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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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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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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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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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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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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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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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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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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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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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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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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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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(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
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omputing **the** penalty value for **the** large set **of** k samples.

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