Abstract:SummaryConstructions of three series of regular GD and semi-regular GD designs are given. Furthermore, a series of rectangular PBIB designs is constructed and particular cases of this series which reduce to PBIB designs with two associate classes are also provided.
Section: Methods V: From Generalised Hadamard Matricesmentioning
confidence: 99%
“…Vartak [52] , Raghavarao and Aggarwal [37] , Kageyama and Tanaka [22] , Banerjee et al, [4] Bhagwandas et al, [6] Suen [51] , Sinha [45] , Kageyama and Miao [23] , Sinha and Mitra [48] , Sinha et al [46,47,49] , Kageyama and Sinha [24] , Bagchi [2,3] have constructed RDs using various approaches. Their construction methods may be summarized as:…”
Section: Earlier Constructionsmentioning
confidence: 99%
“…Raghavaro and Aggarwal [37] From difference sets Kageyama and Tanaka [22] From BIBDs (v, k, λ) and Skew Hadamard designs Bhagwandas et al [6] From BIBDs (v, k, λ) Banerjee et al [4] From Rectangular association scheme Banerjee and Kageyama [5] From association scheme of GDD Puri et al (1987) [54] From partially efficiency-balanced (PEB) designs…”
Section: Earlier Constructionsmentioning
confidence: 99%
“…We observe that the most general class of BIBDs satisfying the conditions of the Theorem [viz. 4 [33] ). Hence the improved version of Theorem 2.7 of Saurabh and Sinha [42] is:…”
Rectangular designs are classified as regular, Latin regular, semi-regular, Latin semi-regular and singular designs. Some series of self-dual as well as α–resolvable designs which belong to the above classes are obtained. The building blocks of the designs are square (0, 1)-matrices. It is more general to view a class of designs based on an array than to view them based on disjoint groups of treatments of equal size. This generality enabled us to identify three subclasses of rectangular designs: Latin regular RDs, Latin semi-regular RDs and semi-regular L2-type designs which deserve further study. In every construction we obtain a matrix N with square (0, 1)-submatrices such that N becomes the incidence matrix of a rectangular design. The method is the reverse of the well-known tactical decomposition of the incidence matrix of a known design. The authors have already obtained some series of Group Divisible and L2-type designs using this method. Tactical decomposable designs are of great interest because of their connections with automorphisms of designs, see Bekar et al.[ 1 ] The rectangular designs constructed here are of statistical as well as combinatorial interest. AMS Subject Classification: 62K10; 05B05
Section: Methods V: From Generalised Hadamard Matricesmentioning
confidence: 99%
“…Vartak [52] , Raghavarao and Aggarwal [37] , Kageyama and Tanaka [22] , Banerjee et al, [4] Bhagwandas et al, [6] Suen [51] , Sinha [45] , Kageyama and Miao [23] , Sinha and Mitra [48] , Sinha et al [46,47,49] , Kageyama and Sinha [24] , Bagchi [2,3] have constructed RDs using various approaches. Their construction methods may be summarized as:…”
Section: Earlier Constructionsmentioning
confidence: 99%
“…Raghavaro and Aggarwal [37] From difference sets Kageyama and Tanaka [22] From BIBDs (v, k, λ) and Skew Hadamard designs Bhagwandas et al [6] From BIBDs (v, k, λ) Banerjee et al [4] From Rectangular association scheme Banerjee and Kageyama [5] From association scheme of GDD Puri et al (1987) [54] From partially efficiency-balanced (PEB) designs…”
Section: Earlier Constructionsmentioning
confidence: 99%
“…We observe that the most general class of BIBDs satisfying the conditions of the Theorem [viz. 4 [33] ). Hence the improved version of Theorem 2.7 of Saurabh and Sinha [42] is:…”
Rectangular designs are classified as regular, Latin regular, semi-regular, Latin semi-regular and singular designs. Some series of self-dual as well as α–resolvable designs which belong to the above classes are obtained. The building blocks of the designs are square (0, 1)-matrices. It is more general to view a class of designs based on an array than to view them based on disjoint groups of treatments of equal size. This generality enabled us to identify three subclasses of rectangular designs: Latin regular RDs, Latin semi-regular RDs and semi-regular L2-type designs which deserve further study. In every construction we obtain a matrix N with square (0, 1)-submatrices such that N becomes the incidence matrix of a rectangular design. The method is the reverse of the well-known tactical decomposition of the incidence matrix of a known design. The authors have already obtained some series of Group Divisible and L2-type designs using this method. Tactical decomposable designs are of great interest because of their connections with automorphisms of designs, see Bekar et al.[ 1 ] The rectangular designs constructed here are of statistical as well as combinatorial interest. AMS Subject Classification: 62K10; 05B05
“…Clatworthy (1973) tabulated 443 parameters' combinations of GD designs with their solutions. Since then Freeman (1976), Kageyama and Tanaka (1981), Bhagwandas and Parihar (1982), Kageyama (1985aKageyama ( , 1985b, Mohan (1985a, 1985b), Banerjee et al (1985aBanerjee et al ( , 1985b, Bhagwandas et al (1985), Dey and Nigam (1985), Banerjee and Kageyama (1986), Sinha and Kageyama (1986), and Sinha (1987) have given several methods of constructing GD designs. From another point of view, Hanani (1975) also presented some methods of constructing GD designs.…”
We describe a class of combinatorial design problems which typically occur in professional sailing league competitions. We discuss connections to resolvable block designs and equitable coverings and to scheduling problems in operations research. We in particular give suitable boolean quadratic and integer linear optimization problem formulations, as well as further heuristics and restrictions, that can be used to solve sailing league problems in practice. We apply those techniques to three case studies obtained from real sailing leagues and compare the results with previously used tournament plans.
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