Ordering policies for a dual sourcing supply chain with disruption risks

Purpose: The main purpose of this article is to explore the trade-off between ordering policies and disruption risks in a dual-sourcing network under specific (or not) service level constraints, assuming that both supply channels are susceptible to disruption risks. Design/methodology/approach: Stochastic newsvendor models are presented under both the unconstrained and fill rate constraint cases. The models can be applicable for different types of disruptions related among others to the supply of raw materials, the production process, and the distribution system, as well as security breaches and natural disasters. Findings: Through the model, we obtain some important managerial insights and evaluate the value of contingency strategies in managing uncertain supply chains. Originality/value: This paper attempts to combine explicitly disruption management with risk aversion issues for a two-stage supply chain with two unreliable suppliers.Peer Reviewe

The Ninth Visual Object Tracking VOT2021 Challenge Results

acceptedVersionPeer reviewe

Ordering policies for a dual sourcing supply chain with disruption risks

Purpose: The main purpose of this article is to explore the trade-off between ordering policies and disruption risks in a dual-sourcing network under specific (or not) service level constraints, assuming that both supply channels are susceptible to disruption risks. Design/methodology/approach: Stochastic newsvendor models are presented under both the unconstrained and fill rate constraint cases. The models can be applicable for different types of disruptions related among others to the supply of raw materials, the production process, and the distribution system, as well as security breaches and natural disasters. Findings: Through the model, we obtain some important managerial insights and evaluate the value of contingency strategies in managing uncertain supply chains. Originality/value: This paper attempts to combine explicitly disruption management with risk aversion issues for a two-stage supply chain with two unreliable suppliers.Peer Reviewe

Ordering policies for a dual sourcing supply chain with disruption risks

Purpose: The main purpose of this article is to explore the trade-off between ordering policies and disruption risks in a dual-sourcing network under specific (or not) service level constraints, assuming that both supply channels are susceptible to disruption risks. Design/methodology/approach: Stochastic newsvendor models are presented under both the unconstrained and fill rate constraint cases. The models can be applicable for different types of disruptions related among others to the supply of raw materials, the production process, and the distribution system, as well as security breaches and natural disasters. Findings: Through the model, we obtain some important managerial insights and evaluate the value of contingency strategies in managing uncertain supply chains. Originality/value: This paper attempts to combine explicitly disruption management with risk aversion issues for a two-stage supply chain with two unreliable suppliers

A New Equilibrium Strategy of Supply and Demand for the Supply Chain of Pig Cycle

The pig market had experienced a cycle of price rise and fall, also known as the “pig cycle.” This paper analyzes the fluctuation relationship between pig price, pig supply, and pork demand, constructs a system dynamics model of the pig industry by decomposing the structure of the pig supply chain, and then discusses the causes of “pig cycle,” as well as the supply chain management strategy and industrial policy, to stabilize the pig industry market. Research shows that reducing the cost of pig breeding, countercyclical adjustment, and government macrocontrol can effectively reduce the fluctuation of pig prices. Among them, reducing the pig breeding cost is the most effective long-term strategy to stabilize the pig price

Simultaneous Pickup and Delivery Traveling Salesman Problem considering the Express Lockers Using Attention Route Planning Network

This paper presents a simultaneous pickup and delivery route designing model, which considers the use of express lockers. Unlike the traditional traveling salesman problem (TSP), this model analyzes the scenario that a courier serves a neighborhood with multiple trips. Considering the locker and vehicle capacity, the total cost is constituted of back order, lost sale, and traveling time. We aim to minimize the total cost when satisfying all requests. A modified deep Q-learning network is designed to get the optimal results from our model, leveraging masked multi-head attention to select the courier paths. Our algorithm outperforms other stochastic optimization methods with better optimal solutions and O(n) computational time in evaluation processes. The experiment has shown that reinforcement learning is a better choice than traditional stochastic optimization methods, consuming less power and time during evaluation processes, which indicates that this approach fits better for large-scale data and broad deployment

Analysis of Factors Affecting Freight Demand Based on Input-Output Model

Freight demand is a highly variable process over economic and industrial structure, and accurate freight demand forecasting is the basis of transportation planning. In order to clarify the influencing factors of freight volume so as to analyze and predict the change trend of freight volume accurately, this paper analyzes the impact of changes in economic, industrial structure, and complete consumption coefficients on freight demand, through constructing an input-output model for transportation value analysis and forecasting freight volume by fitting data of transportation value and freight traffic. Studies have shown that the growth in economic aggregate is the main reason for the increase in the value of transportation, and the change in the complete consumption coefficient is the main reason for the increase in freight traffic

Results of strategies (i).

Results of strategies (i).</p

Values of main factors in the simulation.

Values of main factors in the simulation.</p

Main feedback loops in the CLD.

Main feedback loops in the CLD.</p