Università degli studi di Modena e Reggio Emilia

Decision support systems (DSS) are used daily to make complex and hard decisions. Developing a DSS is not an easy task and may require combining different approaches to reach accurate and timely responses. In this paper, we present a DSS based on a micro-service architecture that we developed to handle a variant of the vehicle routing problem. The DSS has been implemented for a service company operating in the field of pharmaceutical distribution, and it helps decision-makers define the routes that different types of vehicles need to perform during the day to serve the customers’ demands. The underlying optimization problem assumes that a vehicle can perform multiple routes daily and is constrained to operate within a given time horizon. Customers are characterized by hard time windows on the delivery times. The proposed DSS first handles geo-referencing and distance calculation tasks. Then, it invokes a two-step optimization approach in which vehicle routes are generated and combined to reduce the number of vehicles used. For the latter task, we propose and evaluate four solution methods: two greedy heuristics, a metaheuristic, and a mathematical model. All the methods are applied to solve real and randomly generated instances, showing that the metaheuristic algorithm is superior to the others in terms of solution quality and computing time. The company had a very positive feedback on the proposed DSS and is now using it to support its daily operations.

Emerging trends, driven by industry 4.0 and Big Data, are pushing to combine optimization techniques with Decision Support Systems (DSS). The use of DSS can reduce the risk of uncertainty of the decision-maker regarding the economic feasibility of a project and the technical design. Designing a DSS can be very hard, due to the inherent complexity of these types of systems. Therefore, monolithic software architectures are not a viable solution. This paper describes the DSS developed for an Italian company based on a micro-services architecture. In particular, the services handle geo-referenced information to solve a multi-trip vehicle routing problem with time windows. To face the problem, we follow a two-step approach. First, we generate a set of routes solving a vehicle routing problem with time windows using a metaheuristic algorithm. Second, we calculate the interval in which each route can start and end, and then combine the routes together, with an integer linear programming model, to minimize the number of used vehicles. Computational tests are conducted on real and random instances and prove the efficiency of the approach.