National audienceThe aim of this work is to propose a new model that we call the City Logistics Facility Location Problem (CLFLP). Our purpose when introducing the CLFLP, is to capture essential aspects of distribution in cities, while maintaining a reasonable level of genericity and simplicity in the defintion of the problem. Practically, this model was adapted to the case of the city of Marseilles (France) and inserted into a Decision Support System. With a more academic point of view, the model could serve as a cornerstone for the development of new models and methods for strategic issues in city logistics

Absi, Nabil

Feillet, Dominique

Garaix, Thierry

Guyon, Olivier

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The City Logistics Facility Location ProblemN. Absi1, D. Feillet1, T. Garaix2, O. Guyon11 Ecole Nationale Supérieure des Mines de Saint-Etienne,CMP Georges Charpak, F-13541 Gardanne, France{absi, feillet,guyon}@emse.fr2 Ecole Nationale Supérieure des Mines de Saint-Etienne,Centre Ingénierie Santé, F-42023 Saint-Etienne cedex 2, Francegaraix@emse.frMots-clés : logistique urbaine, modélisation, problème de localisation1 IntroductionCity logistics has raised the interest of many researchers from different communities andcountries in the last decade [5, 1]. The subject of this paper is the location of logistics platformsin the context of fast parcel delivery in urban areas.A strong tendency that could be observed in most urban areas during last decades wasto limit the presence of the logistics platforms within the city. Due to many recent factors(sustainability, e-business), the benefits of this policy, both for local authorities, carriers, andeventually inhabitants, can be questioned.Because of the special context of city logistics, usual location models cannot be used todetermine optimal location of city distribution platforms. While some works started addressingthe subject [4, 1] , the literature still lacks from general models on this topic [3, 2].The aim of this work is to propose a new model that we call the City Logistics FacilityLocation Problem (CLFLP). Our purpose when introducing the CLFLP, is to capture essentialaspects of distribution in cities, while maintaining a reasonable level of genericity and simplicityin the definition of the problem. Practically, this model was adapted to the case of the cityof Marseilles (France) and inserted into a Decision Support System. With a more academicpoint of view, the model could serve as a cornerstone for the development of new models andmethods for strategic issues in city logistics.2 The City Logistics Facility Location ProblemThe City Logistics Facility Location Problem mainly addresses solutions for two stakeholdersin city logistics : (i) carriers which want to optimize the location of their logistics platformsand the organization of their distribution scheme, (ii) local authorities which want to evaluatethe relevance of available zones for the setting up of distribution platforms or compare differentscenarios of distribution in the city.The CLFLP involves a set of spatially distributed delivery zones which represent the city,a set of available surfaces for distribution platforms and a set of existing vehicle types totransport goods : (i) from the logistics platforms to the delivery zones, (ii) within the deliveryzones through routes.Delivery zones represent districts in the city. The principle of aggregating the demand offinal customers into districts replicates the actual organization of carriers. We address themodeling of distribution with a compromise modeling between the simple assignment of zonesto platforms and location-routing. This modeling is based on the notion of compatible zones,where two zones are compatible if they can be serviced in the same route. In our context,a location-routing approach is not satisfactory for at least two reasons. First, in practice,vehicle routes are limited to a very restricted number of zones (usually 1 or 2) ; secondly, theimportant part when evaluating the impacts of transportation concerns the initial and finalportions between the platforms and the zones, and the distribution within the zones : travelsbetween successive zones are generally very short and also very difficult to evaluate.In addition to deciding which platforms to open and to allocating demand zones to platforms,we introduce decision variables for the selection of vehicles for the distribution (includingbattery-driven vehicles).Solutions are optimized according to a mix of economic, environmental and societal criteria.We propose two different integer programming formulations for the CLFLP. Roughly spea-king, the difference between these two formulations is that the first formulation explicitlyintroduces flow variables for each vehicle and considers the assignment of zones to vehiclesindependently. In the second formulation, the set of all feasible combinations of compatiblezones is introduced and items from this set are selected and assigned to vehicle types (vehiclesare not explicitly considered).In order to evaluate these models, a set of instances was introduced, based on realistic datafrom the city of Marseilles. Results demonstrate the superiority of the second model unlesscompabitility between zones is very high (which is not relevant in practice). Real-size instancesfor a city of the size of Marseilles can be solved in a few seconds. Optimal solutions cannotalways be obtained in a reasonable time for larger instances. Perspectives are the developmentof ad hoc heuristic methods or more efficient mathematical programming models. Practically,several contacts have been established with carriers and cities to implement the model.Références[1] T.G.Crainic. City logistics, Technical Report CIRRELT-2008-25, CIRRELT, 2008.[2] M.T. Melo, S. Nickel and F. Saldanha-Da-Gama. Facility location and supply chain mana-gement - a review, European Journal of Operational Research, 196(2), 401-412, 2009.[3] C.S. Revelle, H.A. Eiselt and M.S. Daskin. A bibliography for some fundamental problemcategories in discrete location science, European Journal of Operational Research, 184(3,817-848, 2008.[4] E. Taniguchi, M. Noritake, T. Yamada and T. Izumitani. Optimal size and location planningof public logistics terminals, Transportation Research, 35, 1999.[5] E. Taniguchi and R.G. Thompson. Modeling city logistics, Transportation research record,1790 :45-51, 2002.

The City Logistics Facility Location Problem

International audienceThe aim of this work is to propose a new model that we call the City Logistics Facility Location Problem (CLFLP). Our purpose when introducing the CLFLP, is to capture essential aspects of distribution in cities, while maintaining a reasonable level of genericity and simplicity in the defintion of the problem. Practically, this model was adapted to the case of the city of Marseilles (France) and inserted into a Decision Support System. With a more academic point of view, the model could serve as a cornerstone for the development of new models and methods for strategic issues in city logistics

The City Logistics Facility Location ProblemNabil Absi, Dominique Feillet, Thierry Garaix, Olivier GuyonTo cite this version:Nabil Absi, Dominique Feillet, Thierry Garaix, Olivier Guyon. The City Logistics FacilityLocation Problem. ODYSSEUS 2012, Fifth International Workshop on Freight Transportationand Logistics, May 2012, Mykonos, Greece. <emse-00718904>HAL Id: emse-00718904https://hal-emse.ccsd.cnrs.fr/emse-00718904Submitted on 18 Jul 2012HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.L’archive ouverte pluridisciplinaire HAL, estdestine´e au de´poˆt et a` la diffusion de documentsscientifiques de niveau recherche, publie´s ou non,e´manant des e´tablissements d’enseignement et derecherche franc¸ais ou e´trangers, des laboratoirespublics ou prive´s.The City Logistics Facility Location ProblemN. Absi, D. Feillet, T. Garaix, O. GuyonEcole Nationale Supe´rieure des Mines de Saint-Etienne,CMP Georges Charpak, F-13541 Gardanne, FranceEmail: feillet@emse.fr1 IntroductionCity logistics has raised the interest of many researchers from different communities andcountries in the last decade [1, 2]. The subject of this paper is the location of logisticsplatforms in the context of fast parcel delivery in urban areas.A strong tendency that could be observed in most urban areas during last decades wasto limit the presence of the logistics platforms within the city. Several simple reasons canexplain this phenomenon. Inhabitants do not appreciate living around these platformsthat might cause increases of traffic (and specially traffic of large vehicles), generate noiseand pollution, or have some unpleasant visual impact. Also, available surfaces in citiesare rare and expensive. From a pure economical point of view, carriers used to preferless expensive locations, at some distance of the city, though the inconveniences of beingdistant from final customers. Also, local authorities gave priority to more noble activitiesfor these available surfaces: commercial centers, apartments, public services as librariesor concert halls. . .Due to many recent factors, the benefits of this policy, both for local authorities andcarriers (and eventually inhabitants), can be questioned. Environmental issues becomemore and more important. New purchase channels as e-business modify profiles of carriercustomers and imply different organizations and services. Disposing of urban platforms canthen offer several possibilities as using electric vehicles (whose limited autonomy preventsfrom traveling long distances) or scheduling several successive deliveries or collection routes(which is not tractable with distant platforms).Because of the special context of city logistics, usual location models cannot be usedto determine optimal location of city distributon platforms. While some works startedaddressing the subject [3, 2, 4, 5], the literature still lacks from general models on thistopic [6, 7].The aim of this work is to propose a new model that we call the City Logistics FacilityLocation Problem (CLFLP). Our purpose when introducing the CLFLP, is to captureessential aspects of distribution in cities, while maintaining a reasonable level of genericityand simplicity in the defintion of the problem. Practically, this model was adapted to thecase of the city of Marseilles (France) and inserted into a Decision Support System. Witha more academic point of view, the model could serve as a cornerstone for the developmentof new models and methods for strategic issues in city logistics.2 The City Logistics Facility Location ProblemThe City Logistics Facility Location Problem mainly addresses solutions for two stake-holders in city logistics: (i) carriers which want to optimize the location of their logisticsplatforms and the organization of their distribution scheme, (ii) local authorities whichwant to evaluate the relevance of available zones for the setting up of distribution platformsor compare different scenarios of distribution in the city.The model involves a set D of spatially distributed delivery zones which represent thecity, a set L of available surfaces for distribution platforms and a set V of existing vehicletypes to transport goods: (i) from the logistics platforms to the delivery zones, (ii) withinthe delivery zones through routes.Delivery zones represent districts in the city. The principle of aggregating the demandof final customers into districts replicates the actual organization of carriers. Indeed, asdistribution in cities is a highly dynamic problem, considering desaggregate demand ofcustomers at a strategic level is not possible. Also, the lack of automatic sorting systemsin platforms generally imposes to define simple and permanent rules to gather customerstogether.Each delivery zone d ∈ D is characterized with a demand γd and a length δd whichrepresents the length of the delivery route within d.Two extreme possibilities exist in the literature to model transportation here. Eitherzones are simply assigned to platforms and transportation is simplified to back and forthtravels between the platforms and the zones, or a location-routing modeling is involved soas to better evaluate impacts of transportation.In our context, a location-routing approach is not satisfactory for at least two reasons.First, in practice, vehicle routes are limited to a very restricted number of zones (usually 1or 2); secondly, the important part when evaluating the impacts of transportation concernsthe initial and final portions between the platforms and the zones, and the distributionwithin the zones: travels between successive zones are generally very short and also verydifficult to evaluate (remember that precise location of customers is not known).We propose a compromise modeling by defining two types of zones: single-vehicle zones(DS ⊆ D) and multi-vehicle zones (DM ⊆ D).For each multi-vehicle zone d ∈ DM , parcels can be supplied by more than one vehiclebut cannot be consolidated with parcels of other zones. Each vehicle that delivers parcelsin a multi-vehicle zone thus follows a simple route that goes from a platform, serves thezone and come back to the platform. It corresponds to the simplified modeling evokedabove. However, as vehicles are explicitly considered in the model, amount of deliveryare limited by vehicle capacity constraints, thus potentially implying the use of severalvehicles.For each single-vehicle zone d ∈ DS , parcels have to be delivered by a single vehicle.Such single-vehicle zones are typically zones with a limited demand. In a same deliveryroute, a vehicle can deliver parcels in several single-vehicle zones. Travel distances betweenzones are not considered. In order to avoid including distant zones in a route, a set∆d ⊆ DS of compatible zones is assigned to each single-vehicle zone d ∈ DS . Each routehas then to be composed exclusively of zones compatible one to another. We then evaluatetransportation costs as follows: the transportation cost of a route depends on the nearestzone served (indicating the cost of entering and leaving the city from a distant platform)and the set of delivery zones (indicating the distances traveled during the deliveries).Constraints are introduced, through the use of the compatibility between zones, to avoidincluding distant zones in a same delivery route.Each available surface l ∈ L for logistics platforms has a capacity ql (measured innumber of doors which can be built). Each door of l can ensure the distribution of givenamount of parcels per day. The total cost of selecting l is composed of a fixed cost plus avariable cost proportional to the number of doors opened.Vehicles of type v have a capacity βv and a distance-autonomy αdistv . Their drivingtime per day is limited to αtimev (specific technical characteristics, workload limit. . . ). Theyare furthermore limited to travel on a subset Jv ⊆ D of delivery zones (because of thelegislation, the width of the streets. . . ). The travel times involved in our model dependon the vehicle types. The total cost of using a vehicle of type v is composed of a fixed costplus a variable cost proportional to traveled distance.3 Mathematical modeling and experimentsWe proposed two different integer programming formulations for the CLFLP. Roughlyspeaking, the difference between these two formulations is that the first formulation ex-plicetly introduces flow variables for each vehicle and considers the assignment of zones tovehicles independantly. In the second formulation, the set of all feasible combinations ofcompatible zones is introduced and items from this set are selected and assigned to vehicletypes (vehicles are not explicitely considered).In order to evaluate these models, a set of instances was introduced, based on realisticdata from the city of Marseilles. Results demonstrate the superiority of the second modelunless compability between zones is very high (which is not relevant in practice). Real-sizeinstances for a city of the size of Marseilles can be solved in a few seconds. Optimal solu-tions cannot always be obtained in a reasonable time for larger instances. Perspectives arethe development of ad hoc heuristic methods or more efficient mathematical programmingmodels. Practically, several contacts have been esatablished with carriers and cities toimplement the model.References[1] E. Taniguchi and R.G. Thompson, “Modeling city logistics”, Transportation researchrecord, 1790:45-51, 2002.[2] T.G.Crainic, “City logistics”, Technical Report CIRRELT-2008-25, CIRRELT, 2008.[3] E. Taniguchi, M. Noritake, T. Yamada and T. Izumitani, “Optimal size and locationplanning of public logistics terminals”, Transportation Research, 35, 1999.[4] T. G. Crainic, N. Ricciardi and G. Storchi, “Advanced freight transportation systemsfor congested urban areas”. Transportation Research Part C: Emerging Technologies,12, 119-137, 2004.[5] G. Perboli, R. Tadei and D. Vigo, “The Two-Echelon Capacitated Vehicle RoutingProblem: models and math-based heuristics”. Transportation Science, 45, 364-380,2011.[6] C.S. Revelle, H.A. Eiselt and M.S. Daskin, “A bibliography for some fundamentalproblem categories in discrete location science”, European Journal of OperationalResearch, 184(3), 817-848, 2008.[7] M.T. Melo, S. Nickel and F. Saldanha-Da-Gama, “Facility location and supply chainmanagement - a review”, European Journal of Operational Research, 196(2), 401-412,2009.

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