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SELL - Smart Energy for Leveraging LPG use - White Paper
SELL - Smart Energy for Leveraging LPG use - White Paper
SELL - Smart Energy for Leveraging LPG use - White Paper
SELL - Smart Energy for Leveraging LPG use - White Paper
SELL - Smart Energy for Leveraging LPG use - White Paper
SELL - Smart Energy for Leveraging LPG use - White Paper
SELL - Smart Energy for Leveraging LPG use - White Paper
SELL - Smart Energy for Leveraging LPG use - White Paper
SELL - Smart Energy for Leveraging LPG use - White Paper
SELL - Smart Energy for Leveraging LPG use - White Paper
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SELL - Smart Energy for Leveraging LPG use - White Paper

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This white paper presents a spatial decision support system (SDSS) aimed at generating optimized vehicles routes for multiple vehicles routing problems that involves serving the demand located at …

This white paper presents a spatial decision support system (SDSS) aimed at generating optimized vehicles routes for multiple vehicles routing problems that involves serving the demand located at nodes of a transportation network. The SDSS incorporates MapPointTM (cartography and network data), a database and a metaheuristic developed generate routes.

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  • 1. SELL - Smart Energy for Leveraging LPG use White Paper Acknowledgements This work has been framed under the ISA Academy, and totally supported by the SELL - Smart Energy for Leveraging LPG use project (QREN). Keywords: Vehicle routings, decision support systems, heuristic, optimization routings. Abstract: This paper presents a spatial decision support system (SDSS) aimed at generating optimized vehicles routes for multiple vehicles routing problems that involves serving the demand located at nodes of a transportation network. The SDSS incorporates MapPointTM (cartography and network data), a database and a metaheuristic developed to generate routes.
  • 2. Smart Energy for Leveraging LPG use Abstract This white paper presents a spatial decision support system (SDSS) aimed at generating optimized vehicles routes for multiple vehicles routing problems that involves serving the demand located at nodes of a transportation network. The SDSS incorporates MapPointTM (cartography and network data), a database and a metaheuristic developed generate routes. Keywords: Vehicle routings, decision support systems, heuristic, optimization routings. ii
  • 3. Smart Energy for Leveraging LPG use 1 Introduction Transportation of goods and services imposes considerable cost on both the public and private sector for the economy as well as he environment. More efficient vehicle routing can improve a firm’s competitive advantage, increase the efficiency of supplying public services, and reduce energy consumption, traffic congestion and air pollution, which are growing problems in many urban areas. Vehicle travel increased substantially in recent decades and 16 to 50% of all air pollutants resulting from transportation [1]. This paper will present an optimization of distributed logistics of LPG - Smart Logistics, that consist in a computing platform that allows for optimized logistics planning deliveries, for several days, affecting vehicles to LPG tanks to supply daily, to determine efficient routes and display them on maps. This platform adapts to each consumer’s according to specific costumers’ needs and taking into account the need to reduce logistics costs. In this way, the process consists of several steps: audit (survey procedures used and the specific problems); adaptation of computer platform to the specific problem to be addressed; implementation of a prototype in a specific area and expanding the process to a broader area, and monitoring (evaluation of the procedures adopted and gains achieved). Smart Logistics solution is composed by: - Optimization algorithms of supply routes able to impressively reduce the vehicle kilometers traveled, and consequently reduce the logistic costs, in the distribution of LPG; - Computer platform that integrates algorithms and the database that stores the daily data from the telemetry applied in LPG tanks. With this platform the distributors can plan deliveries for the entire week by selecting, for each day the fuel tanks to supply and the vehicles to affect; - Component to visualize the routes selected. Regarding the computer platform, this contains extremely efficient algorithms for optimization in transport logistics, as well as layouts that allow users to make planning for several days, including some variables. Thus, it is possible to visualize the expected levels in each tank over several consecutive days, selecting those that intend to refuel every day, also select the vehicles to be allocated for distribution in each day and assess the costs estimated for each day of supply. This new approach is an innovation in this market niche. The computer platform is also simple to use, easy to parameterization (containing only the parameters strictly necessary to treat the problem in question) and low cost service. The SmartLogistic consists of an integrated platform and adjusted to each costumer in order to reduce logistics costs in the distribution of LPG. Thus it will be possible to avoid the use of multiple platforms by the customer (route management software, database for client’s management and information system telemetry), from different vendors, which sometimes present difficulties interconnection / communication. 1
  • 4. Smart Energy for Leveraging LPG use 2 State-of-the art Urban freight transportation is on one hand an important economic activity but on the other hand is rather disturbing /traffic congestion, noise and other environmental impacts). Issues related to freight transportation are pertinent in an urban context (where the number of vehicles, congestion and pollution levels are increasing fast) and therefore they need to be well understood and quantified. In what concerns environmental impacts, some recent studies emphasize the optimization of route choice based on the lowest total fuel consumption and thus the emission of CO2 [7]. Many see the need for a threefold strategic approach: improving fuel economy, decreasing vehicle miles of travel (VMT) and lowering the carbon content of fuels. Woodcock et al. [8] also refer to several main strategies jointly required for moving to lowcarbon transport, being shortening trip distance one of them. The importance of transportation problem has been acknowledged by the scientific community. However, the optimization of transportation routing is computationally intractable for most real-world problems (e.g. [9, 10]) It was estimated that in Western Europe the costs associated with transport have represented, in 2000, about 7.3% of its GDP, and the road transport sub-sector accounts for about 84% of this value [2]. Some of these costs are supported directly by operators (costs associated with fleet vehicles, crews, etc.) and other convert into indirect costs that are passed on society and on environment (costs associated with greenhouse gas emissions, consumption of energy resources not renewable, traffic congestion, accidents, etc.). Efficient management of fleets of vehicles perform an important role in reducing these costs and contributes, in particular, to achieve the objective set out in the strategy of 20-20-20. The problems involving the optimization of vehicle routes are the most studied in Operations Research. The basic mathematical problems are the famous "Traveling Salesman Problem" and "Chines Postman Problem". New versions of these two problems have been proposed over the recent years (notably, the "Vehicle Routing Problem" and "Arc Routing Problem") in order to make them more approximate to reality problem to deal with. The mathematical models that currently exist to determine the optimum solution to such problems have a high computational complexity, implying generally unacceptable execution times (which may require days). In this way, have been developed heuristic algorithms that obtain approximate solutions in acceptable computational times (in the order of minutes). Some of these algorithms are based on behavior observed in nature, such as genetic algorithms [3] and those based on optimization with ant colonies [4,5, 15], and others are based on strategies supported almost exclusively in determination of shorter journeys [6]. This has justified the development of heuristic approaches to generate (optimal or near optimal) solution in acceptable computer times. As a consequence, the design and implementation of exact and heuristic algorithm for such problem constitute an interesting challenge for operations research (OR) and transportation science, both from methodological perspective and practical decision support purposes to address all the issues mentioned above. The potential benefits of OR models and methods applied to transportation systems, having in mind the implementation in practice, has constitute a research avenue followed by 2
  • 5. Smart Energy for Leveraging LPG use the authors, namely concerning the development of strategies to deal with real-world urban vehicles collection/delivery problems [11,12] and new approaches for routing problems [13, 14, 15] Due to the data requirements and the complexity of urban planning and transportation problems, there has been a growing interest in the use of decision support system (DSS) to analyze them at the operational (e.g., [16,17]), the tactical (e.g., [18]) and the strategic planning levels (e.g., [19,20]). Adequate graphical interfaces are important to represent solutions in routing problems given their strong spatial component. Information and communication technologies (ICT) can play an important role for constructing tools embedding algorithms, graphical interfaces and access to remote data through the Internet. Due to the spatial nature of these problems, geographical information system (GIS) have been a natural components of such DSS as they are important tools for collecting, organizing, and displaying spatial data in a layer variety of planning applications, such as in vehicle touting problems [21, 22]. Hans [23] enhanced the importance of the development of GIS for urban transportation planning and modeling, including network based urban transportation planning and the incorporation of network data into a GIS framework in order to have a high-speed interactive system suitable for providing near real-time alternatives and policy analysis. Although transportation research has been “late to embrace GIS as a key technology to support its research and operational needs” [24], there has been an increase of such research in recent years. Much of this research also incorporates exact and heuristic solution algorithms with the GIS (Geographical information system) The development of decision support tools profiting from state-of the-art ICT is an important avenue of research. World Wide Web technologies have transformed the design, development, implementation and deployment of DSS; however, it is recognized that the use of Web-based computation to deploy DSS applications for remote access remains less common [25]. In the field of transportation, some recent developments can be found, e.g., Ray [26] has developed a web based spatial DSS for managing the movement of oversize and overweight vehicles over highways. We present a -SDSS integrating optimization methodologies for the LPG distribution The system can be used for short-term analysis (e.g., the design of daily vehicle routes) and longterm analysis (e.g., deciding how many vehicles to operate in a fleet). The most complete solutions known to the area of distribution management fuel integrate different platforms (software route management, database management to costumers and computer system telemetry), from different vendors, which sometimes present difficulties interconnection / communication. On the other hand, usually computer applications management routes used are too costly, too broad, difficult parameterization and not always cover all the specifics of costumers. This paper aims to shown as innovative platform developed by ISA – Smart Logistics, that integrated all of these components in one, and adaptive to each costumer in order to reduce logistics costs in the distribution of LPG. 3
  • 6. Smart Energy for Leveraging LPG use 3 Solution Design The SDSS is a standalone application which integrates a module containing the algorithms, the MapPointTM component (that allows users to view, edit and integrate maps) and a local database (Figure 1). Every day, the tanks with the ISA telemetry equipment installed send their levels to a server in the company. After that, the SDSS request these information, via a web service, and store it in the local database. The data from the other tanks (without telemetry equipment) are imported to the local database via excel files. These data requires user validation before be imported. Users can generate several planning for each day and then select one of them to be executed by the vehicle’s crew, based on the routes information. The SDSS contains 3 main modules: import, planning and reports. Import module is used to: a) Import tanks data from excel files; b) Import data from the executed routes (e. g., route length, delivery schedule and delivery quantities), used to compare planned routes and executed routes. In the planning module users can perform the following tasks: a) Select the tanks that need to be serviced in each day (Figure 2); b) Introduce the required amounts of gas on each select tank (Figure 2); c) Select the vehicles that can be used by the algorithm (solutions generated by the algorithm can discard some vehicles selected by the users if not necessary); d) See the routes generated by the algorithm on the maps and some related information (route start date, route length, route duration, percentage of vehicle load, km/ton and €/ton) (Figure 2); e) Manually update the generated routes based on its configuration displayed on the map (e. g., swap serviced tanks between routes, add a new tank to be serviced to an existing route, remove a tank serviced on an existing route and add a new available vehicle to the solution). In the reports module users can create: a) Reports with information for each planned route contained in a selected planning (e. g., delivery sequence, tanks coordinates and planned quantities); b) Reports comparing the planned routes and the executed routes (e. g., route length, route duration, delivered quantities, number of deliveries, total delivery time, km/number of deliveries and €/ton). 4
  • 7. Smart Energy for Leveraging LPG use 3.1 Initial solution The heuristic used to obtain the initial solution is based on the path-scanning heuristic [28]. However, the original version had to be adapted to deal with service located at nodes, instead of arcs, and with additional real-world constraints, such as: a) The route “drop-off point” is not at the same location as the depot where the vehicles start and end their shifts; b) Vehicles can serve more than one route in a day (all routes for a particular vehicle must include a “drop-off point” and only the last route for each vehicle must return to the starting depot immediately after visiting the “drop-off point); c) Vehicles can begin one route in one day and finish it in the day after; d) The vehicle’s crew has limit shift duration by day, which represents the maximum hours that they can work that day; f) Some tanks cannot be serviced by all types of vehicles – the greatest vehicles cannot circulate in the tiniest locals. The greatest vehicles selected by the user are the first that the algorithm tries to assign to the solution, because they have the best transportation ratio cost/capacity. Similar to the original heuristic, each route is obtained by adding one node at a time, instead of an arc, according to a given criteria. In this case, to select the first tank to be serviced in each route users can select one of the following criteria: a) The one requiring the greatest amount of gas; b) The farthest from the vehicle depart local. To select the next tanks to be serviced in the routes users can select one of the following criteria: a) The closest to the last tank added to the route; b) Random selecting one of the closest tanks from the last tank added to the route. 3.2 Final optimization A final optimization step is available in the SDSS. The goal in this step is to improve the initial solution (i. e., reducing the total travelled distance), by evaluating “neighborhood moves”. All the constraints referred in the previous section must be satisfied also in the final solution. Three types of moves were considered: a) Removal and insertion of a tank – Evaluate the removal of each tank from the current route and its insertion in another route or in another position in the current route; 5
  • 8. Smart Energy for Leveraging LPG use b) Swap tanks – Evaluate the swap of two tanks serviced in different routes or in the same route; c) Removal and insertion of an entire route – Evaluate the removal of each route from the current vehicle and its insertion in another available vehicle. In each iteration of the final optimization if the evaluated move improves the current solution then it is updated and all the moves are evaluated again. The stop criterion of this step is the maximum CPU time defined by the user in the SDSS or the evaluation of all moves without any improvement. 6
  • 9. Smart Energy for Leveraging LPG use 4 References [1] - L. Dablanc, Goods transport in large European cities: Difficult to organize, difficult to modernize, Transportation Research Part A 41 (3) (2007) 280–285. [2] - Federação Europeia de Transportes e Ambiente, 2005, "The Eurovignette Directive. Background briefing". Disponível em: www.transportenvironment.org/Publications/prep_hand_out/lid:359 [3] - Lacomme, P., Prins, C., Ramdane-Chérif, W., 2004a, "Competitive memetic algorithms for arc routing problems", Annals of Operations Research, Vol. 131, pp. 159-185. [4] - Lacomme, P., Prins, C., Tanguy, A., 2004b, "First competitive ant colony scheme for the CARP", Research Report LIMOS/RR-04-21. Disponível em: http://www.isima.fr/limos/publi/RR-04-21.pdf [5] - Reimann, M., Ulrich, H., 2006, "Comparing backhauling strategies in vehicle routing using ant colony optimization", Central European Journal of Operations Research, Vol. 14, No. 2, pp. 105-123. [6] - Santos, L., Coutinho-Rodrigues, J., Current, J.R., 2009, "An improved heuristic for the capacitated arc routing problem", Computers and Operations Research, Vol. 36, No. 9, pp. 2632-2637. [7] - E. Ericsson, H. Larsson, K. Brundell-Freij, Optimizing route choice for lowest fuel consumption: Potential effects of a new driver support tool, Transportation Research Part C 14 (6) (2006) 369–383. [8] - J. Woodcock, D. Banister, P. Edwards, A.M. Prentice, I. Roberts, Energy and transport, Lancet 370 (9592) (2007) 1078–1088. [9] - M.R. Garey, D.S. Johnson, Computers and intractability: A guide to the theory of NPcompleteness, W.H. Freeman, New York, 1979. [10] T.L. Magnanti, R.T. Wong, Network design and transportation planning: Models and algorithms, Transportation Science 18 (1) (1984) 1–55. [11] - J. Coutinho-Rodrigues, N. Rodrigues, J. Clímaco, Solving an urban routing problem using heuristics: A successful case study, International Journal of Computer Applications in Technology 6 (2–3) (1993) 176–180. [12] - L. Santos, J. Coutinho-Rodrigues, J.R. Current, Implementing a multi-vehicle multi route spatial decision support system for efficient trash collection in Portugal, Transportation Research Part A 42 (6) (2008) 922–934. [13] – L. Santos, J. Coutinho-Rodrigues, J.R. Current, An improved solution algorithm for the constrained shortest path problem, Transportation Research Part B 41 (7) (2007) 756–771. [14] – L. Santos, J. Coutinho-Rodrigues, J.R. Current, An improved heuristic for the capacitated arc routing problem, Computers and Operations Research 36 (9) (2009) 2632– 2637. 7
  • 10. Smart Energy for Leveraging LPG use [15] – L. Santos, J. Coutinho-Rodrigues, J.R. Current, An improved ant colony optimization based algorithm for the capacitated arc routing problem, Transportation Research Part B 44 (2010) 246–266. [16] – J. Maria, J. Coutinho-Rodrigues, J. Current, Interactive destination marketing system for small- and medium-sized tourism destinations, Tourism: An Interdisciplinary Journal 53 (2005) 45–54. [17] – A. Simão, J. Coutinho-Rodrigues, J. Current, A management information system for urban water supply networks, ASCE Journal of Infrastructure Systems 10 (4) (2004) 176–180. [18] – P.A.L. Matos, P.L. Powell, Decision support for flight re-routing in Europe, Decision Support Systems 34 (4) (2002) 397–412. [19] – J. Coutinho-Rodrigues, J. Current, J. Climaco, S. Ratick, An interactive spatial decision support system for multiobjective HAZMAT location-routing problems, Transportation Research Record 1602 (1997) 101–109. [20] - F. Űlengin, Ş. Őnsel, Y. Topçu, E. Aktaş, K. Őzgur, An integrated transportation decision support system for transportation policy decisions: The case of Turkey, Transportation Research Part A 41 (1) (2007) 80–97. [21] - Z.R. Peng, R. Huang, Design and development of interactive trip planning for webbased transit information systems, Transportation Research Part C 8 (1–6) (2000) 409–425. [22] - L. Santos, J. Coutinho-Rodrigues, J.R. Current, Implementing a multi-vehicle multiroute spatial decision support system for efficient trash collection in Portugal, Transportation Research Part A 42 (6) (2008) 922–934. [23] – Z. Hans, R. Souleyrette, GIS and network models: Issues for three potential applications, Journal of Advanced Transportation 29 (3) (1995) 355–373. [24] – J.C. Thill, Geographic information systems for transportation in perspective, Transportation Research Part C 8 (1–6) (2000) 3–12. [25] – L. Santos, J. Coutinho-Rodrigues, J.R. Current, An improved ant colony optimization based algorithm for the capacitated arc routing problem, Transportation Research Part B 44 (2010) 246–266. [26] – J.J. Ray, A web-based spatial decision support system optimizes routes for oversize/overweight vehicles in Delaware, Decision Support Systems 43 (4) (2007) 1171–1185. [27] - J. Coutinho-Rodrigues, N. Rodrigues, J. Clímaco, Solving an urban routing problem using heuristics: A successful case study, International Journal of Computer Applications in Technology 6 (2–3) (1993) 176–180. [28] – B.L. Golden, J. DeArmon, E.K. Baker, Computational experiments with algorithms for a class of routing problems, Computers and Operations Research (10) (1983) 47–59. 8

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