Abstract
This paper proposes new algorithms for the assembly line balancing problem with hierarchical worker assignment (ALBHW). The ALBHW appears in real industrial contexts, where companies deal with a multi-skilled workforce. It considers task execution times that vary depending on the worker type to whom the task is assigned. Qualification levels among workers are ranked hierarchically, where a lower qualified worker costs less but requires larger execution times then a higher qualified one. The aim is to assign workers and tasks to the stations of an assembly line, in such a way that cycle time and precedence constraints are satisfied, and the total cost is minimised. In this paper, we first present a mathematical model and improve it with preprocessing techniques. Then, we propose a constructive heuristic and a variable neighbourhood descent that are useful to solve large instances. Extensive computational experiments on benchmark instances prove the effectiveness of the algorithms.
Highlights
Lines are essential parts of manufacturing systems for large scale production
Two main simple assembly line balancing problem (SALBP) variants have been considered in the literature: the SALBP-1 aims at minimizing the number of stations for given cycle time, whereas the SALBP-2 aims at minimizing the cycle time for a fixed number of stations
For the variable neighborhood descent (VND), we provide the same information given for CH, but we report the average improvement with respect to CH, computed as %impr = (U BV ND − U BCH )/U BV ND ∗ 100, and the average execution time in seconds (time(s))
Summary
We study the assembly line balancing problem with hierarchical worker assignment (ALBHW). The goal is to find a minimum-cost assignment of tasks and workers to the stations of the line while satisfying maximum cycle time as well as precedence constraints. Studying hierarchical worker assignment is of interest for the ALBHW and because this characteristic might be found in many other types of assembling and disassembling problems (see, e.g., Ozceylan et al 2019), just to cite some. The computational tests that we performed on the benchmark problem instances show that the new formulation improves the previous one for what concerns both the optimality gap and the execution time.
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