Abstract :
Drying operations constitute an important field of food engineering, which is still governed by empiricism. The utilisation of different raw materials to be processed sequentially so that demand constraints are met, broadly characterise dehydration plants as multi-product. Processing of raw materials is implemented through a series of pre-processing operations that together with drying constitute the production procedure of a pre-specified campaign. The basic component of the manufacturing system that a typical dehydration plant involves, is scheduling of operations so that demand is fulfilled within a pre-described time horizon imposed by production planning. The typical scheduling operation that dehydration plants involve, can be formulated as a general job shop scheduling problem. The aim of this study is to describe a new metaheuristic method for solving the job shop scheduling problem of dehydration plants, termed as backtracking adaptive threshold accepting (BATA) method. Our effort focuses on developing an innovative method, which produces reliable and good solutions, requiring reasonable computing effort. The main innovation of the method, towards a typical threshold accepting algorithm, is that the value of the threshold is not only lowered, but also raised or backtracked according to how effective a local search is. BATA is described in detail while its performance and characteristic job shop scheduling case studies for dehydration plant operations are presented.
