Computational study for workforce scheduling and routing problems

We present a computational study on 112 instances of the Workforce Scheduling and Routing Problem (WSRP). This problem has applications in many service provider industries where employees visit customers to perform activities. Given their similarity, we adapt a mathematical programming model from the literature on vehicle routing problem with time windows (VRPTW) to conduct this computational study on the WSRP. We generate a set of WSRP instances from a well-known VRPTW data set. This work has three objectives. First, to investigate feasibility and optimality on a range of medium size WSRP instances with different distribution of visiting locations and including teaming and connected activities constraints. Second, to compare the generated WSRP instances to their counterpart VRPTW instances with respect to their difficulty. Third, to determine the computation time required by a mathematical programming solver to find feasible solutions for the generated WSRP instances. It is observed that although the solver can achieve feasible solutions for some instances, the current solver capabilities are still limited. Another observation is the WSRP instances present an increased degree of difficulty because of the additional constraints. The key contribution of this paper is to present some test instances and corresponding benchmark study for the WSRP

Computational study for workforce scheduling and routing problems

We present a computational study on 112 instances of the Workforce Scheduling and Routing Problem (WSRP). This problem has applications in many service provider industries where employees visit customers to perform activities. Given their similarity, we adapt a mathematical programming model from the literature on vehicle routing problem with time windows (VRPTW) to conduct this computational study on the WSRP. We generate a set of WSRP instances from a well-known VRPTW data set. This work has three objectives. First, to investigate feasibility and optimality on a range of medium size WSRP instances with different distribution of visiting locations and including teaming and connected activities constraints. Second, to compare the generated WSRP instances to their counterpart VRPTW instances with respect to their difficulty. Third, to determine the computation time required by a mathematical programming solver to find feasible solutions for the generated WSRP instances. It is observed that although the solver can achieve feasible solutions for some instances, the current solver capabilities are still limited. Another observation is the WSRP instances present an increased degree of difficulty because of the additional constraints. The key contribution of this paper is to present some test instances and corresponding benchmark study for the WSRP