A Heuristic Master Planning Algorithm Considering Fairness, Substitution, and Recycling for an Integrated Supply Chain Network
Date Issued
2008
Date
2008
Author(s)
Lei, Seak-Tou
Abstract
For a global supply chain, maximizing the benefit of the entire supply chain is the objective of all the members involved in supply chain operations. This study focuses on solving the master planning problem for supply chains by considering product structures with multiple final products using substitutions, common components, and recycling processes and recycled components. Such problems address the difficulties involved in synchronizing manufacturing processes and transporting of materials, semi-finished products, final products, and recycling parts along a supply chain and facilitate decision-making related to the effective and efficient use of production, recycling, and transportation capacities over periods ranging from one month to one year. The priorities and costs of substitution are also taken into consideration. When integrating a recycling process into a supply chain operation, the product structure is changed from a tree configuration to a loop configuration and the supply chain structure is changed from an open loop to a closed loop. This study considers six different goals in the planning process: minimizing delay cost, maximizing fairness, minimizing recycle penalty, minimizing substitution cost, minimizing substitution priority, and minimizing the cost of production, processing, holding, transportation and demand splitting. Mixed Integer Programming is a popular way to solve supply chain master planning problems. However, as such problems increase in complexity, the MIP model becomes insolvable due to the time and computer resources it requires. Therefore, this study proposes a heuristic algorithm, called the genetic algorithm based heuristic master planning algorithm (GAMPA), to solve the supply chain master planning problem efficiently and effectively. GAMPA first transforms the closed-loop supply chain into an open-loop supply chain prone to planning and searching the sub-networks for each final product. GAMPA then uses a genetic algorithm based demand sorting approach to determine the sequence of demands. The sequence of demands is represented by chromosome, and different chromosomes are generated for planning using rule-based and random rules. At the end of planning, GAMPA selects the chromosome generating the best planning result according to the priority of the goals. GAMPA plans each demand sequentially according to the chromosome, and find a production tree randomly. GAMPA tries different production trees for each demand and select the best planning result at the end. To show the effectiveness and efficiency of GAMPA, a prototype was constructed and tested to demonstrate the power of GAMPA using complexity and computational analysis.
Subjects
Supply Chain Management
Advanced Planning and Scheduling
Master Planning
Heuristic Algorithm
Multiple-goal Optimization
Fairness
Substitutions
Recycle Process
Recycle Penalty
SDGs
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