Optimization of express line performance: numerical examination and management considerations

Abstract

Improving the waiting process at checkouts in stores is an important goal of operations management in the era of time-based competition. The paper presents a method for evaluating the effect of express lines on the waiting process. An optimization model is developed which minimizes the average waiting time in line with respect to the maximum number of items allowed in the express lines. The research is based on a real case of a do-it-yourself superstore, but the methodology applied at the store can be used generally.The optimization model includes sensitivity analyses. Sensitivity analyses show how the optimal value of the limit parameter changes if major parameters of the model change. The results of these analyses help managers make decisions about short and medium-term operations of express lines.The major conclusion of the paper is that the optimal operation of express lines does not improve the average waiting time significantly, but the effect of non-optimal operation can be very unfavorable. Therefore, a successful implementation of express lines requires a thorough analysis of operational issues and a careful consideration of perceptional issues as well.

A review of management issues related to express line systems

“…In our analyses, the real data of a do-it-yourself superstore is used [16]. In this store, generally five checkouts operate.…”

“…On the average the part independent of the number of items bought lasts 0.5463 minute, reading a bar code needs 0.1622 minute. With linear regression the standard deviation of these parameters and the service times of customers buying different amounts were determined as well (for details see [16]).…”

“…In Fig. 1, a part of the numerical model can be seen -with the data of the do-ityourself superstore [16]. Introducing express lines into a queuing system can be carried out in several ways.…”

“…By using this relationship, the average service time and the variance of service times can be determined for customers buying the same number of items. With the help of the distribution function of the number of items bought, the average arrival and service rates, and the variances of service times can be determined for all possible customer groups as well (for details see [16]).…”

“…The analytical and simulation results gained by the same type of models are quite close to each other; therefore. the simulation model can be considered valid [16]. The analytical and simulation results are listed later in Table 1.…”