Facility location, inventory control, and vehicle routes scheduling are critical and highly related problems in the design of logistics system for e‐business. Meanwhile, the return ratio in Internet sales was significantly higher than in the traditional business. Many of returned merchandise have no quality defects, which can reenter sales channels just after a simple repackaging process. Focusing on the existing problem in e‐commerce logistics system, we formulate a location‐inventory‐routing problem model with no quality defects returns. To solve this NP‐hard problem, an effective hybrid genetic simulated annealing algorithm (HGSAA) is proposed. Results of numerical examples show that HGSAA outperforms GA on computing time, optimal solution, and computing stability. The proposed model is very useful to help managers make the right decisions under e‐supply chain environment.

The need to reduce carbon emission to cope with climate change has gradually become a global consensus, which also poses a great challenge to cold-chain logistics companies. It forces them to implement green distribution strategies. To help the distribution companies reduce carbon emission, this paper studies two aspects—carbon tax value and investing in the freshness-keeping cost—and proposes corresponding solutions. A new green vehicle-routing model for fresh agricultural products with the goal of minimizing the total cost is proposed. To solve the model proposed, an improved ant-colony optimization (IACO) is designed specifically. On one hand, the experimental results show that the increase in carbon tax will restrict the carbon emission behaviors of the distribution companies, but it will also reduce their economic benefits to a certain extent, at the same time. On the other hand, investing in the freshness-keeping cost can help actively achieve the carbon emission reduction target, reduce the loss of fresh agricultural products in the distribution process, improve the company’s economic benefits and satisfy customers. The comparison results of different algorithms prove that the IACO proposed in this paper is more effective in solving the model, which can help increase the economic benefits of the companies and reduce carbon emission. This study provides a new solution for cold-chain logistics distribution companies to reduce carbon emission in the distribution process, and also provides a reference for government departments to formulate carbon tax policies.

In cold chain logistics, fresh agricultural products are susceptible to deteriorate due to the passage of time in the distribution process. To reduce the loss of cargo, this research integrates the traditional refrigeration cost into the freshness-keeping cost invested in the process of transportation and unloading goods. We rely on the investment of freshness-keeping cost to reduce the cargo damage cost caused by the distribution process and then propose a new vehicle routing problem (VRP). According to all relevant costs, this research builds a mathematical model with the goal of minimizing the total distribution cost. A hybrid ant colony optimization is designed to solve the problem, and the effectiveness of the model and algorithm are verified through two sets of comparative experiments. To determine which products should be invested in freshness-keeping cost to reduce the total distribution cost, we perform numerical analysis on the relevant parameters in the model. Results provide decision-making references for cold chain logistics distribution enterprises in the design of distribution routes.

The stochastic joint replenishment and delivery scheduling (JRD) problem is a key issue in supply chain management and is a major concern for companies. So far, all of the work on stochastic JRDs is under explicit environment. However, the decision makers often have to face vague operational conditions. We develop a practical JRD model with stochastic demand under fuzzy backlogging cost, fuzzy minor ordering cost, and fuzzy inventory holding cost. The problem is to determine procedures for inventory management and vehicle routing simultaneously so that the warehouse may satisfy demand at a minimum long-run average cost. Subsequently, the fuzzy total cost is defuzzified by the graded mean integration representation and centroid approaches to rank fuzzy numbers. To find optimal coordinated decisions, a modified adaptive differential evolution algorithm (MADE) is utilized to find the minimum long-run average total cost. Results of numerical examples indicate that the proposed JRD model can be used to simulate fuzzy environment efficiently, and the MADE outperforms genetic algorithm with a lower total cost and higher convergence rate. The proposed methods can be applied to many industries and can help obtaining optimal decisions under uncertain environment.