An efficient meta-heuristic algorithm for routing product collecting vehicles of dehydration plants. II. Algorithm performance and case studies

Routing of vehicle fleet for collecting newly cropped raw materials for mul ti-product dehydration plants is a component of plant production schedule o f utmost significance. A meta-heuristic algorithm for efficiently solving t he collecting vehicle routing problem was developed and analyzed in detail in Tarantilis and Kiranoudis (2000). Meta-heuristic algorithms are broadly characterized by a stochastic nature in producing tender solution configura tions in linear search terms, which sweep the huge solution space in a guid ed and rational way. Algorithm performance is examined through an analysis of the impact of model parameters on solution procedure during the executio n of typical routing problems. The most important model parameter examined was found to be the value of the initial threshold as well as the way that the value of this actual parameter is appropriately adjusted during the opt imization process. The main characteristic of the algorithm is the way that threshold is not only lowered but also raised, or backtracked, depending o n the success of the inner loop iterations to provide for an acceptable new solution that would replace an older one. An important feature of the algo rithm is the fact that appearance of better configurations within a process run is distributed according to the Poisson probability distribution. The suggested algorithm is tested against typical literature benchmarks as well against real-world problem encountered in the production planning procedur es of dehydration plants in Greece.