13.1 The VehicleRouting Problem 177 The contribution of this work is then to define a powerful yet simple cMA capable of competing with the best known approaches for solving CVRP in terms of accuracy (final cost) and computational effort (the number of evalua- tions made). For that purpose, we test our algorithm over the mentioned large selection of instances (160), which will allow us to guarantee deep and mean- ingful conclusions. Besides, we compare our results against the best existing ones in the literature, some of which we even improve. In  the reader can find a seminal work with a comparison between our algorithm and some other known heuristics for a reduced set of 8 instances. In that work, we showed the advantages of embedding local search techniques into a cGA for solving CVRP, since our hybrid cGA was the best algorithm out of all those compared in terms of accuracy and time. Cellular GAs represent a paradigm much simpler to comprehend and customize than others such as tabu search (TS) [97, 249] and similar (very specialized or very abstract) algorithms [37, 207]. This is an important point too, since the greatest emphasis on simplicity and flexibility is nowadays a must in research to achieve widely useful contributions .
Several approaches were made to solve the VRPTW problems. The use of meta-heuristics is a common solution [Mou08, TM08, BG02, LYL11, GTA99, GGLM03]. Other solutions in- clude heuristics like the one for the distribution of fresh vegetables presented in [OS08] in which the perishability represents a critical factor. The problem was formulated as a VRPTW with time-dependent travel-times, where the travel-times between two locations depend on both the distance and the time of the day. The problem was solved using a heuristic approach based on the Tabu Search and performance was veriﬁed using modiﬁed Solomon’s problems. A somewhat similar work was proposed in [TK02], which deals with distribution problem formulated as an open multi-depot vehiclerouting problem encountered by a fresh meat distributor. To solve the problem, a stochastic search meta-heuristic algorithm, termed as the list-based threshold accepting algorithm, was proposed. In [AS07] a generalization of the asymmetric capacitated vehiclerouting problem with split delivery was considered. The solution determines the dis- tribution plan of two types of products, namely: fresh/dry and frozen food. The problem was solved using a mixed-integer programming model, followed by a two-step heuristic procedure.
Then, the routing technique is required. However, the conventional optimization VRP model can’t produce a result within a small amount of time due to the model itself produces an exponentially growth associated with number of vertexes need to be served . Hence, we decide to use the classical heuristics method for VRP to solve this problem. Sweep algorithm which is introduce since 1972  is selected. The result received from this method is 28 routes without overtime.
This work studies the implementation of heuristics and scatter search (SS) metaheuristic in a real heterogeneous fleet vehiclerouting problem with time windows and split deliveries (HFVRPTWSD) in Brazil. In the vehiclerouting 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.
More recently, a variant of the classical VRP, called the open vehiclerouting 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 . This usually arises in real-world problems, like the planning of train services or bus routes (see ), 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) , 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 , where a new swarm intelligence approach is proposed). The vehiclerouting 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 vehiclerouting with backup provisioning is NP-hard.
extension is done by adding a parameter for setting a minimum value of the tabu list size tls called Threshold. The variation of this parameter improves the exploration of the search space by varying the compromise between intensification and diversification. It allows us to get a dynamic compromise between intensification and diversification. In summary, the more the same solutions found are repeated, the more the tabu list size increases, and vice versa; conversely, the more the solutions are different, the more the tabu list size decreases. This mechanism whereby the number of tabu solutions is increased when reaching local optima allows us to avoid the local optima trap by exploring other solutions in this case because all neighbors have become tabu. The optimization technique for the Reactive tabu with a variable threshold aimed at improving the initial solution (improvement) is developed (Fig. 3) in order to find the best compromise (optimal) solution of the problem. It can quickly check the feasibility of the movement suggested, and then react to the repetition toguide the search. This algorithm is performed via a tabu list size (tls) update mechanism elaborated in five steps, as shown in Fig. 3. The counters and parameters used in Reactive tabu with a variable threshold are defined as follows, and initialized to the following values.
Abstract: Problem statement: In this study, we considered the application of a genetic algorithm tovehiclerouting problem with time windows where a set of vehicles with limits on capacity and travel time are available to service a set of customers with demands and earliest and latest time for serving. The objective is to find routes for the vehicles to service all the customers at a minimal cost without violating the capacity and travel time constraints of the vehicles and the time window constraints set by the customers. Approach: We proposed a genetic algorithm using an optimized crossover operator designed by a complete undirected bipartite graph that finds an optimal set of delivery routes satisfying the requirements and giving minimal total cost. Various techniques have also been introduced into the proposed algorithm to further enhance the solutions quality. Results: We tested our algorithm with benchmark instances and compared it with some other heuristics in the literature. The results showed that the proposed algorithm is competitive in terms of the quality of the solutions found. Conclusion/Recommendations: This study presented a genetic algorithm for solving vehiclerouting problem with time windows using an optimized crossover operator. From the results, it can be concluded that the proposed algorithm is competitive when compared with other heuristics in the literature.
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:
This work addresses the Capacitated VehicleRouting Problem with two-dimensional loading constraints. Given a central depot and a set of clients, where each demands a speciﬁc amount of items, the problem aims to deﬁne minimum cost routes for a ﬂeet of homogeneous vehicles that performs customer service. The items have rectangular shapes, they must be transported in a way that there is no overlap between them, and in some cases, sequential loading restrictions, related to the order of visiting the customers, are required. To solve the problem, two hybrid approaches combining heuristics and Column Generation are proposed. Furthermore, the literature’s Branch-and-Cut was used to solve a reformulation of the original model of the problem. The methods developed were evaluated by means of the instances used in the literature and the results were compared with those previously published. The hybrid methods achieve satisfactory results, sometimes equal to the optima known, and the Branch-and-Cut could attest to optimality for several instances.
Infrastructure based routing: Wu et al. (2013) proposed a moving direction and destination location based routing (MEDAL) algorithm, which takes the moving directions of vehicles and the destination location to select a neighbor vehicle as the next hop for forwarding data. Nzouonta et al. (2009) proposed a set of Road-Based Vehicular Traffic routing (RBVT) protocols, areactive protocol RBVT-R and a proactive protocol RBVT-P that leverage real-time vehicular traffic information to create paths consisting of successions of road intersections. Punithavathi and Duraiswamy (2010) proposed a Client-Server based mobile agent for fast reponse and information reteival. However their protocol requires more server units to store and backup the data. Though most of these algorithm ssupports both V2V and V2I communications, they requires all vehicles to store the periodic hello beacons of other vehicles and also depends on the support of intersections.
Be Precise and Rigorous about How You Will Be Using the Money You have to be quite precise about the amount of money you need. Once again, it is all about trust, and you must be able to tell people precisely which part of their money will be used for which part of your project (including the costs of the rewards and the crowdfunding campaign itself). It is a real asset to present a chart showing how the budget is divided. It is also important to make it clear that you are dealing with experimental efforts, and that it could take months or even years before a conclusive answer and positive outcome is obtained. In the worst-case sce- nario, backers should be aware that you might not reach any conclusion.
the employee type definition is line 14. The field dept is defined as DepartmentType, in the same manner as Employee is defined in line 18, with the Dim of course. Line 21 through 30 use two With-End With blocks to initialize the type data. Employee is the first level type, while .dept, using the dot notation, is the second level type. It is important that you use the dot notation with the second level With block so that the compiler knows that you are referring to a type element within Employee. Lines 33 and 34 prints the header row with the additional department field information. Lines 37 through 42 then print the type data, again using a nested With block. Notice how using the With blocks document the type structure without needing any additional comments. While this isn't the primary reason to use a With block, it does create code that is easily understood. The program is closed in the usual way.
Abstract: Accidents due to drowsiness can be controlled and prevented with the help of eye blink sensor using IR rays. It consists of IR transmitter and an IR receiver. The transmitter transmits IR rays into the eye. If the eye is shut, then the output is high. If the eye is open, then the output is low. This output is interfaced with an alarm inside and outside the vehicle. This module can be connected to the braking system of the vehicle and can be used to reduce the speed of the vehicle. The alarm inside the vehicle will go on for a period of time until the driver is back to his senses. If the driver is unable to take control of the vehicle after that stipulated amount of time, then the alarm outside the vehicle will go on to warn and tell others to help the driver.
A fim de considerar o engarrafamento no planeja- mento da distribuição física, dentre os diversos conceitos de roteamento de veículos já estudados, o que mais se adere a esta realidade é o Time Dependent VehicleRouting Problem (TDVRP). No TDVRP tem-se uma frota de veículos com capacidade limitada que deve coletar ou entregar cargas a clientes a partir de um depósito central. Os clientes devem ser designados aos veículos que realizam rotas, de forma que o tempo total gasto seja minimizado. O tempo de via- gem entre dois clientes ou entre um cliente e o depósito de- pende de suas distâncias e também do momento do dia que o transporte é feito; por exemplo, nos horários de pico o tempo para deslocamento é maior devido ao congestiona- mento. As janelas de tempo para servir os clientes, ou seja, o período que os clientes podem ser atendidos, devem ser consideradas assim como a máxima duração permitida para cada rota (horário de trabalho do motorista) (Malandraki e Daskin, 1992). O TDVRP é, então, uma extensão do Pro- blema de Roteamento de Veículos (VRP) que pode levar em
RVPSE adapted the mathematical model proposed by Fisher & Jaikumar (1981) developed for a typical vehiclerouting problem. The main changes were regarding (i) specific replacement and maintenance nodes for each good in each period, and (ii) limited replacements avoiding successive exchanges for distinct goods in each period. The sequence of arcs at the lowest cost was chosen by exhaustive enumeration using the branch-and-bound algorithm of integer linear programming, which is also available in the Microsoft Excel Solver optimization software.
Dentre as várias classes do VehicleRouting Problem (VRP), a que se mostra mais adequada a este problema é o Open VehicleRouting Problem (OVRP), que difere do VRP pelo fato de os veículos não retornarem ao depósito (nesse caso a garagem) após atenderem o último cliente. Entre os estudos já realizados sobre o OVRP, o trabalho de Bektas e Elmastas (2007) é o que mais se assemelha ao problema de transporte de empregados por uma frota de ônibus fretada, porém, ele foi aplicado ao problema de transporte escolar. Desta forma, utilizou-se o modelo proposto por estes auto- res com o intuito de reduzir o custo total de transporte de empregados por meio de uma frota de ônibus fretada. O mo- delo proposto difere do modelo de Bektas e Elmastas (2007) pelo fato de introduzir limites inferiores e superiores para o número de ônibus utilizados no transporte.
A corporate system is a software used by companies to manage its operations, which must receive data from sys- tem users, web services, and sensors. This data is previously processed to be used by users, or recorded in a black board structure (Figure 4). The agents that manage the subsystems are in charge of monitoring the operations of their entities, always checking if the planned activities are running on time as well as detecting and diagnosing anomalies in such operations. In order to make decisions and act on ongoing operations, these agents not only consider the status of each task, but they must also take into account expectations in terms of system performance. The information used by agents is gathered from corporate databases, which are up- dated by means of continuous communication between the agents and the central database, in order to enable integrated system performance analysis.
The cement industry is not an exception. Cement is the second most consumed substance in the world and with the great number of trucks arriving at cement facilities, every day, the supply chain management of this industry must encompass this management as well. With the lack of assistance and guidance clients have inside the cement facilities, both companies incur in additional costs and clients experience reduced levels of service quality. To overcome these issues, three algorithms were developed and implemented. Each algorithm has different specifications and different goals. However, all the developed algorithms improve the service quality, guiding the truck drivers – the clients – inside the plants and giving the routes in shorter periods of time. One algorithm guides the trucks through the minimum distance route and will serve as a comparison term for the other two. The other two algorithms, named equilibrium approaches, are the main contribution of this dissertation. These dynamic algorithms consider not only the traveled distance, but also the workload both in the servers and in the roads. The entrance management in the facilities is also a crucial aspect cement companies must be aware of. Several thought policies are presented and an algorithm for the entrance management is developed and implemented. With a simulation software, the developed algorithms were tested and simulated. The simulation results are reported and discussed.
A recently-developed music phenomenon-inspired algorithm, HS was introduced and modeled for solving the school bus routing problem. The objective of proposed HS model for the school bus routing is to minimize the total cost of multi-objective function which consists of bus operating cost, bus travel time, and penalties related with bus capacity and time window violations. HS model could find global optimum within far less function evaluations comparing with total enumeration. HS model also found better solution than GA in terms of number of reaching global optimum, average cost out of multiple runs, and computing time.