Alfred L. Guiffrida, Department of Management and Information Systems, Kent State University, Kent, Ohio, USA
Michael J. Messina, Department of Marketing, Gannon University, Erie, Pennsylvania, USA
Mohamad Y. Jaber, Department of Mechanical and Industrial Engineering, Ryerson University, Toronto, Ontario, Canada

In this paper we present a model for estimating the potential market share growth of a supplier who provides a product to customers in a supply chain environment. The model integrates delivery performance and time-based competition with the objective of market share attainment. The model addresses the need for timely delivery by integrating the customer’s specified delivery window and delivery capability index into a model for determining the expected market share that can be attained for a given level of variability in the supplier’s delivery performance. Continuous improvement and the potential for market share growth are illustrated using a cost-based scheme for reducing the supplier’s variability of delivery. A numerical illustration of the model is presented.

Fantahun M. Defersha, Department of Mechanical and Industrial Engineering, Concordia University, Montreal, Quebec, Canada
Mingyuan Chen, Department of Mechanical and Industrial Engineering, Concordia University, Montreal, Quebec, Canada

Lot streaming is a technique used to split the processing of lots (batches) into several sublots (transfer batches) in a multistage manufacturing systems. This allows the overlapping of operations of a batch on successive stages to shorten the production makespan and enable the timely delivery of the batch into the supply chain downstream. In this technique, a production lot may be split into either equal, consistent, or variable sublots. Recent literature shows that significant lead time improvement is possible if variable sublots, instead of equal or consistent sublots, are used when production setup time is considered.

In this research we noted that lot streaming problems with variable sublots are difficult to solve using off-shelf optimization packages even for problems of smaller sizes. Thus efficient solution procedures are needed for solving such problems for practical applications. In this paper, we develop a hybrid genetic algorithm for a model that appeared in recent literature for one-job m-machine lot streaming problems with variable sublots and setup. Computational results showed that the performance of the proposed genetic algorithm is encouraging.