Data Mining Practical Machine Learning Tools and Techniques

A taxonomy of line balancing problems and their solutionapproaches

A discrete version of particle swarm optimization for flowshop scheduling problems

(1962). Management Models and Industrial Applications of Linear Programming. Journal of the Operational Research Society: Vol. 13, No. 3, pp. 274-275.

https://link.springer.com/content/pdf/10.1057%2Fjors.1962.42.pdf

Management Models and Industrial Applications of Linear Programming

Wireless sensor networks (WSNs) have attracted substantial research interest, especially in the context of performing monitoring and surveillance tasks. However, it is challenging to strike compelling tradeoffs amongst the various conflicting optimization criteria, such as the network’s energy dissipation, packet-loss rate, coverage, and lifetime. This paper provides a tutorial and survey of recent research and development efforts addressing this issue by using the technique of multi-objective optimization (MOO). First, we provide an overview of the main optimization objectives used in WSNs. Then, we elaborate on various prevalent approaches conceived for MOO, such as the family of mathematical programming-based scalarization methods, the family of heuristics/metaheuristics-based optimization algorithms, and a variety of other advanced optimization techniques. Furthermore, we summarize a range of recent studies of MOO in the context of WSNs, which are intended to provide useful guidelines for researchers to understand the referenced literature. Finally, we discuss a range of open problems to be tackled by future research.

/pdf/a-survey-of-multi-objective-optimization-in-wireless-sensor-329pcv7jcj.pdf

A Survey of Multi-Objective Optimization in Wireless Sensor Networks: Metrics, Algorithms, and Open Problems

Balancing of mixed-model two-sided assembly lines

http://www.isihome.ir/freearticle/ISIHome.ir-26022.pdf

Multiple-criteria decision-making in two-sided assembly line balancing: A goal programming and a fuzzy goal programming models

Two-sided assembly lines are often used at the assembly of large-sized high-volume products, such as cars, trucks, and buses. In this type of a production line, the left side and the right side of the line are used in parallel. This paper presents a tabu search algorithm for two-sided assembly line balancing. The line efficiency and the smoothness index are considered as the performance criteria. The proposed algorithm is illustrated with two numerical example problems, and its performance is tested on a set of test problems taken from literature. The performance of the proposed algorithm is compared to the existing approaches. The computational results show that the proposed algorithm performs well.

A tabu search algorithm for two-sided assembly line balancing

Many scheduling problems are NP-hard problems. For such NP-hard combinatorial optimization problems, heuristics play a major role in searching for near-optimal solutions. In this paper we develop a genetic algorithm-based heuristic for the flow shop scheduling problem with makespan as the criterion. The performance of the algorithm is compared with the established NEH algorithm. Computational experience indicates that genetic algorithms can be good techniques for flowshop scheduling problems.

A genetic algorithm for flow shop scheduling problems

Mixed-model assembly lines are widely used to improve the flexibility to adapt to the changes in market demand, and U-lines have become popular in recent years as an important component of just-in-time production systems. As a consequence of adaptation of just-in-time production principles into the manufacturing environment, mixed-model production is performed on U-lines. This type of a production line is called a mixed-model U-line. In mixed-model U-lines, there are two interrelated problems called line balancing and model sequencing. In real life applications, especially in manual assembly lines, the tasks may have varying execution times defined as a probability distribution. In this paper, the mixed-model U-line balancing and sequencing problem with stochastic task times is considered. For this purpose, a genetic algorithm is developed to solve the problem. To assess the effectiveness of the proposed algorithm, a computational study is conducted for both deterministic and stochastic versions of the pr...

Bilal Toklu

Papers

Balancing of mixed-model two-sided assembly lines

Multiple-criteria decision-making in two-sided assembly line balancing: A goal programming and a fuzzy goal programming models

A tabu search algorithm for two-sided assembly line balancing

A genetic algorithm for flow shop scheduling problems

A genetic algorithm for the stochastic mixed-model U-line balancing and sequencing problem