Iterative beam search algorithms for the permutation flowshop

We study an iterative beam search algorithm for the permutation flowshop (makespan and flowtime minimization). This algorithm combines branching strategies inspired by recent branch-and-bounds and a guidance strategy inspired by the LR heuristic. It obtains competitive results, reports many new-best-so-far solutions on the VFR benchmark (makespan minimization) and the Taillard benchmark (flowtime minimization) without using any NEH-based branching or iterative-greedy strategy. The source code is available at: https://gitlab.com/librallu/cats-pfsp

Parallel patterns determination in solving cyclic flow shop problem with setups

© 2017 Archives of Control Sciences 2017. The subject of this work is the new idea of blocks for the cyclic flow shop problem with setup times, using multiple patterns with different sizes determined for each machine constituting optimal schedule of cities for the traveling salesman problem (TSP). We propose to take advantage of the Intel Xeon Phi parallel computing environment during so-called 'blocks' determination basing on patterns, in effect significantly improving the quality of obtained results

A survey of scheduling problems with setup times or costs

Author name used in this publication: C. T. NgAuthor name used in this publication: T. C. E. Cheng2007-2008 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Approximate Methods For Solving Flowshop Problems

The flow shop scheduling problem is a classical combinatorial problem being studied for years. The focus of this research is to study two variants of the flow shop scheduling problem in order to minimize makespan by scheduling n jobs on m machines. A solution approach is developed for the modified flow shop problem with due dates and release times. This algorithm is an attempt to contribute to the limited literature for the problem. Another tabu search-based solution approach is developed to solve the classical flow shop scheduling problem. This meta-heuristic (called 3XTS) allows an efficient search of the neighboring solutions leading to a fast solution procedure. Several control parameters affecting the quality of the algorithm are experimentally tested, and certain rules are established for different problem instances. The 3XTS is compared to another tabu search method (that seems to be a champion) in terms of solution quality and computation time

Discrete Particle Swarm Optimization for Flexible Flow Line Scheduling

Previous research on scheduling flexible flow lines (FFL) to minimize makespan has utilized approaches such as branch and bound, integer programming, or heuristics. Metaheuristic methods have attracted increasing interest for solving scheduling problems in the past few years. Particle swarm optimization (PSO) is a population-based metaheuristic method which finds a solution based on the analogy of sharing useful information among individuals. In the previous literature different PSO algorithms have been introduced for various applications. In this research we study some of the PSO algorithms, continuous and discrete, to identify a strong PSO algorithm in scheduling flexible flow line to minimize the makespan. Then the effectiveness of this PSO algorithm in FFL scheduling is compared to genetic algorithms. Experimental results suggest that discrete particle swarm performs better in scheduling of flexible flow line with makespan criteria compared to continuous particle swarm. Moreover, combining discrete particle swarm with a local search improves the performance of the algorithm significantly and makes it competitive with the genetic algorithm (GA)

Cyclic scheduling in flow lines: Modeling observations, effective heuristics and a cycle time minimization procedure

In this paper we address the cyclic scheduling problem in flow lines. We develop a modeling framework and an integer programming formulation of the problem. We subsequently present exact and approximate solution procedures. The exact solution procedure is a branch-and-bound algorithm which uses Lagrangian and station-based relaxations of the integer programming formulation of the problem as the lower bounding method. Our heuristic procedures show a performance superior to the available ones in the literature. Finally, we address the stability issue in cyclic scheduling, demonstrate its relationship to the work-in-progress inventory control of a flow line, and present a very simple procedure to generate stable schedules in flow lines. © 1996 John Wuey & Sons, Inc