Bertrand M. T. Lin

A concise survey of scheduling with time-dependent processing times

Abstract We consider a class of machine scheduling problems in which the processing time of a task is dependent on its starting time in a schedule. On reviewing the literature on this topic, we provide a framework to illustrate how models for this class of problems have been generalized from the classical scheduling theory. A complexity boundary is presented for each model and related existing results are consolidated. We also introduce some enumerative solution algorithms and heuristics and analyze their performance. Finally, we suggest a few interesting areas for future research.

Application of ant colony optimization for no-wait flowshop scheduling problem to minimize the total completion time

Ant colony optimization (ACO) is a meta-heuristic proposed to derive approximate solutions for computationally hard problems by emulating the natural behaviors of ants. In the literature, several successful applications have been reported for graph-based optimization problems, such as vehicle routing problems and traveling salesman problems. In this paper, we propose an application of the ACO to a two-machine flowshop scheduling problem. In the flowshop, no intermediate storage is available between two machines and each operation demands a setup time on the machines. The problem seeks to compose a schedule that minimizes the total completion time. We first present a transformation of the scheduling problem into a graph-based model. An ACO algorithm is then developed with several specific features incorporated. A series of computational experiments is conducted by comparing our algorithm with previous heuristic algorithms. Numerical results evince that the ACO algorithm exhibits impressive performances with small error ratios. The results in the meantime demonstrate the success of ACOs applications to the scheduling problem of interest.

An Ant Colony Optimization Algorithm for the Minimum Weight Vertex Cover Problem

Given an undirected graph and a weighting function defined on the vertex set, the minimum weight vertex cover problem is to find a vertex subset whose total weight is minimum subject to the premise that the selected vertices cover all edges in the graph. In this paper, we introduce a meta-heuristic based upon the Ant Colony Optimization (ACO) approach, to find approximate solutions to the minimum weight vertex cover problem. In the literature, the ACO approach has been successfully applied to several well-known combinatorial optimization problems whose solutions might be in the form of paths on the associated graphs. A solution to the minimum weight vertex cover problem however needs not to constitute a path. The ACO algorithm proposed in this paper incorporates several new features so as to select vertices out of the vertex set whereas the total weight can be minimized as much as possible. Computational experiments are designed and conducted to study the performance of our proposed approach. Numerical results evince that the ACO algorithm demonstrates significant effectiveness and robustness in solving the minimum weight vertex cover problem.

Parallel-machine scheduling to minimize tardiness penalty and power cost

Traditional research on machine scheduling focuses on job allocation and sequencing to optimize certain objective functions that are defined in terms of job completion times. With regard to environmental concerns, energy consumption becomes another critical issue in high-performance systems. This paper addresses a scheduling problem in a multiple-machine system where the computing speeds of the machines are allowed to be adjusted during the course of execution. The CPU adjustment capability enables the flexibility for minimizing electricity cost from the energy saving aspect by sacrificing job completion times. The decision of the studied problem is to dispatch the jobs to the machines as well as to determine the job sequence and processing speed of each machine with the objective function comprising of the total weighted job tardiness and the power cost. We give a formal formulation, propose two heuristic algorithms, and develop a particle swarm optimization (PSO) algorithm to effectively tackle the problem. Since the existing solution representations do not befittingly encode the decisions involved in the studied problem into the PSO algorithm, we design a tailored encoding scheme which can embed all decisional information in a particle. A computational study is conducted to investigate the performances of the proposed heuristics and the PSO algorithm.

Ant colony optimization for the cell assignment problem in PCS networks

Even though significant improvement to communications infrastructure has been attained in the personal communication service industry, the issues concerning the assignment of cells to switches in order to minimize the cabling and handoff costs in a reasonable time remain challenging and need to be solved. In this paper, we propose an algorithm based upon the Ant Colony Optimization (ACO) approach to solve the cell assignment problem, which is known to be NP-hard. ACO is a metaheuristic inspired by the foraging behavior of ant colonies. We model the cell assignment problem as a form of matching problem in a weighted directed bipartite graph so that our artificial ants can construct paths that correspond to feasible solutions on the graph. We explore and analyze the behavior of the ants by examining the computational results of our ACO algorithm under different parameter settings. The performances of the ACO algorithm and several heuristics and metaheuristics known in the literature are also empirically studied. Experimental results demonstrate that the proposed ACO algorithm is an effective and competitive approach in composing fairly satisfactory results with respect to solution quality and execution time for the cell assignment problem as compared with most existing heuristics or metaheuristics.

An empirical investigation of total quality management: a Taiwanese case

Not long ago, the Asia Pacific area suffered economic decline due to financial crises. By contrast, Taiwan, stood apart from its regional competitors and was able to manage and turn the crises into opportunities. Besides the financial factors discussed by economists, quality management should also be considered as a major factor in this development. Investigates how industry in Taiwan implemented quality issues, especially total quality management, within the last decade. Questionnaires were generated and distributed to the largest corporations in Taiwan to gather information. Statistical analysis was performed to evaluate the quality implementation status in Taiwan. The results can be used to study the comparative impacts of quality management in the area of the Asia Pacific.

Complexity results for single-machine scheduling with positional learning effects

This note presents complexity results for a single-machine scheduling problem of minimizing the number of late jobs. In the studied problem, the processing times of the jobs are defined by positional learning effects. A recent paper proposed a polynomial time algorithm for the case with a common due date and conjectured the general problem to be 𝒩𝒫-hard. We confirm that the general problem is strongly 𝒩𝒫-hard and show that the studied problem remains 𝒩𝒫-hard even if there are only two different due-date values.

Single Machine Scheduling to Minimize Batch Delivery and Job Earliness Penalties

We study a problem in which a set of n jobs has to be batched as well as scheduled for processing on a single machine. A constant machine set-up time is required before the first job of each batch is processed. A schedule specifies the sequence of batches, where each batch comprises a sequence of jobs. The batch delivery time is defined as the completion time of the last job in a batch. The earliness of a job is defined as the difference between the delivery time of the batch to which it belongs and the job completion time. The objective is to find a number B of batches and a schedule so as to minimize the sum of the total weighted job earliness and mean batch delivery time. The problem is shown to be strongly NP-hard. It remains strongly

Two-stage flowshop scheduling with a common second-stage machine

NP

Batch scheduling in the no-wait two-machine flowshop to minimize the makespan

-hard if the set-up time is zero and