M/G/ oo with Batch Arrivals

OR 169-87 Revised Version. Original copy, WP1932-87 September 1987, also enclosed was submitted to the University of Rochester.Let po(n) be the distribution of the number N(oo) in the system at ergodicity for systems with an infinite number of servers, batch arrivals with general batch size distribution and general holding times. This distribution is of impotance to a variety of studies in congestion theory, inventory theory and storage systems. To obtain this distribution, a more general problem is addressed . In this problem, each epoch of a Poisson process gives rise to an independent stochastic function on the lattice of integers, which may be viewed as a stochastic impulse response. A continuum analogue to the lattice process is also provided

Product Selection Policies for Perishable Inventory Systems

A comparison is made of the performance characteristics of perishable inventory systems with LIFO and FIFO selection policies. For each system one has Poisson arrivals to inventory and Poisson demand epochs with known rates and deterministic shelf life. The comparison requires development of a new analytical framework for the LIFO systems and extension of earlier results on the FIFO systems via Green's function methods. The performance characteristics derived for both policies are spoilage rate, loss sales rate, mean time between stockouts, inventory level on hand and the distribution of age of items delivered. In particular, the dependance of these characteristics on the replenishment rate, demand rate and shelf life are evaluated both theoretically and numerically. Several important management implications are explored and discussed

Service Systems with Heterogeneous Customers: Investigating the Effect of Telemedicine on Chronic Care

Medical specialists treating chronic conditions typically face a heterogeneous set of patients. Such heterogeneity arises because of differences in medical conditions as well as the travel burden each patient faces to visit the clinic periodically. Given this heterogeneity, we compare the strategic behavior of revenue-maximizing and welfare-maximizing specialists and prove that the former will serve a smaller patient population, spend more time with the patients, and have shorter waiting times. We also analyze the impact of telemedicine technology on patient utility and the specialists' operating decisions. We consider both the case when specialists can freely set their own fee for service and the case when fees are set exogenously by a third-party payer. We prove that with the introduction of telemedicine the specialists become more productive and the overall social welfare increases, though some patients, unexpectedly, will be worse off. Our analytical results lead to some important policy implications for facilitating the further deployment of telemedicine in the care of chronically ill patients

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

Hybrid Genetic Bees Algorithm applied to Single Machine Scheduling with Earliness and Tardiness Penalties

This paper presents a hybrid Genetic-Bees Algorithm based optimised solution for the single machine scheduling problem. The enhancement of the Bees Algorithm (BA) is conducted using the Genetic Algorithm's (GA's) operators during the global search stage. The proposed enhancement aims to increase the global search capability of the BA gradually with new additions. Although the BA has very successful implementations on various type of optimisation problems, it has found that the algorithm suffers from weak global search ability which increases the computational complexities on NP-hard type optimisation problems e.g. combinatorial/permutational type optimisation problems. This weakness occurs due to using a simple global random search operation during the search process. To reinforce the global search process in the BA, the proposed enhancement is utilised to increase exploration capability by expanding the number of fittest solutions through the genetical variations of promising solutions. The hybridisation process is realised by including two strategies into the basic BA, named as â\u80\u9creinforced global searchâ\u80\u9d and â\u80\u9cjumping functionâ\u80\u9d strategies. The reinforced global search strategy is the first stage of the hybridisation process and contains the mutation operator of the GA. The second strategy, jumping function strategy, consists of four GA operators as single point crossover, multipoint crossover, mutation and randomisation. To demonstrate the strength of the proposed solution, several experiments were carried out on 280 well-known single machine benchmark instances, and the results are presented by comparing to other well-known heuristic algorithms. According to the experiments, the proposed enhancements provides better capability to basic BA to jump from local minima, and GBA performed better compared to BA in terms of convergence and the quality of results. The convergence time reduced about 60% with about 30% better results for highly constrained jobs