Modeling nurse shift scheduling in terms of economic objectives, ergonomic criteria, and different work contracts

There are a lot of large-scale Home Improvement Center (HIC) in Japan. In the large-scale HIC,about hundred short time workers are registered. And about forty workers are working every day. A managercreates a monthly shift schedule. The manager selects the workers required for each working day, assigns theworking time and break time for each worker and also work placement. Because there are many requirementsfor the scheduling, the scheduling consumes time costs and efforts. Therefore, we propose the technique to createand optimize the schedule of the short time workers in order to reduce the burden of the manager. A cooperativeevolution is applied for generating and optimizing the shift schedule of short time worker. Several problems hasbeen found in carrying out this research. This paper proposes techniques to automatically create and optimize theshift schedule of workers in large-scale HIC by using a Cooperative Evolution (CE), to solve the situation thatmany workers concentrate in a speci c time zone, and to solve the situation where many breaks are concentratedin a speci c break time zone, and an effective mutation operators.

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Hybrid of Hill Climbing and SAT Solving for Air Traffic Controller Shift Scheduling

JITA - Journal of Information Technology and Applications (Banja Luka) - APEIRON ◽

10.7251/jit1502081s ◽

2016 ◽

Vol 10 (2) ◽

Cited By ~ 1

Author(s):

Mirko Stojadinović

Keyword(s):

Heuristic Method ◽

Optimal Solution ◽

Air Traffic ◽

Hill Climbing ◽

Hybrid Technique ◽

Sat Solving ◽

Shift Scheduling ◽

Exact Methods ◽

Air Traffic Controller ◽

Three Phases

Modern computers solve many problems by using exact methods, heuristic methods and very often by using their combination. Air Traffic Controller Shift Scheduling Problem has been successfully solved by using SAT technology (reduction to logical formulas) and several models of the problem exist. We present a technique for solving this problem that is a combination of SAT solving and meta-heuristic method hill climbing, and consists of three phases. First, SAT solver is used to generate feasible solution. Then, the hill climbing is used to improve this solution, in terms of number of satisfied wishes of controllers. Finally, SAT solving is used to further improve the found solution by fixing some parts of the solution. Three phases are repeated until optimal solution is found. Usage of exact method (SAT solving) guarantees that the found solution is optimal; usage of meta-heuristic (hill climbing) increases the efficiency in finding good solutions. By using these essentially different ways of solving, we aim to use the best from both worlds. Results indicate that this hybrid technique outperforms previously most efficient developed techniques.

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