PENYELESAIAN NURSE ROSTERING PROBLEM (NRP) MENGGUNAKAN CUCKOO SEARCH (CS)

Sektor transportasi, rumah sakit, perusahaan dan institusi akademik adalah beberapa contoh dari bentuk bisnis yang membutuhkan kerangka kerja penjadwalan untuk menjamin kelancaran transaksi. Nurse Rostering Problem (NRP) merupakan salah satu contohnya. NRP dapat didefinisikan sebagai sebuah penentuan pemberian tugas kepada anggota yang seharusnya berdasarkan atas beberpa kategori kualifikasi yang dibutuhkan. Metode yang digunakan adalah algoritma simple cuckoo search. Dari hasil percobaan, cuckoo search dapat digunakan untuk menyelesaikan jadwal jaga perawat namun sebelumnya harus dilakukan perubahan algoritma untuk menyesuaikannya dengan permasalahan. Solusi yang dihasilkan sudah cukup memenuhi syarat dari hard constraint, namun masih melanggar beberapa soft constraint. Hal ini disebabkan karena pembangkitan solusi yang dilakukan masih menggunakan pembangkitan random

A metaheuristics approach to the nurse rostering problem

Health care providers are affected by problems of personnel costs. Usually, the generation of rosters is a hand-made and time-consuming task and does not always comply with the legislation and the internal rules. The article presents an approach to roster generation for nursing technicians according to legal and internal restrictions and in a satisfactory period of time. It is also designed to give employees a higher level of satisfaction concerning their day off preferences and a fair distribution of unpopular shifts.The article’s proposal is to develop a hybrid system formed by a Tabu Search metaheuristic combined with a genetic algorithm. Experiments were carried out with artificial test cases based on real data. The results obtained were satisfactory, showing the feasibility of the solution in all tests performed.Key words: rostering problem, tabu search, genetic algorithm, hybrid systems

A domain transformation approach for addressing staff scheduling problems

Staff scheduling is a complex combinatorial optimisation problem concerning allocation of staff to duty rosters in a wide range of industries and settings. This thesis presents a novel approach to solving staff scheduling problems, and in particular nurse scheduling, by simplifying the problem space through information granulation. The complexity of the problem is due to a large solution space and the many constraints that need to be satisfied. Published research indicates that methods based on random searches of the solution space did not produce good-quality results consistently. In this study, we have avoided random searching and proposed a systematic hierarchical method of granulation of the problem domain through pre-processing of constraints. The approach is general and can be applied to a wide range of staff scheduling problems. The novel approach proposed here involves a simplification of the original problem by a judicious grouping of shift types and a grouping of individual shifts into weekly sequences. The schedule construction is done systematically, while assuring its feasibility and minimising the cost of the solution in the reduced problem space of weekly sequences. Subsequently, the schedules from the reduced problem space are translated into the original problem space by taking into account the constraints that could not be represented in the reduced space. This two-stage approach to solving the scheduling problem is referred to here as a domain-transformation approach. The thesis reports computational results on both standard benchmark problems and a specific scheduling problem from Kajang Hospital in Malaysia. The results confirm that the proposed method delivers high-quality results consistently and is computationally efficient

A domain transformation approach for addressing staff scheduling problems

Staff scheduling is a complex combinatorial optimisation problem concerning allocation of staff to duty rosters in a wide range of industries and settings. This thesis presents a novel approach to solving staff scheduling problems, and in particular nurse scheduling, by simplifying the problem space through information granulation. The complexity of the problem is due to a large solution space and the many constraints that need to be satisfied. Published research indicates that methods based on random searches of the solution space did not produce good-quality results consistently. In this study, we have avoided random searching and proposed a systematic hierarchical method of granulation of the problem domain through pre-processing of constraints. The approach is general and can be applied to a wide range of staff scheduling problems. The novel approach proposed here involves a simplification of the original problem by a judicious grouping of shift types and a grouping of individual shifts into weekly sequences. The schedule construction is done systematically, while assuring its feasibility and minimising the cost of the solution in the reduced problem space of weekly sequences. Subsequently, the schedules from the reduced problem space are translated into the original problem space by taking into account the constraints that could not be represented in the reduced space. This two-stage approach to solving the scheduling problem is referred to here as a domain-transformation approach. The thesis reports computational results on both standard benchmark problems and a specific scheduling problem from Kajang Hospital in Malaysia. The results confirm that the proposed method delivers high-quality results consistently and is computationally efficient

A study of evolutionary multiobjective algorithms and their application to knapsack and nurse scheduling problems

Evolutionary algorithms (EAs) based on the concept of Pareto dominance seem the most suitable technique for multiobjective optimisation. In multiobjective optimisation, several criteria (usually conflicting) need to be taken into consideration simultaneously to assess a quality of a solution. Instead of finding a single solution, a set of trade-off or compromise solutions that represents a good approximation to the Pareto optimal set is often required. This thesis presents an investigation on evolutionary algorithms within the framework of multiobjective optimisation. This addresses a number of key issues in evolutionary multiobjective optimisation. Also, a new evolutionary multiobjective (EMO) algorithm is proposed. Firstly, this new EMO algorithm is applied to solve the multiple 0/1 knapsack problem (a wellknown benchmark multiobjective combinatorial optimisation problem) producing competitive results when compared to other state-of-the-art MOEAs. Secondly, this thesis also investigates the application of general EMO algorithms to solve real-world nurse scheduling problems. One of the challenges in solving real-world nurse scheduling problems is that these problems are highly constrained and specific-problem heuristics are normally required to handle these constraints. These heuristics have considerable influence on the search which could override the effect that general EMO algorithms could have in the solution process when applied to this type of problems. This thesis outlines a proposal for a general approach to model the nurse scheduling problems without the requirement of problem-specific heuristics so that general EMO algorithms could be applied. This would also help to assess the problems and the performance of general EMO algorithms more fairly

Construction-based metaheuristics for personnel scheduling problems

This thesis investigates the idea of balancing different constraints in order to find optimal solutions to two personnel scheduling problems, within the framework of constructive metaheuristic approaches. The two problems considered are a nurse scheduling problem, for which finding feasible solutions is known to be difficult and for which the hard and soft constraints are in direct conflict, and a medical student scheduling problem for which there is little relevant literature this second problem also has conflicting hard and soft constraints, but presents further conflict between the different soft constraints. The methods used to solve these problems are focused on two constructive metaheuristics in particular: Greedy Randomised Adaptive Search Procedures (GRASP) and Ant Colony Optimisation (ACO) and for each approach several construction heuristics are introduced and compared. Using GRASP, a number of local search neighbourhoods are established for each problem, while for ACO the suitability of three trail definitions are compared. In order to further explore the balance which may obtained between the different constraints and objectives for the two problems, hybrid constructions are investigated, incorporating exact methods which take advantage of the underlying structures of each problem with regards to feasibility. For medical student scheduling, this exact method was developed into a new type of construction mechanism providing much improved results over a standard heuristic approach. Further enhancements investigated include the use of problem-specific feedback for nurse scheduling and the use of an intelligent memory procedure for the medical student scheduling problem. For the nurse scheduling problem, the final algorithm developed was able to rival the best in the literature so far and produce optimal solutions for all available datasets. For the medical student scheduling problem, optimal solutions are not known, but the results obtained are very promising and provide a good basis for further study of the problem.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Construction-based metaheuristics for personnel scheduling problems.

This thesis investigates the idea of balancing different constraints in order to find optimal solutions to two personnel scheduling problems, within the framework of constructive metaheuristic approaches. The two problems considered are a nurse scheduling problem, for which finding feasible solutions is known to be difficult and for which the hard and soft constraints are in direct conflict, and a medical student scheduling problem for which there is little relevant literature this second problem also has conflicting hard and soft constraints, but presents further conflict between the different soft constraints. The methods used to solve these problems are focused on two constructive metaheuristics in particular: Greedy Randomised Adaptive Search Procedures (GRASP) and Ant Colony Optimisation (ACO) and for each approach several construction heuristics are introduced and compared. Using GRASP, a number of local search neighbourhoods are established for each problem, while for ACO the suitability of three trail definitions are compared. In order to further explore the balance which may obtained between the different constraints and objectives for the two problems, hybrid constructions are investigated, incorporating exact methods which take advantage of the underlying structures of each problem with regards to feasibility. For medical student scheduling, this exact method was developed into a new type of construction mechanism providing much improved results over a standard heuristic approach. Further enhancements investigated include the use of problem-specific feedback for nurse scheduling and the use of an intelligent memory procedure for the medical student scheduling problem. For the nurse scheduling problem, the final algorithm developed was able to rival the best in the literature so far and produce optimal solutions for all available datasets. For the medical student scheduling problem, optimal solutions are not known, but the results obtained are very promising and provide a good basis for further study of the problem