Study of Client Reject Policies under Lead-Time and Price Dependent Demand

Delivery lead-time has become a factor of competitiveness for companies and an important criterion of purchase for the customers today. Thus, in order to increase their profit, companies must not focus only on price but also need to quote the right delivery lead time to their customers. Some authors to find a way in quoting the right delivery lead-time while considering an M/M/1 system. In M/M/1, all customers are accepted. This can lead to longer lead times in the queue. Firms can react by quoting longer lead times in order to cope with this situation. However, this leads to lower demand and revenue. Starting from this observation, we investigate in this paper whether a customer rejection policy can be more beneficial for the firm than an all-customers’ acceptance policy. Indeed, our idea is based on the fact that rejecting some customers might help to quote shorter lead time for the accepted customers, which might lead to higher demand and profit. We model this rejection policy based on an M/M/1/K system. We analytically determine the optimal firm’s policy (optimal price and quoted lead time) in case of M/M/1/1 system. Then, we compare the optimal firm’s profit under M/M/1/1 with the optimal profit obtained by M/M/1. Two situations are considered: a system without holding and penalty costs and a system where these costs are included

A connection-level call admission control using genetic algorithm for MultiClass multimedia services in wireless networks

Call admission control in a wireless cell in a personal communication system (PCS) can be modeled as an M/M/C/C queuing system with m classes of users. Semi-Markov Decision Process (SMDP) can be used to optimize channel utilization with upper bounds on handoff blocking probabilities as Quality of Service constraints. However, this method is too time-consuming and therefore it fails when state space and action space are large. In this paper, we apply a genetic algorithm approach to address the situation when the SMDP approach fails. We code call admission control decisions as binary strings, where a value of “1” in the position i (i=1,…m) of a decision string stands for the decision of accepting a call in class-i; a value of “0” in the position i of the decision string stands for the decision of rejecting a call in class-i. The coded binary strings are feed into the genetic algorithm, and the resulting binary strings are founded to be near optimal call admission control decisions. Simulation results from the genetic algorithm are compared with the optimal solutions obtained from linear programming for the SMDP approach. The results reveal that the genetic algorithm approximates the optimal approach very well with less complexity

Analysis of Allocation Rules for Heart Transplantation

In this dissertation, we utilize several mathematical, optimization, and simulation techniques to improve the outcomes of organ transplantation allocation systems. Specifically, in Chapter 1, we build a Monte Carlo simulation model of the heart transplantation system in the United States that can be used to compare the performance of different allocation policies and predict the future of allocation systems. In Chapter 2, we develop a constrained Markov Decision model of the transplant queuing system and investigate optimal allocation rules for heart transplantation in the presence of certain fairness constraints. In Chapter 3, we introduce a new measure of fairness in the organ transplantation queuing systems. We show that this measure helps improve the performance loss of incorporating fairness considerations in organ transplantation systems, and decrease the price of fairness

Location models for airline hubs behaving as M/D/c queues

Models are presented for the optimal location of hubs in airline networks, that take into consideration the congestion effects. Hubs, which are the most congested airports, are modeled as M/D/c queuing systems, that is, Poisson arrivals, deterministic service time, and {\em c} servers. A formula is derived for the probability of a number of customers in the system, which is later used to propose a probabilistic constraint. This constraint limits the probability of {\em b} airplanes in queue, to be lesser than a value $\alpha$. Due to the computational complexity of the formulation. The model is solved using a meta-heuristic based on tabu search. Computational experience is presented.Hub location, congestion, tabu-search

Combined make-to-order and make-to-stock in a food production system

The research into multi-product production/inventory control systems has mainly assumed one of the two strategies: Make-to-Order (MTO) or Make-to-Stock (MTS). In practice, however, many companies cater to an increasing variety of products with varying logistical demands (e.g. short due dates, specific products) and production characteristics (e.g. capacity usage, setup) to different market segments and so they are moving to more MTO-production. As a consequence they operate under a hybrid MTO-MTS strategy. Important issues arising out of such situations are, for example, which products should be manufactured to stock and which ones on order and, how to allocate capacity among various MTO-MTS products. This paper presents the state-of-the-art literature review of the combined MTO-MTS production situations. A variety of production management issues in the context of food processing companies, where combined MTO-MTS production is quite common, are discussed in details. The authors propose a comprehensive hierarchical planning framework that covers the important production management decisions to serve as a starting point for evaluation and further research on the planning system for MTO-MTS situations.