the research needs to be accomplished to deal with these major disruption in routing problems in transportation. Objectives. The study first investigates to find suitable and efficient metaheuristic techniques for solving real-time vehicle routing problems than an experiment is performed with the chosen algorithms which might produce near-optimal solutions. Evaluate the performance of those selected algorithms and compare the results among each other. Methods. To answer research questions, firstly, a literature review has been performed to search out suitable meta-heuristic techniques for solving vehicle routing problems. Then based on the findings an experiment is performed to evaluate the performance of selected meta-heuristic algorithms. Results. Results from the literature review showed that the meta-heuristic approaches such as. Tabu Search, Ant Colony Optimization, and Genetic Algorithmare suitable and efficient algorithms for solving real-time vehicle routing problems. The performance of those algorithms has been calculated and compared with one another with standard benchmarks. Conclusions. The performance of a Tabu Search algorithm is best among the other algorithms, followed by Ant Colony Optimization and Genetic Algorithm. Therefore, it has been concluded that the Tabu Search is the best algorithm for solving real-time disruption problems in VRP. The results are similar to the performance comparison of the selected algorithms and standard benchmarks are presented within the research.

El presente estudio versa sobre la ejecución proyectos eléctricos en baja tensión, los cuales son ejecutados por una empresa de servicios cuyas operaciones se realizan en Lima. El capítulo 1 presenta la literatura asociada a la solución del problema, en donde se desarrolla el marco teórico de las metodologías propuestas. El capítulo 2 muestra el uso de las herramientas de la ingeniería industrial a efectos de identificar el problema: “retrasos en la ejecución de proyectos de baja tensión con reforma”. Esta problemática genera una pérdida anual de 381 miles de soles y un déficit de capital de trabajo de 11,500 miles de soles correspondiente al año 2017. El capítulo 3 plantea la solución de la problemática expuesta, para lo cual se definen dos etapas. La primera permite la eliminación del backlog acumulado hasta el mes de setiembre de 2018. La segunda se refiere al desarrollo de las metodologías: Last Planner System, Vehicle Routing Problem y Workforce Management. En el capítulo 4 se realiza la validación de las propuestas de las mejoras planteadas. En la primera etapa se contrataron 210 personas para eliminar el backlog y se validó una utilidad de 104 miles de soles. Para la segunda etapa, se implementó el proyecto cuadrillas propias y se utilizaron las metodologías Last Planner System y Vehicle Routing Problem.. Finalmente, se integró ambas metodologías con la incorporación de tecnología del tipo Workforce Management. Los resultados muestran una TIR del 20% y un VAN de 229 miles de soles. / The present research is about the implementation of low voltage electrical projects, which are performed by an utility company whose area of operations is the city of Lima. Chapter 1 describes the literature associated with the solution of the problem, where the theoretical framework of the above mentioned methodologies is developed. Chapter 2 describes the results obtained through the utilization of industrial engineering tools, with which the problem is identified as: "delays in the implementation of low voltage projects with reform". This problem generates an annual loss of 381 thousand soles and a working capital deficit of 11,500 thousand soles corresponding to year 2017. Chapter 3 proposes the solution of the exposed problem, for which two stages are defined. The first one allows the elimination of the accumulated backlog until September 2018. The second one refers to the development of the methodologies: Last Planner System, Vehicle Routing Problem and Workforce Management. Chapter 4 validates the proposed improvements. In the first stage, 210 people were hired to eliminate the backlog and as a result there was a profit of 104 thousand soles for the period within July and September. For the second stage, the project of own crews was implemented and there were used Last Planner System and the Vehicle Routing Problem as methodologies in order to optimize the route of the crews. Finally, both methodologies were integrated with the incorporation of the Workforce Management technology. The results show an IRR of 20% and a NPV of 229 thousand soles. / Trabajo de Suficiencia Profesional

Collaborative dispatching allows several dispatchers to view the routing solution as a dynamic model where changes to the vehicle routes can be made in real-time. In this paper we discuss implications of collaborative dispatching on real-time decision support tools for motor carriers. We present a collaborative dispatching system which uses real-time information obtained from a telematics system. Messages sent from the vehicles are automatically analysed and actual data, such as exact arrival and departure times, as well as discrepancies between actual and planned data are identified. The collaborative dispatching system not only allows several dispatchers to concurrently modify the schedule, but also a dynamic optimisation method. The optimisation method is capable of taking into account that input data may change at any time and that dispatchers can concurrently modify the schedule and may add or relax certain constraints relevant to the optimisation model.

Real-life vehicle routing problems encounter a number of complexities that are not considered by the classical models found in the vehicle routing literature. In this paper we consider a dynamic real-life vehicle routing problem which is a combined load acceptance and generalised vehicle routing problem incorporating a diversity of practical complexities. Among those are time window restrictions, a heterogeneous vehicle fleet with different travel times, travel costs and capacity, multi-dimensional capacity constraints, order/vehicle compatibility constraints, orders with multiple pickup, delivery and service locations, different start and end locations for vehicles, route restrictions associated to orders and vehicles, and drivers’ working hours. We propose iterative improvement approaches based on Large Neighborhood Search. Our algorithms are characterised by very fast response times and thus, can be used within dynamic routing systems where input data can change at any time.

In this paper we consider a rich vehicle routing problem where transportation requests are characterised by multiple pickup and delivery locations. The problem is a combined load acceptance and generalised vehicle routing problem incorporating a diversity of practical complexities. Among those are time window restrictions, a heterogeneous vehicle fleet with different travel times, travel costs and capacity, multi-dimensional capacity constraints, order/vehicle compatibility constraints, and different start and end locations for vehicles. We propose iterative improvement approaches based on Large Neighborhood Search and a relatedness measure for transportation requests with multiple pickup and delivery locations. Our algorithms are characterised by very fast response times and thus, can be used within dynamic routing systems where input data can change at any time.

In this thesis, the vehicle routing problem with stochastic, and time dependent, travel times is studied. The stochastic travel times are estimated from historical drive data. The variation of the drive times, as well as that of the variance, during the day was modeled.   The purpose of the thesis was to propose a method of handling the congestion related traffic impediments in an urban setting. Since the majority of times of delivery in the empirical test cases studied correlate with the time period of high traffic load, an efficient and robust handling of such traffic scenarios is of high importance.  It is shown that the stochastic models will shift the estimated arrivals to customers from the more volatile early and late extremes to more central regions of the time window. Previously delivered routes were evaluated both with the standard algorithm and the proposed stochastic algorithm. The difference between the actual drive times and the calculated drive times were analyzed by studying the correlation of the drive times between each customer in the route. It was shown that the routes of the proposed stochastic method increased this correlation. The drive times between nodes where also perturbed with a Gamma distributed noise. The results from the stochastic algorithm showed higher resilience to this disturbance than did the deterministic models. / I detta examensarbete har fordonsruttningsproblemet, VRP, med stokastiska och tidsberonds körtider behandlöats. De stokastiska körtiderna har estimerats från tidigare insamlad hasighetsdata. Modeller för körtidernas och variansernas förändring under dagen har tagits fram.   Syftet med examensarbetet var att föreslå en metod för hur påverkan på körtider av förutsägbar trafikträngsel i en urban trafikmiljö kan hanteras. Eftersom huvuddelen av alla leveranser sammanfaller med de tider på dygnet då trafikbelastning är som högst, ar är en effektiv och robust metod för att hantera sådana störningar av stor vikt. Det visas att den stokastiska modellen kommer att förflyttar ankomster från början och slutet av tidsfönstret till den mer okänsliga mittregionen. Tidigare, utförda leveranser studerades både med den ursprungliga deterministiska modellen och här framtagna stokastiska modellen. Skillnaden mellan de två analyserades genom att studera korrelationen mellan körtiderna som de beräknats av de två modellerna och de upmätta tiderna som de loggats av leveransfordonen. Det visas att korrelationen mellan körtiderna mellan de stokastiska körtiderna och de verkliga körtiderna är högre än korrelationen mellan de deterministiska körtiderna och de verkliga. Rutterna som föreslagits av den stokastiska modellen var också mer tlig mot störningar.

No description available.