Two-echelon inventory model with service level constraint and controllable lead time sensitive to order quantity

The decision-making and optimization of two-echelon inventory coordination were analyzed with service level constraint and controllable lead time sensitive to order quantity.First,the basic model of this problem was established and based on relevant analysis,the original model could be transformed by minimax method.Then,the optimal order quantity and production quantity influenced by service level constraint were analyzed and the boundary of optimal order quantity and production quantity was given.According to this boundary,the effective method and tactics were put forward to solve the transformed model.In case analysis,the optimal expected total cost of two-echelon inventory can be obtained and it was analyzed how service level constraint and safety factor influence the optimal expected total cost of two-echelon inventory.The results show that the optimal expected total cost of two-echelon inventory is constrained by the higher constraint between service level constraint and safety factor.

Profit Allocation Scheme Among Players in Supply-Chain Based on Shapley Value of Fuzzy Bi-cooperative Games

The Shapley value of fuzzy bi-eooperative game is developed based on the conventional Shapley value of bi-cooperative game. From the viewpoint that the players can participate in the coalitions to a certain extent and there are at least two independent cooperative projects for every player to choose, Shapley value which is introduced by Grabisch is extended to the case of fuzzy bi-cooperative game by Choquet integral. Moreover, the explicit fuzzy Shapley value is given. The explicit fuzzy Shapley function can be used to allocate the profits among players in supply-chain under the competitive and uncertain environment.

Risk Assessment of Agricultural Products Supply-Chain Finance Based on D-S Theory

Agricultural products supply-chain finance, as one of the solutions to the issue of “capital problems” of agriculture, countryside and farmers, has proposed a kind of characteristics model to assess the risk of agricultural production, processing and marketing, which can improve the issue of farmers and enterprises lacking of funds. This model is proposed on the basis of uncertain information processing method of D-S theory and its data combination rules, combined with the “discount rate” correction model, and it includes a risk assessment index system of agricultural products supply-chain finance, fully considering the five aspects of production, processing, marketing, cooperation of supply chain and collateral. At last, a taro supply chain is taken for example. And the risk assessment of its supply-chain finance based on this model has been discussed in detail. And the result has proved that the model and its algorithm are practical and feasible.

An Optimal Method for Supply Chain Logistics Management Based on Neural Network

From raw material storage through final product distribution,a cold supply chain is a technique in which all activities are managed by temperature.The expansion in the number of imported meat and other comparable commodities,as well as exported seafood has boosted the performance of cold chain logistics service providers.On the basis of the standard basicpursuit(BP)neural network,a rough BP particle swarm optimization(PSO)neural network model is constructed by combining rough set and particle swarm algorithms to aid cold chain food production enterprises in quickly picking the best cold chain logistics service providers.To reduce duplicate information in the original data and make the input index more compact,the model employs rough set.Instead of using gradient descent to train the weights of the neural network,particle swarm optimization is utilized to ensure that the output results are not readily caught in local minima and that the network’s generalization capacity is improved.Finally,an example is presented to demonstrate the model’s validity and viability.The findings reveal that the model’s prediction error is 40.94 percent lower than the BP neural network model,and the prediction result is more accurate and dependable,providing a new technique for cold chain food production companies to swiftly pick the best cold chain logistics service provider.

RFID’s Impacts on Business Value:A Case Study of Supply Chain in the Discrete Manufacturing Industry

Radio frequency identification （RFID） shall be a revolutionary technological innovation in recent years. Various solutions employing RFID technology have proved their functionality already in such industries as pharmaceuticals, express parcel carrying, and automotive manufacturing, and the increased efficiency and effectiveness has provided a good payback for the investments. But, up to now, fewer researches concentrate on applying REID to heavy-machinery manufacturing enterprises, which are a typical kind of enterprises in discrete manufacturing industry. The main objective of this case study is to extend our understanding of the potential for RFID to delivery of the heavy-machinery manufacturing enterprise, which involves one specific supply chain. A trial to automate the verification activities of delivery is designed and performed. Results show that the stops currently designed into the processes can be eliminated by employing RFID technology and that RFID should be a revolutionary technology as it redesigns the existing processes, eliminate some current inefficiency, improve the accuracy of delivering the products, increases information sharing between supply chain members.

Vehicle flow formulation for two-echelon time-constrained vehicle routing problem

Two-echelon routing problems,including variants such as the two-echelon vehicle routing problem(2E-VRP)and the two-echelon location routing problem(2E-LRP),involve assignment and location decisions.However,the two-echelon time-constrained vehicle routing problem(2E-TVRP)that caters to from-linehaul-to-delivery practices does not involve assignment decisions.This routing problem variant for networks with two eche-lons has not yet attracted enough research interest.Localized or long-distance services suffer from the lack of the assignment decisions between satellites and customers.Therefore,the 2E-TVRP,rather than using assignment decisions,adopts time constraints to decide the routes on each of the two interacting echelons:large-capacity vehicles trans-port cargoes among satellites on the first echelon,and small-capacity vehicles deliver cargoes from satellites to customers on the second echelon.This study introduces a mixed integer linear programming model for the 2E-TVRP and proposes a heuristic algorithm that incorporates the savings algorithm followed by a variable neighborhood search phase.Illustrative examples are used to test the mathematical formulation and the heuristic and a case study is used to demonstrate that the heuristic can effectively solve realistic-size instances of the 2E-TVRP.

Water consumption and biodiversity:Responses to global emergency events

Given that it was a once-in-a-century emergency event,the confinement measures related to the coronavirus disease 2019(COVID-19)pandemic caused diverse disruptions and changes in life and work patterns.These changes significantly affected water consumption both during and after the pandemic,with direct and indirect consequences on biodiversity.However,there has been a lack of holistic evaluation of these responses.Here,we propose a novel framework to study the impacts of this unique global emergency event by embedding an environmentally extended supply-constrained global multi-regional input-output model(MRIO)into the drivers-pressure-state-impact-response(DPSIR)framework.This framework allowed us to develop scenarios related to COVID-19 confinement measures to quantify country-sector-specific changes in freshwater consumption and the associated changes in biodiversity for the period of 2020-2025.The results suggest progressively diminishing impacts due to the implementation of COVID-19 vaccines and the socio-economic system’s self-adjustment to the new normal.In 2020,the confinement measures were estimated to decrease global water consumption by about 5.7% on average across all scenarios when compared with the baseline level with no confinement measures.Further,such a decrease is estimated to lead to a reduction of around 5% in the related pressure on biodiversity.Given the interdependencies and interactions across global supply chains,even those countries and sectors that were not directly affected by the COVID-19 shocks experienced significant impacts:Our results indicate that the supply chain propagations contributed to 79% of the total estimated decrease in water consumption and 84%of the reduction in biodiversity loss on average.Our study demonstrates that the MRIO-enhanced DSPIR framework can help quantify resource pressures and the resultant environmental impacts across supply chains when facing a global emergency event.Further,we recommend the development of more locally based water conservation measures—to mitigate the effects of trade disruptions—and the explicit inclusion of water resources in post-pandemic recovery schemes.In addition,innovations that help conserve natural resources are essential for maintaining environmental gains in the post-pandemic world.

Route planning of truck and multi-drone rendezvous with available time window constraints of drones

During the scenarios of cooperative tasks performed by a single truck and multiple drones,the route plan is prone to failure due to the unpredictable scenario change.In this situation,it is significant to replan the rendezvous route of the truck and drones as soon as possible,to ensure that all drones in flight can return to the truck before running out of energy.This paper addresses the problem of rendezvous route planning of truck and multi-drone.Due to the available time window constraints of drones,which limit not only the rendezvous time of the truck and drones but also the available period of each drone,there are obvious local optimum phenomena in the investigated problem,so it is difficult to find a feasible solution.A two-echelon heuristic algorithm is proposed.In the algorithm,the strategy jumping out of the local optimum and the heuristic generating the initial solution are introduced,to improve the probability and speed of obtaining a feasible solution for the rendezvous route.Simulation results show that the feasible solution of the truck-drones rendezvous route can be obtained with 88%probability in an average of 77 iterations for the scenario involving up to 25 drones.The influence of algorithm options on planning results is also analyzed.

The PACTUM model:Product analysis of cost and time using mathematics

Establishing a mathematical supply-chain model is a proposition that has received attention due to its inherent benefits of evolving global supply-chain efficiencies.This paper discusses the prevailing relationships found within apparel supply-chain environments,and contemplates the complex issues indicated for constituting a mathematical model.Principal results identified within the data suggest,that the multifarious nature of global supply-chain activities require a degree of simplification in order to fully dilate the necessary factors which affect,each subsection of the chain.Subsequently,the research findings allowed the division of supply-chain components into subsections,which amassed a coherent method of product development activity.Concurrently,the supply-chain model was found to allow systematic mathematical formulae analysis,of cost and time,within the multiple contexts of each subsection encountered.The paper indicates the supplychain model structure,the mathematics,and considers how product analysis of cost and time can improve the comprehension of product lifecycle management.