Workforce planning at USPS mail processing and distribution centers using stochastic optimization

Abstract

Service organizations that operate outside the normal 8-hour day and face wide fluctuations in demand constantly struggle to optimize the size and composition of their workforce. Recent research has shown that improved personnel scheduling methods that take demand uncertainty into account can lead to significant reductions in labor costs. This paper addresses a staff planning and scheduling problem that arises at United States Postal Service (USPS) mail processing & distribution centers (P&DCs) and develops a two-stage stochastic integer program with recourse for the analysis. In the first stage, before the demand is known, the number of full-time and part-time employees is determined for the permanent workforce. In the second stage, the demand is revealed and workers are assigned to specific shifts during the week. When necessary, overtime and casual labor are used to satisfy demand. This paper consists of two parts: (1) the analysis of the demand distribution in light of historical data, and (2) the development and analysis of the stochastic integer programming model. Using weekly demand for a three-year period, we first investigate the possibility that there exists an end-of-month effect, i.e., the week at the end of month has larger volume than the other weeks. We show that the data fail to indicate that this is the case.In the computational phase of the work, three scenarios are considered: high, medium, and low demand. The stochastic optimization problem that results is a large-scale integer program that embodies the full set of contractual agreements and labor rules governing the design of the workforce at a P&DC. The usefulness of the model is evaluated by solving a

Risk control for staff planning in e-commerce warehouses

“…Liao et al (2012) aim to derive a constant staffing level throughout the planning horizon and add uncertainty by using random mean arrival times. Similar to Bard, Morton, and Wang (2007), the authors highlight the necessity for sophisticated approaches for situations in which the system is very sensitive to data variation. Varying and unpredictable demand patterns in warehouses in Internet sales channels motivate even further exploration of risk aversion tools.…”

“…Their objective is to minimise all related costs accompanied with hiring, layoff, labour shortage and surplus. Bard, Morton, and Wang (2007) derive a twostage stochastic problem with recourse decisions. First-stage decisions concern full-time labour allocations and the amount of part-time labour.…”

“…Staffing policies that prepare the 'mean case'might fail to provide reasonable performances for the majority of possible scenarios. In their article on staff scheduling in mail processing (Bard, Morton, and Wang 2007) demonstrate the potential for 4% lower costs when recourse decisions are allowed during the planning horizon compared with the outcomes of the problem that solely optimises expectations of the entire time horizon. Multistage stochastic models are more sophisticated in tackling the uncertainties in staffing problems.…”

A three-stage approach for aircraft line maintenance personnel rostering using MIP, discrete event simulation and DEA

“…Bard, Morton and Wang (2007), for instance, address a staff planning and scheduling problem that arises at United States Postal Service (USPS) mail processing & distribution centers (P&DCs) and develop a two-stage stochastic integer program. In the computational phase of the work, three scenarios are considered: high, medium and low demand.…”

Budget allocation for permanent and contingent capacity under stochastic demand

“…Wild and Schneeweiss (1993) develop a hierarchical decision model to optimize the use of temporary workers, overtime and floaters (workers able to work in different departments) under uncertain demand for labor and absenteeism. In the same vein, Bard et al (2007) design a 2-stage stochastic program for staff planning and scheduling decisions. In the first stage, the size of the permanent workforce is determined before demand is known.…”