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departments are in the same floor. x y x I = 1− s ) s (( o + o ) + ( o' + o' ( 1 3 i y x y II = 1− s )( 1− s )((( L −o ) + o ) + (( L −o' ) + o' ( 1 2 i i i i y i )) i x i ))) (46) (47)) x y x y III = s s (( W −o ) + o ) + (( W −o' ) + o' )) (48) 1 2 i i x y x y ( − s ) ( ( W − o ) + ( L − o ) + ( ( W − o' ) + ( L − o ) ) ) IV = s ' 1 1 2 i ⎛ cij He + ⎞ F ∑∑ ⎜ ⎟ 2 = fij ⎜ ⎟ i j j 1 j> i i ⎝( I + II + III + IV ) ⎠ i i i i ( G ( 1− G ) + G ( − G ) ) i i (49) (50) (46) to (50) determine material handling cost between two departments if they are in different floors. x i x x 1 2 ( o + o′ ) + . 5( w w ) Up = + x i i i 0 i i x x 1 2 ( o + o′ ) − . 5( w w ) Lo = + y i i i 0 i i y y y y ( o + o′ ) + 0. ( l + l ) Up = 5 ′ y i i i y y y y ( o + o′ ) − 0. ( l + l ) Lo = 5 ′ F 4 =∑∑ j> i i i ⎛ adj ⎜ ij⎜ ⎝ i i i i i ∀ i, (51) ∀ i, (52) ∀ i, (53) ∀ i, (54) x x x x ( min{ Upi , Upj } −max{ Loi , Loj } ) y y y y + ( min{ Upi , Upj } −max{ Loi , Loj } ) ( GG + ( 1−G )( 1−G ) i j i j ⎞ ⎟ ⎟ ⎠ (55) (51) to (55) calculate summation of closeness rating between departments. 1 ( F1 + F2 + F3 ) p2 4 Min Z = p − F p1 + p2 = 1, p1, p2 ≥ 0 (56) (57) Objectives were formulated in a weighted form using (56) and (57) A = x. y; x ≥ 0, y ∈ A ≤ My; M is big ( y) A ≤ x + M 1− ( y) A ≥ x − M 1− { 0, 1} number (58) (59) (60) (61) Krishnan and Jaafari 7213 Constraints (58) to (61) can afford to linearize product of variable by integer variable. GA and MA IMPLEMENTATION Evolutionary algorithms have been applied to many fields of optimization, and it has been observed that combination of evolutionary algorithms with problem-specific heuristics can lead to highly effective procedures. A memetic algorithm (MA) is a hybrid algorithm that augments a population-based search approach with a local search heuristic. MA is similar to genetic algorithm (GA). However, GA is based on biological evolution while MA imitates cultural evolution- in the sense that memes can be modified during an individual's lifetime whereas genes cannot. Thus, MAs are more likely to improve the quality of an individual. This approach proved to be highly efficient for this problem when large-sized test instances were used. Chromosome definition A flexible bay structure is used to place departments. The bay width is flexibly adjusted for departments assigned to the bay. However, the emphasis is on the location of the bay rather than on the arrangement of the departments. A chromosome is divided into three sections (Figure 1), two of which are composed of numbers that represent the departments for first floor and second one, and there is no explicit indication of the breakpoints between bays. However, the sequentially arranged departments can be grouped if they meet the conditions for forming a bay when the areas of the departments are added sequentially from left to right. If the summed area is in the range of the designated bay size and the length and width of the departments are within permissible intervals, then this set of departments can be grouped into a bay. Thus, a set of candidate breakpoints between each pair of bays can be determined. The third section of the chromosome represents binary variables S1, S2 and S3 to determine the number and location of elevators. There can be one or two elevators. The mathematical model and the solution procedure are to determine the number of elevators after doing a trade-off between vertical material handling cost and elevator setup cost. Parent selection The same first individuals are uniformly randomly generated, and the selection of the parents for the next generation is done through tournament 3; consequently, the best individuals in the generation are more likely to move to the next generation.
7214 Afr. J. Bus. Manage. procedure are to determine the number of elevators after doing a trade-off between vertical material handling cost a Bay 1 Bay 2 Bay 1'… S1 S2 S3 Figure 1. The designed chromosome. Crossover Figure 2. An example to illustrate crossover method. A modified partially matched crossover is used to prevent generation of infeasible solutions; since the partially matched crossover method exchanges genes within a parent, feasible offspring are obtained. The integer number in a chromosome, which displays the department, should not be duplicated after the process of crossover is finished since each number is unique within the chromosome. After exchange between the chromosomes, each chromosome is checked to see whether there are two of the same numbers in the new chromosomes. If there is a repeated number, then the repeated gene outside the crossed-over segment is selected and replaced with the first number from 1 to n, which is absent within the mentioned chromosome. This method is iterated until there is no repeated number. This method was easier to be coded and proved to perform crossover operation with less number of computations rather than partially matched crossover. Figure 2 shows an example of the crossover method. After exchanging genes between the two chromosomes in child 1, the first and third genes (number 5) are the same. So, the first gene which is outside the crossed-over segment must be replaced with 4 1 3 5 9 6 … 8 7 2 … The First floor The Second floor Elevators 4, as the first number from 1 to n which does not already exist within child 1. The same method is applied to child 2. Mutation A swap mutation operator is used for the mutation. Two uniformly randomly selected genes are swapped to generate a new chromosome. As mentioned with the crossover operator, the gene that represents each department is unique in the chromosome and should not be duplicated. The standard mutation operator chooses one of the genes in the chromosome and changes its value, but in this problem, the chromosome becomes an infeasible representation because the new gene would be either one of the integer numbers already in the chromosome or a number that does not stand for one of the departments. Local search A simple 2-opt algorithm, also known as a pairwise- interchange heuristic, was used to enhance the GA to
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African Journal of Business Managem
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Editors Prof. Wilfred Isioma Ukpere
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Electronic submission of manuscript
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Fees and Charges: Authors are requi
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Table of Contents: Volume 5 Number
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Table of Contents: Volume 5 Number
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7264 Afr. J. Bus. Manage. Table 1.
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7270 Afr. J. Bus. Manage. business
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7274 Afr. J. Bus. Manage. Satisfact
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7276 Afr. J. Bus. Manage. Score of
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7282 Afr. J. Bus. Manage. Satisfact
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7292 Afr. J. Bus. Manage. determine
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7298 Afr. J. Bus. Manage. Procedure
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7302 Afr. J. Bus. Manage. Figure 1.
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7308 Afr. J. Bus. Manage. required
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7310 Afr. J. Bus. Manage. This obse
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7318 Afr. J. Bus. Manage. expectati
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7320 Afr. J. Bus. Manage. Compensat
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7324 Afr. J. Bus. Manage. Psychol.,
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7326 Afr. J. Bus. Manage. the resea
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7328 Afr. J. Bus. Manage. Z( T, whe
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7330 Afr. J. Bus. Manage. (10) and
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7332 Afr. J. Bus. Manage. during wh
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7334 Afr. J. Bus. Manage. optimal v
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7336 Afr. J. Bus. Manage. satisfact
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7340 Afr. J. Bus. Manage. female re
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7344 Afr. J. Bus. Manage. Populatio
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7352 Afr. J. Bus. Manage. ln S = ln
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7356 Afr. J. Bus. Manage. but some
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7358 Afr. J. Bus. Manage. investiga
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7360 Afr. J. Bus. Manage. when conf
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7362 Afr. J. Bus. Manage. PCs in a
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7364 Afr. J. Bus. Manage. supported
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7366 Afr. J. Bus. Manage. Psychol.,
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7368 Afr. J. Bus. Manage. social pr
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Table 2. Companies and countries co
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to CSR clearly and reliably. The st
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consumers to participate with the t
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A Liao 7379 Figure 1. Example of ev
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Table 3. Summary statistics of the
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Table 5. Utilities for subject 1 Li
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REFERENCES Anderson JC, Narus JA (1
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7386 Afr. J. Bus. Manage. aggregate
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7388 Afr. J. Bus. Manage. Analysis
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(Bass and Avolio, 1994). Leaders fo
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the employees. When employees are e
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Table 1. Means, standard deviation
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negativity in the organization. Tra
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of psychological ownership. Thus te
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Meyer JP, Becker TE, Vandenberghe C
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goods industry in the future. LITER
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F + 2 0. 110 * F + Table 3. Factor
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APPENDIX Data for evaluation on the
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anks. Camanho and Dyson (2005) enha
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Step A-4: Correlation analysis Star
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Table 1. Original raw data of input
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Table 5. Scenario table. Wang and W
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Delmas M, Toka Y (2005). Deregulati
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Technology managers need to attract
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λ is the parameter for risk aversi
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specified ranges of return, for eac
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Carlsson C, Fulle´r R, Majlender P
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Stock Exchange, (Rao, 2010) in Nati
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etween size and unsystematic risk i
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The research schema Figure 1 indica
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Table 3. Result of Freidman rank te
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Table 8. The result of Spearman cor
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significant differences between the
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leadership. The process of manageri
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indices of quality of life work. On
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Figure 2. The values of standardize
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Table 4. The values of coaching ind
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Table 1. Contrast between hedonic a
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in consumers’ shopping behavior.
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et al., 2006; Carpenter, 2008). For
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Table 2. contd Shopping satisfactio
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value in the construct of clothing
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independence, assertiveness, innova
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over those of competitors in order
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Table 2. Demographic profile and ce
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Table 4. Service satisfaction level
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There is a strong significant corre
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Dean AM (2004). Rethinking customer
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as a margin rate of 10% and rest to
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(7) Table 1. Descriptive statistics
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APPENDIX A Table 1. Failure rate te
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Table 1. Results of the multivarian
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Figure 3. Entrepreneurship as a fun
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Furkukawa K, Shafer WE, Lee GM (200
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happen to firms of any and all size
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participants attributed their failu
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Table 1. Descriptive statistics of
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Table 4 Contd. Inconsideration of l
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Table 5 Contd. Manufacturing 2.17 1
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The CLRP is an NP-hard problem, so
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No START Form a new cluster. Select
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Table 1. Computational results of G
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Table 1. Correlation of facets of C
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Figure 1. Theoretical approach adop
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quadrant (III), including product v
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consistently showed negative (posit
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signal. This hypothesis predicts a
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Variables are defined as in the ful
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Table 3. The results of model (2).
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around the world. Q. Rev. Econ. Fin
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tourism industry development. In th
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Table 2. Canonical correlation. 2 C
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Table 5. Bilateral analysis and ran
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strategies (Hart, 1995; Tello and Y
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Table 1. Reliability and construct
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Table 3. Latent variables test. She
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Haines R, Street M, Haines D (2008)
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owned insurance firm to enter the m
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affective commitment; organizationa
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Figure 1. Structural relationships
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Prior to analysis, variables were e
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Table 1. Effect decomposition for M
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Figure 5. Unstandardized estimates
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prospects. In other words, less wor
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goal of a SCM process is to create
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Sale Lean Supply Chain (LSC) Growth
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Upper Saddle River: Pearson Prentic
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the green awareness movement by ado
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usiness practices than smaller firm
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Table 3. Descriptive statistics and
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China. Green leadership could be em
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APPENDIX Appendix 1: Measures of ob
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Emotional intelligence Self monitor
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ship between a people’s perceived
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Table 1. Analysis of the reliabilit
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Table 4. Results from hypothesis te
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Ajzen I, Driver BL (1992). Applicat
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mono production economy, and rural
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Table 2. Pearson correlation betwee
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Table 1. Behavioral Phenomenon that
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Table 3. Test period data. Table 4.
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Prospect value Figure 3. Expected v
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Tversky A, Gilovich T (1989). The H
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Table 1. Components of a mentoring
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Table 3. Perceptions on the success
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Table 5. Characteristics of a mento
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terms of sustained development, are
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Table 7. Ideal characteristics of m
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qualitative analysis of fifteen hou
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section. In Section 3, we will disc
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Table 1. Participants’ demographi
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of Am. Acad. of Bus., Cambridge. 4(
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directors and propose the board ind
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composition is the outcome of a neg
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Table 1. Descriptive statistics. Ti
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Table 3. The determinants of board
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Booth JR, Cornett M, Tehranian H (2
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provides a wider response and swift
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Correlation “Customary Approach
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Table 2. Correlation matrix of EAC
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Table 9. Intra-EAC imports as share
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Martin JC (1999). The European Mone
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participate in the highest degree o
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innovation. A secondary innovation
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Figure 2. The switching ecology of
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Table 1. Revenue growth rate and CA
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variables (Beattie and Jones, 1999a
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Table 3. Explanations of dependent
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Courtis JK (1997). Corporate annual
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treated in the literature, its rela
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company. METHODOLOGY Table 1. Quest
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as an important factor in the manag
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satisfaction may also reduce the ov
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Table 1. Customer expectations on s
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Table 2. Contd. Machirori and Fatok
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and travelling may lead to satisfac
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and professionally developmental pr
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Table 1. Organizational elements mo
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Table 2. Studying the demographical
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education might involve plenty of c
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(Deng et al., 2010). To date, there
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Table 2. Results of hierarchical re
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WOM. The comparison can be seen in
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UPCOMING CONFERENCES International
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