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Transportation decisions and production decisions can usually be made separately. Under certain conditions, however, there are important interactions between transportation and production. An example of a combined production and routeing environment exists in the United States Postal Service (USPS). The daily movement and sorting of first class letter mail requires balancing transportation and mail sorting production costs, while considering the time dependent accumulation of mail during the day. The focus of this paper is on the practical application of a combined production/routeing model to a specific USPS division and the use of insights from this model to re-design the operation. A mixed integer linear program using candidate route integer variables is solved to near optimality. Specific routes and production schedules detailed by the model indicate that the division could save up to 5% of their combined production and routeing costs, amounting to $298 000 per year. More importantly, the model provides insight on potential heuristics to choose routes under these circumstances.

It is demonstrated that a master production schedule (MPS) policy that significantly reduces schedule instability also has superior cost and customer service features. The stable policy uses safety stock for 2 purposes: as a buffer against inaccurate forecasts and to reduce the scheduling instability caused by lot sizing. If a company has a textbook implicit policy in place, substantial gains in schedule stability can be made by switching to a stable policy. However, it must be realized that to achieve the cost and service-level benefits, both the scheduling policy and safety-stock level for triggering production must be changed. Retaining the same safety-stock level will result in lower service. In implementing the stable policy, the rescheduling logic need only be extended to the period of time where rescheduling presents a problem. If an MPS exists for the next year, but MPS instability is troublesome only for the immediate month, then a proper use of the stable policy is to apply it only to the next month and use conventional MPS logic for the remainder of the year.

Data envelopment analysis (dea) has become an increasingly popular method to measure performance for service firms with multiple sites. DEA is superior to many traditional methods for firms that have multiple goals. The promise of DEA is that the complex, multi‐objective problem of performance measurement can be reduced to a single number. Unfortunately, the practice of DEA often belies the promise. Misconceptions concerning the purpose and implementation of DEA can cause DEA applications to be less than successful. Here, the technique is explained, and a guide to the implementation of DEA is proposed, utilizing DEA studies of retail bank branches.