Decision Support System For Routing Research Articles

A mobile application-based decision support system for routing and decision making problems

Industry 4.0 applications and related topics open up new opportunities in problem solving for states, companies, production systems and individuals. These developments make data gathering and processing easier. Moreover, mobile devices offer more sophisticated approaches since they can gather data and process the collected data by various algorithms, which are embedded to devices via applications. Appro-priate problem related algorithms can be coded and they can be deployed to mobile devices to increase mobility and ease of use for end users. In the present paper, a prototype of mobile application-based decision support system is developed for industrial systems including routing and multi-criteria decision making problems. The developed application is comprised of several modules including registry and signup modules as well as problem solving modules. Problem solving modules can gather data from both user and outer sources such as GPS in order to solve both routing problems and multi-criteria decision-making problems. The mentioned application adopts a Simulated Annealing Algorithm to find promising routes for the users, while multi-criteria decision-making module uses both Analytical Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) based on the choice of the users. The proposed application is coded on Java and Python due to their strong integration capabilities with Android. According the results of the developed prototype, the proposed application offer promising results and ease of use.

Train routing in shunting yards using Answer Set Programming

We use Answer Set Programming (ASP), a modern method for knowledge representation and reasoning, to build a fully automated decision support system for routing of trains in shunting yards. The system leverages the knowledge of experienced shunting yard operators and yields optimal, consistent and transparent routing decisions. In addition, the system remains easily adaptable to new expert knowledge that may become available in the future. We embedded this routing system into a simulation environment and conducted a study in order to investigate and confirm the validity and limits of this new approach. The study is based on the track layout and legal regularities of an actual shunting yard and therefore ensures the applicability to real world problem instances. The results confirm that ASP can be used to solve complex routing problems, but cannot yet match the solving speed of proprietary and custom fit algorithms. Therefore the suitability of ASP to solve complex routing problems is subject to the trade-off between transparency, adaptability and flexibility vs. speed.

Bi-objective decision support system for routing and scheduling of hazardous materials

This paper presents a Pareto-based bi-objective optimization of hazardous materials vehicle routing and scheduling problem with time windows and shows its application to a realistic hazardous material logistics instance. A meta-heuristic solution algorithm is also proposed, which returns a set of routing solutions that approximate the frontier of the Pareto optimal solutions based on total scheduled travel time and total risk of whole transportation process. It works in a single-step fashion simultaneously constructing the vehicle route and selecting the optimal paths connecting the routed locations from a set of non-dominated paths obtained in terms of travel time and risk value.

RuttOpt — a decision support system for routing of logging trucks

We describe the decision support system RuttOpt, which is developed for scheduling logging trucks in the forest industry. The system is made up of a number of modules. One module is the Swedish road database NVDB, which consists of detailed information of all of the roads in Sweden. This also includes a tool to compute distances between locations. A second module is an optimization routine that finds a schedule, i.e., set of routes for all trucks. This is based on a two-phase algorithm where linear programming and a standard tabu search method are used. A third module is a database storing all relevant information. At the center of the system is a user interface where information and results can be viewed on maps, Gantt schedules, and result reports. The RuttOpt system has been used in a number of case studies and we describe four of these. The case studies have been made in both forest companies and hauling companies. The cases range from 10 to 110 trucks and with a planning horizon ranging between 1 and 5 days. The results show that the system can be used to solve large case studies and that the potential savings are in the range 5%–30%.

RuttOpt - A Decision Support System for Routing of Logging Trucks

We describe the decision support system RuttOpt, which is developed for scheduling logging trucks in the Swedish forest industry. The system is made up of a number of modules. One module is the Swedish road database NVDB which consists of detailed information of all the roads in Sweden. This also includes a tool to compute distances between locations. A second module is an optimization routine that finds a schedule i.e. set of routes for all trucks. This is based on a two phase algorithm where Linear Programming and a standard tabu search method are used. A third module is a database, storing all relevant information. At the heart of the system is a user interface where information and results can be viewed on maps, Gantt schedules and result reports. We also describe the characteristics of the general routing problem in forestry together with a focus on the planning process and systems in use in the Swedish situation. The system RuttOpt has been used in a number of case studies and we describe four of these. The case studies have been made in both forest companies and hauling companies. The cases range from ten to 110 trucks and with a planning horizon ranging from between one and five days. The results show that the system can be used to solve large case studies and that the potential savings are in the range 5-30%.