Cold chain distribution: How to deal with node and arc time windows?
Commonly encountered in cold chain logistics, third-party distribution firms are required to deliver temperature-sensitive food products to various retailers with two kinds of time-window constraints: (1) the delivery service must begin within the time windows imposed by the retailers (called node time windows) and (2) each vehicle route is available only in a predefined time interval prescribed by the government (called arc time windows). We study the effects of the retailer time window type (i.e., density of the node time-window constraints) and other cost-related factors on a distribution firm’s legitimacy choice (i.e., the firm chooses to either comply with or violate the governmental time-window policy), food quality, and pollutant emissions in the urban environment. We model the problem as an intractable vehicle routing problem with node and arc time windows and develop a genetic algorithm to tackle it. We conduct a case study to generate the managerial insights on dealing with time windows. We find that the governmental time windows will increase the distribution cost. The governmental time windows has a negative effect on pollutant emissions while showing a positive effect on food safety. Given governmental time windows, a higher demand for node time windows will result in more governmental time-window violations or lower vehicle load factor, which depends on the vehicle fixed cost, fuel price, and government penalty.
KeywordsCold chain distribution Urban freight transport Node time windows Arc time windows Vehicle routing
This work was supported by the NSFC under Grant Numbers 71831001 and 71772016; Beijing Philosophy and Social Science Foundation under Grant Number 13JGB042; and Fundamental Research Funds for the Central Universities under Grant Number 2015jbwy011. Cheng was also supported in part by The Hong Kong Polytechnic University under the Fung Yiu King-Wing Hang Bank Endowed Professorship in Business Administration.
- Kokubugata, H., Moriyama, A., & Kawashima, H. (2007). A practical solution using simulated annealing for general routing problems with nodes, edges, and arcs. In Engineering stochastic local search algorithms: Designing, implementing and analyzing effective heuristics (pp. 136–149). Berlin: Springer.Google Scholar
- Miller, B. L., & Goldberg, D. E. (1995). Genetic algorithms, tournament selection, and the effects of noise. Complex Systems, 9(3), 193–212.Google Scholar
- MOEA-IDB. (2001). A handout of training class for industry technology talent in 2001, Taiwan (in Chinese).Google Scholar
- Prins, C., & Bouchenoua, S. (2005). A memetic algorithm solving the VRP, the CARP and general routing problems with nodes, edges and arcs. In Recent advances in memetic algorithms (pp. 65–85). Berlin: Springer.Google Scholar
- Quak, H. J., & De Koster, M. B. M. (2006). Urban distribution: The impacts of different governmental time-window schemes. ERIM report series reference no. ERS-2006-053-LIS.Google Scholar
- Reghioui, M., Prins, C., & Labadi, N. (2007). GRASP with path relinking for the capacitated arc routing problem with time windows. EvoWorkshops 2007: Applications of evolutionary computing (Vol. 4448, pp. 722–731). Berlin: Springer.Google Scholar
- Zhao, G., & Zhang, Y. (2011). On the cold chain logistics distribution routing optimization. In 30th Chinese IEEE control conference (pp. 2063–2068).Google Scholar