Study On End Distribution Problem After Online Shopping

The fast development of online shopping brings great opportunity and challenge to city distribution.The express enterprises do not meet their requirements because of terminal customers'characteristics,such as various uncertainties,decentralization of locations,smaller batch.In order to face the challenges,the express enterprises cooperate with each other to serve customers.In addition,with the development of information technology and big data technology,the e-commerce enterprises own the data of orders,location information of customers and more than one express enterprise;they also get involved in city distribution to optimize the process.Therefore,the express enterprises not only cooperate with each other,but cooperate with e-commerce enterprises.As a result,this paper focuses on the cooperative distribution between express enterprises and the coordination between the express enterprise and e-commerce enterprise.The contents in details are as follows:?1?We first focus on the vehicle routing problem with dynamic customers considering dual service in the last mile delivery in which three decisions have to be made:?1?Determine routes that lie along the HD points and CP facilities;?2?Optimize routes in real time;?3?Which mode is better between simultaneous dual service?SDS,HD points and CP facilities are served simultaneously by the same vehicle?and respective dual service?RDS,HD points and CP facilities are served respectively by different vehicles??Then a mixed integer linear programming model is established for the dynamic vehicle routing problem considering simultaneous dual services?DVRP-SDS?.To increase the practical usefulness and to solve large instances,we designed a two-phase matheuristic including construction-improvement heuristics to solve the deterministic model and dynamic programming to adjust routes to dynamic customers.Our computational experiments show that the CP facilities offer greater flexibility for adjusting routes to dynamic customers and that the SDS delivery system outperforms the RDS delivery system in terms of cost and number of vehicles used.?2?In order to avoid the second or third delivery,the roaming delivery locations of customers are further considered on the foundation of basic model built in above section.Still,only one of the roaming delivery locations of customers is served once.Meanwhile,considering real-life situations,we focus on the stochastic travel times during the delivery.Then a two-stage stochastic recourse formulation is established for the problem and related recourse strategies are designed in case of time window violation and route duration violation.An effective two-stage heuristic is designed to solve the complicated problem.In the first stage,a random selection and greedy insertion?RSGI?heuristic is designed to generate initial feasible solutions,and then the initial solutions are improved further by improvement heuristic with route re-optimization strategy in the second stage.In addition,the classic sample average approximation?SAA?sampling method is applied to dealing with stochastic travel times.Finally,the effectiveness and superiority of model and algorithm is validated based on a large amount of instances.?3?In this section,the collaborative multi-depot vehicle routing problem with roaming delivery location and stochastic travel times.The customers are shared and vehicles can start from one depot and return back to another depot.Then the mathematical formulation is built.Two sampling strategies?the fixed sample sampling,FSS and the SAA?are applied to dealing the stochastic variants.An adaptive large neighborhood search heuristic is designed to solve the problem.In order to explore further the solution spaces,eight effective neighborhood search strategies are established with terms of the problem.The comparison results?the comparisons between deterministic model and stochastic model as well as between two stochastic models?show the superiority of our model and algorithm.?4?This section investigates the issue of coordination for two-stage online shopping supply chain consisting of one online retailer and one thirty party logistics enterprise?TPL?.Meanwhile,the demand is influenced by the retailer's sales effort level and TPL's logistics improvement effort level except price.Using the game theory,we found that the two-part tariff contract cannot coordinate the online shopping supply chain by comparing the optimal solutions in the two cases of the centralized decision-making and decentralized decision-making.In order to effectively coordinate the online shopping supply chain,a bilateral effort cost-sharing contract is designed to by integrating the endeavors of the online retailer and the TPL.We identify the optimal level of sales effort,optimal level of logistics effort and optimal profit of online shopping supply chain.Further,we found that the effort levels of both parties are highest than the unilateral effort cost-sharing contract and no effort cost-sharing contract when sharing coefficients satisfy certain conditions,and the profit of online shopping supply chain is also improved,especially,when?₁?28??₂?28?0.5,the effort levels of both parties in the case of bilateral effort cost-sharing contract are equal the ones in the case of centralized decision-making,and the profit of both parties realize the Pareto improvement.Sensitivity analyses are conducted to examine the impacts of changes in the unit effort cost on the performance of the online shopping supply chain.Finally,numerical examples are applied to illustrate the validity of the proposed method.The research results of this paper can provide a reference for the online retailer and the TPL to make decisions to share effort cost in the customer-centered market environment.