Top PDF A study of flow-based models for the electric vehicle routing problem

A study of flow-based models for the electric vehicle routing problem

A study of flow-based models for the electric vehicle routing problem

A introdu¸ c˜ ao de ve´ıculos el´ etricos tamb´ em conduz a outro tipo de as- petos a considerar relativamente ` a topologia da rede de estradas e clientes subjacente ao problema. Uma rua a subir implica um maior consumo de energia por parte de um ve´ıculo el´ etrico, enquanto que uma rua a descer pode at´ e permitir a recupera¸ c˜ ao de energia. Al´ em disso, temos tamb´ em de considerar um conjunto novo de v´ ertices, do grafo representativo da rede de estradas, referente ` as poss´ıveis esta¸ c˜ oes de recarga para os ve´ıculos el´ etricos. O Electric Vehicle Routing Problem (EVRP) ´ e ent˜ ao uma variante dos problemas cl´ assicos do Traveling Purchaser Problem (TSP) e do Vehicle Routing Problem (VRP) em que s˜ ao considerados ve´ıculos com motor el´ etrico. Apesar das formula¸ c˜ oes naturais do TSP e do VRP considerarem apenas vari´ aveis que modelam os arcos na(s) rota(s) para os ve´ıculos, no EVRP isso n˜ ao ´ e suficiente e, desta forma, ´ e necess´ ario considerar n˜ ao s´ o vari´ aveis de fluxo para a carga do ve´ıculo mas tamb´ em vari´ aveis de fluxo que repre- sentem o n´ıvel de energia do ve´ıculo. Assim, os modelos matem´ aticos para o EVRP apresentam dois sistemas de fluxo em que o sistema de fluxo de energia depende do sistema de fluxo de carga.
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Towards solving a robust and sustainable Vehicle Routing Problem with Backhauls

Towards solving a robust and sustainable Vehicle Routing Problem with Backhauls

The first set of business-related rules addressed in this study relate to the conditions deter- mining the visit to a backhaul: i) the backhaul can only be visited after all deliveries are performed, here called a precedence constraint, because the reinforcement of the truck for transporting the wood chips can only occur after the last delivery of the wood-based panels; ii) there is at most one backhaul visited per route because the amount picked up is always a full truck-load since there are no wood availability constraints at suppliers; iii) if there is a pickup at a backhaul it is mandatory that the same route includes its delivery at a mill. This is another type of precedence constraint ensuring that a mill is visited after a backhaul. However, operational practice indicates that the unloading mill may or may not be the mill of origin, because the company owns several mills geographically dispersed, and the truck can end the route in any of these mills, as long as the compatibility requirements between the types of raw materials available at the backhaul and accepted at the mill are accounted for; iv) a backhaul may or may not be visited, which is known in the literature as selective backhauling; v) routes without a backhaul are also feasible, and in this case, the route ends
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The multi-compartment vehicle routing problem in the collection of recyclable municipal solid waste

The multi-compartment vehicle routing problem in the collection of recyclable municipal solid waste

In the context of RL – and therefore in the context of MSW management –, de Brito et al. (2005) stated that companies have to make several strategic, tactical and operational decisions (pursuing the ideas developed by Fleischmann et al., 1997). Lambert et al. (2011) presented a framework based on these three hierarchical levels of planning and execution, dividing them into seven elements: coordination system; gatekeeping; collection; separation; treatment; information system and disposal system. The latter authors applied their framework to three case studies, analysing each of the referred elements in terms of process, cost and performance. A collection system to operate needs vehicles and must count on infrastructures such as transfer stations, which are significantly costly. Rogers et al. (2012) found that in any RL network is necessary to use mathematical models to make an effective planning and management of its system. There are mainly two subjects regarding the modelling of these type of problems: i) logistics network design (number of facilities, their location, size and area of influence); ii) route planning (places to visit and in which sequence and moment). According to Lambert et al. (2011) the number and the location of facilities can be set as strategic, while its dimension, size and area of influence are tactical decisions. Route planning is related to operational ones. The work hereby presented will focus on tactical and operational decisions: changing the type of vehicles from single-compartmented to multi-compartmented ones may force to adjust the area of influence of each facility and the route planning of the company in study. Strategic decisions do not arise since changes in the number, location and size of the depots will not be addressed.
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Estimating the efficacy of mass rescue operations in ocean areas with vehicle routing models and heuristics

Estimating the efficacy of mass rescue operations in ocean areas with vehicle routing models and heuristics

A second goal of this dissertation aims to study the procedures to retrieve drifting objects from the water and how these are influenced by available information on the scene. The procedure is defined by the sequence of choices made by SRUs in rescuing several dispersed survivors, which is associated to a priority rule (for example, a living person has has more priority than a deceased one when choosing who is to be retrieved from the water by a rescue boat). Particularly, we are interested in perceiving how the availability of survival time’s data can influence the overall efficacy of the rescue operation, if this information is available to be implemented in a rescue procedure at the tactical level (or on-scene level). Having multiple vehicles, conventional procedures for retrieving drifting objects from the water are based on the vehicle’s speed to reach the object’s location (assuming it can retrieve it). In this sense, the vehicles expected time arrival (ETA) to a specific object’s location stands as the “conventional” criteria (or standard priority) for obtaining a vehicle route and the respective sequence of retrieved objects. What if survival times were “available” to vehicles? Would a similar procedure based on the survival times provide better rescue solutions? These questions require the assumption that there is available technology that would provide the SAR system with the knowledge of the persons survival times and location with great accuracy. To answer these questions several variants of heuristics approaches are investigated that incorporate the priorities used by SRU vehicles during the recovery operations. To assess the quality of the heuristics that make use of standard priorities or available survival times, a more sophisticated heuristic approach based on a look ahead method is used for larger instances that cannot be solved optimally.
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Repositorio ISMT: Network flow models for the local access network expansion problem

Repositorio ISMT: Network flow models for the local access network expansion problem

This paper is organized in the following way. In Section 2, we define the problem, present several assumptions for the problem and describe parameters that are needed for deriving and describing the formulations. In Section 3, we show how the problem under study can be seen as a variation of a CMST problem and present network flow-based formulations. We discuss aggregated (Section 3.1) and disaggregated (Section 3.2) versions of these models and we introduce some valid inequalities that strengthen the linear programming relaxation of the models. Section 4 gives pre-processing techniques for variable elimination and coefficient reduction and presents several computational results for evaluating the different models with respect to previous approaches described in the literature. Section 5 presents some conclusions and points out several areas for future research.
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Vehicle routing and tour planning problem: a cement industry case study

Vehicle routing and tour planning problem: a cement industry case study

the clients [5]. Industry 4.0 has also a great impact in the transportation sector as well. Using ICT, it is possible to develop a more efficient and profitable transportation system. The work presented in this dissertation is developed under a scientific project, that aims to develop systems for smart plants, specifically cement plants. The UH4SP – Unified Hub for Smart Plants – aims to develop simulation models and heuristic optimization models to take cement plants to another level [6]. More specifically, one of the main goals of the project is the development of architectures of software and methodologies orientated to services, promoting the corporative and aggregate vision of the operations in each one of the cement plants dispersed by several geographic regions [7]. The UH4SP addresses several segments of the supply chain of a cement plant. The problem addressed in this dissertation is the one dealing with the management of the trucks entering the plant.
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A mixed load rural school bus routing problem with heterogeneous fleet: a study for the Brazilian problem

A mixed load rural school bus routing problem with heterogeneous fleet: a study for the Brazilian problem

The underdevelopment of Brazilian rural families is largely explained by their historical process of formation and by their poor access to a functional education and transportation systems. In the last decade, the federal government has been encouraging the nucleation of rural schools to offer better structured schools to the rural students. Multi-grade rural schools, often located closer to the rural families but with students of different grades being taught by the same teacher at the same class, are being shutdown and transfered to bigger, better installed facilities located near to the counties’ downtown area. The success of such endeavor relies on offering a transportation system for the rural students. Hence the Brazilian federal government has been making a great effort to support local administrators to provide better transport to rural students. One of such efforts gave rise to a central decision support system which solves the mixed load capacitated rural school bus routing problem with heterogeneous fleet. The mixed load feature allows students from different schools to ride the same bus during at the same time. This is an important but neglected problem in vehicle routing literature. In this thesis, four based meta-heuristic algorithms are devised and embedded into the support system. The computation performance of the proposed algorithms was assessed on solving four different datasets, including a real case from Brazil. The proposed methods were also compared with one known method from the literature. The attained cost savings and reduction of the number of buses required to serve the rural students showed the suitability of the mixed load approach over the single load one for the Brazilian rural context. Furthermore four based meta-heuristic based multi-objective algorithms to solve the multi-objective capacitated mixed load rural bus routing problem with heterogeneous fleet were also devised. The three involved objectives were the routing costs, the average weighted riding distances and the routes balance. The proposed multi-objective methods were compared with one from literature adapted for the problem and evaluated by assessing the metrics of cardinality, coverage and hyper-volume, followed by a statistical analyses. The work also introduces a new approach to help decision makers to selected a suitable solution from a Pareto set. All of the four devised multi-objective heuristics outperformed the literature procedure.
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Ant Colony Optimization for Capacitated Vehicle Routing Problem

Ant Colony Optimization for Capacitated Vehicle Routing Problem

To apply the ACO for solving the CVRP, Voss (1999) first developed an ACO algorithm which is called Ant System (AS) for the problem and then presented an improved AS in Bullnheimer et al. (1999). Since then, many researchers have proposed new methods to improve the original ACO especially by applying other algorithms into the ACO to tackle the large-scaled CVRP. For instance, Doerner et al. (2002) proposed a hybrid approach for solving the CVRP by combining the AS with the savings algorithm. After that, Reimann et al. (2002) improved on the method in Doerner et al. (2002) by presenting a Savings based Ant System (SbAS) and then Reimann et al. (2004) proposed an approach called D-Ants which is competitive with the best Tabu Search (TS) algorithm in terms of solution quality and computation time. Also, Mazzeo and Loiseau (2004); Bell and McMullen (2004); Yu et al. (2009) and Zhang and Tang (2009), have made major contributions to the development of ACO to tackle the CVRP. This study aims to compare the solution quality of different basic heuristics combined with an original ACO in solving the problem.
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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

Despite the advantages of adopting consistent routes, few papers have addressed the conVRP and most approaches resort to approximation methods. Groer et al. (2009) formulate the conVRP as a Mixed-Integer Program (MIP) and improve the algorithm used by Li et al. (2005) to solve very large VRPs. A real-world data set is used to generate instances with up to 700 customers which are solved by the algorithm. The obtained consistent routes are less than 10% longer on average, compared to inconsistent routes. Recently, Ridder (2014) shows that some optimal solu- tions provided by Groer et al. (2009) are not feasible because service times were not considered. The author develops an algorithm that improves solutions provided by the latter paper. Tarantilis et al. (2012) propose a Tabu Search (TS) algorithm to iteratively generate template routes and to improve the daily routes that are derived from the template routes. These routes are used as the basis to construct the vehicle routes and service schedules for both frequent and non-frequent customers over multiple days. The best reported cumulative and mean results over all conVRP- benchmark instances is improved. Kovacs et al. (2014b) construct template routes by means of an Adaptive Large Neighbourhood Search (ALNS), which uses several operators in order to destroy and repair a given solution. It is shown that solving daily VRPs may lead to inconsistent routes whereas consistent long-term solutions can be generated by using historic template routes. Kovacs et al. (2014a) state that assigning one driver to each customer and bound the variation in the arrival times over a given planning horizon may be too restrictive in some applications. They propose the generalized conVRP in which a customer is visited by a limited number of drivers and the vari- ation in the arrival times is penalized in the objective function. A Large Neighbourhood Search (LNS) metaheuristic generates solutions without using template routes. The computational results on different variants of the conVRP prove the efficiency of the algorithm, as it outperforms all published algorithms. Sungur et al. (2010) consider a real-world courier delivery problem where customers appear probabilistically. Although the authors do not call it a conVRP, their assump- tions are completely in line with this type of problem. The proposed approach generates master plans and daily schedules with the objective of maximizing both the coverage of customers and the similarity between the routes performed in each day. In order to deal with uncertain service times, it is assumed that the master plans serves frequent customers with the worst-case service times found in historical data. Once again, a mathematical formulation is proposed but the real-world problem is tackled by means of a two-phase heuristic based on insertion and TS.
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Multiple Charging Station Location-Routing Problem with Time Window of Electric Vehicle

Multiple Charging Station Location-Routing Problem with Time Window of Electric Vehicle

A network is normally represented by a graph that is composed of a set of nodes and edges. The task of network clustering is to divide a network into different clusters based on certain principles. Each cluster is called a community. The LRP combines two classical planning tasks in logistics, that is, optimally locating depots and planning vehicle routes from these depots to geographically scattered customers [8]. These two interdependent problems have been addressed separately for a long time, which often leads to suboptimal planning results. The idea of LRP started in the 1960s, when the interdependence of the two problems was pointed out [9,10]. The variants of the LRP have been frequently studied in recent years. Such variants include the capacitated LRP (CLRP) with constraints on depots and vehicles [20,21], the LRP with multi-echelon of networks [11,12], the LRP with inventory management [13,14], and the LRP with service time windows [15–17]. For the variant problem with time windows, Semet and Taillard incorporated the time window constraint to the LRP for a special case of the road–train- routing problem [15]. Zarandi et al. studied the CLRP with fuzzy travel time and customer time windows, in which a fuzzy chance-constrained mathematical program was used to model the problem [16]. Later, they extended the problem by adding the fuzzy demands of customers and developed a cluster-first route-second heuristic to solve the problem [17]. A detailed review of the LRP variants can be found in two recent surveys [18,19].
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The Vehicle Scheduling Problem of Electric Buses

The Vehicle Scheduling Problem of Electric Buses

A. Pepin (2008) compared five different heuristics to solve the multiple depot VSP - two as an integer multi-commodity formulation and the others as a set partitioning type formulation. The fist formulation describes the problem as a time-space network, being each service trip symbolized by a node and each possible deadheading trip by an arc. Its goal is therefore focused on the minimization of the costs regarding the arcs assignment. The second model represents each possible schedule as a decision variable, minimizing, once again, its total cost. In this study, it is proposed a Branch-and-Cut method, based on a time-space network, using CPLEX MIP solver, for the first formulation, as well as a Lagrangian heuristic relying on a Lagrangian relaxation. Another common heuristic was also recommended for the second type of formulation denominated Truncated Column Generation which decomposes the problem into a restriction master problem (RMP) and a subproblem per depot. A metaheuristic Large Neighborhood Search (LNS) was also proposed which destroys a part of the current solution and reoptimizes it again, in order to find a better solution. Finally, it presented a solution based on a Tabu Search metaheuristic, one of the most famous local search technique. These procedures analyze the neighbours of each solution, searching for an improved one. However, Tabu Search decreases the tendency to be stuck in a local optimal solution by allowing worse moves whenever an improved one does not exist and by discouraging coming back to already observed solutions (A. Pepin, 2008).
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Modelos matemáticos baseados no Time Dependent Vehicle Routing Problem para planejamento da logística urbana sob a ótica ambiental

Modelos matemáticos baseados no Time Dependent Vehicle Routing Problem para planejamento da logística urbana sob a ótica ambiental

Abstract: Urban logistics companies are seeking solutions to reduce their cost, but must of them are not paying attention to environmental issues. This is due to the belief that environmentally friendly solutions are more expensive. However, with the growing of environmental concerns, companies have been taking into account the environmental factors, seeking for their social responsibility. Thus, this paper presents two mathematical models, both based on the Time Dependent Vehicle Routing Problem (TDVRP), one to evaluate the reduction in the time of the routes and the other to evaluate the reduction of greenhouse gas emissions. In order to evaluate the model, a real case of a food distribution company in the metropolitan area of Vitória, ES was done. CPLEX 12.6 was used to run both models considering scenarios based on data from a real company. The results showed that environmentally friendly solution may be also financially advantageous for the company.
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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

The only approach for the MVRPDSP in which we must perform an analysis to calibrate its parameters is the hybrid constructive heuristic. In its routing phase the heuristic tspKnapsackBased from the SVRPDSP is used. Notice that this heuristic has a parameter rclSize and as in the SVRPDSP it must be calibrated. For that end we ran 30 times the hybrid constructive for all instances using each rclSize value from the set of values {1, 2, 3}. The best solution values were normalized and an ANOVA was performed on these values. The results of such analysis are shown in Figure 5.4a. Clearly the rclSize = 1 is the best with 95% of confidence. However, since for the SVRPDSP the rclSize = 2 proved to be also a good choice, we decided to perform an ANOVA only considering our biggest instances, with size 72. The results can be seen in Figure 5.4b and shows that the difference between the rclSize = 1 and rclSize = 2 is much smaller and the rclSize = 1 is no longer proved to be the best. This behavior could be due to the fact that in bigger instances the solutions tend to have more customers per route, which greatly increases the number of possibilities to insert the pickup customers in the routing phase. Therefore, we foresee that for instances with a greater number of customers the value rclSize = 2 may be equally a good choice or even a better one. Nevertheless, since the constructive is not time consuming we decided to use both, rclSize = 1 and rclSize = 2, then choose the best solution generated.
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Optimization Algorithms for the Inventory Routing Problem

Optimization Algorithms for the Inventory Routing Problem

In the last few years many European Countries have been facing economic crisis and in order to overcome it many cost-cutting policies have been developed and applied by many companies seeking to keep market shares, reduce operational costs and increase or maintain profits. Supply chains reflect how competitiveness responsibilities do not fall on only one entity as it represents a chain of operators who may depend on each other to survive. Among Supply Chain costs, logistics costs represent a big slice of many companies budget, and in order to tackle this issue, many entities apply what is called Vendor-Managed Inventory (VMI). VMI defines a collaboration between supplier and customer allowing the supplier to control when and how much to supply each customer. This embodies a win-win relation for both supplier and customer as it allows the supplier to better manage inventory while granting a better coordination of its entire fleet enabling an optimization of routes and removing the responsibility of scheduling deliveries from the customers equation. In order to accomplish a doable VMI, companies must have a solid Inventory and Routing plan.
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Design of a High-Speed Transmission for an Electric Vehicle

Design of a High-Speed Transmission for an Electric Vehicle

A tribological system is composed by a base body (e.g. rolling element), a mating body (e.g. bearing ring) and an intermediary medium (e.g. a lubricating oil). The lubricant oil has to keep the base body apart from the mating body, under all considerable loads, to avoid excessive wear in the respective components which can lead to their early failure. There are three major lubricant regimes according to the Stribeck curve, as presented in figure 7.1 , dry friction, mixed friction and hydrodynamic friction. Meshing gears preferably work in between mixed and hydrodynamics friction regimes (figure 7.2 ), also referred as an elastohydrodynamic lubrication regime (EHL), which is also preferred for other friction parts where elastic deflection of the contact surfaces is considered, such as ball bearings [ 35 ].
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Application of Harmony Search to Vehicle Routing

Application of Harmony Search to Vehicle Routing

Abstract: A phenomenon-inspired meta-heuristic algorithm, harmony search, imitating music improvisation process, is introduced and applied to vehicle routing problem, then compared with one of the popular evolutionary algorithms, genetic algorithm. The harmony search algorithm conceptualized a group of musicians together trying to search for better state of harmony. This algorithm was applied to a test traffic network composed of one bus depot, one school and ten bus stops with demand by commuting students. This school bus routing example is a multi-objective problem to minimize both the number of operating buses and the total travel time of all buses while satisfying bus capacity and time window constraints. Harmony search could find good solution within the reasonable amount of time and computation.
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Mathematical Modeling and Simulation of an Electric Vehicle

Mathematical Modeling and Simulation of an Electric Vehicle

As electric vehicles become promising alternatives for sustainable and cleaner energy emissions in transportation, the modeling and simulation of electric vehicles has attracted increasing attention from researchers. This paper presents a simulation model of a full electric vehicle on the Matlab-Simulink platform to examine power flow during motoring and regeneration. The drive train components consist of a motor, a battery, a motor controller and a battery controller; modeled according to their mathematical equations. All simulation results are plotted and discussed. The torque and speed conditions during motoring and regeneration were used to determine the energy flow, and performance of the drive. This study forms the foundation for further research and development.
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Electric vehicle smart charging

Electric vehicle smart charging

The EV charging system prototype is compose by a coordinator, server and charging station. With the interaction of this three components, the EV power consumption can be remotely controlled by a server that has energy management algorithms that considers the building or house power consumption status and power limit. This is done by modifying the PWM signal applied by the charging station on the EV, representing a way of introducing controllable charging without the need to modify locally the PWM signal. In the simulations, the plug and charge scenario is not the ideal to apply on the developed charging system because the charging power rate is fixed, low and limited to the maximum value of the peak consumption. If only night charging is permitted the power rate has a considerable value to charge the EV, comparing with the power rate applied throughout the 24 hours. In this scenario, a power blackout event is more likely to occur if the peak power consumption has an abnormal value and if the unpredicted renewable power generation is considered.
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A MIP based local search heuristic for a stochastic maritime inventory routing problem

A MIP based local search heuristic for a stochastic maritime inventory routing problem

(C(X ∗ ) + Q(X ∗ , ξ) − z k (X ∗ )) 2 ; and the running time, in se onds, of the omplete solution pro edure. The running time in ludes solving the M sto hasti problems, and for ea h solution, omputing the penalty value for the large set of k samples.

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Tesla and the electric vehicle market in 2018

Tesla and the electric vehicle market in 2018

The emerging electric vehicle market is characterized by these network effects. Therefore, designing and implementing effective business strategies to promote the acceptance of these alternative-fuelled vehicles is imperative to reach the critical mass, beyond which the adoption becomes self-sustaining (Zhou and Li 2018). As the technological leader, Tesla will profit substantially from an expansion of the electric vehicle global market. As such, in an attempt to push e-mobility towards the commercial tipping point, Tesla has not only focused on significant technology improvements, but also the industry´s network effects, by expanding its distribution, service and charging infrastructures – which the firm hopes will become the industry´s standard. In fact, Tesla´s ability to deliver on its value proposition depends on the development and commercial deployment of other critical parts of the electric vehicle ecosystem- technologies, services, standards and regulations (Adner 2006). Therefore, the firm has devoted a large amount of resources and employed a set of strategies to make their technology fully functional, grow the user base and move towards the critical mass point.
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