Non-discrete ant colony optimisation (NdACO) to optimise the development cycle time and cost in overlapped product development

In order to expedite the process of introducing a product to market, organisations have shifted their paradigm towards concurrent engineering. This involves the simultaneous execution of successive activities on the basis of information available in rudimentary form. For this, cross-functional teams sporadically communicate to exchange available updated information at the cost of augmented time and money. Therefore, the aim of this paper is to present a model-based methodology to estimate the optimal amount of overlapping and communication policy with a view to minimising the product development cycle time at the lowest additional cost. In the first step of the methodology, an objective function comprising the cycle time and the cost of the complete project is formulated mathematically. To reach the optimal solution, a novel meta-heuristic, non-discrete ant colony optimisation, is proposed. The algorithm derives its governing traits from the traditional ant algorithms over a discrete domain, but has been modified to search results in a continuous search space. The salient feature of the proposed meta-heuristic is that it utilises the weighted sum of numerous probability distribution functions (PDFs) to represent the long-term pheromone information. This paper utilises a novel approach for pheromone maintenance to adequately update the PDFs after each tour by the ants. The performance of the proposed algorithm has been tested on a hypothetical illustrative example of mobile phones and its robustness has been authenticated against variants of particle swarm optimisation.