Regarding added value to **the** company, as it has been raised, this research project had **the** virtue **of** having collected a large amount **of** data and turning it into knowledge **with** implications on daily decisions. Today, for instance, each truck is linked to a cost per ton delivered, as well as a number **of** kilometers to distribute one ton. Moreover, **the** cost structure **of** each truck is detailed from insurance and fines to fuel, tolls and maintenance. This is important to control not only **the** performance **of** each truck, but also **of** each driver. Furthermore, **the** detailed level **of** understanding **of** **the** **distribution** planning allowed to identify a set **of** problems that **the** company will have to properly manage **in** order to improve outbound logistics. **The** evolution **in** **the** departure **time** **of** **the** drivers **in** Ovar, which is a good business unit from this point **of** view, is illustrative **of** **the** difficulties that challenge **the** output **of** this process. Additionally, **the** credit notes related to drivers’ lack **of** **time** are also a symptom **of** **the** problems identified.

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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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Since **the** CVRP is a NP-hard **problem**, only instances **of** small sizes can be solved to optimality using exact solution methods (Toth and Vigo, 2002; Baldacci et al., 2010). As a result, heuristic methods are used to find good, but not necessarily guaranteed optimal solutions using reasonable amount **of** computing **time**. Starting **with** **the** simple constructive approaches such as **the** savings algorithm proposed by Clarke and Wright (1964) or basic improvement methods such as **the** 2-opt heuristic, **the** general-purpose heuristic methods (which are called metaheuristics) have then been developed to guide subordinate heuristics to avoid or overcome local optimality. During **the** past two decades, an increasing number **of** literatures on heuristic approaches have been developed to tackle **the** CVRP. **The** summary and discussion **of** several important and state-**of**-**the**-art modern heuristics for **the** **problem** can be found **in** **the** study by Cordeau et al. (2002) and Szeto et al. (2011).

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These results are somehow expected due to **the** fact that **the** algorithm No.2 does not give always **the** same route for trucks requiring **the** same locations. By choosing always **the** least occupied server at **the** **time** **of** reaching **the** servers and by computing different routes, avoiding **the** congested ones, **the** facility reaches a higher level **of** equilibrium. Usually, when computing a system that is static (always giving **the** same route, **in** **the** case **of** **the** algorithm No.1, **the** minimum distance one), there will be one (or several) road(s) and server(s) that will represent **the** so-called bottleneck(s). **The** algorithm No.2 tries to equilibrate **the** occupation both **in** **the** servers and **in** **the** roads, not overloading any **of** them, thus eliminating these bottlenecks or, at least, mitigating its effects. Besides **the** results **of** **the** simulations, it was possible to observe that, **in** **the** simulations using **the** algorithm No.2, **the** trucks were much more dispersed inside **the** facilities, decreasing **the** queues **in** **the** servers, per example. Also, it was possible to observe that, **in** some cases, **the** algorithm No.2 has chosen some roads that **in** **the** simulations **of** **the** Algorithm No.1 had not been used at all. **In** **the** roads case, as **the** sets have a limited and relatively lower number **of** trucks, **the** division may not be so present. However, **in** a day to day **of** a factory, **with** hundreds **of** trucks, **the** roads would become much more congested. **In** that case, **the** algorithm No.2 will provoke a much more highlighted division **of** **the** trucks **in** **the** roads.

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This paper studies **the** integration **of** inbound and outbound logistics **in** **the** context **of** **the** wood-based panel industry. **The** case study is driven from a real-life industrial application that operates on a multi-mill setting. **The** production strategy **of** **the** wood-based panels at each mill is Make-to-Order. **The** finished **products** are shipped to **the** customers **in** **the** day after its production. **The** stock **of** raw materials should be at least one week to overcome fluctuations **in** wood supply. **The** outbound logistics are planned locally, **in** **the** transporta- tion department **of** each mill, while **the** inbound logistics are planned centrally, considering **the** bulk demand for all **the** mills. **The** goal here is to find daily minimum-cost outbound and inbound routes (OIRs) where **the** **vehicle** departing from each mill firstly performs a sequence **of** deliveries **of** **the** amounts ordered by **the** customers, and secondly, whenever is cost-effective, picks up a full truck-load **of** raw materials at a nearby supplier, and de- livers it at **the** closest company’s mill. OIRs allow better use **of** **the** delivery truck, when compared **with** ORs and further avoid dedicated IRs. This is possible because **the** driver can easily adapt **the** same truck that transported **the** wood boards **with** reinforcements **in** its structure so it can transport a full truck-load **of** wood chips. For wood-based supply chains,

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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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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**.

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Production and **distribution** problems **with** perishable goods are common **in** many **in**- dustries. For **the** sake **of** **the** competitiveness **of** **the** companies, **the** supply chain planning **of** **products** **with** restricted lifespan should be addressed **with** an integrated approach. Par- ticularly at **the** operational level, **the** sizing and scheduling **of** production lots have to be decided together **with** **vehicle** **routing** decisions to satisfy **the** customers. However, such joint decisions make **the** problems hard to solve for industries **with** a large product portfolio. This paper proposes an adaptive large neighbourhood search (ALNS ) framework to tackle **the** **problem**. This metaheuristic is well-known to be effective for **vehicle** **routing** problems. **The** proposed approach relies on mixed-integer linear programming models and tools. **The** adap- tive large neighbourhood search outperforms traditional procedures **of** **the** literature, namely exact methods and fix-and-optimize, **in** terms **of** quality **of** **the** solution and computational **time** **of** **the** algorithms. Nine **in** ten runs **of** ALNS yielded better solutions than traditional procedures and **the** best solution value found by **the** latter methods 12.7% greater than **the** former, on average.

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Extreme events as large-scale disasters can cause partial or total disruption **of** basic ser- vices such as water, energy, communication and transportation. **In** particular, recovering **the** transportation infrastructure is **of** ultimate importance **in** post-disaster situations, to enable **the** evacuation **of** victims and **the** **distribution** **of** supplies to affected areas. Road restoration, one **of** **the** main activities **in** this context, is a complex activity due to its inherent decisions that must be taken quickly and under uncertainty, such as **the** allocation **of** resources and **the** schedul- ing/**routing** **of** **the** crews that perform **the** restoration activities. **In** this thesis, we address road restoration by means **of** **the** Crew Scheduling and **Routing** **Problem** (CSRP), which integrates scheduling and **routing** decisions. **The** **problem** also involves **the** design **of** relief paths to con- nect a supply depot to demand nodes that become accessible only after **the** damaged nodes **in** these paths are repaired. We start addressing **the** basic variant **of** **the** CSRP, which considers a single crew available to perform **the** repair operations and minimizes **the** accessibility **time** **of** **the** demand nodes. Then, we extend **the** **problem** to consider multiple heterogeneous crews and uncertainties **in** **the** repair times via robust optimization. Also, we introduce **the** minimization **of** **the** latency **of** **the** demand nodes, where **the** latency **of** a node is defined as **the** accessibility **time** plus **the** travel **time** from **the** depot to that node. To solve **the** CSRP and **the** proposed ex- tensions, effective solution methods based on Benders decomposition are proposed. We propose three types **of** solution approaches: branch-and-Benders-cut algorithms (BBC), metaheuristics based on simulated annealing and genetic algorithm, and hybrid branch-and-Benders-cut algo- rithms (HBBC). We develop two BBC algorithms. **The** first BBC has a master **problem** **with** scheduling decisions while **the** crew **routing** and **the** design **of** relief paths are considered **in** **the** subproblems. **The** second

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• Particle Swarm Optimization (PSO)- PSO is a stochastic population-based metaheuris- tic Talbi ( 2009 ) **in** which **problem** solving system mimic collective intelligence **in** nature ( Alam et al. ( 2015 )). Each particle **in** a population represents a possible solution to **the** **problem** and is defined by its position where each position has its fitness evaluated by **the** optimized fitness function ( Sethanan and Neungmatcha ( 2016 )) and velocity which redirects its movement (replicating birds behavior when flying). Each particle adjusts it is position taking into account two previous solutions: **the** best personal value and **the** best populational value ( Talbi ( 2009 )) which are also kept **in** memory and thus allowing a quick convergence to global optima ( Lynn and Suganthan ( 2015 )). **In** Belmecheri et al. ( 2013 ) is presented a PSO algorithm **with** a local search for **the** **vehicle** **routing** **problem** **with** heterogeneous fleet, mixed backhauls and **time** **windows** (HVRPMBTW), which when comparing its results **with** an exact method and best know solutions **of** **the** literature for specific VRPTW problems, returns that HVRPMBTW achieves optimal solutions for small problems and was able to improve **the** GAP, which measures **the** distance to **the** optimal solution, by over 5.5% **with** 29 **of** 56 cases displaying its quickness **in** convergence, few parameter settings and fewer memory needed.

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Real world applications for **vehicle** collection or delivery along streets usually lead to arc **routing** problems, **with** additional and complicating constraints. **In** this paper we focus on arc **routing** **with** an additional constraint to identify **vehicle** service routes **with** a limited number **of** shared nodes, i.e. **vehicle** service routes **with** a limited number **of** intersections. This constraint leads to solutions that are better shaped for real application purposes. We propose a new **problem**, **the** bounded overlapping MCARP (BCARP), which is deﬁned as **the** mixed capacitated arc **routing** **problem** (MCARP) **with** an additional constraint imposing an upper bound on **the** number **of** nodes that are common to diﬀerent routes. **The** best feasible upper bound is obtained from a modiﬁed MCARP **in** which **the** minimization criteria is given by **the** overlapping **of** **the** routes. We show how to compute this bound by solving a simpler **problem**. To obtain feasible solutions for **the** bigger instances **of** **the** BCARP heuristics are also proposed. Computational results taken from two well known instance sets show that, **with** only a small increase **in** total **time** traveled, **the** model BCARP produces solutions that are more attractive to implement **in** practice than those produced by **the** MCARP model.

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