A Novel Approach to the Common Due-Date Problem on Single and Parallel Machines

This paper presents a novel idea for the general case of the Common Due-Date (CDD) scheduling problem. The problem is about scheduling a certain number of jobs on a single or parallel machines where all the jobs possess different processing times but a common due-date. The objective of the problem is to minimize the total penalty incurred due to earliness or tardiness of the job completions. This work presents exact polynomial algorithms for optimizing a given job sequence for single and identical parallel machines with the run-time complexities of $O(n \log n)$ for both cases, where $n$ is the number of jobs. Besides, we show that our approach for the parallel machine case is also suitable for non-identical parallel machines. We prove the optimality for the single machine case and the runtime complexities of both. Henceforth, we extend our approach to one particular dynamic case of the CDD and conclude the chapter with our results for the benchmark instances provided in the OR-library.Comment: Book Chapter 22 page

Are We Wise About Sub-Fields in IS? Lessons from Forming and Sustaining a Research Community

Sub-fields within I.S. generate benefits for their participants and for the larger research discipline. Sub-fields can also fragment and compete with the broad field they emerge from. One of the largest and most active research groups in the ICIS community is the researchers examining Information Systems Economics. After 20 years of the Workshop on Information Systems and Economics (WISE), this is a moment to identify what sub-fields contribute in I.S. and look forward to what sub-fields can do for ICIS researchers and I.S. practice in the future

Value of Public Information in Sender-Receiver Games

I find in two classes of sender–receiver games that the receiver’s equilibrium payoff is not increasing in the informativeness of a public signal because the sender may transmit less information when the public signal is more informative