An Energy-Efficient No Idle Permutations Flow Shop Scheduling Problem Using Grey Wolf Optimizer Algorithm

Energy consumption has become a significant issue in businesses. It is known that the industrial sector has consumed nearly half of the world's total energy consumption in some cases. This research aims to propose the Grey Wolf Optimizer (GWO) algorithm to minimize energy consumption in the No Idle Permutations Flowshop Problem (NIPFP). The GWO algorithm has four phases: initial population initialization, implementation of the Large Rank Value (LRV), grey wolf exploration, and exploitation. To determine the level of machine energy consumption, this study uses three different speed levels. To investigate this problem, 9 cases were used. The experiments show that it produces a massive amount of energy when a job is processed fast. Energy consumption is lower when machining at a slower speed. The performance of the GWO algorithm has been compared to that of the Cuckoo Search (CS) algorithm in several experiments. In tests, the Grey Wolf Optimizer (GWO) outperforms the Cuckoo Search (CS) algorithm

Iterated Greedy methods for the distributed permutation flowshop scheduling problem

[EN] Large manufacturing firms operate more than one production center. As a result, in relation to scheduling problems, which factory manufactures which product is an important consideration. In this paper we study an extension of the well known permutation flowshop scheduling problem in which there is a set of identical factories, each one with a flowshop structure. The objective is to minimize the maximum completion time or makespan among all factories. The resulting problem is known as the distributed permutation flowshop and has attracted considerable interest over the last few years. Contrary to the recent trend in the scheduling literature, where complex nature-inspired or metaphor-based methods are often proposed, we present simple Iterated Greedy algorithms that have performed well in related problems. Improved initialization, construction and destruction procedures, along with a local search with a strong intensification are proposed. The result is a very effective algorithm with little problem-specific knowledge that is shown to provide demonstrably better solutions in a comprehensive and thorough computational and statistical campaign.Ruben Ruiz is partially supported by the Spanish Ministry of Economy and Competitiveness, under the project "SCHEYARD - Optimization of Scheduling Problems in Container Yards" (No. DPI2015-65895-R) financed by FEDER funds. Quan-Ke Pan is supported by the National Natural Science Foundation of China (Grant No. 51575212).Ruiz García, R.; Pan, Q.; Naderi, B. (2019). Iterated Greedy methods for the distributed permutation flowshop scheduling problem. Omega. 83:213-222. https://doi.org/10.1016/j.omega.2018.03.004S2132228

A parameter tuned hybrid algorithm for solving flow shop scheduling problems with parallel assembly stages

In this paper, we study the scheduling problem for a customized production system consisting of a flow shop production line with a parallel assembly stage that produces various products in two stages. In the first stage of the production line, parts are produced using a flow shop production line, and in the second stage, products are assembled on one of the parallel assembly lines. The objective is to minimize the time required to complete all goods (makespan) using efficient scheduling. A mathematical model is developed; however, the model is NP-hard and cannot be solved in a reasonable amount of time. To solve this NP-hard problem, we propose two well-known metaheuristics and a hybrid algorithm. To calibrate and improve the performance of our algorithms, we employ the Taguchi method. We evaluate the performance of our hybrid algorithm with the two well-known methods of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) and demonstrate that our hybrid algorithm outperforms both the GA and PSO approaches in terms of efficiency

Hybrid flow shop scheduling problems using improved fireworks algorithm for permutation

Prior studies are lacking which address permutation flow shop scheduling problems and hybrid flow shop scheduling problems together to help firms find the optimized scheduling strategy. The permutation flow shop scheduling problem and hybrid flow shop scheduling problems are important production scheduling types, which widely exist in industrial production fields. This study aimed to acquire the best scheduling strategy for making production plans. An improved fireworks algorithm is proposed to minimize the makespan in the proposed strategies. The proposed improved fireworks algorithm is compared with the fireworks algorithm, and the improvement strategies include the following: (1) A nonlinear radius is introduced and the minimum explosion amplitude is checked to avoid the waste of optimal fireworks; (2) The original Gaussian mutation operator is replaced by a hybrid operator that combines Cauchy and Gaussian mutation to improve the search ability; and (3) An elite group selection strategy is adopted to reduce the computing costs. Two instances from the permutation flow shop scheduling problem and hybrid flow shop scheduling problems were used to evaluate the improved fireworks algorithm’s performance, and the computational results demonstrate the improved fireworks algorithm’s superiority

Deterministic Assembly Scheduling Problems: A Review and Classification of Concurrent-Type Scheduling Models and Solution Procedures

Many activities in industry and services require the scheduling of tasks that can be concurrently executed, the most clear example being perhaps the assembly of products carried out in manufacturing. Although numerous scientific contributions have been produced on this area over the last decades, the wide extension of the problems covered and the lack of a unified approach have lead to a situation where the state of the art in the field is unclear, which in turn hinders new research and makes translating the scientific knowledge into practice difficult. In this paper we propose a unified notation for assembly scheduling models that encompass all concurrent-type scheduling problems. Using this notation, the existing contributions are reviewed and classified into a single framework, so a comprehensive, unified picture of the field is obtained. In addition, a number of conclusions regarding the state of the art in the topic are presented, as well as some opportunities for future research.Ministerio de Ciencia e Innovación español DPI2016-80750-

A Multi-Objective Variable Neighborhood Search Algorithm for Precast Production Scheduling

In real life, precast production schedulers face the challenges of creating a reasonable schedule to satisfy multiple conflicting objectives. Practical constraints and objectives encountered in the precast production scheduling problem (PPSP) were addressed, with the goal to minimize makespan and total earliness and tardiness penalties. A multi-objective variable neighborhood search (MOVNS) algorithm was proposed and the performance was tested on 11 problem instances. Ten of these were generated using precast concrete production information taken from the literature. One real industrial problem from a precast concrete company was considered as a case study. Extensive experiments were conducted, and the spread and distance metrics were used to evaluate the quality of the non-dominated solutions set. Statistical analysis demonstrated that the result was statistically convincing. Computational results showed that the proposed MOVNS algorithm was significantly better when compared to the other nine algorithms. Therefore, the proposed MOVNS algorithm was a very competitive method for the considered PPSP

Assembly flowshop scheduling problem: Speed-up procedure and computational evaluation

In this paper, we address the assembly flowshop scheduling problem, which is a generalisation of two well-known scheduling problems in the literature: the three-stage Assembly Scheduling Problem (ASP) and its variant with two stages denoted as the two-stage ASP. For this problem, we prove several theoretical results which are used to propose a speed-up procedure. This acceleration mechanism can be applied in any insertion-based method for the problem under study and, consequently, also for their special cases. In addition, we propose four efficient constructive heuristics for the problem, based on both Johnson’s algorithm and the NEH heuristic. These proposals are compared against 47 algorithms existing in the literature for related problems. The results show the excellent performance of the proposals.Ministerio de Ciencia e Innovación PID2019-108756RB-I00Junta de Andalucía P18-FR-1149, 5835Junta de Andalucía US- 126451

A Batch Scheduling Model for a Three-stage Hybrid Flowshop Producing Products with Hierarchical Assembly Structures

This paper addresses a batch scheduling problem for a three-stage hybrid flowshop consisting of a machining stage processing common and unique components on unrelated parallel machines, an assembly stage combining the components into assembled products with complex assembly structures, and a differentiation stage processing the assembled products on dedicated machines to produce different product types. The common components are the same for all products and are processed in batches, while the unique components are dedicated to respective given product types and are processed individually (one-by-one component). The goal is to schedule all the products with different assembly structures to minimize total actual flow time (TAFT) defined as total time interval of components to be processed from their arrival times to their common due date. A non-linear programming model is proposed, where small size problems can be solved optimally using the LINGO software, and large size problems is to be solved using a heuristic algorithm. The proposed algorithm consists of two sub-algorithms. The first one is constructed using a shortest processing time (SPT) based heuristic to get a job sequence as an initial solution and the second one is to improve the initial solution using the variable neighbourhood descent (VND) method with neighbourhood insert and swap move operators. In solving the problem with the algorithm, two scenarios arise, e.g., the same and the different sequences for all stages. A set of hypothetical data is generated for different hierarchical assembly structures to test the model and the algorithm, and the results show that the different sequences for all stages obtain solutions with better performances than the same ones

Algorithms and Methods for Designing and Scheduling Smart Manufacturing Systems

This book, as a Special Issue, is a collection of some of the latest advancements in designing and scheduling smart manufacturing systems. The smart manufacturing concept is undoubtedly considered a paradigm shift in manufacturing technology. This conception is part of the Industry 4.0 strategy, or equivalent national policies, and brings new challenges and opportunities for the companies that are facing tough global competition. Industry 4.0 should not only be perceived as one of many possible strategies for manufacturing companies, but also as an important practice within organizations. The main focus of Industry 4.0 implementation is to combine production, information technology, and the internet. The presented Special Issue consists of ten research papers presenting the latest works in the field. The papers include various topics, which can be divided into three categories—(i) designing and scheduling manufacturing systems (seven articles), (ii) machining process optimization (two articles), (iii) digital insurance platforms (one article). Most of the mentioned research problems are solved in these articles by using genetic algorithms, the harmony search algorithm, the hybrid bat algorithm, the combined whale optimization algorithm, and other optimization and decision-making methods. The above-mentioned groups of articles are briefly described in this order in this book