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<p>Introduction ix</p> <p><b>Chapter 1. A Review of Maritime Operations 1</b></p> <p>1.1. Maritime transportation 1</p> <p>1.2. Types of ships and cargo 3</p> <p>1.3. Containerization 5</p> <p>1.4. Handling equipment in seaports 8</p> <p>1.4.1. Quay cranes 10</p> <p>1.4.2. Vehicles 11</p> <p>1.4.3. Storage equipment 13</p> <p>1.5. Optimization of maritime operations 15</p> <p>1.6. Conclusion 19</p> <p><b>Chapter 2. Metaheuristic Algorithms 21</b></p> <p>2.1. Basics of metaheuristics 21</p> <p>2.2. Simulated annealing algorithm 22</p> <p>2.3. Tabu search algorithm 26</p> <p>2.4. Genetic algorithms 28</p> <p>2.5. Particle swarm optimization 32</p> <p>2.6. Ant colony optimization 34</p> <p>2.7. Conclusion 37</p> <p><b>Chapter 3. Metaheuristics for Ship Operations 39</b></p> <p>3.1. Ship routing problem 39</p> <p>3.1.1. Formulation of the tramp SRP 42</p> <p>3.1.2. A simple GA for the tramp SRP 46</p> <p>3.1.3. An ACO for the liner SRP 48</p> <p>3.2. Green maritime transportations 51</p> <p>3.2.1. Mathematical formulation of GSRP 54</p> <p>3.2.2. A PSO for the GSRP 59</p> <p>3.3. Conclusions 62</p> <p><b>Chapter 4. Optimization of Seaside Operations 65</b></p> <p>4.1. Berth allocation problem 65</p> <p>4.1.1. Formulation of the BAP 68</p> <p>4.1.2. Representation of the BAP solution 70</p> <p>4.1.3. An SA algorithm for the BAP 72</p> <p>4.1.4. A bi-objective GA for the BAP 74</p> <p>4.2. BAP in bulk seaports 76</p> <p>4.2.1. Formulation of the BAP in bulk seaports 77</p> <p>4.2.2. SA for the BAP in bulk seaports 80</p> <p>4.3. Quay crane scheduling problem 82</p> <p>4.3.1. An MILP for the QCSP 84</p> <p>4.3.2. Genetic algorithms for the QCSP 87</p> <p>4.3.3. Tabu search for the QCSP 90</p> <p>4.3.4. Double cycling QCSP 92</p> <p>4.4. Integrated berth allocation problem 95</p> <p>4.4.1. A parallel GA for the B&CAP 98</p> <p>4.4.2. PSO for the B&CAP 101</p> <p>4.4.3. A simple hybrid GA for the B&CSP 108</p> <p>4.5. Conclusions 111</p> <p><b>Chapter 5. Problems in Yard Operations 113</b></p> <p>5.1. Storage space allocation problem 113</p> <p>5.1.1. Formulation of the SSAP 116</p> <p>5.1.2. GA for the SSAP 120</p> <p>5.2. Yard crane scheduling 124</p> <p>5.2.1. Mathematical formulation of the YCSP 126</p> <p>5.2.2. A hybrid TS algorithm for the YCSP 130</p> <p>5.3. Intra-terminal transportation 135</p> <p>5.3.1. Formulation of the dispatching of multi-load vehicles 139</p> <p>5.3.2. A Tabu search for the vehicle routing 142</p> <p>5.3.3. Formulation of the vehicle scheduling problem 147</p> <p>5.3.4. GA for the vehicle scheduling problem 150</p> <p>5.4. Integrated storage space allocation problem 153</p> <p>5.4.1. A PSO for integrated SSAP–VSP 155</p> <p>5.4.2. GA for the integrated SSAP–VSP and dispatching of YCs 158</p> <p>5.5. Integrated scheduling of handling equipment 161</p> <p>5.5.1. Mathematical formulation of the 3D scheduling 163</p> <p>5.5.2. SA for the 3D scheduling 172</p> <p>5.5.3. A GA for the 3D scheduling 176</p> <p>5.6. Conclusions 179</p> <p>Conclusion 181</p> <p>Bibliography 185</p> <p>Index 207</p>

<strong>S. Mahdi Homayouni</strong>, Laboratory of Artificial Intelligence and Decision Support - INESC TEC, Portugal. <p><strong>Dliala B.M.M. Fontes</strong>, IAU, Iran.

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