In this work, a probabilistic bid generation problem with the pricing of a bundle of lanes and carrier’s vehicle routing is considered as it is an importation in transportation service procurement. Depending on the network of the vehicle, there exist multiple lanes for traveling between two locations. To solve the bid generation problem efficiently, a two-phase method approach is presented. At the core of the procedure a feasible vehicle routing problem on a multidigraph is solved by an exhaustive search algorithm to enumerate all routes concerning routing constraints and treat each route as a decision variable in the set partitioning formulation. We examine our model both analytically and empirically using a simulation-based analysis.

During the spread of the epidemic, the home delivery service (HDS) has been quickly introduced by retailers which helps customers avoid the risk of viral infection while shopping at offline stores. However, the operation cost of HDS is a huge investment for O2O retailers. How to minimize the operating costs of HDS is an urgent issue for the industry. To solve this problem, we outline those management decisions of HDS that have an impact on operating costs, including dynamic vehicle routing, driver sizing and scheduling, and propose an integrated optimization model by comprehensively considering these management decisions. Moreover, the dynamic feature of online orders and the heterogeneous workforces are also considered in this model. To solve this model, an efficient adaptive large neighborhood search (ALNS) and branch-and-cut algorithms are developed. In the case study, we collected real data from a leading O2O retailer in China to assess the effectiveness of our proposed model and algorithms. Experimental results show that our approach can effectively reduce the operating costs of HDS. Furthermore, a comprehensive analysis is conducted to reveal the changing patterns in operating costs, and some valuable management insights are provided for O2O retailers. The theoretical and numerical results would shed light on the management of HDS for O2O retailers.

Transportation planning in forest industry is a challenging activity since it involves complex decisions about raw material allocation, vehicle routing and scheduling of trucks arrivals to both harvest areas and the plants. In the Argentine context, specifically in the Argentinean Northeast (NEA) region, the forest industry plays essential role for the economic development and, among the included activities, the transportation is the key element considering the volumes that must be moved and the distances to be traveled. Therefore, enhancing efficiency in the transportation activity improves significantly the performance of this industry. In this work, a Mixed Integer Linear Programming (MILP) model is presented, where raw material allocation, vehicle routing and scheduling of trucks arrivals are simultaneously addressed. Since the resolution times of the proposed integrated MILP model are prohibitive for large instances, a hierarchical approach is also presented. The considered decomposition approach involves two stages: in the first phase, the raw material allocation and vehicle routing problems are solved through a MILP model, while in the second phase, fixing the route for each truck according to the results of the previous step, the scheduling of truck arrivals to both the harvest areas and the plants is solved through a new MILP model. The obtained results show that the proposed approach is very effective and could be easily applied in this industry.

This paper introduces a covering model for collaborative bidirectional multi-period vehicle routing problems under profit-sharing agreements (CB-VRPPA) in bulk transportation (BT) networks involving one control tower and multiple shippers and carriers. The objective is to maximize the total profits of all parties subject to profit allocation constraints among carriers, terminal capability limitations, transport capability limitations and time-window constraints. The proposed method includes three stages: (a) generate all feasible routes of each carrier, (b) eliminate unattractive feasible routes via a proposed screening technique to reduce the initial problem size, and (c) solve the reduced problem using a branch-and-bound algorithm. Computational experiments are performed for real-life, medium- and large-scale instances. The proposed method provides satisfactory results when applied to solve the CB-VRPPA. We also conduct a sensitivity analysis on a critical parameter of the profit-sharing agreement to confirm the effectiveness of the proposed method.

Nowadays, most manufacturing units try to locate their requirements and the depot vehicle routing in order to transport the goods at optimum cost. Needless to mention that the locations of the required warehouses influence on the performance of vehicle routing. In this paper, a mathematical programming model to optimize the storage location and vehicle routing are presented. The first objective function of the model minimizes the total cost associated with the transportation and storage, and the second objective function minimizes the difference distance traveled by vehicles. The study uses Imperialist Competitive Algorithm (ICA) to solve the resulted problems in different sizes. The preliminary results have indicated that the proposed study has performed better than NSGA-II and PAES methods in terms of Quality metric and Spacing metric.

In this paper, we consider the multi depot heterogeneous vehicle routing problem with time windows in which vehicles may be replenished along their trips. Using the modeling technique in a new-generation solver, we construct a novel formulation considering a rich series of constraint conditions and objective functions. Computation results are tested on an example comes from the real-world application and some cases obtained from the benchmark problems. The results show the good performance of local search method in the efficiency of replenishment system and generalization ability. The variants can be used to almost all kinds of vehicle routing problems, without much modification, demonstrating its possibility of practical use.