A Heuristic Master Planning Algorithm for Recycling Supply Chain Management Considering Uncertain Supply and Demand
Date Issued
2011
Date
2011
Author(s)
Chen, Pei-Yu
Abstract
Because of the environmental awareness and economical reasons, the manufacturers are pushed to recover the used products and thus the recycling supply chain is recently receiving a lot of attentions. However, disassembly and assembly processes are asymmetric so that the planning problem for the recycling process is different from the one for the regular production process. Although some studies have focused on solving such problems, their models are simplified with unrealistic assumptions.
In this study, we focus on solving the master planning problems for the recycling supply chain with uncertain supply and demand. The recycling supply chain network includes members such as collectors, disassemblers, re-manufacturers and re-distributors with the recycling processes from collecting the returned goods to distributing these recovery products to market. The objective of this study is to maximize the total profit of the entire recycling supply chain. Considering the stochastic property, this study institutes the stocking and processing policies for each member of the recycling supply chain to better respond to the unknown upcoming demand.
To solve the master planning problems for the recycling supply chain with supply and demand uncertainties, we propose a stochastic model. To improve the effectiveness and efficiency of finding a solution, a heuristic algorithm, Heuristic Stochastic Recycling Process Planning Algorithm (SRPPA) is proposed. The idea of SRPPA is to narrow down the search space and compares the long-term profit result of some valuable sets by simulation to find the best one.
The main process of SRPPA consists of three phases. In Leaders Finding Algorithm, SRPPA determines the most important node to be the leader of the recycling supply chain. In Candidate Policies Sets Generating, SRPPA defines the searching range of the inventory policy for the leader and forms the candidate policies sets based on the characteristics of the leader. In Step Best Policies Set Selecting, SRPPA constructs the simulation process for each inventory policy candidate to find the best one. To show the effectiveness and efficiency of SRPPA, a scenario analysis is conducted.
Subjects
Master Planning (MP)
Uncertain Supply and Demand
Heuristic Algorithm
Recycling Supply Chain
Type
thesis
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