Multi-objective vehicle routing problem with time windows: Improving customer satisfaction by considering gap time

Abstract

The vehicle routing problem with time windows is a combinatorial optimisation problem in distribution logistics. It has been infrequently measured as a multi-objective optimisation problem for the benefit of customers. For the purposes of this research, the measurement of multi-objective vehicle routing problem with time windows will be in terms of a minimisation of the total distance travelled by all vehicles, the total number of vehicles used (management beneficial objectives) and the total gap between ready time and issuing time (customer beneficial objective). It is possible to satisfy customers by issuing the goods as close as possible to the customer ready time. This is because possibilities for going out of stock during the above said time gap (by chance of improper inventory maintenance) are reduced with a minimised total time gap, which in turn increases the sales orders for the manufacturing management. An improved genetic algorithm, called the fitness aggregated genetic algorithm, has been implemented to resolve the problem. The proposed algorithm incorporates a fitness aggregation approach and dedicated operators, such as selection based on aggregate fitness value and best cost route crossover, to resolve the multi-objective problem. The algorithm was validated on Solomon’s bi-objective benchmark models for the minimisation of the total distance travelled and total number of vehicles used, and the results formed by proposed algorithm are competitive to best known results. After validating the proposed algorithm on bi-objective models, the third objective – namely, the total time gap between ready time and issuing time – is included in the bi-objective model. The results show that the suggested algorithm creates improved customer-satisfied routes without drastically affecting the total distance travelled and total number of vehicles used.

Optimization of a Low-Carbon Two-Echelon Heterogeneous-Fleet Vehicle Routing for Cold Chain Logistics under Mixed Time Window

“…Zhang et al used an improved fuzzy due-time window to represent customer satisfaction degree [40]. Sivaramkumar et al introduced a gap time which is difference between ready time and issuing time to improve customer satisfaction which helps managers to retain customers [41]. Guerriero et al evaluated customer satisfaction according to the instance of a drone's arriving time at event's location under a soft time condition [38].…”

Smart Cyber-Physical Manufacturing: Extended and Real-Time Optimization of Logistics Resources in Matrix Production

“…The financial aspects of manufacturing supply are analyzed from price [68,69], operational costs [70], and profit [71] points of view. Responsiveness, robustness, and resilience (known as "Triple R") become more and more important in logistics and material handling [72] because customer satisfaction is based on "Triple R"-based performance of manufacturing and related logistics operations [73]. The objective functions and constraints are based on the problems of typical material handling related problems, like facility location [74], allocation [75], lot sizing [76][77][78], shortage planning [79], scheduling [80], inventory planning [81], and ergonomic [82] and trade policy aspects [83].…”

Vehicle Routing Optimization of Instant Distribution Routing Based on Customer Satisfaction

“…The multi-objective vehicle routing problem (MOVRP) is an extension of VRPs. Time windows are introduced into VRPs in logistics distribution, resulting in a combinatorial optimization problem [14]. Different from single objective optimization, the optimal solution is a set of compromise solutions, namely the Pareto optimal solution set.…”

“…Researchers have proposed a series of multi-objective evolutionary algorithms (MOEAs) based on heuristic algorithms, such as the tabu search algorithm [14], genetic algorithm [17], simulated annealing algorithm [18], and ant colony algorithm [19], to solve the multi-objective vehicle routing problems. Deb [20] proposed the fast nondominated sorting genetic algorithm version II (NSGA-II) with elitist strategy based on the NSGA, and the algorithm has become a comparative mark for performance comparison of multi-objective optimization.…”