With the rapid development of the courier industry, customers are placing higher demands on the cost and delivery time of courier services. Therefore, this paper focuses on the bi-objective express transportation network design problem (BO-ETNDP) to minimize the operation cost and maximum arrival time. A multi-structure parallel design methodology (MS-PDM) is proposed to solve the BO-ETNDP. In this methodology, all topological structures commonly used in designing transportation networks are sorted out. For each topological structure, a novel bi-objective nonlinear mixed-integer optimization model for BO-ETNDP is developed considering the impact of the hub’s sorting efficiency on the operation cost and arrival time. To solve these models, a preference-based multi-objective algorithm (PB-MOA) is devised, which embeds the branch-and-cut algorithm and Pareto dominance theory in the framework of this ranking algorithm. In the case study, the applicability of the proposed methodology is verified in a real-world leading express company. The results show that our methodology can effectively avoid the limitation of solving the BO-ETNDP with a specific structure. Besides, the suitable topology for designing express transportation networks in different scenarios are explored through the sensitivity analysis.

This paper investigates an identical parallel machine scheduling problem with flexible maintenance and job release times and attempts to optimize two objectives: the minimization of the makespan and total tardiness simultaneously. A mixed-integer programming (MIP) model for solving small-scale instances is presented first, and then a modified NSGA-Ⅱ (M-NSGA-Ⅱ) algorithm is constructed for solving medium- and large-scale instances by incorporating several strategies. These strategies include: (ⅰ) the proposal of a decoding method based on dynamic programming, (ⅱ) the design of dynamic probability crossover and mutation operators, and (ⅲ) the presentation of neighborhood search method. The parameters of the proposed algorithm are optimized by the Taguchi method. Three scales of problems, including 52 instances, are generated to compare the performance of different optimization methods. The computational results demonstrate that the M-NSGA-Ⅱ algorithm obviously outperforms the original NSGA-II algorithm when solving medium- and large-scale instances, although the time taken to solve the instances is slightly longer.

During the past few years, transportation of agricultural products is increasingly becoming a crucial problem in supply chain logistics. In this paper, we present a new mathematical formulation and two solution approaches for an intermodal transportation problem. The proposed bi-objective model is applied to the transportation of agricultural products from Morocco to Europe to minimise both the transportation cost either in the form of uni-modal or intermodal, as well as the maximal overtime to delivery products. The first solution approach is based on a non-dominated sorting genetic algorithm improved by a local search heuristic and the second one is the GRASP algorithm (Greedy Randomised Adaptive Search Procedure) with iterated local search heuristics. They are tested on theoretical and real case benchmark instances and compared with the standard NSGA-II. Results are analysed and the efficiency of algorithms is discussed using some performance metrics.

The increase competition and decline economy has increased the relevant importance of having reliable supply chain. The primary objective of many supply chain problems is to reduce the cost of services and, at the same time, to increase the quality of services. In this paper, we present a multi-level supply chain network by considering multi products, single resource and deterministic cost and demand. The proposed model of this paper is formulated as a mixed integer programming and we present two metaheuristics namely MOPSO and NSGA-II to solve the resulted problems. The performance of the proposed models of this paper has been examined using some randomly generated numbers and the results are discussed. The preliminary results indicate that while MOPSO is able to generate more Pareto solutions in relatively less amount of time, NSGA-II is capable of providing better quality results.

In recent years, death toll of natural and man-made disasters has increased at an appalling rate. Thus, disaster management and especially efficient management of humanitarian relief efforts seem to be essential. This paper presents a bi-objective mixed-integer mathematical model for Humanitarian Relief Logistics (HRL) operations planning, as an important part of the humanitarian relief efforts. This model determines optimal policies including location of warehouses, quantity of emergency relief items that should be held at each warehouse, and distribution plan to provide an emergency response pre-positioning strategy for disasters by considering two objectives. The first one minimizes the average response time and the second one minimizes the total operational cost including the fixed cost of establishing warehouses, the holding cost of unused supplies and the penalty cost of unsatisfied demand. The survival of pre-positioned supplies, demand amount and routes condition following an event are considered under uncertainty in the model solved by a robust scenario-based approach. The robust approach is applied to reduce the effects of fluctuations of the uncertain parameters with regards to all the possible future scenarios. The research demonstrates the applicability and usefulness of the proposed model on a case study on earthquake preparation in the Seattle area in USA. In addition, the work applies the Reservation Level Tchebycheff Procedure (RLTP) method to solve the bi-objective model in an interactive way with decision maker. This work provides practitioners, specifically planning teams, with a new approach to assist with disaster preparedness and to improve their logistics decisions.