Spatial decision support system for milk collection

Transcription

1 Ref: C0543 Spatial decision support system for milk collection Javier Bueno, Jacobo J. Salgado, José M. Pereira and Carlos Amiama, University of Santiago de Compostela, Campus Universitario, Lugo (Spain) Abstract The cooperative FEIRACO collects milk from over 600 dairy farms. This cooperative has a fleet of 11 trucks with isothermal tankers and different capacities to perform various daily collection routes. The dairy farms have a collection rate of daily or every two days, and each has an associated volume that has remained relatively stable throughout the year, and a quality, which can vary. Additionally, there are restrictions on access to farms, so that not all vehicles have access to all farms. To found the route that ensures the lowest cost of milk collection is a routing problem (VRP) and also a variant of the traveling salesman problem (TSP). Many applications have been developed to provide feasible s, with different configurations depending on the type of operation, the time available for decision making, objectives, and constraints to consider. The complexity of the problem and our experience in previous works, advices to relate metaheuristic algorithms with geographic information system (GIS) tools to improve the results, obtaining spatial decision support systems. It is necessary to provide tools with the ability to easily modify the s generated by the heuristic algorithms, ie, the operator must have control over the routes. It is necessary to use GIS tools which allow the planner debug the provided by the heuristic or provides to the manager information to select the farms included in each route. This paper describes a spatial decision support system (SDDS) that integer heuristics algorithm a GIS. With the use of these techniques we want to reduce the number of km traveled by trucks, creating routes that are compatible with the existing constraints. We want to improve too the sequence of vehicles arrival at the plant, because it has been found that truck queues or download reduce the process efficiency. In this work the impact of the introduction of GIS tools in the results obtained are evaluated. Additionally, the decision support system is used to simulate different scenarios by changing the capacity of the vehicles involved, the number of vehicles and access restrictions. Our objective is to find combinations that minimize the collection costs. Keywords: route management, optimization, logistics, DSS Proceedings International Conference of Agricultural Engineering, Zurich, /6

2 1 Introduction In this work, we deal with a vehicle route problem (VRP), applied to the reality of a dairy factory located in Galicia, Spain. The factory collects milk from dairy farms, located in the northwest of Spain. The geographical area, which this factory covers is characterised by high dispersion and small sized farms. These farms have a reduced milk production in line with the storage capacity. As a consequence of these limitations, frequent visits to collect the milk are needed, resulting, thereby, in having to travel an elevated number of kilometers. At the present time, the process of generating routes, is not computerised and is carried out manually, based on the factory technicians thorough knowledge of the field of operations. In order to carry out these routes, only a single datasheet is used to make some basic calculations. Three qualities of milk are differentiated and are collected separately, as milk of different qualities cannot be mixed on the same route. First quality milk is collected first and then the second and third qualities are collected, paying attention to a criterion of minimizing kilometers covered by the fleet. The trucks have different geographical zones assigned to them, in relation to the proximity to the domicile of the truck operator, although the said areas are not separate entities. In the last few years heuristic and meta-heuristic focuses have been advancing to resolve the vehicle route problems. Some of these foci begin to take into account the differences existing between different trucks which comprise the fleet of vehicles, called "Heterogeneous VPR" (Ruiz et al., 2004). Also, Distance constrained VPR" metaheuristic, where the duration of each route is fixed to an established time (Mendoza et al., 2009). e. Although science is making rapid advances in the field of vehicle route, there are still very few monographs, which confront real problems, where there are a high number of restrictions. Due to the data requirements and complexity of transportation problems, there has been an increasing interest in the use of Decision Support Systems (DSS) in order to analyse them at the operational level. At the present moment, a new pathway is opening up in the field of logistics with the integration of the GIS systems (Geographic Information Systems). Many of these systems, which rely on GIS, incorporate exact and heuristic algorithm s. These systems are known as Spatial Decision Support Systems, SDSS (Jha & Schonfeld, 2004). The application of techniques previously described in existing plants and factories, is not an easy task. As we have seen, many of the techniques cannot allow for flexibility and capacity of response which a real logistical environment requires. Many of the references quoted, only work with simplistic versions of the VRP problem, without taking into account specific problems: such as the limited number of stops per route; that the fleets of vehicles in reality are heterogeneous or they do not take into account other criteria except minimising distances. With the exception of the research by Ruiz et al. (2004), no research until now has taken into account the great number of restrictions, which are inherent in milk collection. Recent studies point to the benefit of giving planning a more important role in the process of resolving problems (Van Wezel, et al., 2011; Gacias, et al., 2012). The present research has focused on this aspect, developing a tool, where the interaction of the DSS with the operator is fundamental. The main objective of this research has been to develop a Spatial Decision Support System, which facilitates the to the logistical problem in milk collection for the route manager. The objective is not to substitute the logistic operator, since he has superior knowledge of the reality to any system based on heuristic algorithms. This system has to be sufficiently agile and flexible to be able to adapt to the possible changes which can occur with the fleet of vehicles or on the dairy farms. This program has to be able to be used as a tool for strategic planning for the company carrying out simulations of "What if" type. 2 The Structure of the Spatial Decision Support System The SDSS, called Loxislact-Edit is divided into three components: the database, the user interface and the route generator. The integration of these three components is represented by an information flow diagram (Figure 1). In addition, capacity for intercommunication with Proceedings International Conference of Agricultural Engineering, Zurich, /6

3 the Entreprise Resource Planning (ERP) has been added, in order to import the kg of milk produced by the milk producers and quality produced by each farm. ERP Database Dairy fams Dairy factory Road Networks Trucks User Interface Data entry window Route map editor Preliminary Routes Final routes Route Generator Heuristic (Simulated Annealing) LOXISLACT-EDIT Figure 1: Diagram of information flow in Loxilact-Edit A local search technique based on simulated annealing algorithm has been used for the construction of routes (Kirkpatrick, et al., 1983). This algorithm requires an initial, generated by an algorithm of sequential insertion. The function of cost is given by the expression 1. F(s) = ( ) [1] Where: N: Number of trucks : Cost of each kg transported by the truck :: kg transported by each truck : Cost for each km covered by each truck : km covered by each truck The route generator provides a which is transferred to the route map editor. The route manager can accept or carry out the changes which he considers opportune, due to the thorough knowledge of the actual situation that he possesses. The route map editor (see fig. 2), allows any two routes to be selected from the generated planning by the route generator. The farms collected by route 1 and by route 2 are shown in two different colours. We can zoom in on the map to better observe the position of the farms or roads used. The route editor allows the route manager to have knowledge of all the data necessary for route optimization: - Graphic visualization of the position of the farms visited on each route. - The order in which the farms will be collected and the expected arrival each farm. Proceedings International Conference of Agricultural Engineering, Zurich, /6

4 - Percentage occupation of the trucks tanks. - Km covered on each route. - Percentage of kg of milk which are collected on day 1 and day 2. Figure 2: Route editing interface Furthermore, the route manager has knowledge of all the data pertinent to each farm, such as the quality of milk generated, the kg of milk that it produces and the frequency of collection from the farm. The route editor allows the route manager, to interact with the graphic information, in such a way that it permits the selection of two routes for milk collection, and carry out movements between the farms themselves, provided that each and every one of the following restrictions is complied with: - The truck can access the farm. - Milk is not collected during the milking window and preference is maintained in respect to said time frame in all collections. - Collection days are not swapped on farms collected on a daily basis. If the route manager tries to carry out a movement that infringes one of these restrictions, the program will prevent this movement being performed and will issue a notice warning him that he is in breach of a restriction. Any movement carried out by the route editor entails an automatic update of data displayed in the editor. Thus, the SDSS provides the route manager information that he needs to make decisions at the time of modifying the milk collection routes. 3 Results and Discussion The activity of the fleet was monitored for two days in January 2013 to evaluate the usefulness of Loxislact-Edit. The total time of the routes carried out has been separated into time invested in moving between farms, time spent on the collection of milk at the farm (independent to the planning of the routes carried out), time unloading at the factory (conditioned by the planning carried out, since if the number of routes are reduced, the unloading time also reduces) and the waiting the unloading bay (conditioned by the sequence of arrivals of the trucks at the factory). From the data collected (original ), a new with the SDSS has been generated (see Table 1). In line with previous epigraphs, the meth- Proceedings International Conference of Agricultural Engineering, Zurich, /6

5 odology applied has consisted in generating a with the route generator (heuristic ) modifying it later with the route map editor (edited ). Table 1: Comparative maintaining the fleet Original Heuristic Edited Travelling time Loading farm Unloading factory Waiting bay Total time Total km Cost per cycle ( ) 85h 26m 65h 36m 24h 30m 3h 54m 179h 26m h 45m 65h 36m 24 h 0m 0h 0m 180h 21m +0.1% % % 76h 24m 65h 36m 23h 30m 0h 0m 165h 30m -7.8% % % The provided by the route generator, is slightly worse than the actual situation, increasing the number of km by 2.4% and the time employed by 0.1%. From this heuristic, 7 hours have been invested in manual editing of the routes achieving savings of 13.0% in km covered, and 10.6% in time employed, compared to the actual situation. The improvements have been obtained fundamentally by a reduction in the km covered and by the elimination of the waiting times to unload. The period invested in manual editing may seem excessive, but the problems in planning milk collection are static, as there are no fluctuations in the farms to be collected during long periods of time and this must be taken into account. To calculate the costs per cycle (two collection days), the methodology suggested by the Ministry of Public Works and Transport (Ministerio de Fomento, MFOM, 2006) of the Spanish Government has been followed. It has been considered that all the trucks are new and have a useful life of 8 years and the useful life of the tank is 10 years, differentiating the fixed costs and the variable costs. With the use of this tool the costs would be seen to reduce by 3.8%, which means an annual saving of euros. The reason for not being able to reduce the costs even more significantly, is that, on maintaining the same fleet of vehicles, we cannot decrease the fixed costs of the vehicles, which assume on average 80% of the total costs of the fleet. The possibility for the route manager to carry out modifications offers great versatility in the use of the tool, allowing the carrying out of a multitude of what-if type simulations. In the first simulation (S1) the vehicle combination that provides the minimum cost will be determined, within the restrictions of the problem. Vehicles with capacities between 10,000 and 18,000 litres have been considered as they are the most frequently used. The method of successive approximations has been used for the calculation, obtaining a fleet composed of 7 trucks with capacities of 18,000 litres and a truck with 10,000 litres (required to access the farms with difficult access). Original S1.Heuristic S.1.Edited Table 2: Fleet that provides the lowest cost Travelling time Loading farm Unloading factory Waiting bay Total time Total km Cost per cycle ( ) 85h 26m 65h 36m 24h 30m 3h 54m 179h 26m h 48m 65h 36m 18h 30m 0h 0m 167h 54m -6.4% % % 71h 24m 65h 36m 17h 30m 0h 0m 154h 30m -13.9% % % Proceedings International Conference of Agricultural Engineering, Zurich, /6

6 4 Conclusions Loxilact-Edit has been designed to ease the process of route generation for its users, not to automate the process of decision-making. With the implementation of the GIS in the DSS, the user is provided with a powerful tool which allows the creation of new routes, to make changes to them and to evaluate alternative plans with respect to the given criteria. The SDSS will allow the logistic operator to consider all the elements and aspects that his company in particular possesses, and to carry out the changes to the routes to adapt them accordingly. The route manager has a tool which allows him to see the results of his suggestions instantaneously and efficiently, which allows him to evaluate the decision to make changes in the routes or leave them as they are. This design allows him to draw on the computer s superior ability to calculate together with the human capacity to recognise and create patterns. In general, the benefits of SDDS are difficult to quantify. The true value of the SDDS developed is to improve the ability of the logistic operator to make decisions giving him the capacity to carry out a long term strategic plan, or to act quickly when confronted with unexpected situations. These advantages cannot be measured directly with a cost-benefit analysis. However, some of the benefits brought by the SDDS can be measured, such as the reduction in fuel costs, amortization of the trucks and labour costs. 5 References Gacias, B., Cegarra, J., Lopez, P., Scheduler-oriented algorithms to improve humanmachine cooperation in transportation scheduling support systems. Engineering Applications of Artificial Intelligence, 25, Jha, M., Schonfeld, P., A highway alignment optimization model using geographic information systems. Transportation Research Part A, 6(38), Kirkpatrick, S., Gelatt, C.,Vecchi, M., Optimization by simulated annealing. Science, New Series, 220(4598), Mendoza, J., Medaglia, A., Velasco, N., An evolutionary-based decision support system for vehicle routing: The case of a public utility. Decision Support Systems, 3(46), Ministerio de Fomento, MFOM. (2006): Observatorio de Costes del Transporte de Mercancías por Carretera. Ruiz, R., Maroto, C., Alcaraz, J., A decision support system for a real vehicle routing problem. European Journal of Operation Research, 3(153), Van Wezel, W., Cegarra, J., Hoc, J., Allocating function to humans and algorithms in scheduling. Behavioral Operations in Planning and Scheduling. Ed.: Springer Science. Proceedings International Conference of Agricultural Engineering, Zurich, /6