Due to the diversity and the different distribution conditions of agricultural products, split delivery plays an important role in the last mile distribution of agricultural products distribution. The time-dependent split delivery green vehicle routing problem with multiple time windows (TDSDGVRPMTW) is studied by considering both economic cost and customer satisfaction. A calculation method for road travel time across time periods was designed. A satisfaction measure function based on a time window and a measure function of the economic cost was employed by considering time-varying vehicle speeds, fuel consumption, carbon emissions and customers’ time windows. The object of the TDSDGVRPMTW model is to minimize the sum of the economic cost and maximize average customer satisfaction. According to the characteristics of the model, a variable neighborhood search combined with a non-dominated sorting genetic algorithm II (VNS-NSGA-II) was designed. Finally, the experimental data show that the proposed approaches effectively reduce total distribution costs and promote energy conservation and customer satisfaction.

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Research on the Time-Dependent Split Delivery Green Vehicle Routing Problem for Fresh Agricultural Products with Multiple Time Windows

This study focuses on the collaborative multicenter vehicle routing problem with time windows and mixed deliveries and pickups (CMVRPTWMDP), which is a variant of the vehicle routing problem (VRP) with mixed deliveries and pickups, and VRP with simultaneous deliveries and pickups and time windows. Collaboration and transportation resource sharing are adopted to optimize vehicle routes in the CMVRPTWMDP, to integrate the delivery and pickup services with time windows, and to construct open–closed mixed vehicle routes. First, the CMVRPTWMDP is formulated as a mixed-integer programming model to minimize logistics operating costs. The effect of transportation resource sharing on reducing the number of needed vehicles and the maintenance cost is considered in the model formulation. Second, a two-stage hybrid algorithm combining customer clustering and vehicle routing optimization is designed to solve the CMVRPTWMDP. An improved 3D k-means clustering algorithm based on space–time distances and the customer demand is proposed to reassign customers to logistics facilities (e.g., delivery or pickup centers). Furthermore, a hybrid heuristic algorithm that combines the genetic algorithm (GA) and particle swarm optimization (PSO) algorithm, called GA–PSO, is designed to optimize the vehicle routes. A coordination operator between GA and PSO is designed to allow particles and chromosomes to interact, increasing the diversity of particle swarms and the possibility of finding a feasible solution. Third, the performance and effectiveness of the proposed approach are tested by comparing them with the CPLEX solver using 30 small-scale instances and other existing algorithms using 25 benchmark instances. Fourth, the minimum costs-remaining savings (MCRS) model is adopted to design a fair and reasonable profit allocation scheme for participants in the collaborative alliance and maintain alliance stability. Finally, the optimization results of a real-world case study from Chongqing, China, show that transportation resource misuse and logistics operating costs are significantly reduced, demonstrating the proposed approach’s effectiveness and applicability. This study provides insights for logistics enterprises and transportation departments on effectively allocating and utilizing the transportation resources and optimizing the local logistics network. 

Collaborative multicenter vehicle routing problem with time windows and mixed deliveries and pickups

In this paper, we study on how to achieve balance between minimizing the objectives, satisfying the constraints, and avoiding the population to stuck at locally optimal or locally feasible regions. We propose a new constraints handling technique based on diversity distance measure for constrained multiobjective optimization problems. We innovatively measure all effects of the constraints on the objectives by the interaction effects and direct effects of constraints on the objectives. The interactive effects of constraints on the objective functions are firstly expressed and quantified by using Mahalanobis distance and computing the value of the distance measure by involving a Lebesgue measure. Based on interactive and direct effects, we propose a new modified mechanism of objective space, called objective space modified mechanism based on diversity distance measure, so that the good infeasible solution could be more effectively used to find the optimal solution. The Pull and Push search is used to adaptively adjust the location of the Pareto front, which could preclude the population from being stuck at some locally optimal or locally feasible regions and could decrease time complexity. Comprehensive experiments completed for several benchmark problems demonstrate the competitiveness of the proposed algorithm, in comparison to the existing state-of-art constrained evolutionary multiobjective optimization.

Constraint handling technique based on Lebesgue measure for constrained multiobjective particle swarm optimization algorithm

Services such as Meituan and Uber Eats have revolutionized the way the customer can find and order from restaurants Numerous independent restaurants are competing for orders placed by customers via online food ordering platforms Ordering takeout food on smartphone apps has become more and more prevalent in recent years There are some operational challenges that takeout food service providers have to deal with, eg, customer demand fluctuates over time and region In this sense, the service providers sometimes ignore the fact that some riders may be idle in several periods in regions, while, in contrast, there may be a shortage of riders in other situations In order to address this problem, we introduce a two-stage model to optimize scheduling of riders for instant food deliveries A service provider platform expectantly schedules the least quantity of riders to deliver within expected arrival time to satisfy customer demand in different regions and time periods We introduce a two-stage model that adopts the method of mixed-integer programming (MIP), characterize relevant aspects of the scenario, and propose an optimization algorithm for scheduling riders We also divide the delivery service region and time into smaller parts in terms of granularity The large neighborhood search algorithm is validated through numerical experiments and is shown to meet the design objectives Furthermore, this study reveals that the optimization of rider resource is beneficial to reduce overall cost of the delivery Takeout food service platforms decide scheduling shifts (start time and duration) of the riders to achieve a service level target at minimum cost Additional sensitivity analyses, such as the tightness of the order time windows associated with the orders and riders’ familiarity with delivery regions, are also discussed

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Optimization of Rider Scheduling for a Food Delivery Service in O2O Business

Problems of allocating joint costs in a reasonable way arise in many practical situations where people decide to work together to save costs. Cost allocation methods based on game theoretical concepts take into account the strategic aspects of cost allocation situations. We give a survey of cost allocation methods based on the nucleolus and the Shapley value, and introduce also a new one, the so-called cost gap allocation method which is based on the I-value. It is shown that for some large subclasses of cost allocation problems this new cost allocation method coincides with old separable cost methods proposed in the thirties by the Tennessee Valley Authority and also with the separable costs-remaining benefits SCRB method. Properties of this cost gap allocation method are also treated.

Game theory and cost allocation problems

Collaboration such as resource sharing among logistics participants (LPs) can effectively increase the efficiency and sustainability of logistics operations, especially in the transportation and distribution of fresh and perishable products that require special infrastructure (e.g., refrigerated trucks/vehicles). This study tackles a collaborative multi-center vehicle routing problem with resource sharing and temperature control constraints (CMCVRP-RSTC). Solving the CMCVRP-RSTC by minimizing the total cost and the number of refrigerated vehicles returns a fresh logistics operational strategy that pinpoints how a multi-center fresh logistics distribution network can be reorganized to highlight potential collaboration opportunities. To find the solution to the CMCVRP-RSTC, we develop a hybrid heuristic algorithm that combines the extended k-means clustering and tabu search non-dominated sorting genetic algorithm-II (TS-NSGA-II) to search a large solution space. This hybrid heuristic algorithm ensures that the optimal solution is found efficiently through initial solution filtering and the combination of local and global searches. Furthermore, we explore how to motivate individual LPs to collaborate by analyzing the benefits of collaboration to each LP. Using the minimum costs remaining savings method and the strictly monotonic path rule, a cost saving calculation model is proposed to find the best profit allocation scheme where each collaborating LP keeps benefiting from long-term collaboration. An empirical case study of Chongqing City, China indicates the efficiency of our proposed collaborative mechanism and optimization algorithms. Our study will help improve the efficiency of logistics operation significantly and contribute to the development of more intelligent logistics systems and smart cities.

Collaborative multiple centers fresh logistics distribution network optimization with resource sharing and temperature control constraints

Collaboration among logistics companies offers a simple and effective way of increasing logistics operation efficiency. This study designs an optimal collaboration strategy by solving the collaborative logistics pickup and delivery problem with eco-packages (CLPDPE). This problem seeks to minimize the total operational costs by forming collaborative alliances and allocating trucking resources based on time–space (TS) network properties. The synchronization of two-echelon logistics networks is improved by solving this problem. Moreover, this study considers the stability of collaboration (i.e., the willingness of logistics companies to join and remain in collaborative alliances) by comparing different profit allocation strategies in the CLPDPE solving process. A novel methodology that combines multi-objective mixed integer programming, multidimensional K-means clustering, reference point based non-dominated sorting genetic algorithm-II (RP-NSGA-II), forward dynamic programming and improved Shapley value method is developed to formulate and solve CLPDPE. Our results show that the proposed algorithm outperforms most other algorithms in minimizing the total cost, waiting time and number of vehicles. An empirical case study in Chongqing city, China suggests that the proposed collaborative mechanism and transportation resource sharing strategy based on TS network can reduce cost, improve distribution efficiency, and contribute to efficient, smart, intelligent and sustainable urban logistics and transportation systems.

Zheng Wang

Papers

Collaborative multiple centers fresh logistics distribution network optimization with resource sharing and temperature control constraints

Collaborative logistics pickup and delivery problem with eco-packages based on time–space network