Preventive Planning of ‘Product-as-a-Service’ Offers Using Genetic Population-Driven Stepping Crawl Threads

Abstract

Unlike the precise methods implemented in constrained programming environments, the proposed approach to preventive planning of Product-as-a-Service offers implements a competitive solution based on Genetic Population Stepping Crawl Threads (GPSCT).GPSCT techniques are used to determine the so-called stepping crawl threads (SCT) that recreate, in subsequent steps, variants of the allocation of sets of leased devices with parameters that meet the expectations of the customers ordering them by means of genetic algorithms. SCTs initiated at a selected point of the Cartesian product space of the functional repertoire of the equipment offered penetrate it in search of offer variants that meet the constraints imposed by the size of the budget and the risk level (i.e., expressed as the likelihood of damaging the device or losing part of its functionality) of individual customers. Two approaches of implementation techniques were used to determine the initial SCT population for the genetic algorithm—branch and bound (BBA) and linear programming (LPA). Many experiments assessed their impact on the computation time and the quality of the obtained solution. The performed computational experiments indicate that the effectiveness of both approaches depends on the specificity of the problem considered each time. Interestingly, for different instances of the problem, an alternative solution can always be selected that is competitive with the exact methods, allowing for a 10-fold increase in scalability.