ÖÇÄÜ¿Æѧ¼¼ÊõÖø×÷´ÔÊéÈáÐÔ×÷Òµ³µ¼äµ÷¶ÈÎÊÌâ¼°ÆäÖÇÄÜÓÅ»¯Ëã·¨(Ó¢ÎÄ°æ)

Preface Chapter 1 A hybrid tabu search algorithm for FJSP 1.1 Introduction 1.2 Problem description and formulation 1.3 Related algorithm and theory 1.3.1 Tabu search algorithm 1.3.2 Critical path theory 1.4 The hybrid algorithm framework 1.4.1 Coding 1.4.2 Initialization of solutions 1.4.3 Public critical blocks 1.4.4 Neighborhood for machine assignment component 1.4.5 Neighborhood for operation scheduling component 1.4.6 The hybrid algorithm framework 1.5 Experimental results 1.5.1 Experimental setup 1.5.2 Test instances of the Kacem instances 1.5.3 Test instances of the BRdata 1.6 Conclusion References Chapter 2 A hybrid tabu search for multi-objective FJSP 2.1 Introduction 2.2 Problem formulation 2.3 Framework of the hybrid algorithm 2.4 Assignment algorithm: tabu search algorithm 2.4.1 Tabu search algorithm 2.4.2 Encoding 2.4.3 Parameter settings 2.4.4 Local search 2.5 Scheduling algorithm: variable neighborhood search 2.5.1 Left-shift based decoding 2.5.2 Public critical block 2.5.3 Variable neighborhood search 2.6 Experimental results 2.6.1 Results of Kacem instances 2.6.2 Results of BRdata 2.7 Conclusion References Chapter 3 A hybrid VNS algorithm for multi-objective FJSP 3.1 Introduction 3.2 Problem formulation 3.3 Framework of the hybrid algorithm 3.4 Machine assignment algorithm: the genetic algorithm 3.4.1 Genetic algorithm 3.4.2 Encoding 3.4.3 Initialization of machine assignment component 3.4.4 Crossover operation 3.4.5 Mutation operation 3.5 Operation sequencing algorithm: variable neighborhood search algorithm 3.5.1 Initialization of the operation sequencing component 3.5.2 Public critical block theory 3.5.3 Effective neighborhood structure 3.6 Experimental results 3.6.1 Setting parameters 3.6.2 Results of the Kacem instances 3.7 Conclusion References Chapter 4 Pareto-based ABC for multi-objective FJSP 4.1 Introduction 4.2 Problem formulation 4.3 Artificial bee colony algorithm 4.3.1 The basic concept of ABC algorithm 4.3.2 Initialization of the parameters 4.3.3 Initialization of the population 4.3.4 Local search operator 4.3.5 Global search operator 4.3.6 Random search operator 4.4 The hybrid algorithm P-DABC 4.4.1 Food source representation 4.4.2 Local search approaches 4.4.3 Employed bee phase 4.4.4 Crossover operator 4.4.5 Onlooker bee phase 4.4.6 Scout bee phase 4.4.7 Multi-objective optimizer 4.5 Experimental results 4.5.1 Setting parameters 4.5.2 Results comparisons 4.6 Conclusion References Chapter 5 An effective shuffled frog-leaping algorithm for multi-objective FJSP 5.1 Introduction 5.2 Literature review 5.3 Problem formulation 5.4 Shuffled flog-leaping algorithm 5.5 The hybrid algorithm HSFLA 5.5.1 Solution representation 5.5.2 Population initialization 5.5.3 Multi-objective SFLA 5.5.4 The framework of HSFLA 5.6 Experimental results 5.6.1 Setting parameters 5.6.2 Results comparisons 5.6.3 The three Kacem instances 5.6.4 The three Kacem instances with release dates 5.6.5 The BRdata instances 5.7 Conclusion References Chapter 6 A hybrid Pareto-based local search algorithm for multi-objective FJSP 6.1 Introduction 6.2 Problem description 6.3 Related theory 6.3.1 Variable neighbourhood search 6.3.2 Critical path theory 6.4 The hybrid algorithm 6.4.1 Coding 6.4.2 Population initialization 6.4.3 Neighboring approaches 6.4.4 VNS based self-adaptive strategy 6.4.5 Pareto archive set 6.4.6 The framework of PLS 6.5 Experimental results 6.5.1 Setting parameters 6.5.2 Results comparisons 6.6 Conclusion References