In this paper, a mathematical model and an improved imperial competition algorithm (IICA) are proposed to solve the multi-objective two-sided assembly line rebalancing problem with space and resour...

Improved imperialist competitive algorithms for rebalancing multi-objective two-sided assembly lines with space and resource constraints

In this paper, we conduct a systematic literature review to examine several logistics network designs and evaluate their performance with respect to cost, quality, delivery, flexibility, and resili...

Logistics and supply chain network designs: incorporating competitive priorities and disruption risk management perspectives

Scheduling a cellular manufacturing system based on price elasticity of demand and time-dependent energy prices

In the current section, several metaheuristics involving the evolutionary of a population in order to create new generations of genetically superior individuals are presented. These algorithms are usually significantly influenced by the most prominent (and earliest) among them, the Genetic Algorithm (GA). Details about their basic characteristics and function, as well as some important variants, are described and applications in the field of industrial engineering are highlighted. A detailed description of the basic features of the genetic algorithm is presented at the beginning of this chapter and afterwards, other Evolutionary Algorithms (EA) are summarized. In specific, both relatively older and well established, as well as newer but promising methods are included, namely Differential Evolutionary, Memetic Algorithm, Imperialist Competitive Algorithm, Biogeography-Based Optimization algorithm, Teaching-Learning-Based optimization, Sheep Flock Heredity algorithm, Shuffled Frog-Leaping algorithm, and Bacteria Foraging Optimization algorithm.

Evolutionary-Based Methods

An innovative approach for resource sharing and scheduling in a sustainable distributed manufacturing system

In this article, a novel bi-objective integer model is presented to integrate reliability and intra-cell layout in designing a cellular manufacturing system CMS. Minimising the total costs e.g. inter and intra-cell material handling, machine overhead and operation, and setting up routes is the first objective with considering operation time, operation sequence, intra-cell layout, alternative process routing, routes selection, machines capacity, parts demand and parts movements in batches. Maximising the processing routes reliability is the second objective. The presented model is capable of modelling different failure characteristics including a decreasing, increasing, or constant value for machine failure rate. An illustrative example is solved to represent the capability of the presented model using the e-constraint method in order to demonstrate the conflict between the maximum value of the system reliability and the total costs of the system. Next, a multi-objective imperialist competitive algorithm MOICA is employed to find near-optimal solutions for medium-and large-sized test problems. Also, the efficiency of the proposed MOICA is revealed by comparison with the performance of a non-dominated sorting genetic algorithm NSGA-II. The computational results demonstrate that the performance of the proposed MOICA is superior to the NSGA-II. Furthermore, a real-world case study is conducted to validate the proposed model.

A multi-objective imperialist competitive algorithm for integrating intra-cell layout and processing route reliability in a cellular manufacturing system

S. Shirzadi

Papers

A multi-objective imperialist competitive algorithm for integrating intra-cell layout and processing route reliability in a cellular manufacturing system