Routing of Supply Vessels to with Deliveries and Pickups of Multiple Commodities

AbstractThis paper considers a single vehicle routing problem with pickups and deliveries of multiple commodities where each customer requires both pickup and delivery of several types of goods from a single depot. This problem arises in offshore upstream logistics and is relevant for the oil and gas companies operating offshore. Offshore installations need to be supplied with several types of goods from an onshore base, and also some cargo need to be transported from the installations back to the base. Supply operations from and to the base are performed by supply vessels, which have separate compartments for different types of cargo. In this paper we present a mathematical formulation for the problem and describe a metaheuristic algorithm yielding non- Hamiltonian routes where customers may be visited once or twice. Computational tests show that the algorithm outperforms CPLEX optimization solver in speed on instances of medium size and generates high quality solutions for large-size instances compared to the Unified Tabu Search algorithm

El problema del viajante de comercio con recogida y entrega de una mercancía con demanda dividida.

This thesis focuses on a kind of problems thoroughly studied in operational research, in particular in Vehicle Routing Problems. Despite there are a lot of studies about them, the most basic are still difficult to solve optimally, even for small size and the lacking practical use. Because of that, this thesis develops exact and heuristic algorithms to solve a particular problem known as The split-demand one-commodity pickup-and-delivery travelling salesman problem (SD1PDTSP). This combines three vehicle routing problems. The first one is the Capacitated Vehicle Routing Problem (CVRP), aiming at designing the routes for a vehicle fleet to deliver a commodity from the depot to a set of customers. Each route starts and ends at the depot, and the load of a vehicle through a route should never exceed the vehicle capacity. A fundamental assumption is that each customer cannot be visited more than once. The aim of the CVRP is to satisfy the demand of each customer while minimizing the total distance travelled by the fleet. The second problem is the Split Delivery Vehicle Routing Problem (SDVRP), which allows a customer to be visited more than once. The third problem still moves one commodity while it allows more than one pickup location. It is the One Commodity Pickup-and-Delivery Travelling Salesman Problem (1-PDTSP). Using the elements of these three problems, the SD1PDTSP is defined as follows. Let us consider a finite set of locations. The travel distances (or costs) between the locations are assumed to be known. Each location is related to a customer, with a known positive or negative demand of a commodity (e.g. bicycles). We assume that the sum of all demands is equal to zero. Customers with negative demands correspond to pickup locations, and customers with positive demands correspond to delivery locations. It is assumed that there is one vehicle with a given capacity that must visit each location at least once through a route to move the commodity and satisfy all the customer demands. Each visit may partially satisfy a customer, and all the visits to that customer must end up with exactly its full demand. The SD1PDTSP consists of finding a minimum-cost route for the vehicle such that it satisfies the demand of all customers. Note that such a route may not exist, and in general checking whether a feasible solution exists is a NP-complete problem due to the limitation in the number of visits to each location and the vehicle capacity. The importance of these kind of problems is not due to the computational complexity only, but also the variety of practical applications. In fact, the idea of Operational Research appears formally in 1938 in the SecondWorldWar, in the frame in collaborative researches between soldiers and scientists about planning of fight military operations. A part of the most obvious examples in logistic (distribution of commodities, scholar routes,etc.), there are more examples as operational control of traffic light, those that can be found in robotic systems that allow to solve production problems, in genetic, etc. Other kind of practical applications arise due to the growing worry about environment. This thesis studies the case of the management of bicycle sharing systems. These are increasingly in demand due to the need of space, the high traffic density and the high emission of noise and CO2

Heuristics and policies for online pickup and delivery problems

Master ThesisIn the last few decades, increased attention has been dedicated to a speci c subclass of Vehicle Routing Problems due to its signi cant importance in several transportation areas such as taxi companies, courier companies, transportation of people, organ transportation, etc. These problems are characterized by their dynamicity as the demands are, in general, unknown in advance and the corresponding locations are paired. This thesis addresses a version of such Dynamic Pickup and Delivery Problems, motivated by a problem arisen in an Australian courier company, which operates in Sydney, Melbourne and Brisbane, where almost every day more than a thousand transportation orders arrive and need to be accommodated. The rm has a eet of almost two hundred vehicles of various types, mostly operating within the city areas. Thus, whenever new orders arrive at the system the dispatchers face a complex decision regarding the allocation of the new customers within the distribution routes (already existing or new) taking into account a complex multi-level objective function. The thesis thus focuses on the process of learning simple dispatch heuristics, and lays the foundations of a recommendation system able to rank such heuristics. We implemented eight of these, observing di erent characteristics of the current eet and orders. It incorporates an arti cial neural network that is trained on two hundred days of past data, and is supervised by schedules produced by an oracle, Indigo, which is a system able to produce suboptimal solutions to problem instances. The system opens the possibility for many dispatch policies to be implemented that are based on this rule ranking, and helps dispatchers to manage the vehicles of the eet. It also provides results for the human resources required each single day and within the di erent periods of the day. We complement the quite promising results obtained with a discussion on future additions and improvements such as channel eet management, tra c consideration, and learning hyper-heuristics to control simple rule sequences.The thesis work was partially supported by the National ICT Australia according to the Visitor Research Agreement contract between NICTA and Martin Damyanov Aleksandro

Heuristics and policies for online pickup and delivery problems

Master ThesisIn the last few decades, increased attention has been dedicated to a speci c subclass of Vehicle Routing Problems due to its signi cant importance in several transportation areas such as taxi companies, courier companies, transportation of people, organ transportation, etc. These problems are characterized by their dynamicity as the demands are, in general, unknown in advance and the corresponding locations are paired. This thesis addresses a version of such Dynamic Pickup and Delivery Problems, motivated by a problem arisen in an Australian courier company, which operates in Sydney, Melbourne and Brisbane, where almost every day more than a thousand transportation orders arrive and need to be accommodated. The rm has a eet of almost two hundred vehicles of various types, mostly operating within the city areas. Thus, whenever new orders arrive at the system the dispatchers face a complex decision regarding the allocation of the new customers within the distribution routes (already existing or new) taking into account a complex multi-level objective function. The thesis thus focuses on the process of learning simple dispatch heuristics, and lays the foundations of a recommendation system able to rank such heuristics. We implemented eight of these, observing di erent characteristics of the current eet and orders. It incorporates an arti cial neural network that is trained on two hundred days of past data, and is supervised by schedules produced by an oracle, Indigo, which is a system able to produce suboptimal solutions to problem instances. The system opens the possibility for many dispatch policies to be implemented that are based on this rule ranking, and helps dispatchers to manage the vehicles of the eet. It also provides results for the human resources required each single day and within the di erent periods of the day. We complement the quite promising results obtained with a discussion on future additions and improvements such as channel eet management, tra c consideration, and learning hyper-heuristics to control simple rule sequences.The thesis work was partially supported by the National ICT Australia according to the Visitor Research Agreement contract between NICTA and Martin Damyanov Aleksandro

GRASP with path relinking for the selective pickup and delivery problem

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Solution of a practical Vehicle Routing Problem for monitoring Water Distribution Networks

In this work, we introduce a generalisation of the Vehicle Routing Problem for a specific application in the monitoring of a Water Distribution Network (WDN). In this problem, multiple technicians must visit a sequence of nodes in the WDN and perform a series of tests to check the quality of water. Some special nodes (i.e., wells) require technicians to first collect a key from a key centre. The key must then be returned to the same key centre after the test has been performed, thus introducing precedence constraints and multiple visits in the routes. To solve the problem, a Mixed Integer Linear Programming model and an Iterated Local Search have been implemented. The efficiency of the proposed methods is demonstrated by means of extensive computational tests on randomly created and real-world instances

A Study of the Static Bicycle Reposition Problem with a Single Vehicle

The Bicycle Sharing System (BSS), a public service system operated by the government or a private company, provides the convenient use of a bicycle as a temporary method of transportation. More specifically, this system allows people to rent a bike from one location, use it for a short time period and then return it to either to the same or a different location for an inexpensive fee. With the development of IT technology in the 1990s, it became possible to balance the bicycle inventory among the various destinations. In fact, a critical aspect to maintaining a satisfactory BSS is effectively rebalancing bicycle inventory across the various stations. In this research, we focus on the static bicycle repositioning problem with a single vehicle which is abstracted from the operation issue in the bicycle sharing system. The mathematical model for the static bicycle reposition problem had been created and several variations had been analyzed. This research starts to solve the problem from a very restrictive and constrained model and relaxes the constraints step by step to approach the real world case scenario. Several realistic assumptions have been considered in our research, such as a limited working time horizon, multiple visit limitation for the same station, multiple trips used for the vehicle, etc. In this research, we use the variable neighborhood search heuristic algorithm as the basic structure to find the solution for the static bicycle reposition problem. The numeric results indicate that our algorithms can provide good quality result within short solving time. By solving such a problem well, in comparison to benchmark algorithms, this research provides a starting place for dynamic bicycle repositioning and multiple vehicle repositioning

Shared Mobility Optimization in Large Scale Transportation Networks: Methodology and Applications

abstract: Optimization of on-demand transportation systems and ride-sharing services involves solving a class of complex vehicle routing problems with pickup and delivery with time windows (VRPPDTW). Previous research has made a number of important contributions to the challenging pickup and delivery problem along different formulation or solution approaches. However, there are a number of modeling and algorithmic challenges for a large-scale deployment of a vehicle routing and scheduling algorithm, especially for regional networks with various road capacity and traffic delay constraints on freeway bottlenecks and signal timing on urban streets. The main thrust of this research is constructing hyper-networks to implicitly impose complicated constraints of a vehicle routing problem (VRP) into the model within the network construction. This research introduces a new methodology based on hyper-networks to solve the very important vehicle routing problem for the case of generic ride-sharing problem. Then, the idea of hyper-networks is applied for (1) solving the pickup and delivery problem with synchronized transfers, (2) computing resource hyper-prisms for sustainable transportation planning in the field of time-geography, and (3) providing an integrated framework that fully captures the interactions between supply and demand dimensions of travel to model the implications of advanced technologies and mobility services on traveler behavior.Dissertation/ThesisDoctoral Dissertation Civil, Environmental and Sustainable Engineering 201