Designing an Assembly Line for Reliability
Read this article. It deals with production efficiency and human behavior. Despite advanced technology and automation, systems are still dependent on human interaction. How can the human component enhance performance, and conversely, how does this human interaction contribute to system failure?
Problem Description
In most of the above mentioned cases, the only important consideration for assuring efficiency in line balancing was to minimize the cost of assignment through balancing loss. These methods are very useful for transfer lines where lines are fully automated and line elements are preferably performed by machines or robots in a nearly deterministic manner. But in case of Assembly lines, human beings are involved and they have the problem of variable operation times for the same task. So, assembly line balancing problem is not only the problem of line design with nearly equal distribution of tasks among the stations or the adaptation of tasks to the speed of the workers but also to provide some amount of slackness in each workstation to take care of the stability of the system. It may be pointed out that the success of an organization depends not only on quality and reliability of the final product, but also on the reliability of the production set up. Otherwise, system failure may result in irregular supply of the item which will reduce the customer base and hence the profit of the organization by increasing the cost of production or loss of customers or both. So, there should be both reliable products with reliable production set up for smooth and stable functioning of the production activities.
The objective of the current work is to present a mathematical formulation for designing stable assembly line where both chance of system failure and number of workstations will be minimum. Equivalently, expected balancing loss has been minimized under the stochastic domain to generate an initial set of feasible solutions and then the reliability of the assembly line has been maximized. Thus, we propose a two-stage optimization method and use stochastic simulation approach to solve the final problem.