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comparison with GRASP-CST (3.289 se
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machines and also the sequence of b
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3 Heuristic Approaches 3.1 ATC-BATC
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3. Create a sample of a [ 0, 1] U d
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three metaheuristics the ACS approa
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3.1 Construction method to build in
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A bias is introduced in the perturb
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average normalized time 1 0.8 0.6 0
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Instance Avg. COEV Best COEV Avg. t
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MISTA 2009 Empowerment-based Workfo
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Problem formulation is a very impor
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5 Prototype For the model outlined
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physicians requires satisfying a ve
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o Similarly, a physician cannot als
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U i lL Ail jJ kK lL Xijkl i I (1
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optimization models with meta-heuri
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2.2 Problem formulation In order to
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( 0 ≤1) ≤ HMCR . Other values t
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Step 1. Initialize parameters Step
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MISTA 2009 A Guided Search Genetic
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In order to test the performance of
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Considering the scale of integratio
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process. The processing time of all
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processing times regardless of the
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e) Non-negativity constraint: δ ,
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Figure 2L: Heuristics Comparison by
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MISTA 2009 An exact algorithm for t
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3 Lagrangian relaxation and dynamic
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3.4 Recovering the relaxed constrai
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4.2 Reduction by dominance theorem
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Stage 2 The multipliers are re-adju
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to the Uj criterion) of any job se
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x + ki and x− ki since, if ytk =
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subject to: Multidisciplinary Inter
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function scheduleActivity schedule
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#feasible[-] #feasible Fig. 5 Param
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A List of Variables Multidisciplina
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pattern is λ R = 9 and it gives th
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vr: the number of non-idle cycles o
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Let h =< Tj1 , · · · , Tjc , Sto
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Fig. 1 Sweep Width [18] Multidiscip
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Destress Call Or Intelligence Fig.
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asearchefforts relocation heuristic
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Fig. 7 Flight Plan γ 1 2 τ (t 2 )
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5 Computational Study Multidiscipli
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A new schedule Ψ’ could be gener
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Orders 1, 2, ..., k − 1 Order k O
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An Illustrative Example: We conside
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need for methodology that allows fi
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Theorem 1 DVP1 is NP-complete even
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Step 2. Calculate the minimum amoun
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14. Slotnick, S.A. and Sobel, M.J.,
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Pr: the cancellation cost for non-p
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the list considering, if necessary,
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load processing was proposed first
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(8). By constraints (9) schedule le
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1E+2 1E+1 distance from LoBo BR1 BR
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Φ denote the density function and
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To illustrate the type of data we c
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The system energy loss curve repres
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T I ⎛ 0. 746γ ⎞ ⎡Qt , iH Min
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X2 0 10 20 30 40 50 60 X1 Objective
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problem involving the minimization
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Consider the following graph which
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2 2 2 2 2 For setξ 2 : p =3, p =6,
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the objective is to find a feasible
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a1 A1 A2 A3 A4 A7 A5 A6 (a) An inpu
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graphs: since the values of of a an
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the Minimum DISJOINT PATH COVER pro
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the currently omitted jobs are to b
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Data sets Carter et al. [24] Di Gas
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such as scheduling, data mining, cu
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and it was found that using a RCL w
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which can be adapted to construct g
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obtained. Finally the paper is conc
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instances (that is, using a differe
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At each iteration, the exams are sc
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Table 7. Best results from our IP b
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The fitness of an individual is a f
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7 Conclusion and Future Work The st
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5. k[a, b] is defined as the set of
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n v(Jk, Mq, Jj, ri) ≤ C(Jj, ri)
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pij: processing time of job Ji on m
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4.1 The main procedure The proposed
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t A B In summary, after repairing t
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paper, hence, is of importance in t
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5 Conclusions In this paper we have
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where Multidisciplinary Internation
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