Storage planning and utilization are among the most important considerations of practical batch process scheduling. Modeling the available storage options appropriately can be crucial in order to find practically applicable solutions with the best objective value. In general, there are two main limitations on storage: capacity and time. This paper focuses on the latter and investigates different techniques to tackle limited storage time within the S-graph framework. The S-graph framework is a collection of combinatorial algorithms and a directed graph based model that has been introduced three decades ago and has been under development ever since. In this work, several options for addressing storage time limitations within the framework were implemented and tested for efficiency. The empirical results over a huge number of tests have unequivocally favored one of the approaches, which will be applied in later developments.

Nowadays the successful operation of a company is unimaginable without fast and reliable communication. As a result, so-called Communication Service Providers play an important role in today’s business life. Their orders have to be carried out promptly and dependably, let them be requests for new installations, modifications, or maintenance tasks. These orders have to be performed at different locations and they often have deadlines or strict starting times. Violating such a timing requirement usually implies penalties. In this paper, scheduling problems arising at a Hungarian service provider are examined. At this company, orders are decomposed into smaller tasks, which can be performed by specially trained personnel. Transportation of these specialists contributes a lot to the costs and to the complexity of their scheduling, as well. The goal is to minimize the overall cost of satisfying all orders within the given time horizon with the available assets of the company. The proposed approach relies on the S-graph framework, which has been applied to various production scheduling problems in the literature. In addition to an unambiguous and sound S-graph model of the examined problem, slight modifications of the scheduling algorithms for cost minimization, and new bounding methods have been developed. Several of such bounds have been provided and tested for performance and scalability over a large number of generated examples. The sensitivity of the approach for certain problem features has also been examined.