Top PDF Contributions to the single and multiple vehicle routing problem with deliveries and selective pickup

Contributions to the single and multiple vehicle routing problem with deliveries and selective pickup

Contributions to the single and multiple vehicle routing problem with deliveries and selective pickup

In the Single Vehicle Routing Problem with Deliveries and Selective Pickups (SVR- PDSP) there are a set of customers to be served and a depot from where a vehicle departs to serve the customers. Each customer has a certain demand of goods either to be delivered or to be picked up, which generates a revenue if collected. It is possible for a customer to have both demands. In such case, if both are going to be served, they can be performed simultaneously or in two different visits, each completely fulfilling one of the demands. The vehicle that departs from the depot shall perform a route that visits a subset of customers performing deliveries and pickups, then return to the depot. All delivery demands must be fulfilled exactly once. The pickup demands, however, are not mandatory, therefore they are only performed if there is enough space in the vehicle and if they are profitable. Serving a pickup demand is profitable if the revenue generated by collecting it is greater than the additional routing cost. One can notice that some pickups might not be served at all and it is possible to argue that they would need to be performed at some point. To address this issue these pickup demands could either be delayed to be served in the following day, or a third party service can be used to collect these pickups, which could be a less costly option than forcing all pickups to be fulfilled or sending another vehicle only to perform a few pickups. The objective is to find a route that minimizes the total routing cost, which is the travel cost to visit the customers minus the revenue generated by the collected pickups. Fig- ure 1.1a shows a small example with 8 customers and a vehicle with capacity equals to 35. In the figure, d is the delivery demand of a customer, while p stands for the pickup demand and r is the revenue generated by performing the respective pickup demand of the customer. The transportation cost of the solution presented is equal to 5 + 4 + 4 + 1 + 10 + 8 + 5 + 4 = 41 and the total revenue generated by the three pickups collected is 5 + 20 + 8 = 33. Therefore the total cost of this solutions is 41 − 33 = 8. In
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Multi-Objective Forest Vehicle Routing Using Savings-Insertion and Reactive Tabu with a Variable Threshold

Multi-Objective Forest Vehicle Routing Using Savings-Insertion and Reactive Tabu with a Variable Threshold

resolution method used was a bi-objective tabu search algorithm. The first objective is to minimize the total number of vehicles used, and the second, to minimize the total cost, which is the weighted sum of the total distance traveled and the corresponding total time. Ref. [13], based on the work of [12], established a multi-criteria optimization model of long-haul VRP and scheduling integrating working hours rules. The solution method used was a bi- objective tabu search algorithm determining a set of heuristic non-dominated solutions. The mechanism consists of a single thread in which the weights assigned to the two objectives, namely, operating costs and driver inconvenience, are dynamically modified, and in which dominated solutions are eliminated throughout the search. Ref. [14] proposed a multi-depot VRP with a simultaneous delivery and pick-up model. The resolution method used was the iterated local search embedded adaptive neighborhood selection approach. Ref. [15] tested local search move operators on the VRP with split deliveries and time windows. To that end, they used eight local search opera-tors, in combinations of up to three of them, paired with a max-min ant system. Ref. [16] developed a dynamic model for solving the mixed integer programming of forest plant location and design, as well as production levels and flows between origins and destinations. Ref. [17] proposed a multi-depot forest transportation model solving the tactical problem of the flow between origins and destinations without solving the operational problem of VRP. The solution method used was column generation. Ref. [6] proposed a model for forest transportation, solving the problem of flow between origins and destinations, and involving a sedimentation constraint
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Scatter search para problemas de roteirização de veículos com frota heterogênea, janelas de tempo e entregas fracionadas.

Scatter search para problemas de roteirização de veículos com frota heterogênea, janelas de tempo e entregas fracionadas.

This work studies the implementation of heuristics and scatter search (SS) metaheuristic in a real heterogeneous fleet vehicle routing problem with time windows and split deliveries (HFVRPTWSD) in Brazil. In the vehicle routing problem with time windows and split deliveries (VRPSD) each client can be supplied by more than one vehicle. The problem is based in a single depot, the demand of each client can be greater than the vehicle’s capacity and beyond the time windows constraints, and there are also vehicle capacity and accessibility constraints (some customers cannot be served by some vehicles). The models were applied in one of the biggest retail market in Brazil that has 519 stores distributed in 12 Brazilian states. Results showed improvements over current solutions in a real case, reducing up to 8% the total cost of the operation.
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13 Logistics: The Vehicle Routing Problem

13 Logistics: The Vehicle Routing Problem

The VRP is a very important source for problems, since solving it is equiv- alent to solving multiple TSP problems at once [96]. Due to the difficulty of this problem (NP-hard) and because of its many industrial applications, it has been largely studied both theoretically and in practice [47]. There is a large number of extensions to the canonical VRP. One basic extension is known as the capacitated VRP –CVRP–, in which vehicles have fixed capacities of a single commodity. Many different variants can be constructed from CVRP; some of the most important ones [248] are those including time windows re- strictions –VRPTW– (customers must be supplied following a certain time schedule), pickups and deliveries –VRPPD– (customers will require goods to be either delivered or picked up), and backhauls –VRPB– (like VRPPD, but deliveries must be completed before any pickups are made).
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Planning of vehicle routing with backup provisioning using wireless sensor technologies

Planning of vehicle routing with backup provisioning using wireless sensor technologies

More recently, a variant of the classical VRP, called the open vehicle routing problem (OVRP), attracted the attention of practitioners and researchers. In this case, vehicles are not required to return to the depot after serving the last customer on a route [9]. This usually arises in real-world problems, like the planning of train services or bus routes (see [10]), or when industries do not own a vehicle fleet or their private fleet is inadequate to fully satisfy customer demand, and distribution services (or part of them) are either entrusted to external contractors or assigned to a hired vehicle fleet. In these cases, vehicles are not required to return to the central depot after their deliveries have been satisfied. The main difference between VRP and OVRP is that in VRP, the routes are Hamiltonian cycles, and in the OVRP, the routes are Hamiltonian paths originated at the depot and ending at one of the customers, so the shortest Hamiltonian path problem with a fixed source node has to be solved for each vehicle in the OVRP. The traveling salesperson problem, known to be NP-hard, consists of finding the Hamiltonian cycle with the lowest cost. This, together with the fact that the Hamiltonian cycle problem (HCP) is NP-hard and can be reduced to the Hamiltonian path problem (HPP) [11], allows us to conclude that the shortest HPP is NP-hard. Consequently, the OVRP is also an NP-hard problem, justifying the development of heuristics and meta-heuristics (see [12], where a new swarm intelligence approach is proposed). The vehicle routing with backup provisioning, under discussion here, can be seen as a variant of the OVRP applied to the transportation of persons, considering multiple depots and having the possibility of backup provision to certain critical stops. Therefore, the vehicle routing with backup provisioning is NP-hard.
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A matheuristic for the consistent vehicle routing problem with service level agreements: a case study in the pharmaceutical distribution sector

A matheuristic for the consistent vehicle routing problem with service level agreements: a case study in the pharmaceutical distribution sector

This research mainly focuses on a less studied VRP extension which is the conVRP. This optimization problem demands the definition of vehicle routes for several periods, maintaining a certain level of consistency on pre-selected metrics. For instance, when distribution companies make an agreement for the deliveries to be made always by same driver, they are adding consis- tency constraints in order to take into account customer satisfaction. Therefore, the objective is to achieve minimum cost routing plans satisfying the classical routing constraints as well as con- sistency requirements taking into account customer satisfaction. Generally, this type of customer- oriented routing considers two types of consistency for customer satisfaction: driver consistency, and time consistency (Kovacs et al., 2014a). Driver consistency is measured by the number of different drivers that visit a customer whereas time consistency is related to the maximum dif- ference between the earliest visit and the latest arrival at each customer. The conVRP arises in many industries where customer satisfaction is considered as a distinctive factor of competitive- ness. Particularly in industries transporting small packages, providing a standard service with a single driver and approximately at the same time of the day enables the customers to prepare them- selves for a delivery, strengthening supplier/customer relationships (Kovacs et al., 2014b). Since the conVRP considers several periods, it can be seen as a tactical extension of the classical VRP with customer-focused routes.
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Contributions to the single and multiple vehicle routing problems with deliveries and selective pickups

Contributions to the single and multiple vehicle routing problems with deliveries and selective pickups

Su¨ral and Bookbinder [30] are the first to directly address this problem. They present the problem using the notation α/β/γ, where α denotes the number of ve- hicles (1 for single and M for multiple), β the pickup service options (must or f ree if the pickup is respectively mandatory or optional) and γ the precedence order for visiting the customers (prec if all deliveries must precede the pickups, or any if they can be visited in any order). While the SVRPDSP is 1/free/any, according to this notation, the MVRPDSP can be described as M/free/any. They cited papers dealing with the 1/free/prec and 1/must/any problems, and claimed to be the first to address the 1/free/any. For the multiple vehicle versions, they list papers for the M/must/prec and M/free/prec, however no mention is made about the M/free/any, which is one of our objects of study. They propose a mixed integer linear programming formulation for the SVRPDSP along with some improvements on the constraints to strenghten the formulation, such as constraint disaggregation, coefficient improvement, cover and logi- cal inequalities, and lifted subtour elimination constraints. They modified 24 instances from the literature, with sizes of 10, 20 and 30 customers, to test their formulation. These instances were adapted for the SVRPDSP by setting some of the delivery de- mands as pickups in 3 different ways (20% of the customers reset as pickups, then 30% and 40%), generating a total of 72 instances, which were tested with some combina- tions of the formulation and the improvements resulting in about 75% of the instances optimally solved in a reasonable computational time for the best combination.
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NUMERICAL AND EXPERIMENTAL ANALYSIS OF THE FLOW OVER A COMMERCIAL VEHICLE- PICKUP

NUMERICAL AND EXPERIMENTAL ANALYSIS OF THE FLOW OVER A COMMERCIAL VEHICLE- PICKUP

A numerical and experimental study of the aerodynamics of a generic pickup was performed. Studied geometry was established on the dimensions of the most representative models of the light pickup fleet in Brazil. Baseline model is composed only by sharp edges and flat surfaces; a second version was prepared filleting those edges (rounded model). Numerical steady simulations (RANS) were performed on commercial software Star-CCM+ using the SST k-w turbulence model; tetraedrical meshes have an average of 8 million elements. All simulations were prepared on an i7 processor with 12 cores, 48 GB of RAM. For wind-tunnel testing, model was 3D printed in 1:10 scale. On experimental procedures, qualitative (wall tufts visualization) and quantitative (hot-wire anemometry velocity profiles) tests revealed main structures on wake and on the close wall.
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A solution for a real-time stochastic capacitated vehicle routing problem with time windows

A solution for a real-time stochastic capacitated vehicle routing problem with time windows

This work presented the vehicle routing optimization system developed to be integrated with an existing ERP. The optimization procedure takes into consideration the need for a near real-time routing solution under dynamic orders and interactions with the system administrator. In this sight, this work described the interactions and dependencies between the system’s four main components, namely: i3FR-Opt (where the computation of the routes is done), the i3FR-Hub (implementing a channel to all the communications inside the system and to the exterior), the i3FR-DB (provider of local storage to the information relevant to the optimization procedure), and i3FR-Maps (a cartography subsystem of routing informations). With this structure it is possible to deal with late orders and different states for the routes, which allows to do a phased picking and loading of the vehicles. As mere examples, some results for the Algarve’s region were presented showing different solution depending on the time windows restrictions.
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A guide to vehicle routing heuristics

A guide to vehicle routing heuristics

Rochat and Taillard22 have developed an adaptive memory mechanism for the capacity and route duration constrained VRP and for the VRP with time windows, based on the earlier [r]

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Ant colony optimization techniques for the hamiltonian p-median problem

Ant colony optimization techniques for the hamiltonian p-median problem

Furthermore, for attainment of improved solutions to the problem, we can use the route improvement strategies in the algorithms. One of these strategies involves the inclusion of a local exchange procedure to act as an improvement heuristic within the routes found by individual ants. One of the techniques used for this purpose is the common 2-opt heuristic where all possible pairwise exchanges of customer locations visited by individual vehicle are tested to see if an overall improvement in the objective function can be attained.
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Ant Colony Optimization for Capacitated Vehicle Routing Problem

Ant Colony Optimization for Capacitated Vehicle Routing Problem

Pheromone update: After all the artificial ants have improved the solutions through the heuristics, the pheromone trails will be updated. This is the main feature of an ACO algorithm which assists at improving future solutions since the updated pheromone trails would reflect the ants’ performance and the quality of their solutions found. In this context, there are two main phases of the pheromone update in an AS algorithm (Dorigo and Stutzle, 2004), which are the pheromone evaporation and the pheromone deposition. In the proposed ACO, modifications would be made to the usual pheromone evaporation whereas the pheromone deposition would be referred to Bullnheimer et al. (1999) which comprises of the elitist strategy and also the concept of ranking. The details of the pheromone update procedures implemented in the proposed ACO are described as follows:
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A multi agent based system to enable dynamic vehicle routing

A multi agent based system to enable dynamic vehicle routing

tasks in the sequence {15,16, … , 25} should be transferred to another vehicle agent. Then, the vehicle agent starts to examine which one of the 11 remaining tasks should be transferred. By examining task 16, it notes that there are prospective gains due to two types of reduction: (a) reduced mileage, and (b) reduced down time when visiting client 16, which should be transferred from the route of the regular vehicle agent. The gain is the difference between the cost generated by the remaining basic routing sequence, and the new routing that excludes the selected task. Therefore, by analyzing every single task on sequence {15,16, … , 25} the system will select the tasks to be transferred that will mostly reduce cost. The third form of selecting the task to be trans- ferred is an extension of the previous form. After some at- tempts to transfer a task from the routing, such as task 16, the VRP algorithm is once again applied to the other activ- ities, resetting the routing sequence but considering all the other remaining tasks except number 16. Finally, the task that most reduces mileage in the regular VRP sequence is chosen.
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Problema de Roteamento de Veículos – Vehicle Routing Problem (VRP)

Problema de Roteamento de Veículos – Vehicle Routing Problem (VRP)

geograficamente dispersas, encontrar o melhor conjunto de rotas para atender todos os. clientes[r]

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Analysis of Structure and Abrasion Resistance of the Metal Composite Based on an Intermetallic FeAl Phase with VC and TiC Precipitates

Analysis of Structure and Abrasion Resistance of the Metal Composite Based on an Intermetallic FeAl Phase with VC and TiC Precipitates

The resulting carbide is thermodynamically stable and saturates carbon in the liquid solution. The degree of bath saturation is dependent on the amount of vanadium added. From reaction (1) it follows that 1 g of vanadium binds 0.236 g of carbon, which gives 1.236 g of VC. In this way, with the appropriate addition of vanadium, all carbon in the solution can be bound into VC, which will make the crystallisation of aluminium carbide hardly probable. Then, the material is obtained which after solidification can be treated as an "in situ" composite consisting of an FeAl matrix reinforced with vanadium carbides.
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Algoritmos para o problema de roteamento de veículos capacitado com restrições de carregamento bidimensional

Algoritmos para o problema de roteamento de veículos capacitado com restrições de carregamento bidimensional

Além do CVRP, outras versões do VRP são muito estudadas. O Problema de Roteamento de Veículos com Janelas de Tempo (VRPTW, do inglês Vehicle Routing Problem with Time Windows) é uma extensão do CVRP onde cada cliente deve ter seu atendimento iniciado em uma janela de tempo e o veículo associado deve atendê-lo durante um tempo previamente estipulado. Por sua vez, o Problema de Roteamento de Veículos com Backhauls (VRPB, do inglês Vehicle Routing Problem with Backhauls) consiste em um CVRP onde o conjunto de clientes é particionado em dois subconjuntos: linehaul e backhaul. O primeiro subconjunto consiste nos clientes que necessitam de itens a serem entregues, enquanto o segundo representa os clientes que dispõem de itens a serem coletados. No VRPB, todos os clientes linehaul devem ser visitados antes dos clientes backhaul. Uma outra variação do VRP é o Problema de Roteamento de Veículos com Coleta e Entrega (VRPPD, do inglês Vehicle Routing Problem with Pick-ups and Deliveries), onde uma requisição de transporte é associada a dois clientes, de tal forma que a demanda é coletada em um deles e entregue no outro. Nesse problema, uma solução viável requer que a coleta de uma requisição seja feita antes de sua entrega, e que ambas operações ocorram na mesma rota. Informações sobre os trabalhos propostos e os detalhes do VRPTW, VRPB e VRPPD, podem ser encontrados em Alvarenga et al. [2007], Toth & Vigo [2001c] e Desaulniers et al. [2001], respectivamente.
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Chromium and copper influence on the nodular cast iron with carbides microstructure

Chromium and copper influence on the nodular cast iron with carbides microstructure

In this cast iron eutectic transformation takes place in the temperature of 7 C less, than in cast iron with 0,50% Cu, but it is still higher than in cast iron with chromium. Molybdenum pres- ence caused ledeburitic carbides forming (HKL thermal effect, Fig. 10 a). Copper increase caused small amount of martensite forming, but did not change significantly carbides amount. It is important, that ferrite amount is decreased and pearlite disap- peared in cast iron metal matrix microstructure.

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NETWORK FLOW ORIENTED APPROACHES FOR VEHICLE SHARING RELOCATION PROBLEMS

NETWORK FLOW ORIENTED APPROACHES FOR VEHICLE SHARING RELOCATION PROBLEMS

Remark 2 (About MIP Models and Complexity). Modeling VSR through a MIP (Mixed Integer Linear Program) is possible, but inefficient. The reason is that there is no a priori bound about the number of times a given station is going to be visited by a same carrier. As for complexity, in the case when K = 1 (α very large), v(x) values are equal to 1 or −1, CAP = 1 and δ = 0, our problem is equivalent to the Travelling Salesman Problem set on a bipartite graph (the excess stations on one side and the deficit ones on the other side), which is NP-Hard. Non Preemptive VSR also contains the Uncapacitated Swapping Problem, which is also NP-Hard (see [1]).
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REPOSITORIO INSTITUCIONAL DA UFOP: A cooperative coevolutionary algorithm for the multi-depot vehicle routing problem.

REPOSITORIO INSTITUCIONAL DA UFOP: A cooperative coevolutionary algorithm for the multi-depot vehicle routing problem.

In a cooperative coevolutionary algorithm, each population repre- sents a part of a complex decomposable problem (Engelbrecht, 2007). Accordingly, the true fitness of an individual can only be obtained from its interaction with other individuals from the same or other populations. Each individual receives a reward or punishment, be- ing rewarded when it interacts well with the others while getting a punishment otherwise. A cooperative coevolutionary algorithm is considered in this work because the problem can be decomposed into smaller subproblems, each one evolving in parallel. Partial so- lutions for those subproblems cooperate to create a complete solu- tion for the MDVRP. In this situation, a competitive strategy would not be suitable because a complete solution is obtained from infor- mation gathered from all subproblems and there is no competition among these subproblems. Fig. 2 depicts a general cooperative coevo- lutionary algorithm with decomposition. A complex problem is first decomposed into smaller subproblems. Each population is evolved on its subproblem and, after a number of generations, individuals from these populations are combined to create complete solutions to the original problem. Through this process, it is possible to compute the fitness of these complete solutions. Some feedback information is then returned to each population, such as the best solution found so far, any required updates to the individuals in the population, etc.
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Repositório Institucional UFC: Um Algoritmo para o problema de roteirização de veículos com frota heterogênea

Repositório Institucional UFC: Um Algoritmo para o problema de roteirização de veículos com frota heterogênea

No processo de distribuição é necessário fazer a entrega de bens e serviços para clientes dispersos geograficamente, nesse processo encontra-se o Vehicle Routing Problem (VRP). O VRP, ou Problema de Roteirização de Veículos, é o nome de uma classe de problemas para definir a sequência de visita a clientes dispersos geograficamente com um conjunto finito de veículos a partir de um depósito comum. Para resolver este problema e analisar os resultados obtidos, foi desenvolvido um algoritmo utilizando a meta-heurística Variable Neighborhood Descent (VND), ou Descida em Vizinhança Variável, o qual foi aplicado em instâncias conhecidas na literatura e realizado um benchmarking com outros algoritmos. Esse problema aplica-se na prática em coleta de peças automobilísticas, coleta de lixo industrial, coleta de lixo residencial, limpeza de ruas, e entre outras situações. Os VRPs receberam muita atenção nos últimos anos devido a sua aplicabilidade e sua importância econômica na determinação de estratégias eficientes, com o objetivo de reduzir os custos operacionais. Os resultados obtidos com o algoritmo proposto foram próximos dos algoritmos estudados no benchmarking realizado. Contudo, superando alguns destes algoritmos em apenas uma das instâncias das oito instâncias utilizadas.
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