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Recent Advancements of Nurse Scheduling Models and A Potential Path
 Proceedings of the 6th IMTGT Conference on Mathematics, Statistics and its Applications (ICMSA2010). Universiti Tunku Abdul Rahman, Kuala Lumpur, Malaysia
, 2011
"... Abstract. Nurse scheduling problem (NSP) has been a subject that is being studied broadly by both academician and practitioners, over the years. Nurse scheduling concepts are varied due to sophisticated and challenging real world scenarios in nurse management system. Moreover, the importance of solv ..."
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Abstract. Nurse scheduling problem (NSP) has been a subject that is being studied broadly by both academician and practitioners, over the years. Nurse scheduling concepts are varied due to sophisticated and challenging real world scenarios in nurse management system. Moreover, the importance of solving nurse scheduling problems has triggered various approaches or techniques to solve these scheduling problems. In order to develop a good nurse scheduling model, it is crucial to comprehend the problem and the characteristics of the potential techniques so as to cope with the complex scheduling problem. Hence, the main purpose of this paper is to provide a rich literature on the diverse recent solution techniques in the NSP. Subsequently, an evolutionary trend of nurse scheduling models is identified as fruitful and thus, future directions of the scheduling technique are discussed. Finally, a potential algorithm is proposed to solve a nurse scheduling problem.
Moving Block Sequence and Organizational Evolutionary Algorithm for General Floorplanning with Arbitrarily Shaped Rectilinear Blocks
, 2008
"... A new nonslicing floorplan representation, the moving block sequence (MBS), is proposed in this paper. Our idea of the MBS originates from the observation that placing blocks on a chip has some similarities to playing the game, Tetris®. Because no extra constraints are exerted on solution spaces, t ..."
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A new nonslicing floorplan representation, the moving block sequence (MBS), is proposed in this paper. Our idea of the MBS originates from the observation that placing blocks on a chip has some similarities to playing the game, Tetris®. Because no extra constraints are exerted on solution spaces, the MBS is not only useful for evolutionary algorithms, but also for dealing with rectangular, convex rectilinear, and concave rectilinear blocks, similarly and simultaneously, without partitioning rectilinear blocks into subblocks. This is owed to a special structure designed for recording the information of both convex and concave rectilinear blocks in a uniform form. Theoretical analyses show that the computational cost of transforming an MBS to a floorplan with rectangular blocks, in terms of the number of blocks, is between linear and quadratic. Furthermore, as a followup of our previous works, a new organizational evolutionary algorithm (OEA) based on the MBS (MBSOEA) is proposed. With the intrinsic properties of the MBS in mind, three new evolutionary operators are designed in the MBSOEA. To test the performance of the MBSOEA, benchmarks with hard rectangular, soft rectangular, and hard rectilinear blocks are used. The number of blocks in these benchmarks varies from 9 to
A twostage intelligent search algorithm for the twodimensional . . .
, 2011
"... fo im hno Simulated annealing stag nt. I on imusta sive algorithms reported recently, on average. It performs particularly better for large instances. a well has m ing rec glass in are parallel to edges of the rectangular board, i.e. all rectangles are packed orthogonally. A classification of the 2 ..."
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fo im hno Simulated annealing stag nt. I on imusta sive algorithms reported recently, on average. It performs particularly better for large instances. a well has m ing rec glass in are parallel to edges of the rectangular board, i.e. all rectangles are packed orthogonally. A classification of the 2D Bin Packing Problem that can also be applied to the 2D SPP and 2D Knapsack Problem was introduced by Lodi et al. (1999). According to this classification, there are four subtypes of the SPP problem: lems because a large amount of running time is required to obtain optimal solutions. Therefore, constructive heuristic algorithms, which can quickly produce good approximation solutions, are preferred for solving this class of problems. Some examples are the wellknown bottomleft (BL) and bottomleftfill (BLF) (Baker et al., 1980; Chazelle, 1983; Berkey and Wang, 1987), best fit heuristics (Burke et al., 2004), recursive heuristics (Zhang et al., 2006), bricklaying heuristics (Zhang et al., 2008), and new heuristics (Ortmann et al., 2010). These heuristics may be grouped into
A Tree Search Algorithm for the Manufacturer’s Pallet Loading Problem
"... Abstract. Transportation and warehouse costs are the two major components of the total logistics cost. To increase the material handling efficiency both in transit and in the warehouse, pallets have been used widely in logistics systems. The manufacturer’s pallet loading problem (MPLP) is to pack id ..."
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Abstract. Transportation and warehouse costs are the two major components of the total logistics cost. To increase the material handling efficiency both in transit and in the warehouse, pallets have been used widely in logistics systems. The manufacturer’s pallet loading problem (MPLP) is to pack identical smaller boxes onto a rectangle pallet to maximize the pallet utilization. In this research, we solve the MPLP based on a twophase algorithm. In the first phase, the maximum number of items is found by solving a mathematical model. In the second phase, a tree search algorithm is proposed to obtain a feasible layout. The tree search algorithm is to move sets of boxes that may be in different orientations instead of moving individual boxes during the search process. The idea here is to reduce the possible loading combinations and only find a feasible pattern for given number of boxes. Different data sets from the literature are tested. The results show that the proposed tree search algorithm can find feasible layout efficiently.