Stochastic Programming For Order Allocation And Production Planning

Stochastic demand is an important factor that heavily affects production planning.It influences activities such as purchasing,manufacturing,and selling,and quick adaption is required.In production planning,for reasons such as reducing costs and obtaining supplier discounts,many decisions must be made in the initial stage when demand has not been realized.The effects of non-optimal decisions will propagate to later stages,which can lead to losses due to overstocks or out-of-stocks.To find the optimal solutions for the initial and later stage regarding demand realization,this study proposes a stochastic two-stage linear program-ming model for a multi-supplier,multi-material,and multi-product purchasing and production planning process.The objective function is the expected total cost after two stages,and the results include detailed plans for purchasing and production in each demand scenario.Small-scale problems are solved through a deterministic equivalent transformation technique.To solve the problems in the large scale,an algorithm combining metaheuristic and sample average approximation is suggested.This algorithm can be implemented in parallel to utilize the power of the solver.The algorithm based on the observation that if the remaining quantity of materials and number of units of products at the end of the initial stage are given,then the problems of the first and second stages can be decomposed.

Order Allocation in Industrial Internet Platform for Textile and Clothing

In order processing in the industrial Internet platform for textile and clothing,assigning optimal order quantities to each factory is the focus and the existing difficulty.The order allocation is a typical NP⁃hard problem in combinatorial optimization,and typical research of this kind is still at the initial stage.This paper aims to improve the optimization approach to select factories and to allocate proper orders to each one.It designs a genetic algorithm by making a deviation constraint rule for the initial population and introducing a penalty function to improve convergence.Remarkably,the objective functions of total cost along with the related constraints undergo optimization in the model.The experimental results indicate that the proposed algorithm can effectively solve the model and provide an optimal order allocation for multi⁃factories with less cost and computational duration.

Research of Order Allocation of E-commerce Logistics Service Supply Chain Considering Customer Evaluation

Based on the importance of customer evaluation for developing e-commerce enterprises,this paper analyzes the customer evaluation as a fuzzy variable and establishes a multi-objective mixed integer order allocation planning model by considering customer satisfaction,which maximizes customer praise and minimizes procurement cost.As the optimization goal,transaction cost is optimized for the order allocation of the secondary e-commerce logistics service supply chain.In order to defuzzify the customer evaluation,a fuzzy evaluation method is designed to transform the customer evaluation from fuzzy language evaluation to numerical measurement.Finally,the feasibility and effectiveness of the model are verified by using a specific example,and the order is made for the e-commerce enterprise.The allocation provides a theoretical reference.

A Deep Reinforcement Learning Algorithm for the Power Order Optimization Allocation of AGC in Interconnected Power Grids

The integration of distributed generations(solar power,wind power),energy storage devices,and electric vehicles,causes unpredictable disturbances in power grids.It has become a top priority to coordinate the distributed generations,loads,and energy storages in order to better facilitate the utilization of new energy.Therefore,a novel algorithm based on deep reinforcement learning,namely the deep PDWoLF-PHC(policy dynamics based win or learn fast-policy hill climbing)network(DPDPN),is proposed to allocate power order among the various generators.The proposed algorithm combines the decision mechanism of reinforcement learning with the prediction mechanism of a deep neural network to obtain the optimal coordinated control for the source-grid-load.Consequently it solves the problem brought by stochastic disturbances and improves the utilization rate of new energy.Simulations are conducted with the case of the improved IEEE two-area and a case in the Guangdong power grid.Results show that the adaptability and control performance of the power system are improved using the proposed algorithm as compared with using other existing strategies.

Optimization Model in Manufacturing Scheduling for the Garment Industry

The garment industry in Vietnam is one of the country’s strongest industries in the world.However,the production process still encounters problems regarding scheduling that does not equate to an optimal process.The paper introduces a production scheduling solution that resolves the potential delays and lateness that hinders the production process using integer programming and order allocation with a make-to-order manufacturing viewpoint.A number of constraints were considered in the model and is applied to a real case study of a factory in order to viewhowthe tardiness and latenesswould be affected which resulted in optimizing the scheduling time better.Specifically,the constraints considered were order assignments,production time,and tardiness with an objective function which is to minimize the total cost of delay.The results of the study precisely the overall cost of delay of the orders given to the plant and successfully propose a suitable production schedule that utilizes the most of the plant given.The study has shown promising results that would assist plant and production managers in determining an algorithm that they can apply for their production process.