MISTA 2009 Conference Program Committee

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1 Fig. 1 A series-parallel poset Multidisciplinary International Conference on Scheduling : Theory and Applications (MISTA 2009) 10-12 August 2009, Dublin, Ireland 2 5 9 3 4 6 7 8 (b) and their parallel composition P = P1 + P2 is the poset on J1 ∪ J2 with i
4 7 S3 P1 Multidisciplinary International Conference on Scheduling : Theory and Applications (MISTA 2009) 10-12 August 2009, Dublin, Ireland S2 8 10 1 P4 2 3 S12 Fig. 2 The binary decomposition tree for the series-parallel poset of Fig. 1 5 on P. On the other hand, if P = P1 + P2, then the parallel chains corresponding to the sequences σ1 and σ2 have to be optimally merged into a sequence σ by some algorithm. For various implementations of this argument we refer the reader to [17,20]. Consider the poset Z defined on four elements by the following precedence relations: a ≺ c, b ≺ c and b ≺ d. The following is a useful alternative characterization of seriesparallel posets (see e.g. [19]). Theorem 1 AposetP is series-parallel if and only if it does not contain any sub-poset isomorphic to the poset Z. Using our earlier interpretation for strong precedence constraints, we “unify" the concept of strong precedence for both chains and trees and in addition extend it to series-parallel posets. Definition 5 Consider a scheduling problem with P =(J, ≺P ) a poset on the job set J with ≺P representing the transitive reduction of relations in P .Wesaythat P represents strong precedence constraints, denoted by strong prec, for the problem if every schedule has to satisfy the following: S5 P9 S8 P6 9 32 S11 12 S7 6 P10 11 13
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Multidisciplinary International Con
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References Multidisciplinary Intern
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m
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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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MISTA 2009 Scheduling Semiconductor
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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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MISTA 2009 Hybrid of the weighted m
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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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MISTA 2009 Decomposed Software Pipe
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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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MISTA 2009 Scheduling policy for on
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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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3.3 Appender heuristics Multidiscip
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1E+2 1E+1 distance from LoBo BR1 BR
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MISTA 2009 Three Heuristic Procedur
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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 estimated standard deviation of
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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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( 1 2 2 2 ( ) 2 O n2 ) P n n O whil
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MISTA 2009 An efficient implicit en
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MISTA 2009 Isomorphic coupled-task
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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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If all incoming jobs can be perfect
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3 ETP-HBMO Multidisciplinary Intern
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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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placed in a restricted candidate li
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and it was found that using a RCL w
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3.2.2 GRASP improvement phase The G
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which can be adapted to construct g
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MISTA 2009 Multidisciplinary Intern
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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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stable sets in G(V, E). A maximal c
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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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MISTA 2009 Parallel Machine Problem
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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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4.2 Perturbation of Xk Multidiscipl
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the output with less computational
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4049 129451 05 3964 122477 05 3842
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t A B In summary, after repairing t
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MISTA 2009 Preemptive scheduling wi
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[Preemptive scheduling problem SS12
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it is possible to further reduce th
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paper, hence, is of importance in t
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6.2 Empirical Results Multidiscipli
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8 Conclusions This paper analyzed t
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Unfortunately, List scheduling in d
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MISTA 2009 A deterministic resource
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are linear and integer programming
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yi(t): be the number of infected in
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where Ai, Bi, Γi are constants dep
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5. Illustrative example Multidiscip
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2. N. Altay, W.G. Green III. OR/MS
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change in the processing conditions
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3 Special case : no release dates T
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n K \m 10 20 30 50 100 Total K=2 60
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Proposition 3 Let us consider a seq
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In this formulation, constraints (1
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n k \m 10 20 30 40 k=2 0.0 0.7 18.7
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MISTA 2009 Tabu Search Techniques f
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Rule Condition Description of tabu
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The experimental analysis shows tha
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MISTA 2009 A Water Flow Algorithm f
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difference between the objective fu
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order of J1 to J5. J4 can usually b
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MISTA 2009 Metaheuristic Approach t
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generation, and the maximal standar
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evaluation of solutions and improve
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MISTA 2009 Generating Cyclic Fair S
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Thus, the second aggregation combin
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The key distinctions between the hi
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i. Set i = S(s, a). Set L i equal t
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Lemma 1 There exists an optimal sch
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5 Conclusions In this paper we have
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where Multidisciplinary Internation
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Table 4 Computational results for S
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jobs 0 → j → n+1, j = 1, . . .
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Fig. 1 Two schedules: one with pree
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Fig. 7 Transformation of K5,2 Proof
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six neighbourhoods for the nurse ro
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the heuristic set is composed of as
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Psychiatry Normal Average Std. Dev.
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MISTA 2009 Reactive Nurse Schedulin
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personnel scheduling process and da
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during a working day in case of ext
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2 Problem formulation The analyzed
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MISTA 2009 Scatter search for a wei
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MISTA 2009 A VNS-based solution app
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Table 1 Comparison of different sol
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3 Problem Definition The Malaysian
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∑xqt (1-αnq) + ∑xq(t+1) (1-αn
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5 Conclusion and future work The de
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MISTA 2009 Round-robin Sports Sched
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MISTA 2009 Workforce Planning and S
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MISTA 2009 Integrated workforce sta
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our method produces the best known
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MISTA 2009 Interactive Course Timet
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plications of the block planning ap
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¡
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MISTA 2009 Approximation algorithm
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3 Approximation algorithm for molda
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MISTA 2009 Solving a dock assignmen
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3 Three-stage flexible flow-shop In
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MISTA 2009 Project scheduling with
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3 Markov decision chain For Markovi
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proportion of precedence-related ac
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MISTA 2009 On the Effective Approac
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2 Manifest Variables Production Mix
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MISTA 2009 Planning Heuristics for
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the lower bound for each P de. Let
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MISTA 2009 Order acceptance and sch
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Morton [7]. The first B&B algorithm
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MISTA 2009 Real World Optimisation
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many alternative plans before going
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MISTA 2009 Scheduling in a micro-bi
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MISTA 2009 A self-organising hyper-
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simple structure and effectiveness.
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MISTA 2009 Multilevel search for ch
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MISTA 2009 HyFlex: A Flexible Frame
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Instance number Size 0-9 20 × 5 10
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it will be conducted in a high-leve
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MISTA 2009 Multilevel Search for Ev
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Terminals Functions Delta (change i
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MISTA 2009 Scheduling of job shop,
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formulations are considered superio
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and independence of the residuals a
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esource levelling means to schedule
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3.3 Branch and Bound Multidisciplin
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Due to its inferior performance on
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increasingly easier to change the p
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Update pheromone value τ ij counte
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In this paper, we present a new evo
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MISTA 2009 Variable Neighborhood De
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3 Computational results We evaluate
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how much fuel to take on at any giv
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Note that the activations of τi do
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and a best-case deadline BDi equal
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functionftransformsatimeunitalready
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MISTA 2009 Speeding up the optimal
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Table 1 Speedup of the modified OPT
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MISTA 2009 Conference Program Committee

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