Robust Dynamic Multi-Objective Vehicle Routing Optimization Method

Abstract

For dynamic multi-objective vehicle routing problems, the waiting time of vehicle, the number of serving vehicles, the total distance of routes were normally considered as the optimization objectives. Except for above objectives, fuel consumption that leads to the environmental pollution and energy consumption was focused on in this paper. Considering the vehicles' load and the driving distance, corresponding carbon emission model was built and set as an optimization objective. Dynamic multi-objective vehicle routing problems with hard time windows and randomly appeared dynamic customers, subsequently, were modeled. In existing planning methods, when the new service demand came up, global vehicle routing optimization method was triggered to find the optimal routes for non-served customers, which was time-consuming. Therefore, robust dynamic multi-objective vehicle routing method with two-phase is proposed. Three highlights of the novel method are: (i) After finding optimal robust virtual routes for all customers by adopting multi-objective particle swarm optimization in the first phase, static vehicle routes for static customers are formed by removing all dynamic customers from robust virtual routes in next phase. (ii)The dynamically appeared customers append to be served according to their service time and the vehicles' statues. Global vehicle routing optimization is triggered only when no suitable locations can be found for dynamic customers. (iii)A metric measuring the algorithms' robustness is given. The statistical results indicated that the routes obtained by the proposed method have better stability and robustness, but may be sub-optimum. Moreover, time-consuming global vehicle routing optimization is avoided as dynamic customers appear.

Dynamic Multiobjective Software Project Scheduling Optimization Method Based on Firework Algorithm

“…The assignment scheme of an employee is defined as C 2 shown in Figure 4, except for the first and last dummy tasks, with the purpose of facilitating the evolutionary operations. By combining the task allocation scheme C 1 with the employee assignment scheme C 2 , a complete software project scheduling scheme expressed by is formed, as shown in Figure 5 [26].…”

Adaptive Cultural Algorithm-Based Cuckoo Search for Time-Dependent Vehicle Routing Problem with Stochastic Customers Using Adaptive Fractional Kalman Speed Prediction

“…p is set as different real numbers from 0.1 to 0.9. e parameters are set as follows: α � 0.3, β � 0.5, m � 50, and τ il � 1. e initial vehicle speeds v are set as stochastic real numbers within the range of [1,6]. Table 5 gives the average results of shortest expected total travelling time and servicing time for P-n16-k8 case with different IT and different p under 20 times calculation.…”

“…Guo studied dynamic multiobjective Vehicle Routing Problem with a corresponding carbon emission model and it was set as an optimization objective [1]. After finding optimal robust virtual routes for all customers by adopting multiobjective particle swarm optimization in the first phase, static vehicle routes for static customers are formed by removing all dynamic customers from robust virtual routes in next phase.…”

Memory-based multi-population genetic learning for dynamic shortest path problems

“…Immigrants strategy [3] in ACO is a popular diversity strategy, which introduces immigrant ants, either using random solutions (random immigrant ACO, RIACO) or the best solutions from the previous environment (elitismbased immigrants ACO, EIACO), to deposit pheromone trails. Guo [17] used the crowding distance to select the global best or the local best particle in particle swarm optimization (PSO) to solve dynamic multi-objective vehicle routing problems.…”