A multiperiod workforce scheduling and routing problem with dependent tasks

Abstract

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.

Algorithms for the multiperiod workforce scheduling and routing problem with dependent tasks

“…The significance of our results for this set of instances is evident by our comparison with the BKS for every instance. Still, a direct comparison with the best overall algorithm for the MWSRPDT in the literature, the ACO of Pereira et al (2020), is presented next in order to further strengthen our claim. Considering small-to medium-sized instances, the best results found by the TLMA record only a single loss against the ACO, in type B instance 8 with n = 35.…”

“…The number of days in the best solution found by the algorithm lies under "ub," followed by the computational time "t" spent in completing such solution. We remark that the experiments in Pereira et al (2020) were conducted on a different computational environment than ours. Results for the GCA are presented under "GCA," where the number of days in its solution lies under "ub" and the computational time, in seconds, spent to reach that solution is presented under "t." Different from the other algorithms in this paper, the algorithm in Section 3.2 carries a stochastic component, imposed by the pseudo-random choosing of activities to apply (24) cuts.…”

“…Therefore, heuristics and hybrid algorithms that attempt to use both exact and heuristic concepts have emerged as very desirable solution methods for these types of problems. In this paper, we present two hybrid exact-heuristic algorithms for the MWSRPDT that use a mathematical model for the problem recently proposed in Pereira et al (2020). The first uses a simplified version of the model to perform client scheduling followed by a routing procedure imbued with a local search technique applied to precedence-free spaces that plans routes for every team.…”

New general mixed-integer linear programming model for mobile workforce management

“…Instead, MILP models can be effectively included in algorithmic frameworks, as the integer decision variables can be assigned by heuristics as well (Goel and Meisel 2013). A mobile workforce management problem with precedence relationships were solved by Pereira et al (2020) with an ACO solution approach, which was based on an MILP model. The authors remark that the presence of dependencies between tasks often make local neighbourhood search methods difficult to implement.…”

Modeling and Planning Multimodal Transport Paths for Risk and Energy Efficiency Using AND/OR Graphs and Discrete Ant Colony Optimization

“…There are few control parameters and well optimization effects in the ACO algorithm [34]- [39], which are its main advantages. Due to its simplicity and ease of implementation, it has gained more and more attention and has been used to solve many practical and complex industrial optimization problems [40]- [45]. Hence, we used ACO to solve our proposed multimodal transport route sequence optimization problem.…”