TY - THES
T1 - Joint optimization of successive manufacturing processes and cycle service levels in the paper supply chain
AU - Malik, Muhammad Mohsin
PY - 2012
Y1 - 2012
N2 - [Truncated abstract] The objective of the thesis is to integrate three core production considerations in an optimization model namely: cycle service level, trim loss when cutting stock, and grade changeover costs associated with lot-sizing. Various industries encounter the cutting stock and lot-sizing problems in successive manufacturing processes. The lot-sizing problem (LSP) finds a trade-off between setup and inventory holding costs, whereas the cutting stock problem (CSP) involves cutting large objects into smaller ones while minimizing the trim loss. These two processes are strongly interlinked because the latter is governed by the customer demand and the demand for production lots is derived from it. Particularly for the paper industry, the end demand for smaller rolls of different grades drives the production schedules for the paper machine producing jumbo reels. However, the literature has mostly dealt with these two processes separately, which has important repercussions especially for cycle service levels. A separate optimization approach restricts cycle service levels by putting an upper bound on the total number of different grades of jumbo reels to be produced on the paper machine. This study jointly optimizes the two successive manufacturing processes of lotsizing at the paper machine and determining the cutting pattern during paper conversion with cycle service level considerations. Initially, an integrated formulation is developed as a conventional single objective function embracing the costs of trim loss, grade changeover and inventory holding as well as the tardiness penalty incurred whenever an order fails to meet its due date. Standard genetic algorithm is used as the solution method for the joint problem of simultaneously solving the two NP-hard combinatorial problems. The results reveal that the service levels are maximized by simultaneously solving the trim loss and lot-sizing problem...
AB - [Truncated abstract] The objective of the thesis is to integrate three core production considerations in an optimization model namely: cycle service level, trim loss when cutting stock, and grade changeover costs associated with lot-sizing. Various industries encounter the cutting stock and lot-sizing problems in successive manufacturing processes. The lot-sizing problem (LSP) finds a trade-off between setup and inventory holding costs, whereas the cutting stock problem (CSP) involves cutting large objects into smaller ones while minimizing the trim loss. These two processes are strongly interlinked because the latter is governed by the customer demand and the demand for production lots is derived from it. Particularly for the paper industry, the end demand for smaller rolls of different grades drives the production schedules for the paper machine producing jumbo reels. However, the literature has mostly dealt with these two processes separately, which has important repercussions especially for cycle service levels. A separate optimization approach restricts cycle service levels by putting an upper bound on the total number of different grades of jumbo reels to be produced on the paper machine. This study jointly optimizes the two successive manufacturing processes of lotsizing at the paper machine and determining the cutting pattern during paper conversion with cycle service level considerations. Initially, an integrated formulation is developed as a conventional single objective function embracing the costs of trim loss, grade changeover and inventory holding as well as the tardiness penalty incurred whenever an order fails to meet its due date. Standard genetic algorithm is used as the solution method for the joint problem of simultaneously solving the two NP-hard combinatorial problems. The results reveal that the service levels are maximized by simultaneously solving the trim loss and lot-sizing problem...
KW - Pulp and paper industry
KW - Supply chain management
KW - Genetic algorithms
KW - Production planning
KW - Multi-objective optimization
KW - Industrial management
KW - Operations management
M3 - Doctoral Thesis
ER -