An intelligent logistics system is an important branch of intelligent transportation systems. It is a great challenge to develop efficient technologies and methodologies to improve its performance in meeting customer requirements while this is highly related to people's life quality. Its high efficiency can reduce food waste, improve food quality and safety, and enhance the competitiveness of food companies. In this paper, we investigate a new integrated planning problem for intelligent food logistics systems. Two objectives are considered: minimizing total production, inventory, and transportation cost and maximizing average food quality. For the problem, a bi-objective mixed integer linear programming model is formulated first. Then, a new method that combines an ε-constraint-based two-phase iterative heuristic and a fuzzy logic method is developed to solve it. Computational results on a case study and on 185 randomly generated instances with up to 100 retailers and 12 periods show the effectiveness and efficiency of the proposed method.

International audienceThe zero sum gains data envelopment analysis models (ZSG-DEA models) are non-linear. In this paper, we first show that the ZSG-DEA models can be transformed to linear or parametric linear models and discuss the feasible domains of the parameters. Second, we show that the linear formulations of ZSG-DEA models under the equal output reduction strategy and the proportional output reduction strategy in a single output case are equivalent to the output-oriented super-efficiency model under variable returns-to-scale (VRS) assumption. As a matter of course, the models may encounter infeasibility. Third, we propose the linear transformations of ZSG-DEA models under constant returns-to-scale (CRS) assumption and compare them with the VRS models. In the end, we evaluate the participant countries at the Olympic Games by the linear equivalent models with multiple outputs under different weight restrictions. Our results are compared with the efficiencies obtained from the original ZSG-DEA model with an aggregated output under both CRS and VRS assumptions. It is found that the original method with aggregated output tends to underestimate the efficiencies of DMUs

China is one of the disaster‐prone countries in the world. Constructing a rapid and effective relief logistic system is important for disaster‐responding at country level. Strategic prepositioning of emergency items, especially the decision of appropriate emergency warehouses location, has significant impacts on rapid disaster response to ensure sufficient relief supplies. The emergency warehouse location decision is a complex problem, where a wide variety of criteria need to be considered and the preference information of decision makers (DMs) may be imprecise or even absent. In this paper, we identify key effectiveness‐oriented criteria used to evaluate the alternative emergency warehouse locations and make an attempt to propose a new multicriteria ranking method to solve the problem of inaccurate or uncertain weight information based on stochastic pairwise dominant relations and the pruning procedure of ELECTRE‐II method. The proposed method extends the conventional ELECTRE‐II method by incorporating inaccurate information and broadens its application to emergency warehouse location field. The feasibility and applicability of the proposed method are illustrated with a simulated example.