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1、table of contentsfront matterchapters1.introduction1.1.introduction to sequencing and scheduling1.2.scheduling theory1.3.philosophy and coverage of the book2.single-machine sequencing2.1.introduction2.2.preliminaries2.3.problems without due dates: elementary results2.3.1flowtime and inventory2.3.2mi
2、nimizing total flowtime2.3.3minimizing total weighted flowtime2.4.problems with due dates: elementary results2.4.1lateness criteria2.4.2minimizing the number of tardy jobs2.4.3minimizing total tardiness2.4.4due dates as decisions2.5.summary3.optimization methods for the single-machine problem3.1.int
3、roduction3.2.adjacent pairwise interchange methods3.3.a dynamic programming approach3.4.dominance properties3.5.a branch and bound approach3.6.summary4.heuristic methods for the single-machine problem4.1.introduction4.2.dispatching and construction procedures4.3.random sampling4.4.neighborhood searc
4、h techniques4.5.tabu search4.6.simulated annealing4.7.genetic algorithms4.8.the evolutionary solver4.9.summary5.earliness and tardiness costs5.1.introduction5.2.minimizing deviations from a common due date5.2.1.four basic results5.2.2.due dates as decisions5.3.the restricted version5.4.asymmetric ea
5、rliness and tardiness costs5.5.quadratic costs5.6.job-dependent costs5.7.distinct due dates5.8.summary6.sequencing for stochastic scheduling6.1.introduction6.2basic stochastic counterpart models6.3.the deterministic counterpart 6.4.minimizing the maximum cost 6.5.the jensen gap6.6.stochastic dominan
6、ce and association6.7.using risk solver6.8.summary7.safe scheduling7.1introduction7.2meeting service-level targets7.3trading off tightness and tardiness7.4the stochastic e/t problem7.5setting release dates7.6the stochastic u-problem: a service-level approach7.7the stochastic u-problem: an economic a
7、pproach7.8summary8.extensions of the basic model8.1.introduction8.2.nonsimultaneous arrivals8.2.1.minimizing the makespan8.2.2.minimizing maximum tardiness8.2.3.other measures of performance8.3.related jobs8.3.1.minimizing maximum tardiness8.3.2.minimizing total flowtime with strings8.3.3.minimizing
8、 total flowtime with parallel chains8.4.sequence-dependent setup times8.4.1.dynamic programming solutions8.4.2.branch and bound solutions8.4.3.heuristic solutions8.5.stochastic models with sequence-dependent setup times8.5.1.setting tight due dates8.5.2.revisiting the tightness-tardiness trade off8.
9、6.summary9.parallel-machine models9.1.introduction9.2.minimizing the makespan9.2.1.nonpreemptable jobs9.2.2.nonpreemptable related jobs9.2.3.preemptable jobs9.3.minimizing total flowtime9.4.stochastic models9.4.1.the makespan problem with exponential processing times9.4.2.safe scheduling with parall
10、el machines9.5.summary10.flow shop scheduling10.1.introduction10.2.permutation schedules10.3.the two-machine problem10.3.1.johnsons rule10.3.2.a proof of johnsons rule10.3.3.the model with time lags10.3.4.the model with setups10.4.special cases of the three-machine problem10.5.minimizing the makespa
11、n10.5.1.branch and bound solutions10.5.2.heuristic solutions10.6.variations of the m-machine model10.6.1.ordered flow shops10.6.2.flow shops with blocking10.6.3.no-wait flow shops10.7.summary11.stochastic flow shop scheduling11.1.introduction11.2.stochastic counterpart models11.3.safe scheduling mod
12、els with stochastic independence11.4.flow shops with linear association11.5.empirical observations11.6.summary12.lot streaming procedures for the flow shop12.1.introduction12.2.the basic two-machine model12.2.1.preliminaries12.2.2.the continuous version12.2.3.the discrete version12.2.4.models with s
13、etups12.3.the three-machine model with consistent sublots12.3.2.the continuous version12.3.3.the discrete version12.4.the three-machine model with variable sublots12.4.1.item and batch availability12.4.2.the continuous version12.4.3.the discrete version12.5.the m-machine model with consistent sublot
14、s12.5.1.the two-sublot solution12.5.2.a heuristic procedure for s sublots12.6.summary13.scheduling groups of jobs13.1.introduction13.2.scheduling job families13.2.1.minimizing total weighted flowtime13.2.2.minimizing maximum lateness13.2.3.minimizing makespan in the two-machine flow shop13.3.schedul
15、ing with batch availability13.4.scheduling with a batch processor13.4.1.minimizing the makespan with dynamic arrivals13.4.2.minimizing makespan in the two-machine flow shop13.4.3.minimizing total flowtime with dynamic arrivals13.4.4.batch dependent processing times13.5.summary14.the job shop problem
16、14.1.introduction14.2.types of schedules14.3.schedule generation 14.4.the shifting bottleneck procedure 14.4.1.bottleneck machines 14.4.2.heuristic and optimal solutions14.5.neighborhood search heuristics14.6.summary15.simulation models for the dynamic job shop15.1.introduction15.2.model elements15.
17、3.types of dispatching rules 15.4.reducing mean flowtime15.5.meeting due dates15.5.1.background15.5.2.some clarifying experiments15.5.3.experimental results15.6.summary16.network methods for project scheduling16.1.introduction16.2.logical constraints and network construction16.3.temporal analysis of
18、 networks16.4.the time/cost trade-off16.5.traditional probabilistic network analysis16.5.1.the pert method16.5.2.theoretical limitations of pert16.6summary17.resource-constrained project scheduling17.1.introduction17.2.extending the job shop model17.3.extending the project model17.4heuristic constru
19、ction and search algorithms17.4.1.construction heuristics17.4.2.neighborhood search improvement schemes17.4.3.selecting priority lists and tie-breakers17.5summary18.safe scheduling for projects18.1.introduction18.2.stochastic balance principles for activity networks18.2.1.the assembly coordination m
20、odel 18.2.2.balancing general project networks18.2.3.additional examples18.2.4.hierarchical balancing18.3.crashing stochastic activities18.4.summaryappendicesa.practical processing time distributionsa.1.important processing time distributionsa.1.1the uniform distributiona.1.2the exponential distributiona.1.3the normal distributiona.1.4the logno
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