Economic lot size scheduling problem (ELSP) for a multi-product single machine system is a classical problem. This paper considers ELSP with budgetary constraint as an important aspect of such systems. In the real world situations the available funds for investment in inventory is limited. By adopting the common Cycle Time approach to ELSP, we obtain the optimal common Cycle which minimizes the total inventory ordering and holding costs for the case of nonzero setup Times. One aspect of the scheduling is to decide what should be the sequence of production runs and how the idle Times shall be distributed in the common Cycle Time. For such a sequencing problem, we consider two cases: a) the common Cycle Time is given, and b) the common Cycle Time is a decision variable. In the literature, scheduling rules are introduced for both cases, which assume that the total idle Time is located at the end of each Cycle. This paper relaxes this assumption and provides: i) a rule to optimize the production sequence and the length of idle Times before (or after) producing each item, for both cases (a) and (b), and ii) the optimal common Cycle for case (b). The presented rule is interestingly general, simple and easy-to-apply.

Business Process Management Systems (BPMS) is a complex information system that provides designing, administrating, and improving the business processes. Task allocation to human resources is one of the most important issues which should be managed more efficiently in BPMS. Task allocation algorithms are defined in order to meet the various policies of organizations. The most important of these policies could be reducing the average Cycle Time, balancing the resource workload, increasing the product quality and minimizing the production costs. Therefore, choosing an appropriate resource in task allocation algorithms could influence on overall policy of the organization. In heavy load conditions or when the number of human resources is limited, workload balancing can increase the stability of the system. In this paper, a task allocation algorithm is proposed to rebalance the resource workload in runTime while minimizing Cycle Time by offering dependent pair tasks to resources for concurrent processing. The experimental results show that the combination of previous algorithms with the proposed algorithms would have 4. 42% reduction in Cycle Time in contrast to most efficient stateof-the-art algorithms.