Multi-Objective Management in Freight Logistics: Increasing Capacity, Service Level, Sustainability, and Safety with Optimization Algorithms, Caramia Massimiliano, Dell`olmo Paolo

Автор: Caramia Massimiliano, Dell`olmo Paolo
Название: Multi-Objective Management in Freight Logistics: Increasing Capacity, Service Level, Sustainability, and Safety with Optimization Algorithms
ISBN: 3030508110 ISBN-13(EAN): 9783030508111
Издательство: Springer
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Цена: 22359.00 р.
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Описание: The second edition of Multi-Objective Management in Freight Logistics builds upon the first, providing a detailed study of freight transportation systems, with a specific focus on multi-objective modelling.

Автор: M.C. Bhuvaneswari
Название: Application of Evolutionary Algorithms for Multi-objective Optimization in VLSI and Embedded Systems
ISBN: 8132219570 ISBN-13(EAN): 9788132219576
Издательство: Springer
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Цена: 19564.00 р.
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Описание: This book describes how evolutionary algorithms (EA), including genetic algorithms (GA) and particle swarm optimization (PSO) can be utilized for solving multi-objective optimization problems in the area of embedded and VLSI system design.

Автор: Schьtze Oliver, Hernбndez Carlos
Название: Archiving Strategies for Evolutionary Multi-Objective Optimization Algorithms
ISBN: 3030637727 ISBN-13(EAN): 9783030637729
Издательство: Springer
Цена: 19564.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: This book presents an overview of archiving strategies developed over the last years by the authors that deal with suitable approximations of the sets of optimal and nearly optimal solutions of multi-objective optimization problems by means of stochastic search algorithms.

Автор: M.C. Bhuvaneswari
Название: Application of Evolutionary Algorithms for Multi-objective Optimization in VLSI and Embedded Systems
ISBN: 8132235398 ISBN-13(EAN): 9788132235392
Издательство: Springer
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Цена: 14365.00 р.
Наличие на складе: Есть у поставщика Поставка под заказ.

Описание: This book describes how evolutionary algorithms (EA), including genetic algorithms (GA) and particle swarm optimization (PSO) can be utilized for solving multi-objective optimization problems in the area of embedded and VLSI system design.