The effective distribution of relief to an emergency logistics system plays a crucial role during the disaster response phase. Considering stochastic characteristics of relief demand, this study investigates the robust optimization of a multi-objective multi-period location-routing problem for epidemic logistics, a special emergency logistics, with uncertain scenarios. A corresponding robust multi-objective multi-period optimization model is proposed, which aims to determine the optimal location of temporary relief distribution centres and route planning simultaneously. The optimization objectives include the total travel time, the total cost, and the disutility of relief service. To solve the above optimization model, a preference-inspired co-evolutionary algorithm with Tchebycheff decomposition (PICEA-g-td) is given. The performance of the proposed PICEA-g-td is evaluated by comparing it with NSGA-II, MOEA/D and PICEA-g. The experimental results show that the proposed algorithm performs better than the other three algorithms in terms of the solution quality. Finally, some useful management insights are obtained.

This paper investigates the joint optimization problem on the logistics infrastructure investment and CO₂ emission taxes for a sustainable city logistics network design by a goal programming approach where the cost recovery, service level and CO₂ emission reduction goals are involved. The above multi-objective logistics infrastructure capacity investment and CO₂ emission taxes problem is formulated as a bi-level goal programming model. Given the priority structure of the goals, the total deviations from predetermined goals are minimized in the upper level, while the lower level of the model serves as the service route choice equilibrium problem of logistics users. To solve the proposed model, a genetic algorithm is developed, where the method of successive average (MSA) is embedded. The case study focusing on the urban logistics network of Changsha, China demonstrates the effectiveness of the bi-level goal programming model and the genetic algorithm. The findings reveal that the priority rankings of the goals have a significant impact on the joint decisions of CO₂ emission taxes and logistics infrastructure capacity investment. The proposed methodology provides an avenue to balance multiple conflicting objectives and obtain an economical and environmental city logistics network.

This paper outlines an exact and a heuristic algorithm for the electric vehicle routing problem with a nonlinear charging function (E-VRP-NL) introduced by Montoya et al. (2017). The E-VRP-NL captures several realistic features of electric vehicles including the battery limited driving range and nonlinear charging process at the charging stations. We formulate this problem as a set-partitioning and solve it using a column generation based algorithm. The resulting pricing problem of the column generation is a complicated problem as, next to the usual operational constraints e.g. time windows and vehicle capacity, electric vehicle related features are also considered. In particular, the nonlinear nature of the battery charging process requires the incorporation of a set of sophisticated recursive functions in the pricing algorithm. We show how these recursive functions allow for the simultaneous evaluation of the routing and charging decisions. Moreover, we illustrate how they can efficiently be embedded in the pricing algorithm. The column generation algorithm is integrated in a branch and bound algorithm and cutting planes are added resulting in a branch-and-price-and-cut algorithm for the E-VRP-NL. Next to the exact algorithm, we also develop a tabu search based heuristic to solve the problem quickly. To prove the efficiency of the proposed algorithms, their performance is tested on benchmark instances from the literature. Our exact algorithm can optimally solve instances with up to 40 customers, including several instances previously unsolved to optimality. The tabu search heuristic proves to be superior to state-of-the-art heuristics in the literature both on solution quality and computation times.