Greedy-search-based multi-objective genetic algorithm for emergency logistics scheduling

To enable the immediate and efficient dispatch of relief to victims of disaster, this study proposes a greedy-search-based, multi-objective, genetic algorithm capable of regulating the distribution of available resources and automatically generating a variety of feasible emergency logistics schedules for decision-makers. The proposed algorithm dynamically adjusts distribution schedules from various supply points according to the requirements at demand points in order to minimize unsatisfied demand for resources, time to delivery, and transportation costs. The proposed algorithm was applied to the case of the Chi–Chi earthquake in Taiwan to verify its performance. Simulation results demonstrate that under conditions of a limited/unlimited number of available vehicles, the proposed algorithm outperforms the MOGA and standard greedy algorithm in ‘time to delivery’ by an average of 63.57% and 46.15%, respectively, based on 10,000 iterations.     

A hybrid hub-and-spoke postal logistics network with realistic restrictions: A case study of Korea Post

Postal logistics has a complex transportation network for efficient mail delivery. Therefore, a postal logistics network consists of various functional sites with a hybrid hub-and-spoke structure. More specifically, there are multiple Delivery & Pickup Stations (D&PSs), multiple Mail Processing Centers (MPCs), and one Exchange Center (EC). In this paper, we develop two mathematical models with realistic restrictions for Korea Post for the current postal logistics network by simultaneously considering locations and allocations. We propose an Integer Linear Programming (ILP) model for transportation network organization and vehicle operation and a Mixed Integer Linear Programming (MILP) model that considers potential ECs for decision making while simultaneously regarding the EC location, transportation network organization, and vehicle operation. We use modified real data from Korea Post. Additionally, we consider several scenarios for supporting EC decision makers. The proposed models and scenarios are very useful in decision making for postal logistics network designers and operators.     

Novel multi-objective resource allocation and activity scheduling for fourth party logistics

In order to reduce logistic costs, the scheduling of logistic tasks and resources for fourth party logistics (4PL) is studied. Current scheduling models only consider costs and finish times of each logistic resource or task. Not generally considered are the joint cost and time between two adjacent activities for a resource to process and two sequential activities of a task for two different resources to process are ignored. Therefore, a multi-objective scheduling model aiming at minimizing total operation costs, finishing time and tardiness of all logistic tasks in a 4PL is proposed. Not only are the joint cost and time of logistic activities between two adjacent activities and two sequential activities included but the constraints of resource time windows and due date of tasks are also considered. An improved nondominated sorting genetic algorithm (NSGA-II) is presented to solve the model. The validity of the proposed model and algorithm are verified by a corresponding case study.     

景德镇陶瓷物流的构建研究

物流服务作为对地区生产力提高和社会经济增长有重要意义的四个服务性行业之一，在全国范围内已经被广泛地得到认可。本文分析了景德镇经济发展、陶瓷物流业发展和景德镇物流需求的状况，结合实际提出了景德镇现代物流业发展建议。     

基于VB的模具企业生产物流信息系统的研究

通过分析模具企业生产物流信息的特点,运用Visual Basic 6.0和Access数据库对该系统进行研究和开发,包括系统的注塑模具BOM表、明细表的查询模块;零件加工工艺过程表的添加、修改、删除管理模块;各零件物流量计算模块;零件工艺过程图的调用模块及选择决策等各功能模块的实现。然后把选择决策模块中的F-D图反应出的车间物流状况的优劣作为平面布置调整的依据,并制定出改善方案,从而使得车间设备布局及组合方式更为合理,以更好地支持生产。     

两级定位-路径问题的变邻域人工蜂群算法

建立了两级定位-路径问题的数学模型,提出了一种求解该问题的人工蜂群算法。针对该算法容易出现早熟现象,将近年来国外出现的一种新颖的轨迹式启发式算法--变邻域搜索融入其中,由此提出三种变邻域搜索策略。基于不同变邻域搜索策略的人工蜂群算法和人工鱼群算法的求解效果进行对比仿真。实验结果表明,变邻域人工蜂群算法能有效求解两级定位-路径问题。     

基于需求更新的救灾品配送公平与效率协调模型

鉴于需求准确度与救灾品配送效率之间的悖反关系,研究了基于需求更新的救灾品配送效率与公平问题.通过对某市民政局进行调研,了解到台风灾害发生时的需求变化规律,设计一种通过已知需求灾区对未知需求灾区的需求进行贝叶斯更新的需求更新方式,由此建立救灾品配送模型.将该模型应用于某台风救灾实例,通过实例分析表明了该模型的求解速率与精度,并找到了最优"决策点",实现了救灾品配送公平与效率的平衡.     

Coordinating efficiency and equity in disaster relief logistics via information updates

This paper addresses a multi-supplier, multi-affected area, multi-relief, and multi-vehicle relief allocation problem in disaster relief logistics. A multi-objective optimisation model based on disaster scenario information updates is proposed in an attempt to coordinate efficiency and equity through timely and appropriate decisions regarding issues such as vehicle routing and relief allocation. An optimal stopping rule is also proposed to determine the optimum period of delay before responding to disaster, because decision making requires accurate disaster information. The main contribution of this paper is solving relief allocation problem in a novel way by correlating operational research with statistical decision making and Bayesian sequential analysis. Finally, a case is presented based on the post-disaster rescue in Eastern China after supertyphoon Saomai to test the applicability and show the potential advantages of the proposed model.     

An order-picking operations system for managing the batching activities in a warehouse

Nowadays, customer orders with high product variety in small quantities are often received and requested for timely delivery. However, the order-picking process is a labour-intensive and costly activity to handle those small orders separately. In such cases, small orders are often grouped into batches so that two or more orders can be served at once to increase the picking efficiency and thus reduce the travel distance. In this paper, an order-picking operations system (OPOS) is proposed to assist the formulation of an order-picking plan and batch-handling sequence. The study integrates a mathematical model and fuzzy logic technique to divide the receiving orders into batches and prioritise the batch-handling sequence for picking, respectively. Through the proposed system, the order-picking process can be managed as batches with common picking locations to minimise the travel distance, and the batch-picking sequence can be determined as well. To demonstrate the use of the system, a case study in a third-party logistics warehouse is presented, and the result shows that both the order-picking activity and labour utilisation can be better organised.     

物流监控系统人工免疫数据融合算法研究

为实现对果蔬等农产品物流过程的低能耗实时监控,提出一种适用于现代果蔬物流过程的实时监控系统监测数据处理方案。通过在运输车辆的车厢内部署具有RFID功能的温湿度传感器,获取物流过程中果蔬储存环境的实时温湿度数据;采用生物免疫机理对节点感知数据进行预处理,降低节点数据传输量;计算节点数据融合信度获取融合权值,对节点数据加权融合获取对监测对象的完整一致性监测结果;用感知数据与融合结果相似度差值,对节点感知数据的融合信度进行更新;根据实际情况选取通信模式,降低节点通信能耗及紧急事件的响应时间。仿真结果表明,该方案可有效降低果蔬物流过程实时监控的网络通信能耗。