S.S. Lam

Single machine scheduling with a variable common due date and resource-dependent processing times

The problem of scheduling n jobs with a variable common due date on a single machine is studied. It is assumed that the job processing times are non-increasing linear functions of an equal amount of a resource allocated to the jobs. The due date and resource values can be continuous or discrete. The objective is to minimize a linear combination of scheduling, due date assignment and resource consumption costs. The resource consumption cost function may be non-monotonous. Algorithms with O(n2 log n) running times are presented for scheduling costs involving earliness/tardiness and number of tardy jobs. Computational experiments show that the algorithms can solve problems with n= 5,000 in less than a minute on a standard PC.

A genetic fuzzy expert system for stock market timing

Buying and selling of stocks is an essential activity in financial markets. Financial experts invented many market indicators to monitor the movement of stock prices. Trading rules were defined on these indicators for generating buy-sell signals. These rules are fuzzy in nature and they can predict to a certain extent but not always the price movement of stocks. Their accuracy is time-varying and it is impossible to have the best trading rule. A certain combination of trading rules will generate more reliable buy-sell signals for a particular stock in a certain period of time. The selection of these trading rules can be formulated as an optimization problem. We propose a new stock market timing system by integrating a genetic algorithm with a fuzzy expert system. A genetic algorithm is used to optimize the selection of fuzzy trading rules. Experiments were conducted to evaluate the performance of the system and the results indicate that the system can generate more reliable buy-sell signals even in a declining market.

Hospital lift system simulator: A performance evaluator-predictor

Abstract Hospitals often experience lift congestion as a result of their heavy traffic, complex user types, and relatively slow-moving lifts (due to concerns over safety). Given the increasing number of current and new hospital building blocks that consist of many storeys, a visual simulation-based decision support system (DSS) is recommended. We present the modelling approach and development of a tool capable of being used for lift performance evaluation/prediction of existing/new hospital designs. These are also applicable to other general-purpose lift systems. A new data modelling approach, based on collected empirical traffic data, was developed to estimate the inter-floor passenger traffic. The DSS is flexible enough to allow the input of any zoning policy. The integrated zoning analysis offered here has not been found in existing lift simulators. This paper is the first to model a special feature designed to disable certain lift buttons in order to ensure fair use of the lift service. We carried out field studies of two existing hospitals, and we projected lift demand for a new hospital under construction. Performances at all three hospitals with different design structures under different operational control policies and lift features are given.

Group sequencing around a common due date

The problem of scheduling groups of jobs on a single machine under the group technology assumption is studied. Jobs of the same group are processed contiguously and a sequence independent setup time precedes the processing of each group. All jobs have a common fixed due date, which can be either unrestrictively large or restrictively small. The objective is to minimize the total weighted earliness-tardiness. Properties of optimal solutions are established, and dynamic programming algorithms are derived to solve several special cases of this problem. Computational experiments show that the algorithms can easily solve problems with 500 groups of jobs and each group has 10 to 50 jobs on a standard PC.

Maximum Resolution Topology for Online Auction Markets

A topological model of an online auction market is a simultaneous graphical display of all the dimensions of its relevant database, providing a geometrical shape as a visually descriptive statistics of the market at any particular instance of its development. In particular, various dimensions were identified for constructing a multi-attribute dichotomy that can help discern relative advantages to buyers and sellers, using only available, operational data, and without expert knowledge of the items involved or the prices attained. With a reference subset of prejudged cases, the configuration of the dimensions and the angles among them can be optimized for a topology that maximizes the resolution of such dichotomies. The approach is illustrated in a global comparative study of four markets in five countries. It can be a useful tool for data mining and visualization in the design, study, and analysis of online auction markets.

Early-tardy scheduling under fuzzy due dates using a genetic algorithm

We consider the problem of scheduling n jobs on a single machine. Each job i has a processing time p/sub i/, a weight w/sub i/, and a due date D/sub i/, which is a fuzzy number with a triangular membership function. The problem is to determine: (i) a job sequence, and (ii) a set of idle times each before one job, so as to minimize the total weighted earliness and tardiness cost under the fuzzy due dates. We first introduce a fuzzy distance function to measure the deviation of the completion time of a job from its fuzzy due date. We show that, given a job sequence, the problem of determining the optimal idle times is a continuous and convex optimization problem with a differentiable objective function, although the objective function after applying the fuzzy distance measure becomes nonlinear. We devise a genetic algorithm (GA) to tackle the problem, using a pigeon-hole coding scheme to represent a sequence and a nonlinear optimizer to determine the idle times. We evaluate the solutions obtained by such a GA as compared to the solutions obtained by treating the due dates as crispy numbers equal to the mean values of its fuzzy partners.

Least cost route planning DSS for telecommunication operators

This article employs a mathematical programming model to solve the capacity planning problem of international direct dialling (IDD) services. Our study involves allocating forecasted IDD demand traffic to destination countries via partner suppliers at minimum cost. To facilitate its use, the model has been integrated into a decision support system (DSS). Evaluating the data provided by a telecommunication operators shows that the system can handle the problem very effectively and efficiently.

Virtual Distribution Systems: an integrated model with research initiatives

In this article, we attempt to give a comprehensive review of how classical supply chain models have evolved with advances in information technology and its related branches of knowledge. To illustrate a possible solution to meet the challenges of the present day, we propose a model of a Virtual Distribution System for a supplier (or a group of suppliers) to use in planning and operating the distribution of goods and merchandise to customers over the entire region. These customers often demand that the products they have ordered be delivered to their preferred destinations in a highly efficient way. The modular structure of this system enables it to be more flexible and responsive to dynamic changes in the market. Illustrative example of the solution obtained by the main distribution model is given in the paper.

Earliness and tardiness scheduling with a fuzzy due date and job dependent weights

Abstract The paper considers the problem of scheduling n jobs on a single machine. Each job j is associated with a weight wj and a processing time pj, and the objective of the problem is to minimize the weighted earliness and tardiness of job completions from a common due date D̃, where D̃ is a fuzzy number, governed by a triangular membership function. A fuzzy distance function is introduced first to measure the deviations of job completions from the fuzzy due date. We then show that an optimal job sequence must be V-shaped in terms of weighted processing time when the problem is agreeably weighted, in the sense that pi < pj implies wi ≥ wj . We further show that, for arbitrary weights, the optimal sequence must be W-shaped if the support of the due date is smaller than p min, the smallest processing time. Algorithms are derived which can find the best V-shaped or W-shaped sequences in pseudo-polynomial time. A polynomial algorithm is also developed for the special case with equal processing times. Numerical experiments were conducted, to examine the applicability of the algorithms proposed to general cases without any conditions.