Balancing perfectly periodic service schedules: An application from recycling and waste management

Abstract

Abstract:A national recycling and waste management company provides periodic services to its customers from over 160 service centers. The services are performed periodically in units of weeks over a planning horizon. The number of truck-hours allocated to this effort is determined by the maximum weekly workload during the planning horizon. Therefore, minimizing the maximum weekly workload results in minimum operating expenses. The perfectly periodic service scheduling (PPSS) problem is defined based on the practices of the company. It is shown that the PPSS problem is strongly NP-hard. Attempts to solve large instances by using an integer programming formulation are unsuccessful. Therefore, greedy BestFit heuristics with three different sorting schemes are designed and tested for six real-world PPSS instances and 80 randomly generated data files. The heuristics provide effective solutions that are within 2% of optimality on average. When the best found BestFit schedules are compared with the existing schedules, it is shown that operational costs are reduced by 18% on average.

The multi-period service territory design problem – An introduction, a model and a heuristic approach

“…In these problems, the time period between consecutive executions of a task is constant, corresponding to regularity type (2) with the only difference that 240 time is not necessarily discretized into days. Applications of this kind of multi-period scheduling problems can be found in maintenance scheduling (e.g., Wei and Liu, 1983), processor scheduling (e.g., Korst et al, 1991), and logistics (e.g., Campbell and Hardin, 2005;Delgado et al, 2005;Kazan et al, 2012). However, these problems have in common that geographical aspects are not taken into account, i.e., compactness is not considered a relevant planning criterion.…”