Crowdsource enabled integrated production and transportation scheduling for smart city logistics
With city logistics becoming more and more important, increasing attention has been paid to the 'last mile delivery' in urban areas. We investigate a novel crowdsource enabled integrated production and transportation scheduling problem in the paper. The problem is first formulated into a mixed integer linear program and its strong NP hardness is proved. To better understand this complex problem, two sub problems: a production and transportation scheduling problem and a crowdsourced bid selection problem are analysed. Based on problem properties, a Genetic Algorithm (GA) and a lower bound (LB) are developed to solve the original problem. Experimental results with up to 100 customers show that the GA outperforms the well known commercial MIP solver CPLEX. Especially, (1) the GA can yield near optimal solutions for all the tested instances with an average gap of 10.17% from the lower bound, while CPLEX provides feasible solutions only for instances with no more than 30 customers; (2) the average computation time of the GA is only 0.93% of that required by CPLEX; Besides, sensitivity analysis demonstrates advantages of introducing crowdsourced delivery into city logistics.
International Journal of Production Research