An iterative solution approach for truck routing and scheduling in the forest industry

Log transportation in forest industry is a resource-intensive operation and represents a great challenge for logistic planners. Several trips must be generated in order to satisfy plants demand; in addition, trucks arrivals at each plant must be considered in order to avoid unproductive waiting times. In the Argentine context, specifically in the Argentinean Northeast (NEA) region, these activities represent the main sustenance of the regional economies, so enhancing efficiency in the transport operation would represent a considerable improvement for these economies. In this work, an iterative solution approach for the truck routing and scheduling problems is presented. The proposed strategy involves two stages which are iteratively solved: product allocation, trip composition and truck routing problems are first solved through a Mixed-Integer Linear Programming model (MILP), while in the second stage, fixing the route for each truck according to the results of the previous step, a MILP model for the scheduling of truck arrivals at plants is considered. If no feasible solution for the scheduling problem is obtained, then an integer cut is applied in order to exclude from the search space truck routes already explored in previous iterations. The solution approach is tested in a case study representative of the Argentine context and conclusions are detailed.Sociedad Argentina de Informática e Investigación Operativ

An iterative solution approach for truck routing and scheduling in the forest industry

Log transportation in forest industry is a resource-intensive operation and represents a great challenge for logistic planners. Several trips must be generated in order to satisfy plants demand; in addition, trucks arrivals at each plant must be considered in order to avoid unproductive waiting times. In the Argentine context, specifically in the Argentinean Northeast (NEA) region, these activities represent the main sustenance of the regional economies, so enhancing efficiency in the transport operation would represent a considerable improvement for these economies. In this work, an iterative solution approach for the truck routing and scheduling problems is presented. The proposed strategy involves two stages which are iteratively solved: product allocation, trip composition and truck routing problems are first solved through a Mixed-Integer Linear Programming model (MILP), while in the second stage, fixing the route for each truck according to the results of the previous step, a MILP model for the scheduling of truck arrivals at plants is considered. If no feasible solution for the scheduling problem is obtained, then an integer cut is applied in order to exclude from the search space truck routes already explored in previous iterations. The solution approach is tested in a case study representative of the Argentine context and conclusions are detailed.Sociedad Argentina de Informática e Investigación Operativ

An iterative solution approach for truck routing and scheduling in the forest industry

Log transportation in forest industry is a resource-intensive operation and represents a great challenge for logistic planners. Several trips must be generated in order to satisfy plants demand; in addition, trucks arrivals at each plant must be considered in order to avoid unproductive waiting times. In the Argentine context, specifically in the Argentinean Northeast (NEA) region, these activities represent the main sustenance of the regional economies, so enhancing efficiency in the transport operation would represent a considerable improvement for these economies. In this work, an iterative solution approach for the truck routing and scheduling problems is presented. The proposed strategy involves two stages which are iteratively solved: product allocation, trip composition and truck routing problems are first solved through a Mixed-Integer Linear Programming model (MILP), while in the second stage, fixing the route for each truck according to the results of the previous step, a MILP model for the scheduling of truck arrivals at plants is considered. If no feasible solution for the scheduling problem is obtained, then an integer cut is applied in order to exclude from the search space truck routes already explored in previous iterations. The solution approach is tested in a case study representative of the Argentine context and conclusions are detailed.Sociedad Argentina de Informática e Investigación Operativ

PENJADWALAN TRUK SAMPAH KOTA PONTIANAK DENGAN MODEL ROLL ON ROLL OFF VEHICLE ROUTING PROBLEM

Increased consumption and need for goods that generate waste has an impact on environmental hygiene Pontianak city. Production of waste generated by the resident according to the Office of Sanitation and Park of Pontianak City in 2015 reached 1339.98 tons per day over 64 locations with a fleet of only 35 units available. This makes the process of transporting waste needs to be done effective and efisienct. The aims of this study are to analyze the exizting condition of the transport arm roll garbage truk in Pontianak City and use the model of Roll on Roll off Vehicle Routing Problem to generate better waste transportation schedule in Pontianak city.The steps in solving scheduling problem of Pontianak City garbage trucks using the model of Roll On Roll Off Vehicle Routing Problem are by mapping the locations of waste in Pontianak city, calculating the distance using the road map, describing the problem of scheduling garbage trucks, determining the types of trip used for Pontianak city garbage truck and calculating the service time of each garbage truck.Based on the research on Pontianak city garbage truck scheduling with model of Roll On Roll Off Vehicle Routing Problem, the following conclusions can be drawn: Amr roll truck fleet consisting of 22 units of vhicle, there are 8 vehicles that exceed the time limit of service, so do balancing the workload between vehicles has been conducted. This proves that amr roll waste transportation trucks in Pontianak are not optimal. The Model of Roll On Roll Off Vehicle Routing Problem has not been able to obtain the optimal schedule for a solution model using the heuristic method. However, the proposed heuristic method has been able to offer a solutions for better waste transpotation fares in the sense that it reduced the schedule of vehicles that exceed the time limit. The waste transportation services schedule originally had 8 vehicles that exceed the time limit of 80.1398 service hours to 4 vehicles that exceed the time limit of 39.0832 hour service so that there is a difference of total service time of 41.0566 hours. This proves that the scheduling for garbage transportation is better.Keywords: Scheduling, Roll On Roll Off Vehicle Routing Problem, Garbage Truck

Assessing dynamic models for high priority waste collection in smart cities

Waste Management (WM) represents an important part of Smart Cities (SCs) with significant impact on modern societies. WM involves a set of processes ranging from waste collection to the recycling of the collected materials. The proliferation of sensors and actuators enable the new era of Internet of Things (IoT) that can be adopted in SCs and help in WM. Novel approaches that involve dynamic routing models combined with the IoT capabilities could provide solutions that outperform existing models. In this paper, we focus on a SC where a number of collection bins are located in different areas with sensors attached to them. We study a dynamic waste collection architecture, which is based on data retrieved by sensors. We pay special attention to the possibility of immediate WM service in high priority areas, e.g., schools or hospitals where, possibly, the presence of dangerous waste or the negative effects on human quality of living impose the need for immediate collection. This is very crucial when we focus on sensitive groups of citizens like pupils, elderly or people living close to areas where dangerous waste is rejected. We propose novel algorithms aiming at providing efficient and scalable solutions to the dynamic waste collection problem through the management of the trade-off between the immediate collection and its cost. We describe how the proposed system effectively responds to the demand as realized by sensor observations and alerts originated in high priority areas. Our aim is to minimize the time required for serving high priority areas while keeping the average expected performance at high level. Comprehensive simulations on top of the data retrieved by a SC validate the proposed algorithms on both quantitative and qualitative criteria which are adopted to analyze their strengths and weaknesses. We claim that, local authorities could choose the model that best matches their needs and resources of each city

Optimization of Vehicle Routing Problem with Tight Time Windows, Short travel time and Re-used Vehicles (VRPTSR) for Aircraft Refueling in Airport Using Ant Colony Optimization Algorithm

Scheduling in aircraft refueling has an important role in aviation. Scheduling of aircraft refueling is called Airport Ground Service Scheduling (AGSS) that can be formulated as Vehicle Routing Problem with Tight time windows, Short travel time and Re-used Vehicles (VRPTSR) This research is focusing in scheduling design for aircraft refueling with refueller truck in Juanda Airport, Surabaya, so minimum amount of truck will be used using Ant Colony optimization. The result shows that Ant Colony optimization could do scheduling in refueling well so minimum amount of truck will be used

Optimizing departure times in vehicle routes

Most solution methods for the vehicle routing problem with time\ud windows (VRPTW) develop routes from the earliest feasible departure time. However, in practice, temporal traffic congestions make\ud that such solutions are not optimal with respect to minimizing the\ud total duty time. Furthermore, VRPTW solutions do not account for\ud complex driving hours regulations, which severely restrict the daily\ud travel time available for a truck driver. To deal with these problems,\ud we consider the vehicle departure time optimization (VDO) problem\ud as a post-processing step of solving a VRPTW. We propose an ILP-formulation that minimizes the total duty time. The obtained solutions are feasible with respect to driving hours regulations and they\ud account for temporal traffic congestions by modeling time-dependent\ud travel times. For the latter, we assume a piecewise constant speed\ud function. Computational experiments show that problem instances\ud of realistic sizes can be solved to optimality within practical computation times. Furthermore, duty time reductions of 8 percent can\ud be achieved. Finally, the results show that ignoring time-dependent\ud travel times and driving hours regulations during the development of\ud vehicle routes leads to many infeasible vehicle routes. Therefore, vehicle routing methods should account for these real-life restrictions