Efficient Heuristics for MRP Lot Sizing with Variable Production/Purchasing Costs*
针对具有可变成本和通用产品结构的闭环MRP批量问题，提出了两种基于分支定界的启发式算法（LAM和TAM），能高效求解大规模实际问题。
ABSTRACT Two heuristics based on branch and bound (B&B) are developed to solve closed‐loop material requirements planning (MRP) lot‐sizing problems that have general product structures and variable costs. A “look ahead method'’(LAM) heuristic allows for variable production/purchasing costs and uses a single‐level B&B procedure to rapidly improve lower bound values; thus, LAM efficiently uses computer‐storage capacity and allows solution of larger problems. The “total average modification'’(TAM) heuristic uses B&B, applied level by level, and modified setup and carrying costs to solve the variable production/purchasing costs MRP lot‐sizing problem. LAM and TAM are tested on problems and compared to heuristics in the literature. TAM may be used to solve large MRP lot‐sizing problems encountered in practice.