Machine scheduling using the Bees algorithm

Single-machine scheduling is the process of assigning a group of jobs to a machine. The jobs are arranged so that a performance measure, such as the total processing time or the due date, may be optimised. Various swarm intelligence techniques as well as other heuristic approaches have been developed for machine scheduling. Previously, the Bees Algorithm, a heuristic optimisation procedure that mimics honeybee foraging, was successfully employed to solve many problems in continuous domains. In this thesis, the Bees Algorithm is presented to solve various single-machine scheduling benchmarks, all of which, chosen to test the performance of the algorithm, are NP-hard and cannot be solved to optimality within polynomially-bounded time. To apply the Bees Algorithm for machine scheduling, a new neighbourhood structure is defined. Several local search algorithms are combined with the Bees Algorithm. This work also introduces an enhanced Bees Algorithm. Several additional features are considered to improve the efficiency of the algorithm such as negative selection, chemotaxis, elimination and dispersal which is similar to the ‘site abandonment’ strategy used in the original algorithm, and neighbourhood change. A different way to deploy neighbourhood procedures is also presented. ii Three categories of machine scheduling problems, namely, single machine with a common due date, total weighted tardiness, and total weighted tardiness with sequence-dependent setup are used to test the enhanced Bees Algorithm’s performance. The results obtained compare well with those produced by the basic version of the algorithm and by other well-known techniques

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

Applied Methuerstic computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC