Polygon containment under translation
Journal of Algorithms
A new cutting-stock heuristic for scheduling production
Computers and Operations Research
Near-optimal solutions to one-dimensional cutting stock problems
Computers and Operations Research
A solution comparison for dual angular linear programs
Computers and Operations Research
Nonorthogonal two-dimensional cutting patterns
Management Science
Selection and design of heuristic procedures for solving roll trim problems
Management Science
A practical solution to a fuzzy two-dimensional cutting stock problem
Fuzzy Sets and Systems
A computational improvement to Wang's two-dimensional cutting stock algorithm
Computers and Industrial Engineering
On the optimum two-dimensional allocation problem
DAC '78 Proceedings of the 15th Design Automation Conference
A process model of cased-based reasoning in problem solving
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Recursive computational procedure for two-dimensional stock cutting
IBM Journal of Research and Development
An algorithm for optimal two-dimensional compaction of VLSI layouts
Integration, the VLSI Journal
Paper: Production planning and scheduling for an integrated container company
Automatica (Journal of IFAC)
An instance of the cutting stock problem for which the rounding property does not hold
Operations Research Letters
Hi-index | 0.98 |
Stock Cutting Problem (CSP) is an instance of a particularly difficult combinatorial optimization problem where a few geometrical patterns must be selected and arranged so as to minimize the total cost of the underlying process. A survey of the literature on CSP reveals that the scope of this famous problem applications has expanded in the recent thirty-five years from pallet loading, packing, and industrial production planning to computer operations to telecommunications. Three major trends of engineering activities are identified and discussed in detail: integration of applications, shift from optimization to control, and construction of new related problems. On the other hand, the notorious difficulties of the popular Linear Programming approach to solving CSP (e.g., handling non-linear constraints and integer solutions) have been only partially remedied by a host of heuristic search schemes. We propose a learning expert system CUT addressing the above difficulties. CUT achieves efficient cutting pattern knowledge acquisition and inference by combining Simulated Annealing and Case Based Reasoning. CUT is implemented in PROLOG. Logic Programming implementation offers the advantages of natural symbolic data processing, declarative programming, and automatic search.