Intermodal logistics: The interplay of financial,operational and service issues

This research addresses strategic planning for an interregional, hub based, intermodal logistics network operated by a logistics service provider. A tabu search meta-heuristic is used to solve a mathematical optimization model that extends the p-hub median model for interacting hub location-allocation problems to the domain of intermodal logistics. An empirical study based on a subset of US freight flows shows that intermodal logistics networks differ significantly from traditional over-the-road logistics networks in their hub locations, network structure, and their use of direct and inter-hub shipments. Furthermore, intermodal logistics networks are more sensitive to changes in service requirements and costs.     

A reverse logistics cost minimization model for the treatment of hazardous wastes

This study presents a cost-minimization model for a multi-time-step, multi-type hazardous-waste reverse logistics system. A discrete-time linear analytical model is formulated that minimizes total reverse logistics operating costs subject to constraints that take into account such internal and external factors as business operating strategies and governmental regulations. Application cases are presented to demonstrate the feasibility of the proposed approach. By using the proposed model coupled with operational strategies, it is shown that the total reverse logistics costs for the applications cases can be reduced by more than 49%.     

基于多重图的物流链选择决策模型及算法研究

为了解决由物流链结构特性确定的最优物流链的选择问题,提出了基于多重图的物流链选择决策模型。通过讨论物流链的结构特性,在物流节点和物流路径均有费用出现的情况下,建立了同一位置有多个物流节点和物流路径可供选择的,以多重图作为拓扑形式的物流链选择决策模型。针对Dijkstra算法不能解决多重图中节点具有权值的问题,对算法进行改进,通过虚拟的方式将多重图转化为简单图,并将原节点的权值分解到简单图中相应的边上,使原问题转换为简单图的最短路径问题进行求解。最后,用一个算例进行了分析,算例表明,该算法对规模不大的物流链网络选择决策问题求解是可行的。     

发展中枢辐射铁路物流的探讨

传统的铁路物流组织模式有其局限性,已难以满足市场对快速运输的需求,铁路企业的改革势在必行。在阐述中枢辐射铁路物流表现形式的基础上,分析铁路企业发展中枢辐射铁路物流的必要性和可行性及所面临的困难,同时就铁路企业如何发展中枢辐射铁路物流提出了建设性意见。     

Comparing strategies for addressing delivery shortages in stochastic demand settings

In logistics networks involving one supply point (depot) and several geographically dispersed demand points (e.g., retail stores), delivery shortages will result if the design of delivery routes ignores random period-to-period fluctuations in customer demands. Delivery shortages may be costly enough for the depot to seek strategies to prevent them. A requirement for rational comparison of strategies is quantifying their effects on total supply chain costs. Accurate distance prediction models are developed to help satisfy this prerequisite for the transportation cost element. These models are integrated into a comparison of strategies on the basis of how these strategies affect inventory and transportation. The focus of findings from the comparison involves identifying the information cost thresholds for accepting/rejecting a demand-responsive strategy. The study's implications for choosing a strategy are presented. ©     

Urban freight demand forecasting: A mixed quantity/delivery/vehicle-based model

The research germinates from the statement that the cities have to solve the impacts due to freight transport in order to improve their sustainability implementing sets of city logistics measures. But city logistics measures involve several actors and choice dimensions. It is therefore important to have methods and models able to assess the effectiveness of the measures to be implemented. The current models were mainly developed to simulate some aspects of urban freight transport, and are not able to forecast many impacts of implementing traffic and transportation measures at an urban scale.This paper presents a modelling approach that tries to point out the relations existing among city logistics measures, actors and choice dimensions. It comprises three model sub-systems to estimate the quantity O–D matrices by transport service type (e.g. retailer on own account or wholesaler on own account or by carrier), the delivery O–D matrices by delivery time period, and the vehicle O–D matrices according to delivery tour departure time and vehicle type.This modelling system is a multi-stage model and considers a discrete choice approach for each decisional level. It was first tested using some data collected in the inner area of Rome, including traffic counts and interviews with retailers and truck-drivers. The model estimations were also compared with the experimental ones, and quite satisfactory results were obtained.     

Green supply chain management,reverse logistics and nuclear power generation

Built on the concepts of green supply chain management (G-SCM), this paper presents a multi-objective optimization programming approach to address the issue of nuclear power generation. In this study, a linear multi-objective optimization model is formulated to optimize the operations of both the nuclear power generation and the corresponding induced-waste reverse logistics. Factors such as the operational risks induced in both the power generation and reverse logistics processes are considered in the model formulation. Numerical results indicate that using the proposed approach, the induced environmental impact including the corresponding costs and risks can be improved up to 37.8%.     

Reliable design of a forward/reverse logistics network under uncertainty: A robust-M/M/c queuing model

This paper presents a novel model for designing a reliable network of facilities in closed-loop supply chain under uncertainty. For this purpose, a bi-objective mathematical programming formulation is developed which minimizes the total costs and the expected transportation costs after failures of facilities of a logistics network. To solve the model, a new hybrid solution methodology is introduced by combining robust optimization approach, queuing theory and fuzzy multi-objective programming. Computational experiments are provided for a number of test problems using a realistic network instance.     

Reliable design of a forward/reverse logistics network under uncertainty: A robust-M/M/c queuing model

This paper presents a novel model for designing a reliable network of facilities in closed-loop supply chain under uncertainty. For this purpose, a bi-objective mathematical programming formulation is developed which minimizes the total costs and the expected transportation costs after failures of facilities of a logistics network. To solve the model, a new hybrid solution methodology is introduced by combining robust optimization approach, queuing theory and fuzzy multi-objective programming. Computational experiments are provided for a number of test problems using a realistic network instance.     

Towards enhancing the last-mile delivery: An effective crowd-tasking model with scalable solutions

In urban logistics, the last-mile delivery from the warehouse to the consumer’s home has become more and more challenging with the continuous growth of E-commerce. It requires elaborate planning and scheduling to minimize the global traveling cost, but often results in unattended delivery as most consumers are away from home. In this paper, we propose an effective large-scale mobile crowd-tasking model in which a large pool of citizen workers are used to perform the last-mile delivery. To efficiently solve the model, we formulate it as a network min-cost flow problem and propose various pruning techniques that can dramatically reduce the network size. Comprehensive experiments were conducted with Singapore and Beijing datasets. The results show that our solution can support real-time delivery optimization in the large-scale mobile crowd-sourcing problem.     

A conceptual framework for agent-based modelling of logistics services

This paper presents an agent-based microsimulation framework that represents the diversity of roles and functions of actors in the freight system, how they interact through markets and how interactions between actors are established in markets through contracts. The framework provides sensitivity to technology trends, business trends, and policy scenarios. Logistics costs, outsourcing of logistics services to third party logistics firms, growth or retraction of various industry sectors, and the impact of new supply channels are explicitly represented. Data sources available in Toronto, Canada and new data collection efforts required for model estimation are described.     

A scheduling model of logistics service supply chain based on the mass customization service and uncertainty of FLSP’s operation time

Building on previous research, this paper establishes a new multi-objective program scheduling model of Logistics service supply chain that considers the uncertainty of operation time for functional logistics service providers (FLSPs) in a mass customization service environment. This model aims to minimize total scheduling costs, minimize the difference between the scheduled and actual time of each service process, and maximize the average satisfaction of FLSPs. Considering the differences in target priority, the goal programming method is applied to solve the model hierarchically. In addition, the effects of scheduling parameters on scheduling objectives are provided after numerical analysis.     

TPL的物流服务水平与物流成本决策

物流服务水平与物流成本之间存在效益悖反,对于第三方物流,进行物流服务水平与物流成本的权衡与决策是一项重要的战略性工作。借助经济学中的弹性概念进行TPL服务-成本能力分析,用以表明不同环境下物流成本对于物流服务水平变动的敏感程度,结合竞争环境和竞争对手水平、顾客需求等相关影响因素,实现对物流服务水平与物流成本的综合决策。     

China's economic rise and its implications for logistics: The Australian case

China's economic rise has had a significant impact on the global economy in terms of trade patterns and orientation. Much has been done to quantify the impact of China's economic rise on international trade, but very little has been done to assess its implications for logistics. In this paper, we examine the effects of China's continued economic rise on the Australian logistics sector. Unlike previous work that used partial equilibrium models, we use a computable general equilibrium (CGE) model. This allows us to capture the direct and indirect ramifications of trade and other economic developments associated with China's economic rise. The results are mixed between sub-sectors, but overall the Australian logistics sector would potentially benefit significantly from China's continued economic rise. However, these potential benefits will hinge partly on the Australian logistics sector's ability to respond to these opportunities.     

Congestion,port expansion and spatial competition for US container imports

The purpose of this paper is to analyze spatial competition, congestion and flows of container imports into the United States. An intermodal network flow model is developed and used to analyze congestion in the logistics system for container imports. The results indicate that congestion exists at most ports and its consequences are to raise costs at these nodes, and in some cases to divert traffic to other routes. Finally, if each of the ports expanded, the value of marginal capacity would converge to nil, and expansion would reduce congestion costs and waiting times.     

Congestion,port expansion and spatial competition for US container imports

The purpose of this paper is to analyze spatial competition, congestion and flows of container imports into the United States. An intermodal network flow model is developed and used to analyze congestion in the logistics system for container imports. The results indicate that congestion exists at most ports and its consequences are to raise costs at these nodes, and in some cases to divert traffic to other routes. Finally, if each of the ports expanded, the value of marginal capacity would converge to nil, and expansion would reduce congestion costs and waiting times.     

A metaheuristic approach to the reliable location routing problem under disruptions

This paper examines a reliable capacitated location–routing problem in which depots are randomly disrupted. Customers whose depots fail must be reinserted into the routes of surviving depots. We present a scenario-based mixed-integer programming model to optimize depot location, outbound delivery routing, and backup plans. We design a metaheuristic algorithm that is based on a maximum-likelihood sampling method, route-reallocation improvement, two-stage neighborhood search and simulated annealing. Numerical tests show that the heuristic is able to generate results that would keep operating costs and failure costs well balanced. Managerial insights on scenario identification, facility deployment and model simplification are drawn.     

Fulfillment source allocation,inventory transshipment,and customer order transfer in e-tailing

We consider an inventory fulfillment-allocation and transshipment problem in an e-tailing environment. For a typical e-tailer, each customer demand is fulfilled from the closest fulfillment center if there are enough inventories. Otherwise, the e-tailer would transship stock from a nearby facility or transfer the customer order so it is fulfilled from another facility, depending on the economics of transportation. We develop a mixed-integer programming model to help e-tailers optimally fulfill customer orders while minimizing logistics costs. We propose a Benders decomposition-based approach to efficiently find optimal solutions. Our computational results demonstrate the importance of considering inventory transshipments in online deliveries.     

A profit-maximization location-capacity model for designing a service system with risk of service interruptions

This paper considers the design of an immobile service system in which each facility’s service process is subject to the risk of interruptions. The location-capacity decisions and allocations are simultaneously made to maximize the difference between the service provider’s profit and the sum of customers’ transportation and waiting costs. An efficient Lagrangian-based solution algorithm is developed, which solves large-sized instances with up to 50 service facilities and 500 customers in a few seconds. Several sensitivity analyses and managerial insights are presented. The model is also applied to a case study on a logistics network design problem in the zinc mining industry.     

“新零售”背景下智慧物流治理策略探讨

"新零售"商业形态给物流产业带来了巨大商机,同时给物流产业提出了全新要求。智慧物流是现代物流创新转型适应"新零售"发展的重要途径。阐述"新零售"背景下我国智慧物流发展现状及存在的问题,针对"新零售"对智慧物流发展提出依托供应链整合全渠道物流、订单驱动导向的精准物流服务、高强度的城市配送承载能力等要求,提出应充分发挥政府引导作用,加快智能物流基础设施建设;赋予市场引导功能,建构智慧物流信息平台;彰显社会共治效用,建设智慧物流多重保障体系,形成"政府引导、市场主导、社会共治"三位一体的智慧物流治理策略,以实现对"新零售"的高质量支撑。