Study On Integrated Optimization Of Manufacturing And Distribution With Learning Effects

Batch and integrated scheduling problems are the important research topics in production scheduling area.Recently,researchers in Management and Operation Research field focus on these topics.In traditional research of operations management,a common assumption is that the processing time of a task is known in advance.However,in practice workers finish the same task more and more quickly when they perform the task over and over.That is,learning effects exist widely in manufacturing companies.Therefore,it is quite meaningful and useful to consider integrated optimization of manufacturing and distribution with learning effects.We first research production scheduling problem with cost under cloud manufacturing.Motivated by applications in art tile manufacturing and metal working industries,we study the optimization problem with a truncated batch-position-based learning effect.In production,a set of semi-products need to be processed on a single batch facility which has a fixed capacity.Several semi-products can be processed together in one batch if their total size does not exceed the facility capacity.We consider a truncated batch-position-based learning effect which is a typical behavior of workers.During the learning period,the worker can finish the task more and more quickly because of learning effects.After the learning period,the worker reaches the best ability and the ability keeps steady.Then we consider two models of manufacturing with batch operations.In the first model,semi-products have identical sizes and we propose an optimal algorithm with time complexity of nnO)log(.In the second model,semiproducts have arbitrary sizes which are proportional to their processing times and the model is shown to be NP-hard in the strong sense.We propose two types of learning effects including fast and slow truncated batch-position-based learning effects.Then we propose an approximation algorithm with an absolute and asymptotic worst-case ratio less than 2.Finally,we conduct computational experiments and the results show the effectiveness of our algorithms.We also provide managerial insights and detailed suggestions for decision makers of manufacturing companies based on our results.Then,we extend our research field to integration production and delivery scheduling problem.We consider the learning effects in the coordination of production and outbound distribution for manufacturers.The objective is to minimize service span,which lasts from the beginning of production to the completion of delivery of products.Batch-position-based learning effects are considered.In production,a batch-processing facility is used to process jobs which have different sizes.In distribution,a vehicle with a fixed capacity is used to deliver products the customer and the transportation time from the manufacturer to the customer is a constant.We show the coordinated scheduling problem is NP-hard in the strong sense.We propose properties of optimal solutions and provide an approximation algorithm for the problem.The absolute performance guarantee of the algorithm is 1.667 and the asymptotic performance guarantee is 1.223.Then we consider the problem where there are infinite vehicles and the performance guarantees are respectively 1.5 and 1.223.Finally we analyze the performance of the algorithm by the change of the problem scale,the learning index and operational factors.We propose managerial suggestions for decision makers of manufacturers according to our results.