Stochastic multi-objective integrated disassembly-reprocessing-reassembly scheduling via fruit fly optimization algorithm

Scheduling automated transport vehicles for material distribution systems

An iterated greedy algorithm (IGA) is a simple and powerful heuristic algorithm. It is widely used to solve flow-shop scheduling problems (FSPs), an important branch of production scheduling problems. IGA was first developed to solve an FSP in 2007. Since then, various FSPs have been tackled by using IGA-based methods, including basic IGA, its variants, and hybrid algorithms with IGA integrated. Up until now, over 100 articles related to this field have been published. However, to the best of our knowledge, there is no existing tutorial or review paper of IGA. Thus, we focus on FSPs and provide a tutorial and comprehensive literature review of IGA-based methods. First, we introduce a framework of basic IGA and give an example to clearly show its procedure. To help researchers and engineers learn and apply IGA to their FSPs, we provide an open platform to collect and share related materials. Then, we make classifications of the solved FSPs according to their scheduling scenarios, objective functions, and constraints. Next, we classify and introduce the specific methods and strategies used in each phase of IGA for FSPs. Besides, we summarize IGA variants and hybrid algorithms with IGA integrated, respectively. Finally, we discuss the current IGA-based methods and already-solved FSP instances, as well as some important future research directions according to their deficiency and open issues. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Many practical scheduling problems can be transformed into flow-shop scheduling problems (FSPs), most of which are NP-hard. In order to solve them in an industrial system setting, designing effective heuristics is important and practically useful and has, thus, attracted much attention from both researchers and engineers. As an easy and high-performance heuristic, an iterated greedy algorithm (IGA) is widely used and adapted to solve numerous FSPs. Its simple framework makes it easy to be implemented by practitioners, and its high performance implies its great potential to solve industrial scheduling problems. In this work, we aim to give practitioners a comprehensive overview of IGA and help them apply IGA to solve their particular industrial scheduling problems. We review the papers that solve FSPs with IGA-based methods, including basic IGA, its variants, and hybrid algorithms with IGA integrated. First, we provide practitioners with a tutorial on IGA, where an example for solving an FSP is introduced and an open platform is constructed. The platform collects and shares the related materials, e.g., open-source code, benchmarks, and website links of important papers. Then, we introduce various FSPs and specific designs of IGA-based methods. Finally, we discuss the current research and point out future research issues. 

Iterated Greedy Algorithms for Flow-Shop Scheduling Problems: A Tutorial

A population-based iterated greedy algorithm for no-wait job shop scheduling with total flow time criterion

Scheduling algorithms for job-shop-type remanufacturing systems with component matching requirement

This study addresses a variant of job-shop scheduling in which jobs are grouped into job families, but they are processed individually. The problem can be found in various industrial systems, especially in reprocessing shops of remanufacturing systems. If the reprocessing shop is a job-shop type and has the component-matching requirements, it can be regarded as a job shop with job families since the components of a product constitute a job family. In particular, sequence-dependent set-ups in which set-up time depends on the job just completed and the next job to be processed are also considered. The objective is to minimize the total family flow time, i.e. the maximum among the completion times of the jobs within a job family. A mixed-integer programming model is developed and two iterated greedy algorithms with different local search methods are proposed. Computational experiments were conducted on modified benchmark instances and the results are reported.

Jung Hyeon Park

Papers

Iterated greedy algorithms to minimize the total family flow time for job-shop scheduling with job families and sequence-dependent set-ups