Modelling of Simple Assembly Line Balancing Problem Type 1 (SALBP-1) with Machine and Worker Constraints

Abstract

Abstract-This paper presents a mathematical model for Simple Assembly Line Problem Type 1 (SALBP-1) with resource constraints; machine and worker. The existing model of SALBP-1 assumes that all the workstations have similar capability, while in reality the workstation has different capability because of limitation in term of machines and worker skills. The proposed model is aimed to mathematically represent the SALBP-1 with resource constraints. Besides that, three objective functions which to minimize number of workstation, machine used and number of worker are also presented. The machine considered to have different types of machine needed to produce a product in an assembly line while worker are considered to have different abilities and skills. The model is then illustrated and validated using some examples. The proposed model for SALBP-1 with machine and worker constraints is able to minimize the resources in assembly. Therefore, the assembly cost can be reduced.

Simple Assembly Line Balancing Problem Type 2 By Variable Neighborhood Strategy Adaptive Search: A Case Study Garment Industry

“…The results showed that the heuristics and lower bounds method could solve the problem well and was the most effective in this regard. The work carried out by Kamarudin et al (2018) [18] presented a mathematical model with resource constraints. The study found that the mathematical model could minimize the resources and decrease costs.…”

Optimal Control of Industrial Assembly Lines

“…In Type-E problems, the objective function is the efficiency of the assembly line. In Type-F problems the objective is to find a feasible balance between the number of workstations and cycle time which are both fixed [6], [7]. The above standard problem types can be further particularized to specific applications by customizing the mathematical formulation of the optimization problem.…”

“…In [9], the authors solve a MMALBP addressing simultaneously the line balancing and sequencing problems, comparing the performances of two algorithms: the multi-objective particle swarm optimization (MOPSO) and the non-dominated sorting genetic algorithm (NSGA-II); results show that the latter has better performances. In [7], the authors address a SALBP-1 problem with resources constraints. Three different objective functions are taken into account: minimization of workstations, machines used and the number of multi-skilled workers.…”