Single-machine scheduling

About: Single-machine scheduling is a research topic. Over the lifetime, 2473 publications have been published within this topic receiving 56288 citations.

Integrating target analysis and tabu search for improved scheduling systems

Abstract: This paper explores the integration of the Artificial Intelligence/Operations Research approach known as target analysis with tabu search to create a more effective system for machine scheduling. Target analysis is designed to give heuristic and optimization procedures the ability to learn what rules are best for solving particular classes of problems. The authors focus on the development of rules that depend on memory functions to incorporate diversifying elements in a tabu search method which is tailored to find optimal or near optimal solutions for a class of single machine scheduling problems with delay penalties and setup costs.

An exact algorithm for single-machine scheduling without machine idle time

Abstract: This study proposes an exact algorithm for the general single-machine scheduling problem without machine idle time to minimize the total job completion cost. Our algorithm is based on the Successive Sublimation Dynamic Programming (SSDP) method. Its major drawback is heavy memory usage to store dynamic programming states, although unnecessary states are eliminated in the course of the algorithm. To reduce both memory usage and computational efforts, several improvements to the previous algorithm based on the SSDP method are proposed. Numerical experiments show that our algorithm can optimally solve 300 jobs instances of the total weighted tardiness problem and the total weighted earliness-tardiness problem, and that it outperforms the previous algorithms specialized for these problems.

Solving the integrated scheduling of production and rail transportation problem by Keshtel algorithm

Abstract: An integrated production and transportation model, which considers rail transportation, is developed.A heuristic, two metaheuristics and some related procedures are developed.Keshtel algorithm is developed for the first time for a mathematical model.Taguchi experimental design method is utilized to improve their performance.Performance evaluation, comparison, and analysis are employed for the proposed algorithms. Nowadays, scheduling of production cannot be done in isolation from scheduling of transportation since a coordinated solution to the integrated problem may improve the performance of the whole supply chain. In this paper, because of the widely used of rail transportation in supply chain, we develop the integrated scheduling of production and rail transportation. The problem is to determine both production schedule and rail transportation allocation of orders to optimize customer service at minimum total cost. In addition, we utilize some procedures and heuristics to encode the model in order to address it by two capable metaheuristics: Genetic algorithm (GA), and recently developed one, Keshtel algorithm (KA). Latter is firstly used for a mathematical model in supply chain literature. Besides, Taguchi experimental design method is utilized to set and estimate the proper values of the algorithms' parameters to improve their performance. For the purpose of performance evaluation of the proposed algorithms, various problem sizes are employed and the computational results of the algorithms are compared with each other. Finally, we investigate the impacts of the rise in the problem size on the performance of our algorithms.