The aggregate planning literature fails to account correctly for productivity losses incurred by employee layoffs and hires. Moreover, the literature is mostly silent on the productivity losses associated with other capacity changes such as multiple shifts and overtime. In those cases where productivity losses are considered, traditional approaches impute associated costs but fail to account for lost capacity. It was found that when linear programming formulations have capacity constraints that explicitly account for productivity losses, the resulting production plans are superior to those obtained when productivity losses are modelled solely as costs in the objective function. Some avenues for future research are also proposed.

AbstractIt has been shown by G. Roodman that useful postoptimization capabilities for the 0‐1 integer programming problem can be obtained from an implicit enumeration algorithm modified to classify and collect all fathomed partial solutions. This paper extends the the approach as follows: 1) Improved parameter ranging formulas are obtained by higher resolution classification criteria. 2) Parameters may be changed so as to tighten the original problem, in addition to relaxing it. 3) An efficient storage structure is presented to cope with difficult data collection task implicit in this approach. 4) Finally, computer implementation is facilitated by the elaboration of a unified set of algorithms.