Implicit modeling of flexible break assignments in optimal shift scheduling
Management Science
Off-day scheduling with hierarchical worker categories
Operations Research
Sufficient working subsets for the tour scheduling problem
Management Science
Solving large-scale tour scheduling problems
Management Science
Improved implicit optimal modeling of the labor shift scheduling problem
Management Science
Overlapping start-time bands in implicit tour scheduling
Management Science
Optimal shift scheduling with multiple break windows
Management Science
Personnel Tour Scheduling When Starting-Time Restrictions Are Present
Management Science
An efficient two-phase algorithm for cyclic days-off scheduling
Computers and Operations Research
Implicit optimal tour scheduling with flexible break assignments
Computers and Industrial Engineering
Flexible 4-day workweek scheduling with weekend work frequency constraints
Computers and Industrial Engineering
Staff scheduling at the United States postal service
Computers and Operations Research
Mathematical and Computer Modelling: An International Journal
Cyclic staff scheduling: optimization models for some real-life problems
Journal of Scheduling
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Unlike manufacturing, where standard shifts and days off are the rule, the service industry operates every day of the week across a month and a year. To maintain the morale and productivity of the workers in the service industry, the weekend off requirements, one of the important work preferences for the workers, should be respected and balanced for a longer planning horizon beyond a week. This paper deals with the monthly tour scheduling problem with mixed skills considering the weekend off requirements in contrast to the weekly planning horizon that is typical in most literature. The objective is to obtain the most economical mix of types of workers satisfying the patterns of demands for the workers and desired work characteristics. Two model formulations are developed based on implicit programming techniques. One model uses a general integer programming (GIP) formulation and assigns the lunch break hours aggregately to the workers based on the worker types. The other one adopts a binary integer programming (BIP) formulation and assigns the lunch break hours explicitly to the individual workers. The effectiveness of the two models is illustrated by the numerical tests and the results show that the BIP formulation is more efficient than the GIP formulation.