A couple of years ago while working on my PhD and before joining Opex Analytics, I had the privilege of working with some amazing people as part of solving a customer product delivery and transportation problem for GE Appliances & Lighting. The results of this collaboration is published in a journal paper and we believe it makes an important contribution by applying Operations Research models and algorithms to solve a large, complex, real-world transportation problem faced by a major appliance manufacturer.
Here is the paper’s abstract:
We consider a special case of the vehicle routing problem where not only each customer has specified delivery time window, but each route has limited time duration. We propose a solution algorithm using network reduction techniques and simulated annealing meta-heuristic. The objective is twofold: minimising the travel time and minimising the total number of vehicles required. The time-window constraint ensures delivery without delay, thus, a potentially higher level of customer satisfaction. The algorithm has helped the transportation planning team at General Electric Appliances & Lighting to significantly reduce the number of required trucks in two real cases, while its performance on randomly generated cases is also efficient when compared to properly selected benchmarking algorithms.
You can check the full version of the paper here.