OPTIMAL ROUTE DETERMINATION FOR POSTAL DELIVERY USING ANT COLONY OPTIMIZATION ALGORITHM

There are a lot of optimization challenges in the world, as we all know. The vehicle routing problem is one of the more complex and high-level problems. Vehicle Routing Problem is a real-life problem in the Postal Delivery System logistics and, if not properly attended to, can lead to wastage of resources that could have been directed towards other things. Several studies have been carried out to tackle this problem using different techniques and algorithms. This study used the Ant Colony Optimization Algorithm along with some powerful APIs to find an optimal route for the delivery of posts to customers in a Postal Delivering System. When Ant Colony Optimization Algorithm is used to solve the vehicle routing problem in transportation systems, each Ant's journey is mere “part” of a feasible solution. To put it in another way, numerous ants' pathways might make up a viable solution. Routes are determined for a delivery vehicle, with the objective of minimizing customer waiting time and operation cost. Experimental results indicate that the solution is optimal and more accurat

Analysis of the characteristics and applications of vehicle routing systems

El ruteo de vehículos, permite establecer una estrategia para realizar la distribución adecuada de las mercancías, en los diferentes puntos en los cuales lo desee una organización. Esto se logra, a través del diseño de rutas para una flota de vehículos determinada; ya sea homogénea o heterogénea. El estudio de este problema de ruteo, como ha sido considerado, se ha clasificado en diferentes sistemas, de acuerdo a las condiciones del entorno en el cual se desean aplicar. Sin embargo, no todas las tipologías son conocidas a cabalidad por las organizaciones o investigadores, debido a su reciente desarrollo o su poco nivel de aplicación. Es por ello, que en la presente investigación, se plantea realizar un análisis de las características y aplicaciones de los tipos de sistemas de ruteo de vehículos, a través de una revisión bibliográfica de trabajos previos, con el propósito de brindar información sólida y concisa a futuros investigadores. La metodología empleada, conlleva principalmente a una investigación de tipo cualitativa, en la cual se realizó una búsqueda sistemática en bases de datos del problema planteado de los últimos cinco años. A partir de esto, fue posible establecer que durante este período de tiempo, las publicaciones en este campo, presentaron un incremento de aproximadamente el doble, evidenciando el aumento en el interés por el tema objetivo.The vehicle routing allows to establish a strategy for the proper distribution of goods in different points at which you want an organization. This is achieved through the design of routes to a particular fleet vehicle; either homogeneous or heterogeneous. Studying this routing problem, as has been seen, has been classified into different systems, according to the environmental conditions in which is applied. However, not all types are known at all by the organizations or researchers, due to its recent development or some application level. That is why, in this research, we propose an analysis of the characteristics and applications of the types of systems vehicle routing through a literature review of previous works, in order to provide solid and concise information to future researchers. The methodology used primarily involves qualitative research type, in which a systematic search was performed in databases of the problem of the past five years. From this, it was possible to establish that during this period, the publications in this field, showed an increase of about twice, showing increased interest in the subject target

A Time-Constrained Capacitated Vehicle Routing Problem in Urban E-Commerce Delivery

Electric vehicle routing problems can be particularly complex when recharging must be performed mid-route. In some applications such as the e-commerce parcel delivery truck routing, however, mid-route recharging may not be necessary because of constraints on vehicle capacities and maximum allowed time for delivery. In this study, we develop a mixed-integer optimization model that exactly solves such a time-constrained capacitated vehicle routing problem, especially of interest to e-commerce parcel delivery vehicles. We compare our solution method with an existing metaheuristic and carry out exhaustive case studies considering four U.S. cities -- Austin, TX; Bloomington, IL; Chicago, IL; and Detroit, MI -- and two vehicle types: conventional vehicles and battery electric vehicles (BEVs). In these studies we examine the impact of vehicle capacity, maximum allowed travel time, service time (dwelling time to physically deliver the parcel), and BEV range on system-level performance metrics including vehicle miles traveled (VMT). We find that the service time followed by the vehicle capacity plays a key role in the performance of our approach. We assume an 80-mile BEV range as a baseline without mid-route recharging. Our results show that BEV range has a minimal impact on performance metrics because the VMT per vehicle averages around 72 miles. In a case study for shared-economy parcel deliveries, we observe that VMT could be reduced by 38.8\% in Austin if service providers were to operate their distribution centers jointly

A review on delivery routing problem and its approaches

In this paper, a review is conducted specifically in the delivery routing problem, so as to understand its problems and approaches on the current developments and publications. The variants of delivery routing problem were categorized according to the constraints considered in solving the problem. The solution algorithms for the delivery routing problem were classified into hybrid and non-hybrid approaches. A collection of benchmark datasets and real case studies is also presented in relation to the delivery routing problem. The review helps to summarize and record a comprehensive survey on the delivery routing problem. The aims is to organize the variants components of delivery routing problems in a manner that provides a clear view for the readers. New potential research directions resulting from the study is also presented

The median routing problem for simultaneous planning of emergency response and non-emergency jobs

This paper studies a setting in emergency logistics where emergency responders must also perform a set of known, non-emergency jobs in the network when there are no active emergencies going on. These jobs typically have a preventive function, and allow the responders to use their idle time much more productively than in the current standard. When an emergency occurs, the nearest responder must abandon whatever job he or she is doing and go to the emergency. This leads to the optimisation problem of timetabling jobs and moving responders over a discrete network such that the expected emergency response time remains minimal. Our model, the Median Routing Problem, addresses this complex problem by minimising the expected response time to the next emergency and allowing for re-solving after this. We describe a mixed-integer linear program and a number of increasingly refined heuristics for this problem. We created a large set of benchmark instances, both from real-life case study data and from a generator. On the real-life case study instances, the best performing heuristic finds on average a solution only 3.4% away from optimal in a few seconds. We propose an explanation for the success of this heuristic, with the most pivotal conclusion being the importance of solving the underlying p-Medians Problem

Distance-constrained vehicle routing problem: exact and approximate solution (mathematical programming)

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The asymmetric distance-constrained vehicle routing problem (ADVRP) looks at finding vehicle tours to connect all customers with a depot, such that the total distance is minimised; each customer is visited once by one vehicle; every tour starts and ends at a depot; and the travelled distance by each vehicle is less than or equal to the given maximum value. We present three basic results in this thesis. In the first one, we present a general flow-based formulation to ADVRP. It is suitable for symmetric and asymmetric instances. It has been compared with the adapted Bus School Routing formulation and appears to solve the ADVRP faster. Comparisons are performed on random test instances with up to 200 customers. We reach a conclusion that our general formulation outperforms the adapted one. Moreover, it finds the optimal solution for small test instances quickly. For large instances, there is a high probability that an optimal solution can be found or at least improve upon the value of the best feasible solution found so far, compared to the other formulation which stops because of the time condition. This formulation is more general than Kara formulation since it does not require the distance matrix to satisfy the triangle inequality. The second result improves and modifies an old branch-and-bound method suggested by Laporte et al. in 1987. It is based on reformulating a distance-constrained vehicle routing problem into a travelling salesman problem and uses the assignment problem as a lower bounding procedure. In addition, its algorithm uses the best-first strategy and new branching rules. Since this method was fast but memory consuming, it would stop before optimality is proven. Therefore, we introduce randomness in choosing the node of the search tree in case we have more than one choice (usually we choose the smallest objective function). If an optimal solution is not found, then restart is required due to memory issues, so we restart our procedure. In that way, we get a multistart branch and bound method. Computational experiments show that we are able to exactly solve large test instances with up to 1000 customers. As far as we know, those instances are much larger than instances considered for other VRP models and exact solution approaches from recent literature. So, despite its simplicity, this proposed algorithm is capable of solving the largest instances ever solved in literature. Moreover, this approach is general and may be used in solving other types of vehicle routing problems. In the third result, we use VNS as a heuristic to find the best feasible solution for groups of instances. We wanted to determine how far the difference is between the best feasible solution obtained by VNS and the value of optimal solution in order to use the output of VNS as an initial feasible solution (upper bound procedure) to improve our multistart method. Unfortunately, based on the search strategy (best first search), using a heuristic to find an initial feasible solution is not useful. The reason for this is because the branch and bound is able to find the first feasible solution quickly. In other words, in our method using a good initial feasible solution as an upper bound will not increase the speed of the search. However, this would be different for the depth first search. However, we found a big gap between VNS feasible solution and an optimal solution, so VNS can not be used alone unless for large test instances when other exact methods are not able to find any feasible solution because of memory or stopping conditions