A multiperiod workforce scheduling and routing problem with dependent tasks

•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 solutio...

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Vydáno v:Computers & operations research Ročník 118; s. 104930 - 13
Hlavní autoři: Pereira, Dilson Lucas, Alves, Júlio César, Moreira, Mayron César de Oliveira
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Elsevier Ltd 01.06.2020
Pergamon Press Inc
Témata:
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
Vehicle routing
Workforce scheduling and routing
Scheduling
ISSN:0305-0548, 1873-765X, 0305-0548
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Shrnutí:•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.
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ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2020.104930