
handle: 11499/10330
Abstract This paper presents a fuzzy extension of the disassembly line balancing problem (DLBP) with fuzzy task processing times since uncertainty is the main character of real-world disassembly systems. The processing times of tasks are formulated by triangular fuzzy membership functions. The balance measure function is modified according to fuzzy characteristics of the disassembly line. A hybrid discrete artificial bee colony algorithm is proposed to solve the problem whose performance is studied over a well-known test problem taken from open literature and over a new data set introduced in this study. Furthermore, the influence of the fuzziness on the computational complexity of HDABC is evaluated and the solution quality of the proposed algorithm is compared against discrete and traditional versions of the artificial bee colony algorithm. Computational comparisons demonstrate the superiority of the proposed algorithm.
Optimization, Artificial bee colonies, Problem solving, Combinatorial optimization, Artificial bee colony algorithms, Disassembly systems, Artificial bee colony, Membership functions, Meta heuristics, Fuzzy disassembly line balancing, Metaheuristics, Evolutionary algorithms, Disassembly line balancing, 510, 004, Computational complexity, Statistical tests, Triangular fuzzy membership functions, Computational comparisons, Variable neighborhood search
Optimization, Artificial bee colonies, Problem solving, Combinatorial optimization, Artificial bee colony algorithms, Disassembly systems, Artificial bee colony, Membership functions, Meta heuristics, Fuzzy disassembly line balancing, Metaheuristics, Evolutionary algorithms, Disassembly line balancing, 510, 004, Computational complexity, Statistical tests, Triangular fuzzy membership functions, Computational comparisons, Variable neighborhood search
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 120 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 1% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
