A survey on problems and methods in generalized assembly line balancing

Environmentally conscious manufacturing and product recovery (ECMPRO): A review of the state of the art

http://web.nchu.edu.tw/pweb/users/arborfish/lesson/8682.pdf

A survey of variants and extensions of the resource-constrained project scheduling problem

State-of-the-art exact and heuristic solution procedures for simple assembly line balancing

Lecture Notes in Economics and Mathematical Systems

The assembly line balancing problem consists of assigning tasks to an ordered sequence of stations such that the precedence relations among the tasks are satisfied and some performance measure is optimized. Due to the complexity of the problem, heuristic procedures appear to be more promising than the optimum-seeking algorithms. For the single-model, deterministic version, there are numerous exact and heuristic algorithms developed, while for the other more complex but more realistic versions, the research published consists mainly of heuristic procedures. In this paper, the heuristic procedures are critically examined and summarized in sufficient detail to provide a state-of-the-art survey. An evaluation of the procedures and some further research topics have also been presented.

/pdf/a-survey-of-the-assembly-line-balancing-procedures-1n0hz8uylu.pdf

A survey of the assembly line balancing procedures

Multicriteria inventory classification using a genetic algorithm

Assembly Line Balancing (ALB) is one of the important problems of production/operations management area. As small improvements in the performance of the system can lead to significant monetary consequences, it is of utmost importance to develop practical solution procedures that yield high-quality design decisions with minimal computational requirements. Due to the NP-hard nature of the ALB problem, heuristics are generally used to solve real life problems. In this paper, we propose an efficient heuristic to solve the deterministic and single-model ALB problem. The proposed heuristic is a Genetic Algorithm (GA) with a special chromosome structure that is partitioned dynamically through the evolution process. Elitism is also implemented in the model by using some concepts of Simulated Annealing (SA). In this context, the proposed approach can be viewed as a unified framework which combines several new concepts of AI in the algorithmic design. Our computational experiments with the proposed algorithm indicate that it outperforms the existing heuristics on several test problems.

/pdf/assembly-line-balancing-using-genetic-algorithms-3ymu61n1u7.pdf

Assembly line balancing using genetic algorithms

In this paper, the disassembly line balancing problem, which involves determining a line design in which used products are completely disassembled to obtain useable components in a cost-effective manner, is studied. Because of the growing demand for a cleaner environment, this problem has become an important issue in reverse manufacturing. In this study, two exact formulations are developed that utilize an AND/OR Graph (AOG) as the main input to ensure the feasibility of the precedence relations among the tasks. It is also shown that traditional task precedence diagrams can be derived from the AOG of a given product structure. This procedure leads to considerably better solutions of the traditional assembly line balancing problems; it may alter the approach taken by previous researchers in this area.

/pdf/two-exact-formulations-for-disassembly-line-balancing-45sfhvpnkg.pdf

Two exact formulations for disassembly line balancing problems with task precedence diagram construction using an AND/OR graph

http://yoksis.bilkent.edu.tr/pdf/files/10.1016-S0377-2217(98)00115-5.pdf

Erdal Erel

Papers

A survey of the assembly line balancing procedures

Multicriteria inventory classification using a genetic algorithm

Assembly line balancing using genetic algorithms

Two exact formulations for disassembly line balancing problems with task precedence diagram construction using an AND/OR graph

Shortest-route formulation of mixed-model assembly line balancing problem