- Email: [email protected]

S0959-6526(17)32553-2

DOI:

10.1016/j.jclepro.2017.10.249

Reference:

JCLP 11041

To appear in:

Journal of Cleaner Production

Received Date: 28 August 2017 Revised Date:

5 October 2017

Accepted Date: 22 October 2017

Please cite this article as: Ibarra-Rojas OJ, Hernandez L, Ozuna L, The Accessibility Vehicle Routing Problem, Journal of Cleaner Production (2017), doi: 10.1016/j.jclepro.2017.10.249. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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The Accessibility Vehicle Routing Problem O.J. Ibarra-Rojasa,∗, L. Hernandezb , L. Ozunac a Universidad

Aut´ onoma de Nuevo Le´ on, Faclutad de Ciencias F´ısico-Matem´ aticas Aut´ onoma de Nuevo Le´ on, Facultad de Ciencias Qu´ımicas c Universidad Aut´ onoma de Nuevo Le´ on, Facultad de Ingenier´ıa Mec´ anica y El´ ectrica

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In a distribution process where the demand relates to essential products or services, is important to consider the access for people to fulfill their needs. In particular, for land use and urban transportation planning, accessibility relates to appropriately allocating opportunities to satisfy a demand or provide a service considering the cost of mobility. Measuring accessibility is a challenging task, indeed, it depends on the context of the study and has not been properly considered in the definition of vehicle routing problems, which are commonly used to represent distribution processes. In the study reported here, we addressed a vehicle routing problem to optimize accessibility based on six indicators: the number of zones with access to opportunities with delimited mobility, the number of zones covered by the route, the cost of travel, the distance to the nearest opportunity, the number of opportunities, and geographical disaggregation. We defined a mixed-integer linear formulation for the proposed problem that we used to show the potential benefits of our approach compared with a maximum coverage vehicle routing problem for small instances. In turn, we designed an iterated local search algorithm and analyzed its efficiency according to a benchmark of randomly generated instances. Numerical results show that we obtain high-quality solutions for acceptable computational times.

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Given the world’s ever-increasing human population, the sustainability of planning and of the implementation of processes, whether in manufacturing, public services, or land use, among other fields, is important to guarantee equilibrium between populations and their ecosystems. To attain the desired characteristics of such activities, different indicators have been considered. Juwana et al. (2012), for example, have reviewed indicator-based water sustainability assessments, in which access to water resources for people was an important indicator. In general, improving accessibility means guaranteeing more and better-allocated opportunities for people to obtain a service or meet a demand without relying as much on mobility and is therefore undoubtedly important from a socioeconomic perspective. Indeed, measures of accessibility have been applied to land use and transportation analysis, among other studies on socioeconomic factors. However, perspectives on accessibility differ depending on the context, and to the best of our knowledge, no consensus on the definition of the term or best measures for it exist. According to Geurs and van Wee (2004), any measure of accessibility should consider the impedance, or cost, imposed upon an individual to cover the distance between an origin and a destination, as well as the number and locations

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1. Introduction

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Keywords: Accessibility, vehicle routing problem, mixed-integer programming, iterated local search

∗ Corresponding

author Email addresses: [email protected] (O.J. Ibarra-Rojas), [email protected] (L. Hernandez), [email protected] (L. Ozuna)

Preprint submitted to Elsevier

October 24, 2017

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2. Related literature

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Measures of accessibility measure commonly consider different elements or indicators based on the context. For example, Liu et al. (2015) analyzed the impact of different factors on park visitation among citizens, including the accessibility of green areas, defined as a weighted sum of the indicators of the number of parks within a given radius, the walking time to the nearest park, and the number of turns along the shortest path to the nearest park. More recently, S´anchez-Garc´ıa et al. (2017) proposed a method based on geographical information systems to evaluate the location of wood-fired power plants in relation to the availability of biomass feedstock, both geographically and in terms of accessibility. Their indicators related to accessibility used to evaluate the locations of wood-fired plants were the distances from biomass feedstock to major roads and the varying levels of accessibility to direct the supply of wood fuel based on the interaction of slopes in the terrain, road networks, population centers, government restrictions in logging operations, and legal limitations in protected areas such as national parks, natural parks, sites of community importance, and special protection areas. As also done by Liu et al. (2015), their indicators were combined in a measure of cost for the location of wood-fired plants. We consider accessibility to optimize distribution processes that can be represented with a VRP, particularly one with coverage indicators as part of an arguably global measure of accessibility, which, to the best of our knowledge, has not been previously addressed by researchers. Vehicle routing problems with route length constraints are formulated as orienteering problems and maximal covering tour problems. The classic orienteering problem considers an objective function of

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of opportunities along the way, ideally in light of the spatial distribution of the services that meet the demands for those opportunities. Handy and Niemeier (1997) have posited that four interrelated elements should be considered by any measure of accessibility: the degree of spatial disaggregation, the definition of origin and destinations, travel impedance, and the attractiveness of a zone due to the provided services in the area. As Makr´ı and Folkesson (1999) have argued, a function of travel impedance regarding accessibility can be defined in terms of the distance or time estimated in light of straight-line distance, network distance, network models simulating travel demand, or field surveys of actual driving times, if not a combination of those elements. In short, the greater the travel time or distance to reach a destination, the less accessible that destination is. Even when meeting demands is critical in distribution processes—for instance, in supplying humanitarian relief—and accessibility is therefore crucial, previous studies have not consider the concept (see reviews of humanitarian relief of Altay and Green III, 2006; de la Torre et al., 2012). In response, this study propose an accessibility measure based on the definition of accessibility articulated by Bertolini et al. (2005) which is related to amount and diversity of places that can be reached within a given travel time and/or cost. This measure is integrated in a Vehicle Routing Problem with route length constraints. Our major contributions are the definition of a new variant for the VRP including accessibility, a mathematical formulation for the proposed problem, a solution algorithm capable of obtaining good quality solutions in acceptable computational times, and a comparative analysis that exhibits the relevance of considering the accessibility concept in routing. The structure of this paper is as follows. Section 2 presents related literature to our study. Section 3 introduces our Accessibility Vehicle Routing Problem (denoted as AVRP) as well as its mathematical formulation. Section 4 introduces an Iterated Local Search to obtain high quality solutions for large instances of the AVRP. Section 5 presents a comparison of AVRP and a Maximal Covering Vehicle Routing problem, as well as an analysis of the efficiency of our ILS algorithm. Finally, Section 6 presents our conclusion and further research.

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zone coverage based on scores for zones visited and penalties for zones not visited by routes. The latter problem gives a single score for each zone and imposes a hard constraint for route time and length (see review of Vansteenwegen et al., 2011), which in turn allows it consider a weighted objective function for the number of zones on the route. A variant of the problem is the team orienteering problem, in which the goal is to determine a number of paths P , each limited by a maximum route time such that the total collected score is maximized (Tang and Miller-Hooks, 2005). Although those problems can also consider time windows (Tricoire et al., 2010; Vansteenwegen et al., 2009), the objective function is a sum score for visited zones, which is a rather simple objective compared to elements that should be considered by a measure of accessibility. A problem more pertinent to our study is the generalized orienteering problem, in which the total score is defined by nonlinear functions of vertices visited, for a certain combination of vertices can produce a higher or lower score than the sum of individual scores (Geem et al., 2005; Wang et al., 1995, 2008). Some studies on the generalized orienteering problem have focused on sightseeing activities; for example, Wang et al. (2008) defined multiple attributes for each touristic attraction, and the score was not a single value but consisted of multiple attributes considered in a weighted objective function based on the preferences of the different attributes. The authors proposed a genetic algorithm to solve large instances of the problem. The maximal covering tour problem is also related to the concept of accessibility. This problem was proposed by Current and Schilling (1994), who assumed that people can travel from their origin zone to other zones to obtain a service or meet a demand. That problem also involves a coverage radius for each visited zone, although the tour is limited to a maximum number of visits. As such, the goal is to minimize the sum of distances between zones not covered and their nearest coverage radius. In a sense, Current and Schilling (1994) considered indicators of accessibility, for their model can maximize the number of zones covered, as well as guarantee nearby opportunities. However, their model does not consider attributes such as spatial disaggregation or the number of opportunities. More recently, Flores-Garza et al. (2015) presented a generalization of the MCTP that considers a multivehicle case and assumes mandatory coverage for zones impossible to cover by the routes and zones whose coverage is optional. The goal of their model is to minimize the arrival time at all locations visited considering a limited route length, and to obtain good-quality solutions, the authors proposed a greedy randomized adaptive search procedure heuristic. In another study, Nolz et al. (2010) examined a multiobjective coverage tour problem in the context of humanitarian relief, with the proposed objectives of a weighted sum of distances from zones not covered to their nearest coverage radius, in which weights represent the number of inhabitants in each zone; the maximum visit time at each zone along the route in consideration of service time; and route duration. Since their problem is intractable for commercial solvers, the authors implemented a hybrid method based on genetic algorithms and a variable neighborhood search to obtain quality solutions for large instances. Although facility location problems also define coverage objective functions, as with VRPs, the common considerations are radius of coverage in terms of an acceptable service distance or the minimum distance from the farthest clients to service points. Constraints of the problems include single or multiple sources for the satisfaction of demands, limited capacity, mandatory coverage for specific clients, mandatory constraints upon closeness, the allowance or prohibition of partial coverage in terms of demand, and hierarchical location (Farahani et al., 2012). To the best of our knowledge, our study is the first to apply the concept of accessibility to a VRP.

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3. Accessibility Vehicle Routing Problem

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3.1. Assumptions and input

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• A zone i is directly covered if it is in a route and indirectly covered if not but nevertheless it is in neighborhood N (j) of directly covered zone j. • If neither directly nor indirectly covered, then zone i is not covered. • A zone j represents an opportunity for zone i not covered only if zone j is directly covered and j ∈ A(i). Figure 1 illustrates coverage sets N (i), accessibility sets A(i), and the classification of zones using a graph. Two routes depart from one origin (represented with a square node of the graph). Sets N (i) are represented by black dashed lines and, for example, N (5) = {6}. Sets A(i) are represented by red doted lines, for example, A(7) = {4, 5, 6}. The solution shows that zone 6 is indirectly covered by zone 5 and that zone 7 is not covered but presents two opportunities to have its demand met (i.e., by zones 4 and 5). Zones 8, 9, and 10 are not covered, and because they have only one opportunity to have the demand met (i.e., in zone 1), they can be considered to constitute a small cluster of zones not covered—a situation that we would prefer to avoid.

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We assume a set of zones with unitary demand and the number of vehicles with unlimited capacity seeking to cover those zones, as well as known travel times for each pair of zones connected by a direct road (independent of the vehicles). Moreover, the zones should be visited by at most one vehicle, it is possible to cover zones indirectly if they are near other zones covered by the route (proposed by Current and Schilling, 1994)—that is, we consider coverage neighborhoods—and a zone not covered could have the demand met by another zone within a predefined limit of mobility distance (i.e., we consider accessibility neighborhoods). Now, we introduce our notation, I is the set of zones, in which 0 represents the origin of the routes and K the number of homogeneous vehicles. The travel time from zone i to zone j is represented as tij for each pair of zones i and j connected with a direct road, and T is the time limit of the routes. N (i) is the set of zones covered indirectly if zone i is visited by a route, and A(i) is the set of accessible zones for zone i from which zone i can have the demand met. Besides the previous sets and parameters, we define the following classification of zones.

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Figure 1: Example of two routes in a network with 10 zones that illustrate coverage and accessibility neighborhoods.

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Any solution for a vehicle routing problem with constraints of limited length of routes length involves sets I1 and I2 of zones directly covered and not covered, respectively. In the example in Figure 1, those sets are I1 = {1, 2, 3, 4, 5} and I2 = {7, 8, 9, 10}, whereas zones indirectly covered are defined by I − (I1 ∪ I2 ∪ {0}) = {6}. Based on sets I1 and I2 , we define different indicators of accessibility to merge into a single measure. First, we are interested in the number of zones with access to the demand either being directly covered or having at least one opportunity within their mobility radius. Then, we define the following indicator aai taking the value of 1 if the zone has access to obtain a service or meet a demand, and 0 otherwise.

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covered zone j—a distance that we seek to minimize. Lastly, we defined an accessibility indicator in terms of the spatial disaggregation, as = min {tij }, which we maximize in order to avoid a situation in i,j∈I2

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Based on the above, our AVRP determines which zones of a set of given zones I have to be visited by a number of routes K with a limited length such that the accessibility measure A(I1 , I2 ) based on coverage, number and location of opportunities, and spatial disaggregation is maximized.

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3.3. Mixed-integer linear program for the AVRP

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To define our mathematical formulation, we define the following decision variables.

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Objective function (4) represents our accessibility measure. Constraints (5) and (6) limit the number of vehicles, whereas (7) and (8) are flow balance restrictions. The inequalities (9) define the accumulated travel time ui before the visit to each zone i on a route whereas the inequalities (10) guarantee that each variable ui for zone i ∈ I 0 is less than or equal to the maximum route time T . Notice that we do not consider the travel time of vehicles returning to the depot. The definition of zi is given by (11). Constraints (12) force variable yi to be 1 when zone i is either directly or indirectly covered (M1 can be set to |N (i)| + 1) and inequalities (13) force variable yi to be 0 when zone i is not covered. Constraints (14) and (15) guarantee that each zone not covered has only one nearest opportunity j. Equations (16) guarantee that aai takes the value of 1 if the zone is either covered (yi = 1) or has at least one opportunity j within its mobility radius A(i), and 0 otherwise. The inequalities (17) and the objective function (4) define the value of indicator ati . Notice that by maximizing our accessibility measure, we try to define near opportunities for each zone i not covered, i.e., to activate variables zj with small values of tij when 7

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yi = 0. Equation (18) defines the variables ani , whereas inequalities (19) and (20), along with objective function (4), define aoi as the number of opportunities for zone i if not covered and 0 if covered. Lastly, Constraint (21), along with objective function (4), defines the minimum distance between zones not covered, in which M2 can be set to max{tij }. (i,j)

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A simple, quick tool that can be improved by increasing the quality of each of its components, an ILS relies on evading local optimality by way of perturbation (i.e., search diversification) while increasing the quality of the local search (Louren¸co et al., 2010). Given an initial incumbent solution s, which can be a local optimum, we implement a perturbation to obtain an intermediate feasible solution s0 . Subsequently, our local searches could find a new local optimum s0∗ . If the new solution meets an acceptance criterion (typically, the objective function), then the incumbent solution would be s0∗ ; otherwise, s would remain the incumbent. We iterate until the algorithm reaches a maximum number of iterations (these steps are exhibit by Figure 2). Now, we detail all the components of our algorithm.

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By using commercial solvers, our proposed formulation is intractable since we only obtain optimal solutions for small instances. We therefore develop an iterated local search (ILS) metaheuristic to obtain high-quality feasible solutions for large instances of the AVRP.

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4.1. Constructive algorithm

Our proposed constructive algorithm is based on the savings methods proposed by Clarke and Wright (1964). In particular, we define a list LS of pairs (i, j) where zone i is the first or the last zone in a route k whereas zone j is not visited by any route. This list LS is ordered decreasingly in terms of the saving parameter sij = t0i + t0j − λtij , where λ is a route shape parameter (see Yellow, 1970). Moreover, given a solution s and a pair (i, j) ∈ LS, we define a insertion procedure InsertSavings(s, i, j) which determines if zone j is inserted at the beginning or at the end of the route k visiting i, as well as the direction of the route, which is necessary since we do not consider the travel time of vehicles returning to the depot. Figure 3 exhibits an example of solution s consisting of a route visiting zones i and i0 , and a zone j to be inserted in that route. Cases (a)–(d) show the four solutions that are explored with our proposed operator InsertSavings(s, i, j), from which we choose the feasible solution with the minimal route length. We also define an alternative insertion procedure to explore each position of all routes. Given a solution s, and a zone i not visited by any route, the operator Insert(s, i) inserts zone i in the position

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Figure 2: Schematic view of an iterated local search algorithm.

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of a route k that yields the minimal increment of route time, for which we consider both feasible and infeasible insertions along our ILS. We recall that our accessibility measure depends on the zones visited by routes. Then, we define a set L of different ordered lists to use in the insertion of zones leading to different solutions. In particular, L has three lists, where the first list order zones i ∈ I 0 decreasingly in terms of the value of |{j ∈ I 0 : i ∈ A(j), yj = 0}| but breaking ties by allocating first in L the zone i with maximal cardinality |N (i)|; the second list order zones i ∈ I 0 decreasingly in terms of the value of |N (i)| but breaking ties by allocating first in L the zone i with maximal cardinality |{j ∈ I 0 : i ∈ A(j), yj = 0}|; and finally, the third list order zones i ∈ I 0 decreasingly in terms of the value of |{j ∈ I : i ∈ A(j), yj = 0}| but breaking ties by allocating first in L the zone i with maximal value of t0i . Based on the above, we define a constructive algorithm Constructor(λ, L) consisting of three phases detailed in Algorithm 1. Similarly to the classic savings method, Phase I of our constructive algorithm defines K feasible routes visiting a single zone that is chosen from the ordered list L. In, Phase II, we implement our operator InsertSavings(s, i, j) for solution s and pairs (i, j) ∈ LS, such that, all pairs (i, j) ∈ LS where zone j has no access to opportunities (i.e., m = 0) are explored first, followed by pairs (i, j) ∈ LS with zone j not covered but with at least one opportunities (i.e., m = 1) and finally, pairs (i, j) ∈ LS with zone j indirectly covered (i.e., m = 2). Next, Phase III implement feasible operators Insert(s, i) based on the ordered list L. To obtain our initial solution, algorithm Constructor(λ, L) is implemented for different pairs (λ, L) and then, we choose the solution with the best value of the objective function.

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We propose several greedy local searches based on the following operators, some of them have been successfully implemented to solve different SVRPs (e.g., Aras et al., 2011; Palomo-Mart´ınez et al., 2017; Wang et al., 2017). • Exchange(s, k, i, j): inserts zone j at the position of zone i on route k and removes zone i of that route only if that movement is feasible. • Reorder(s, k, i, j, m): cuts the segment of route k starting from zone i and ending in zone j and reinserts it after zone m, for which we consider both feasible and infeasible moves. • Swap(s, k, i, k 0 , i0 ): removes zone i from route k and inserts it at the position of route k 0 that yields the minimal increment on the route length. Similarly, we remove i0 from route k 0 and insert it in route k, for which we consider feasible swaps.

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Algorithm 1: Constructor(λ, L):

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Initialize empty solution s and decision variables equal to 0; Create savings list LS based on λ, T imek = 0, and k = 1; Phase I: while k ≤ K do Choose first zone i in list L with aai = 0 and t0i ≤ T ; Insert i at the beginning of the route, i.e., update decision variables x0i = xi0 = aai = yi = zi = 1, aaj = yj = 1 for each j ∈ N (i), and aaj = 1 for each j ∈ I : i ∈ A(j), and T imek = T imek + t0i ; Update list L = L − ({i} ∪ N (i)) and update k = k + 1;

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Phase II: for m = 0, 1, 2 do Take first pair (i, j) ∈ LS such that aaj + yj + zj = m; s ← InsertSavings(s, i, j); if zj = 1 then Update route k (the one visiting i), and list LS according to the operator InsertSavings(s, i, j); Update decision variables aaj = yj = zj = 1, aaj 0 = yj 0 = 1 for each j 0 ∈ N (j), and aaj 0 = 1 for each j 0 ∈ I : j ∈ A(j 0 ), and list L = L − ({j} ∪ N (j)); else LS = LS − {(i, j)};

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Phase III: while T imek < T for some route k or L 6= ∅ do Take first zone i ∈ L; s ← Insert(s, i) considering only feasible movements; if zi = 1 then Update route k according to the operator Insert(s, i); Update decision variables aai = yi = zi = 1, aaj = yj = 1 for each j ∈ N (i), and aaj = 1 for each j ∈ I : i ∈ A(j);

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• P osition(s, k, i, k 0 ): removes zone i from route k and insert it at the position of route k 0 that yields the minimal increment on the length of that route, for which we consider feasible moves. • Cut(s, k, i, k 0 , i0 ): cuts the route segment starting from i (i0 , resp) to the last zone on route k (k 0 , resp) and reinsert it in route k 0 (k, resp), for which we consider feasible operations.

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Update list L = L − {i};

• F easibility(s): creates a list Lk of zones visited for each infeasible route k and order it decreasingly based on the value of the objective function if zone i is removed. Next, for each infeasible route k, selects txhe first zone i in Lk then, removes i from route k and updates Lk = Lk − {i} and route k until that route reaches feasibility. Subsequently, we define LocalSearchI, a greedy algorithm that implements the operator Insert() iteratively over an ordered list L of zones not covered and indirectly covered, details of which appear in Algorithm 2. LocalSearchI implements only feasible insertions and a necessary reordering procedure because, if zone i is inserted, then yi takes the value of 1 and {j ∈ I : i ∈ A(j), yj = 0} is updated, which affects the order of list L. We also propose LocalSearchIF described by Algorithm 3, which consists of inserting a zone along a 10

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route, implementing feasibility recovery to obtain a feasible solution, and updating the incumbent when an improvement in the objective function is identified.

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Algorithm 2: LocalSearchI(s): Create list L of indirectly covered and not covered zones ordered increasingly in terms of the value of yi (zones not covered first) but breaking ties by allocating first in L the zone i with the maximal cardinality of set {j ∈ I : i ∈ A(j), yj = 0} (zones not covered for which i could be an opportunity); while L 6= ∅ do Take first element i of L; Implement feasible Insertion(s, i); Update zones: L = L − {i};

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Algorithm 3: LocalSearchIF (s):

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improve = true; while improve do improve = false; for each route k and each zone i with zi = 0 do s0 ← Insert(s, i) but considering only route k; s0 ← F easibility(s0 ); if s0 improves the objective value then s ← s0 ; improve =true; else s0 ← s;

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Additionally, we define algorithm LocalSearchE, a greedy local search that implements the operator Exchange() in order to reduce route lengths and increase the value of the objective function by considering different zones on the routes. Details of the local search are described by Algorithm 4.

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Algorithm 4: LocalSearchE(s): improve = true; while improve do improve = false; for each route k and each pair of zones (i, j) with yi = 1 and yj = 0 do s0 ← Exchange(s, k, i, j); if s0 improves the objective value then s ← s0 ; improve =true; else s0 ← s;

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Algorithm 5: LocalSearchS(s):

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improve = true; while improve do improve = false; for each route k and zone i in route k do for each route k 0 6= k and zone i0 in route k 0 do s0 ← Swap(s, k, i, k 0 , i0 ); if s0 diminish the sum of route lengths then s ← s0 ; improve = true; else s0 ← s;

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Finally, we propose local searches to diminish the route length so the value of the objective function can be increased by implementing insertion procedures. We first define LocalSearchS, which implements operator Swap() for each 4-tuple (s, k, i, k 0 , i0 ) and updates the solution if it diminishes the sum of the lengths of routes k; details appear in Algorithm 5. Analogously to LocalSearchS, we define two greedy algorithms, LocalSearchP and LocalSearchC, to implement the operators P osition() and Cut() for tuples (k, i, k 0 ) and (k, i, k 0 , i0 ), respectively.

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4.3. Perturbation procedure and acceptance criterion

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The perturbation procedure of an ILS must be an operator irreversible by local searches. The intensity of this perturbation bias the algorithm to diversification or intensification. We propose the operator P erturbation(s), which consists of implementing the operators Reorder(s, k, i, j, m) for randomly selected 4-tuple (k, i, j, m) and F easibility(s) to obtain a feasible solution. Having examined infeasible solutions and recovering feasibility in the local searches and the perturbation procedure, we define the acceptance criterion Acceptance(s, s0 ) based only on feasibility and the value of the objective function, for which we select the feasible solution with the highest value of accessibility.

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To obtain high-quality solutions for the AVRP, we define—in a preliminary stage—the order of local searches to be sequentially implemented in our ILS. The Algorithm 6 shows the detailed steps where we can notice that the local searches are used in three phases: improving value of objective function with LocalSearchIF and LocalSearchE; diminishing route length with LocalSearchS, LocalSearchP , and LocalSearchC; and improving value of objective function by implementing LocalSearchI. Our ILS stops when a number IterLimit of iterations without any improvement in the objective function is reached.

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In this section, we present a benchmark of randomly generated instances. Moreover, we show that our metaheuristic outperforms the solver of CPLEX for large instances and that we obtain solutions congruent with the concept of accessibility.

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Define s as the best solution of Constructor(λ, L) among all pairs (λ, L); s ← LocalSearchIF (s) and s ← LocalSearchE(s); s ← LocalSearchS(s), s ← LocalSearchP (s), and s ← LocalSearchC(s); s ← LocalSearchI(s); Iter = 0; while Iter < IterLimit do s0 ← P erturbation(s); s0 ← LocalSearchIF (s0 ) and s0 ← LocalSearchE(s0 ); s0 ← LocalSearchS(s0 ), s0 ← LocalSearchP (s0 ), and s0 ← LocalSearchC(s0 ) ; s0 ← LocalSearchI(s0 ); s ← Acceptance(s, s0 ); if s0 6= s then Iter = Iter + 1;

5.1. Instances

We design different types of instances based on the number of zones |I|, the number of routes K, the maximum length T for each route, the size of the grid in which zones are randomly generated, a coverage radius rN to define set N (i) for each i ∈ I 0 (i.e., j ∈ N (i) if tij ≤ rN ), and the accessibility radius rA to define A(i) for each i ∈ I 0 (i.e., j ∈ A(i) if tij ≤ rA ). Table 1 shows the different instance sizes and for each of which we randomly generate 30 instances.

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Table 1: Instances types for the AVRP.

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size of the grid 60 × 60 60 × 60 60 × 60 60 × 60 120 × 120 120 × 120 240 × 240 240 × 240

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For instances A1, B1, C1, and D1, all zones are randomly generated within the grid. In the case of instances types A2, B2, C2, and D2, the depot is randomly generated within the grid and then, |I|−1 3 zones are randomly generated within each one of the three regions in the grid layout illustrated by Figure 4. Notice that the area of the three regions is the same and by using the latter grid layout, we are able of representing scenarios were may exist groups of zones. Finally, all instances satisfy A(i) 6= ∅ for all i ∈ I 0 . We executed all experiments on a MacPro with 2×2.4 GHz quad core Intel Xeon and 20 GB of RAM. We solved MILPs by using CPLEX 16.2 with default settings and a stopping criterion of 3 h of computational time. After a preliminary experimental stage with the AVRP MILP, we fixed weight parameters as w1 = 1000, w2 = 250, w3 = 100, w4 = −1, w5 = 10, and w6 = 0.01. For our constructor algorithm, we use λ within {−30, −29.9, . . . , 29.9, 30} and three ordered lists in L, leading to explore 1800 sets of K routes to find our best initial solution.

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5.2. AVRP versus MCVRP

To highlight the potential benefits of our proposed approach, we compare our AVRP with the VRP maximizing the weighted sum of the number of covered zones and the sum of distances from zones not covered to the nearest zone on the route, as proposed by Current and Schilling (1994). We refer to the P P latter problem as the MCVRP. The objective function of MCVRP is defined as w2 yi + w4 ani , in

since we could therefore consider only indicator ati for zones not covered by routes. Our proposed problem yields slightly worst results for indicators considered in the MCVRP; see negative values in columns for P P indicators yi and ani . However, improvement in the number of opportunities and the accessibility

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indicator based on the impedance function for zones not covered are quite large (i.e., up to 66.68%). With the AVRP, the magnitude of the average improvement for the number of zones not covered with access to opportunities is larger than the magnitude of the average decrement of the number of covered zones for both types of instances.

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which parameters w2 and w4 are defined similarly as set for the AVRP. Table 2 shows the average improvement of accessibility indicators solved with the AVRP over the MCVRP for 60 small instances of types A1 and A2 (the ones we can solve to optimality with commercial P solver). We focus on summation ati (1 − yi ) to analyze the accessibility indicators based on impedance i∈I 0

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For a clearer presentation of results in Table 2, we present Figures 5 which shows the comparison for each accessibility indicator among all small instances, where we exhibits the improvements described by the previous analysis. For example, it can be seen that for 75% of the instances of AVRP we obtain the P same value of coverage indicator yi and there are small losses for the rest of the instances while we i∈I 0 P obtain significant improvements for indicator ati (1 − yi ) for 41.66% of the instances by solving the i∈I 0

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Figure 5: Comparison of access indicators for solutions of instances A1 and A2 obtained by MCVRP and AVRP. Higher is P better except for indicator ani . i∈I 0

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To compare our ILS with the solver of CPLEX 12.6, we focused on computational time in seconds and gaps computed as the relative difference of the best dual bound obtained by CPLEX and the best feasible solution obtained by each method. Table 3 shows the average gap and time obtained by CPLEX and the ILS for instance types A1, A2, B1, and B2. Although CPLEX often outperforms heuristic algorithms for small instances in complex problems, since the average gap obtained by the ILS for instance types A1 and A2 was less than 0.8%, we obtained optimal or nearly optimal solutions for the smallest instances generated in the experimental stage in less than 0.5 s of average computational time. In particular, we found 21 and 18 optimal solutions for types

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Table 3: Comparison of CPLEX and ILS on small instances.

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A1 and A2, respectively. For types B1 and B2, the difference between the average gap obtained by our ILS and CPLEX was 0.36% at most. In particular, we found three and six optimal solutions for instances of type B1 and type B2, respectively. Average computational times of our ILS were less than 1 s, whereas CPLEX reached the time limit for most instances of those types. We also compared CPLEX and ILS in 10 instances of type C1. Table 4 shows the gap and time (in seconds) for each instance. Feasible solutions obtained by CPLEX in 3 h of computational time were of significantly lower quality than those obtained with the ILS for all instances, for a difference of 10.40% in the average gap. That result demonstrates the usefulness of our ILS as a tool to obtain quality solutions for large instances of the AVRP in acceptable computational times.

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CPLEX gap time (secs) 0% 94.59 0% 130.20 1.47% 9703.46 1.38% 7597.24

Table 4: Comparison of CPLEX and ILS on ten instances of type C1.

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CPLEX gap 24.36% 20.07% 3.49% 17.94% 17.13% 7.31% 12.84% 19.31% 12.41% 15.42% 15.03%

gap 8.82% 4.27% 2.66% 4.46% 2.51% 2.86% 3.76% 4.05% 3.86% 4.61% 4.19%

ILS time (secs) 7.06 8.38 8.3 7.55 8.48 8.24 7.84 9.04 3.86 8.24 8.18

5.3.2. ILS on generated instances To showcase the behavior of our ILS on the generated instances, Table 5 presents the average computational time and average improvement of our ILS over the constructive algorithm among all instance types. Figure 8 shows a box plot of the improvements summarized by Table 5. In this Figure, black dots represent the average improvement, black line within the box shows the median, the box is delimited by the first and third quartiles, the dashed lines reach lower and upper limits, and the blank circles are atypical values. All types showed average improvements of at least 0.60%, whereas average computational times were less than 1 min. In particular, for types B1 and B2, the maximum route length allowed visiting almost all zones, even in the constructive phase, which yielded less improvement when the ILS was implemented.

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Table 5: Average improvement of our ILS over the initial solution obtained by Algorithm 1.

improvement of ILS over Constructor

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Even if improvements shown in Figure 8 appear small for some instances, they may represent significant changes in values for the different indicators of our accessibility measure given the value of the weight parameters of the objective function. For example, Figure 9 shows the case of instance 14 of type C1, for which we obtained the greatest improvement of that instance type (12.06%). The improvement of our ILS over the constructor algorithm occurred primarily due to the increment in the number of zones not covered that got access to opportunities (zones 22, 29, 46, and 58) and the number of zones covered. Moreover, the average value of ati based on travel impedance among all zones not covered was 0.89 for the solution obtained by the ILS compared with the value of 0.46 for the solution obtained with the constructor algorithm. In short, there were more, closer opportunities for those zones. Figure 10 shows a better example of instance 26 of type C2 with an improvement of 7.09%. In that case, there were also more zones not covered with access to opportunities (i.e., zone 9), but the more significant improvement was due to 13 more covered zones in the solution obtained by the ILS. Moreover, there were not groups of zones not covered—that is, there was greater separation between those zones— which constitutes an important improvement in the indicator of spatial disaggregation (250%).

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1 of D1 and D2, Figure 11 shows the case of instance 1 In case of larger instances 28 of type D1, which demonstrated an improvement of 6.06%. Similarly to cases shown in Figures 9 and 10, the improvements emerged due to more zones covered and not covered with opportunities for access. That instance represents a scenario in which the maximum route length is not enough to avoid spatial disaggregation, since a large concentration of zones not covered appears in the low area of Figure 11, which could represent the limit of the distribution process. However, we increased the number of opportunities and the value of accessibility indicator based on the impedance function for zones not covered in that delimited area. Finally, we analyzed instance 24 of type D2 (Figure 12), which represented the largest improvement (i.e., 5.07%) for that instance type. In that case, 5.07% consisted of an additional zone not covered with access to opportunities and additional covered zones, among other changes, for the rest of the accessibility indicators.

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Based on the numerical 1results in this section, our iterated local search1 is a tool that can be implemented on large scenarios to improve the accessibility of a distribution processes, in order to guarantee more opportunities requiring less mobility for inhabitants of zones not covered.

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6. Conclusions and further research

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Improving accessibility consists of guaranteeing more opportunities for people to obtain services or have demands met without having to rely as much on mobility. Despite the different definitions of accessibility for different contexts, common elements of measures of accessibility include the number of opportunities to obtain the service, the specific cost of traveling, and geographical disaggregation. In our study, we defined an accessibility measure as a weighted sum of indicators for those elements. 20

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We optimized this measure in our proposed new variant of the SVRP, which we called as AVRP, that considered a radius of coverage for zones along routes and a radius of mobility for zones not covered by any route. We find out that classic coverage metrics for VRPs do not consider all the elements of accessibility defined in our study. To exhibit the latter, we compared a maximal covering vehicle routing problem— focused only on the number of covered zones and the distance to the nearest opportunity—with our AVRP where we obtained more and better-allocated opportunities to fulfill a demand or receive a service and we diminish geographical disaggregation, which is more desirable according to an inclusive philosophy consistent with the definition of accessibility. In particular, the solutions obtained with our approach yielded improvements between 1.95% and 66.68% for indicators included only in the AVRP, while we obtain losses less than 3.84% for indicators optimized in the MCVRP. Our proposed mathematical formulation is intractable by implementing commercial solver, even for small instances. In turn, we designed an ILS capable of obtaining good-quality solutions for large instances of the AVRP in less than 1 min of computational time. As a result, we generated nearly optimal solutions—at most, of 1.83% of the average gap—for small instances types. Furthermore, by implementing our proposed local searches, we also significantly improved elements of accessibility in the objective function over initial solutions generated by our constructive algorithm. A further research area is including the demand, as well as capacities for zones, in order to determine an assignment of opportunities for inhabitants in zones not covered by routes. Moreover, the proposed measure of accessibility can be adapted for facility location problems in cases in which demand is critical for people (e.g., distribution of bare necessities and humanitarian relief) and a delimited mobility for inhabitants is considered. Besides, a comparison of different accessibility measures is of interest as well as the practical implementation of such approaches in real scenarios. Finally, considering stochastic parameters for the problem (for example, Allahviranloo et al., 2014) is also of interest since a potential implementation is in the context of humanitarian relief where uncertainty is present in the input data.

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Appendix: Notation list

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This work was supported by the Programa para el Desarrollo Profesional Docente (PRODEP), Grants 511-6/17-7538 and DSA/103.5/16/14538.

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I: set of zones.

I1 : set of directly covered zones. I2 : set of zones not covered by routes.

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K: number of homogeneous vehicles.

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N (i): set of zones covered indirectly by i if that zone is visited by any route.

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A(i): set of accessible zones for zone i.

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Parameters tij : travel time from zone i to zone j.

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T : time limit of the routes.

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f (tij ) impedance function for traveling from zone i to zone j.

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wk : weight parameter.

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s: solution for the AVRP.

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sij : savings parameter for pair of zones (i, j).

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λ: route shape parameter.

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L: set of ordered lists of zones.

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L: ordered list of zones which denotes an element of L.

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rN : coverage radius to define the set N (i).

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rA : accessibility radius to define the set A(i).

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LS: List of pairs of zones (i, j) ordered accordingly savings parameters sij .

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Altay, N., Green III, W., 2006. OR/MS research in disaster operations management. European Journal of Operational Research 175, 475 – 493.

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Allahviranloo, M., Chow, J.Y., Recker, W.W., 2014. Selective vehicle routing problems under uncertainty without recourse. Transportation Research Part E: Logistics and Transportation Review 62, 68 – 88.

Aras, N., Aksen, D., Tekin, M.T., 2011. Selective multi-depot vehicle routing problem with pricing. Transportation Research Part C: Emerging Technologies 19, 866 – 884. Freight Transportation and Logistics (selected papers from ODYSSEUS 2009 - the 4th International Workshop on Freight Transportation and Logistics).

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