A Novel Clustering Approach for Minimizing Transportation Lanes of Complex Supply Chain Networks based on Economic Significance

Abstract

This work studied a business problem in transportation management identified as an area of future research by Caplice and Sheffi (2003) and Acocella and Caplice (2023) and presented it as a fully formulated mathematical optimisation problem. This new clustering problem, here named the lane elimination clustering problem (LECP), is fundamentally multi-objective. The formulation includes the individual principal objective functions of the LECP as well as extensions of these functions that may represent additional measures of utility to some decision-makers. Known evolutionary algorithm methods were applied to solve the LECP for three problem instances using large-scale, privately owned datasets with up to 400 000 data points. Mutation and crossover operators, as well as constraint handling techniques, were developed as a general evolutional model of the LECP based on a priori knowledge of the problem domain. The applied genetic algorithms included the aggregation selection solution approach to provide a decision-maker with a Pareto front approximation that represents suitable trade-offs with respect to the various fitness functions. Focus, however, was given to Pareto dominance selection approaches to generate these solutions. The Pareto-based NSGA-II, SPEA2, PESA and PAES were applied to the LECP. Parallelization strategies were incorporated into these algorithms to improve performance. The performance data of these algorithms were empirically obtained and presented as a new comparative study of the efficacy of well-known multi-objective optimisation solution approaches applied to a constrained combinatorial problem. Results were compared and discussed as the basis for suggested approaches to solving the LECP. A single integrated pMOEA approach to optimize lanes was therefore provided to the field of transportation science. A new case study of evolutionary methods applied to a combinatorial multi-objective problem was also thereby contributed to the field of operations research. Although the problem was initially framed in the context of transport procurement event design, this research acknowledged the applicability of the LECP and its algorithmic optimisation to other areas of transportation management. This thesis concludes with a discussion regarding the relevance of understanding the trade-offs of this multi-objective optimisation problem with respect to these additional operational and strategic activities. Notes regarding the implementation of the LECP are given for each scenario, both in terms of the specific approaches to be used and the process by which the algorithms should be deployed.