Minimizing total tardiness on one machine is NP-hard
Mathematics of Operations Research
Modern heuristic techniques for combinatorial problems
Modern heuristic techniques for combinatorial problems
Scheduling multiprocessor tasks in a two-stage flow-shop environment
Proceedings of the 21st international conference on Computers and industrial engineering
A 0-1 goal programming model for nurse scheduling
Computers and Operations Research
Real Optimization with SAP APO
Real Optimization with SAP APO
A heuristic algorithm for master planning that satisfies multiple objectives
Computers and Operations Research
Scheduling: Theory, Algorithms, and Systems
Scheduling: Theory, Algorithms, and Systems
Supply Chain Management and Advanced Planning: Concepts, Models, Software, and Case Studies
Supply Chain Management and Advanced Planning: Concepts, Models, Software, and Case Studies
Network modeling and evolutionary optimization for scheduling in manufacturing
Journal of Intelligent Manufacturing
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This study focuses on solving the factory planning (FP) problem for product structures with multiple final products. In situations in which the capacity of the work center is limited and multiple job stages are sequentially dependent, the algorithm proposed in this study is able to plan all the jobs, while minimizing delay time, cycle time, and advance time. Though mixed integer programming (MIP) is a popular way to solve supply chain factory planning problems, the MIP model becomes insolvable for complex FP problems, due to the time and computer resources required. For this reason, this study proposes a heuristic algorithm, called the heuristic factory planning algorithm (HFPA), to solve the supply chain factory planning problem efficiently and effectively. HFPA first identifies the bottleneck work center and sorts the work centers according to workload, placing the work center with the heaviest workload ahead of the others. HFPA then groups and sorts jobs according to various criteria, for example, dependency on the bottleneck work center, the workload at the bottleneck work center, and the due date. HFPA plans jobs individually in three iterations. First, it plans jobs without preempting, advancing, and/or delaying. Jobs that cannot be scheduled under these conditions are scheduled in the second iteration, which allows preemption. In the final iteration, which allows jobs to be preempted, advanced, and delayed, all the remaining jobs are scheduled. A prototype was constructed and tested to show HFPA's effectiveness and efficiency. This algorithm's power was demonstrated using computational and complexity analysis.