An empirical comparison of improvement heuristics for the mixed-model U-line balancing problem

Abstract

Mixed-model assembly lines often create model imbalance due to differences in task times for the different product models. Smoothing algorithms guided by meta-heuristics that can escape local optimums can be used to reduce model imbalance. In this research we utilize the metaheuristics tabu search (TS), the great deluge algorithm (GDA) and record-to-record travel (RTR) to reduce three objective functions: the absolute deviation from cycle time, the maximum deviation from cycle time, and the sum of the cycle time violations. We found that the GDA was significantly superior to the RTR and TS algorithms across all problem sizes and objective functions. For the 19 task problems, RTR performed significantly better than TS for all three objective functions. On the other hand, for the 61 and 111 task problems TS performed significantly better than RTR for all three objective functions.

Evaluation of mixed‐model U‐line operational designs

“…Therefore, we consider various product‐scheduling sequences using a simplification scheme called minimum part set. A minimum part set represents the product mix ratio across product models, e.g., 111 reflecting one of product A, one of product B, one of product C. While a full part set emulates the total demand for each product model over the planning horizon (Thomopoulos, , ), a minimum part set simplifies the scheduling of products maintaining the same product mix (Bard et al , Kara, , , Visich et al ). Scheduling the products using a minimum part set is also ideal for a simulation study because it quickly reaches a steady state (McCormick et al, ).…”

“…A mixed‐integer linear programming formulation to solve small‐sized problems and a heuristic procedure based on COMSOAL, a computer method of sequencing operations for assembly lines (Arcus, ) to solve large‐sized problems that are nondeterministic polynomial time hard (NP‐hard). Visich et al () also adapted the four‐step procedure of Thomopoulos, but modified the procedure to accommodate the use of the minimum part set. The minimum part set is the smallest part set having the same product model proportion as the total demand (Bard et al, , ).…”

Sequencing minimum product sets on mixed-model U-lines to minimise work overload

“…Chutima and Olanviwatchai (2010) attempted to minimise workload smoothness, which is measured by the difference of the total time of a station during a work shift and the maximum total time of all stations. Visich et al (2010) compared the performance of three smoothing algorithms guided by meta-heuristics that can be used to reduce model imbalance. On a MMUL, the workload of a station during a production cycle depends not only on the tasks assigned to its front and back sections, but also on the model mix at the sections, i.e.…”

Balanceamento e sequenciamento de linhas de produção multi-modelo com trabalhadores deficientes

“…Matanachai e Yano (2001) resolvem o problema de balanceamento vertical, em um contexto de multi-modelos, com um algoritmo de Beam Search. Visich et al (2010) utilizam Busca Tabu para balancear uma linha em formato de U (U-line) e minimizar os desvios em relação ao tempo de ciclo, comparando três objetivos diferentes de antecipação: o desvio absoluto, o desvio máximo (máxima divergência) e a soma dos desvios em relação ao tempo de ciclo. McMullen e Tarasewich (2006) e Vilarinho e Simaria (2006) utilizam algoritmos de colônia de formigas para balanceamento antecipativo em um contexto just-in-time e de paralelismo entre as estações, respectivamente.…”