Collaborative Logistics Network Research Articles

A Study on Collaborative Logistics Network Design with Truck Sharing under Demand Uncertainty

The sharing economy is a new innovative business model for more flexible and sustainable logistics. This paper proposes a stochastic programming model for supply chain network design with truck sharing. The model considers demand uncertainty and optimizes allocation and operation of trucks. A case study with realistic data is presented by applying the model to a supply chain with different collaboration level. A sensitivity analysis on cost parameter for sharing service is also conducted to evaluate the impact on business sustainability. The results show that higher collaboration level successfully copes with demand uncertainty, and improves revenues in the entire supply chain.

Collaborative logistics network: a new business mode in the platform economy

ABSTRACT A Collaborative logistics network is a new service style that is oriented from a platform economy and that aims at a win–win of network participants and higher customer satisfaction. As a new business mode in supply chain management, this network style can attract all kinds of logistics entities, schedule various resources distributed in space–time and provide efficient customised services for customers. In the current paper, we first employ the 5W1H method to introduce collaborative logistics networks from a theoretical perspective. First, we construct an analysis system consisting of three parts: market positioning, operation mechanism and profit model. Furthermore, we give the canvas of this business mode, including a service list, core stakeholders, application scenario, operation mode and profit allocation. Finally, we introduce some typical cases operated in the USA, EU and China, discussing the application prospect in green supply chain management.

Two-echelon collaborative multi-depot multi-period vehicle routing problem

Collaboration among logistics operators offers an effective way to improve customer service and freight transportation efficiency. One form of collaboration is the sharing of logistics resources (e.g., delivery vehicles). Existing studies on collaboration and resource sharing have not sufficiently accounted for the time frame within which collaboration happens, and they mostly assume that collaboration among logistics operators occurs in a single time period. This study addresses the issue of collaboration across multiple time periods, in which logistics resources can be shared between different service time periods, by formulating and solving a two-echelon collaborative multi-depot multi-period vehicle routing problem (2E-CMDPVRP). The 2E-CMDPVRP is formulated as a multi-objective integer programming model that minimizes logistics operational costs, service waiting times, and number of vehicles in multiple service periods. A hybrid heuristic algorithm with three-dimensional k-means clustering and improved reference point-based non-dominated sorting genetic algorithm-III (IR-NSGA-III) is proposed to solve the multi-objective optimization model. Comparative analysis results show that the proposed IR-NSGA-III outperforms existing algorithms in terms of the minimization of logistics operational costs, service waiting times, and number of vehicles. The minimum costs remaining saving method and strictly monotonic path selection principle are combined to determine the best profit allocation schemes and the optimal coalition sequences. An empirical case study of a multi-depot multi-period logistics network in Chongqing, China, is used to validate the proposed model and solution algorithm. Results suggest that the proposed collaborative mechanism with multi-depot and multi-period resource sharing can improve the degree of synchronization within a collaborative logistics network, and thus contribute to sustainable development of urban logistics distribution networks.

Fuzzy Optimal Allocation Model for Task–Resource Assignment Problem in a Collaborative Logistics Network

Obtaining a multiresource allocation scheme for multitask influenced by uncertain factors is a critical problem in a collaborative logistics network. This paper presents an optimal allocation model of fuzzy resources for multistage random logistics tasks based on the six-point trapezoidal fuzzy number and the membership function. Besides, considering task demands and resource constraints, a new cost–time–quality multiobjective programming of N–N task–resource assignment is introduced, which can be divided into minimize total logistics cost and execution time, maximize total service quality. Furthermore, by setting the different simulation scenarios, the results show that if the decision maker has a higher risk preference and pursues the optimization of single or multiobjective, the higher degree membership and satisfaction function values can be obtained with a larger compensation coefficient. The allocation scheme of task–resource assignment generated by proposed model has a high global level of utilization efficiency, which can effectively utilize fuzzy resources in collaborative logistics network, and avoid resource shortage caused by the excessive occupation of local resources.

A N–N optimization model for logistic resources allocation with multiple logistic tasks under demand uncertainty

It is challenging to make an optimal allocation scheme for multiple paralleling logistics tasks match suitable resources in collaborative logistics network (CLN) due to the uncertainty. In this paper, we propose a multi-objective optimization mathematical model to solve the N–N task-resource assignment (TRA) problem in CLN under demand uncertainty. Chance constraint in the model is used to indicate demand uncertainty, and resource leveling is considered in the execution of multiple paralleling logistics tasks scheduling. A hybrid heuristic algorithm based on genetic algorithm and tabu search is presented to solve the N–N TRA model. The experimental results demonstrate that the proposed model can promisingly describe the TRA problem, and the hybrid heuristic algorithm can achieve superior result about the size of uncertainty degree on allocation scheme.

Route optimization model in collaborative logistics network for mixed transportation problem considered cost discount based on GATS

A mixed transportation problem with centralized and decentralized transportation is proposed in collaborative logistics network. All tasks can be centralized to transport for once, twice or transported directly to destination node. A route optimization programming model to minimize the total costs is proposed in consideration of cost discount, road section capacity, overload penalty, advance delivery cost and hard time window. The hybrid genetic tabu search algorithm is presented to solve the model. To test the feasibility and validity of proposed model and hybrid algorithm, a simulation example is presented and solved. The optimal solution of experiment shows that the hybrid algorithm can obtain better solution, better convergence speed and high calculation efficiency.

Multi-task transportation scheduling model with backhauls based on hub and spoke in collaborative logistics network

Hub and spoke collaborative logistics network has recently received considerable attention in terms of resource scheduling problem. In view of that problem such as low scale merit, inefficient resource utilization and conflict of interest between the operator and customer in the process of network operation, the paper seeks to formulate the scheduling strategy for multiple logistics tasks oriented to multi-origin and multi-destination on the basis of multiple-allocation-hybrid H&S collaborative network (including two layers). Taking the backhaul resource, resource departure time, capability limitation, task delay penalty, etc. into account, the aim of the problem is to minimize both total cost and total time for the task set. According to the characteristics of the problem, the immune genetic algorithm based on three-layer encoding mechanism is designed to solve the model. Moreover, the models and algorithms are validated by extensive results of numerical examples.

A bi-level optimization model of LRP in collaborative logistics network considered backhaul no-load cost

In collaborative logistics network, all task demands are centralized by logistics platform for resource allocation, and then resource owners execute discretely under final plan. The key point of this study is to effectively balance the different appeals of stakeholders. In this paper, a bi-level programming model is presented to solve multi-deport LRP considered the constraint of hard time window, vehicle capacity and vehicle backhaul cost, which aims at achieving the effective interest coordination between upper layer platform and lower layer vehicle owners. To solve this bi-level model, genetic algorithm is redesigned according to this specific situation. Optimal results of simulation sample show that the satisfied solution for both platform and vehicle owners could be obtained using this method. Feasibility and validity of this model and adaptive algorithm have been verified in the paper. The bi-level model provides a practical and effective method to solve the profit distribution problem between platform and vehicle owners.

Resource Allocation Optimization Model of Collaborative Logistics Network Based on Bilevel Programming

Collaborative logistics network resource allocation can effectively meet the needs of customers. It can realize the overall benefit maximization of the logistics network and ensure that collaborative logistics network runs orderly at the time of creating value. Therefore, this article is based on the relationship of collaborative logistics network supplier, the transit warehouse, and sellers, and we consider the uncertainty of time to establish a bilevel programming model with random constraints and propose a genetic simulated annealing hybrid intelligent algorithm to solve it. Numerical example shows that the method has stronger robustness and convergence; it can achieve collaborative logistics network resource allocation rationalization and optimization.

Design optimization of resource combination for collaborative logistics network under uncertainty

Collaborative logistics networks (CLNs) are considered to be an effective organizational form for business cooperation that provides high stability and low cost. One common key issue regarding CLN resource combination is the network design optimization problem under discrete uncertainty (DU-CLNDOP). Operational environment changes and information uncertainty in network designs, due to partner selection, resource constrains and network robustness, must be effectively controlled from the system perspective. Therefore, a general two-stage quantitative framework that enables decision makers to select the optimal network design scheme for CLNs under uncertainty is proposed in this paper. Phase 1 calculates the simulation result of each hypothetical scenario of CLN resource combination using the expected value model with robust constraints. Phase 2 selects the optimal network design scheme for DU-CLNDOP using the orthogonal experiment design method. The validity of the model and method are verified via an illustrative example.

A bi-level programming model of resource matching for collaborative logistics network in supply uncertainty environment

In collaborative logistics network (CLN) models, the uncertainty of supply often occurs during the resource matching process due to the fact that each individual cooperative enterprise tries to maximize its own operating profit. This paper presents a bi-level programming model which can reduce or eliminate the adverse effects of supply uncertainty in CLN resource matching process. By adding the robust constraints, this model reduces both the frequency and the cost of resource planning changes, which lead to increasing stability of CLN operation. Based on the particular form of the model, a new algorithm combined by Monte Carlo method (MCM) and particle swarm optimization (PSO) is proposed. Results from example analysis demonstrate the effectiveness of the proposed model and algorithm.

Measure Analysis for Order Degree of Complex Manufacturing Collaborative Logistics Network

In order to more effectively study the resource optimization problem of complex manufacturing collaborative logistics network, the study combines the features of complex manufacturing, has a system analysis for complex manufacturing collaborative logistics network and expect to have a better understand about its internal composition. Therefore, under the guidance of Synergetic and dissipative structure theory, the study introduces the hyper cycle dynamical concurrence idea, measures the orderly operation of complex manufacturing collaborative logistics network from synergy degree, stability and self-organization three angles.

Optimal Model of Resource Planning for Collaborative Logistics Network Considering Robust Constraints

Uncertainty factors which influence the resource plan for collaborative logistics network, is the completely uncertain problem that parameter status and probability distribution are unknown, but traditional plan methods can’t ensure the robustness of plan implementation. Based on robust optimal control, the paper carries on simulation about uncertain parameters with Monte Carlo method, and proposes a expected model of robust restraint, the robustness constrains realizes to contract the feasible region and improve robustness of model. Finally, The simulation results illustrate the validity of the improved model.