Study On Delivery And Production Decision Of Operation System With Customer Orders

In a rapidly changing and competitive market, make-to-order meets the customers’ requirements of Individuation and diversification, gets the favor of small and medium-sized enterprises. With the advantages of low cost and flexible production, small and medium-sized enterprises develop rapidly. However, in complex environment, small and medium-sized enterprises face severe challenges due to insufficient funds and lack of talents. In this paper, three key problems from three stages, e.g. before orders arrive→the arrival of orders (negotiation)→orders arrived were studied in line the realities of Chinese small and medium-sized enterprises.The main contents of this paper are as followed:1. In order to make full use of the spare capacity before order arrived, the fourth chapter established the optimal production and inventory decision models without purchasing lead time and with purchasing lead time when the demand is uncertain. The existence of the optimal solution was proved. At the same time, dichotomy solving steps were given. The optimal production quantity and the inventory of raw materials were derived by the numerical investigation data. In addition, We analyzed the influence of demand standard deviation on the optimal solution. We find that production quantity in advance is decreased with increased demand standard deviation. The sum of production quantity and raw materials inventory with purchasing lead time is greater than that without purchasing lead time.2. Facing the complex domestic and international demand and many uncertainties, forecasting order delivery date becomes more and more difficult, the fifth chapter consider price, demand, advance completion, delayed delivery, overtime work, association, loss rate and so on, and formulates delivery-income model with the purpose of largest income. We map the income with promised delivery time changes to intuitive showed the changes of income with the promised delivery time by the numerical enterprise data. We find that promised delivery time is not the longer the better and is not the sooner the better. There is the optimal delivery commitments time to maximize the income.3. Production too much, waste of resources, whereas too little, the delays of delivery date. Chapter 6 established planned quantity put into production decision models to minimize expectation mismatch loss. Then we showed the optimal output expressions under three kinds of situations, e.g., both the pass rate and rework rate were certain value, discrete random variable and continuous random variable. Finally, the effectiveness of the models was illustrated by conducting some numerical examples. We find that (i) the greater the dispersion degree, the bigger the expectation adaptation loss; (ii) when the pass rate obeys normal distribution, the greater discrete degree, the planned quantity put into production is larger; the smaller the average, the bigger the expectation adaptation loss, the planned quantity put into production is larger.