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.

We study a dynamic vehicle routing problem where stochastic customers request urgent deliveries characterized by restricted time windows. The aim is to use a fleet of vehicles to maximize the number of served requests and minimize the traveled distance. The problem is known in the literature as the same-day delivery problem, and it is of high importance because it models a number of real-world applications, including the delivery of online purchases. We solve the same-day delivery problem by proposing a novel branch-and-regret algorithm in which sampled scenarios are used to anticipate future events and an adaptive large neighborhood search is iteratively invoked to optimize routing plans. The branch-and-regret is equipped with four innovation elements: a new way to model the subproblem, a new policy to generate scenarios, new consensus functions, and a new branching scheme Extensive computational experiments on a large variety of instances prove the outstanding performance of the branch-and-regret, also in comparison with recent literature, in terms of served requests, traveled distance, and computational effort.

We study a problem from a real-world application, in which a daily set of orders must be processed following two stages, consisting of preparing perishable products on benches and allocating them to conveyors to be packed in disposable trays. Daily decisions must be made regarding the number and start time of working shifts, the number of workers and their allocation to machines, and the scheduling of orders in a two-stage flexible flow shop environment. The flow shop environment of the studied problem is common in many industries of perishable products, making the problem very general. The problem involves a number of operational constraints, and three objective functions that are minimised in a lexicographic way. To solve the problem, we implement a constructive heuristic and embed it within three metaheuristics: a Random multi-start algorithm (MR), a Biased random key genetic algorithm (BRKGA), and a Variable neighbourhood search (VNS) based one. We perform computational experiments over a set of realistic instances, and present a lower bound obtained from a constraint programming model for the scheduling counterpart. The results of the experiments show that the BRKGA is the most effective in practice for the integrated problem of workforce allocation and scheduling.

The dynamic scheduling of patients to doctors in an emergency department environment is tackled in this work. We consider the case in which patients arrive dynamically during the working hours, and the objective is to minimize the weighted tardiness. We propose a greedy heuristic based on priority queues and a general variable neighborhood search (GVNS). In the greedy heuristic, patients are scheduled by observing their urgency, while in the GVNS, the schedule is optimized every time a patient arrives. The GVNS uses six neighborhood structures and a variable neighborhood descent to perform the local search. The GVNS also handles the static problem whose solution can be used as a reference for the dynamic one. Computational results on 80 instances show that using the GVNS better approximates the static problem, besides giving an overall reduction of 66.8% points over the greedy heuristic.