Production Planning Of Remanufacturing Research Articles

A two-period carbon tax regulation for manufacturing and remanufacturing production planning

The tax price in a carbon tax regulation may vary from time to time. In this paper, we model a manufacturer who produces new products in the first period and makes new and remanufactured products in the second period under carbon tax regulation where the tax price differs over the two periods. It is shown that improving the first period tax price always decreases the total emission, while improving the second period tax price may enlarge the overall emission. With the decrease of the remanufacturing emission intensity, the overall emission could either increase or decrease. To effectively control the total emission, the tax price could be raised selectively by the regulator according to the manufacturer’s production decision and the characteristic of remanufacturing. In addition, subsidizing strategy for employing green technology during remanufacturing pays off in obtaining environmental benefit only when the original remanufacturing emission intensity is low enough. With this two-period tax regulation, regulators are enabled to improve both the economic and environmental benefits.

An optimization algorithm for integrated remanufacturing production planning and scheduling system

It is known that production planning and scheduling are mutual influence and restriction. In this paper, we aim to obtain the minimum remanufacturing time of recycling parts by use of birandom variables and further optimize an integrated remanufacturing production planning and scheduling system under uncertain conditions. An integrated production planning and scheduling optimization model with birandom variable restraints is firstly established. Then we develop a hybrid intelligent algorithm including random simulation technique, neural network, and genetic algorithms to optimize an integrated remanufacturing production planning and scheduling system. Furthermore, we generate a random variable samples matrix through random simulation technique and a trained neural network is embedded into genetic algorithm. Finally, this hybrid intelligent algorithm is applied to optimize an integrated remanufacturing production planning and scheduling system through a case study.

Remanufacturing production planning with compensation function approximation method

Remanufacturing is becoming a strategic emerging industry in China. However, there are many uncertain factors such as remanufacturing rate of recycling products, reprocessing costs, quantity of recycling products during a remanufacturing process. Hence, it is difficult to make an accurate production planning. This paper aims at studying a new remanufacturing production planning model in view of some possible uncertain factors in a remanufacturing enterprise according to the features and characteristics of remanufacturing. Considering the production capacity constraint of recycling, reprocessing and reassembly under the condition of uncertain reprocessing amount, unpredictable reprocessing cost, unknown purchase volume of new parts, and uncertain customer demand, this paper develops a two-stage, multi-period hybrid programming model with compensation function based on uncertainty theory to minimize the total remanufacturing cost. A hybrid intelligent algorithm is designed combined with compensation function approximation, neural network training, and virus particle algorithm to optimize this two-stage uncertain remanufacturing production planning. By use of compensation function approximation method, it is to convert an infinite optimization problem in this algorithm into that of a finite one. Finally, one remanufacturing simulation case is studied to validate the efficiency and rationality of the proposed approach.