Journal of Industrial and Systems Engineering
Volume:1 Issue: 2, Summer 2007

Networked Infrastructure systems deliver services and/or products from point to point along the network. Demand for the services provided by such systems is typically cyclic, creating inefficiencies in capacity utilization. Congestion pricing provides incentives to shift demand from peak time periods to lower demand periods. This effectively increases the capacity of the system without the need for additional capital investment. This paper investigates the potential for congestion pricing to reduce necessary infrastructure investments in the United States. Several types of congestion pricing schemes are presented, along with existing implementations across multiple infrastructure systems. We find over $20 billion in potential annual savings in electricity and road systems alone in the United States from implementing congestion pricing schemes.

This paper employs an interactive possibility linear programming approach to solve a singlemachine scheduling problem with imprecise processing times, due dates, as well as earliness and tardiness penalties of jobs. The proposed approach is based on a strategy of minimizing the most possible value of the imprecise total costs, maximizing the possibility of obtaining a lower total costs, and minimizing the risk of obtaining higher total costs simultaneously. This approach is applicable to just-in-time systems, in which many firms face the need to complete jobs as close as possible to their due dates. The objective of the model is to minimize the total costs of earliness/tardiness penalties. In this paper, the proposed possibility linear programming approach is applied to a fuzzy single machine scheduling problem with respect to the overall degree of decision maker satisfaction. Due to the proposed model’s complexity, conventional optimization methods cannot be utilized in reasonable time. Hence, the particle swarm optimization method is applied toward its solution.

Many extension of the newsboy problem have been solved in the literature. One of thoseextensions solves a newsboy problem with stochastic initial inventory, earlier extensions have focused on quantity discounts offered by suppliers. An important practical extension would address a combination of the two pervious extensions. In this paper we consider a newsboy problem in which the suppliers offer quantity discount and the initial inventory at the beginning of the period is a random variable. We obtain the optimal value of the order quantity which maximizes the total profit and then present the results for some practical distributions of both random variables, demand and initial inventory.

The Mahalanobis-Taguchi System is a diagnosis and predictive method for analyzing patterns in multivariate cases. The goal of this study is to compare the ability of the Mahalanobis- Taguchi System and a neural-network to discriminate using small data sets. We examine the discriminant ability as a function of data set size using an application area where reliable data is publicly available. The study uses the Wisconsin Breast Cancer study with nine attributes and one class.

In this paper we consider a two dimensional strip packing problem. The problem consists of packing a set of rectangular items in one strip of width W and infinite height. They must be packed without overlapping, parallel to the edge of the strip and we assume that the items are oriented, i.e. they cannot be rotated. To solve this problem, we use three exact a branch and bound method, a dichotomous algorithm and a branch and price method. The three methods were carried out and compared on literature instances.

There exist various service systems that have hierarchical structure. In hierarchical servicenetworks, facilities at different levels provide different types of services. For example, inhealth care systems, general centers provide low-level services such as primary health careservices, while the specialized hospitals provide high-level services. Because of demandcongestion in service networks, location of servers and allocation of demand nodes have astrong impact on the length of queues at servers as well as on the response times to service calls. The thrust of this article is the development of hierarchical location-allocation models for congested systems by employing queueing theory in a fuzzy framework. The new models allow partial coverage of demand nodes and approximate determination of parameters. Using queueing theory and fuzzy conditions, both referral and nested hierarchical models are developed for the maximal covering location problem (MCLP). An example is solved for both an existing probabilistic model and the new fuzzy models and the results are compared.