A multiperiod workforce scheduling and routing problem with dependent tasks

• Published in: Computers & operations research 2020-06, Vol.118, p.104930-13, Article 104930
• Main Authors: Pereira, Dilson Lucas, Alves, Júlio César, Moreira, Mayron César de Oliveira
• Format: Article
• Language: English
• Subjects: Algorithms; Ant colony optimization; Computing time; Customers; Heuristic methods; Integer programming; Mixed integer; Operations research; Precedence constraints; Problem solving; Schedules; Scheduling; Search algorithms; Task scheduling; Vehicle routing; Workforce; Workforce scheduling and routing
• ISSN: 0305-0548; 1873-765X; 0305-0548
• DOI: 10.1016/j.cor.2020.104930

•Definition and modeling of a new Workforce Scheduling and Routing Prob- lem.•Proposal of a constructive heuristic and heuristics based on the Ant Colony Optimization Metaheuristic.•Computational experiments demonstrating the efficiency of the best per- forming heuristic in matching the best solution found by the model in a fraction of its time. In this paper, we study a new Workforce Scheduling and Routing Problem, denoted Multiperiod Workforce Scheduling and Routing Problem with Dependent Tasks. In this problem, customers request services from a company. Each service is composed of dependent tasks, which are executed by teams of varying skills along one or more days. Tasks belonging to a service may be executed by different teams, and customers may be visited more than once a day, as long as precedences are not violated. The objective is to schedule and route teams so that the makespan is minimized, i.e., all services are completed in the minimum number of days. In order to solve this problem, we propose a Mixed-Integer Programming model, a constructive algorithm and heuristic algorithms based on the Ant Colony Optimization (ACO) metaheuristic. The presence of precedence constraints makes it difficult to develop efficient local search algorithms. This motivates the choice of the ACO metaheuristic, which is effective in guiding the construction process towards good solutions. Computational results show that the model is capable of consistently solving problems with up to about 20 customers and 60 tasks. In most cases, the best performing ACO algorithm was able to match the best solution provided by the model in a fraction of its computational time.