In the two-sided mixed-model assembly line, there is a process of installing two single stations

in each position left and right of the assembly line with the combining of the product model.

The main aim of this paper is to develop a new mathematical model for the mixed model

two-sided assembly line balancing (MTALB) generally occurs in plants producing large-sized

high-volume products such as buses or trucks.

According to the literature review, authors focus on research gap that indicate in MTALB

problem, minimize the length of the line play crucial role in industry space optimization.In

this paper, the proposed mathematical model is applied to solve benchmark problems of

two-sided mixed-model assembly line balancing problem to maximize the workload on each

workstation which tends to increase the compactness in the beginning workstations which

also helps to minimize the length of the line.

Since the problem is well known as np-hard problem benchmark problem is solved using

a branch and bound algorithm on lingo 17.0 solver and based on the computational results,

station line effectiveness and efficiency that is obtained by reducing the length of the line in

mated stations of the assembly line is increased.

The automotive industry is a highly competitive sector. Manufacturers must effectively control highly complex production processes in order to fulfil all customer orders for customized cars on time, on budget and to the required quality. In this paper, the authors focus on improving the flow time of asynchronous automotive assembly lines by reducing the buffer time. A simulation-search heuristic procedure was developed and confirmed in a 5 workstations asynchronous assembly line installed in an automotive company. The proposed procedure identifies optimal performing buffer profiles for each storage level which guarantees lowest flow time while keeping the same throughput level. Experiments results show that our new algorithm significantly outperforms existing results, especially for large scale problems.

Improving production processes includes not only activities concerning manufacturing itself, but also all the activities that are necessary to achieve the main objectives. One such activity is transport, which, although a source of waste in terms of adding value to the product, is essential to the realization of the production process. Over the years, many methods have been developed to help manage supply and transport in such a way as to reduce it to the necessary minimum. In the paper, the problem of delivering components to a production area using trains and appropriately laid-out carriages was described. It is a milk run stop locations problem (MRSLP), whose proposed solution is based on the use of heuristic algorithms. Intelligent solutions are getting more and more popular in the industry because of the possible advantages they offer, especially those that include the possibility of finding an optimum local solution in a relatively short time and the prevention of human errors. In this paper, the applicability of three algorithms – tabu search, genetic algorithm, and simulated annealing – was explored.