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

Solving blocking flowshop scheduling problem with makespan criterion using q-learning-based iterated greedy algorithms

This study proposes Q-learning-based iterated greedy (IGQ) algorithms to solve the blocking flowshop scheduling problem with the makespan criterion. Q learning is a model-free machine intelligence technique, which is adapted into the traditional iterated greedy (IG) algorithm to determine its parameters, mainly, the destruction size and temperature scale factor, adaptively during the search process. Besides IGQ algorithms, two different mathematical modeling techniques. One of these techniques is the constraint programming (CP) model, which is known to work well with scheduling problems. The other technique is the mixed integer linear programming (MILP) model, which provides the mathematical definition of the problem. The introduction of these mathematical models supports the validation of IGQ algorithms and provides a comparison between different exact solution methodologies. To measure and compare the performance of IGQ algorithms and mathematical models, extensive computational experiments have been performed on both small and large VRF benchmarks available in the literature. Computational results and statistical analyses indicate that IGQ algorithms generate substantially better results when compared to non-learning IG algorithms

Theoretical and Computational Research in Various Scheduling Models

Nine manuscripts were published in this Special Issue on “Theoretical and Computational Research in Various Scheduling Models, 2021” of the MDPI Mathematics journal, covering a wide range of topics connected to the theory and applications of various scheduling models and their extensions/generalizations. These topics include a road network maintenance project, cost reduction of the subcontracted resources, a variant of the relocation problem, a network of activities with generally distributed durations through a Markov chain, idea on how to improve the return loading rate problem by integrating the sub-tour reversal approach with the method of the theory of constraints, an extended solution method for optimizing the bi-objective no-idle permutation flowshop scheduling problem, the burn-in (B/I) procedure, the Pareto-scheduling problem with two competing agents, and three preemptive Pareto-scheduling problems with two competing agents, among others. We hope that the book will be of interest to those working in the area of various scheduling problems and provide a bridge to facilitate the interaction between researchers and practitioners in scheduling questions. Although discrete mathematics is a common method to solve scheduling problems, the further development of this method is limited due to the lack of general principles, which poses a major challenge in this research field

Energy-aware coordination of machine scheduling and support device recharging in production systems

Electricity generation from renewable energy sources is crucial for achieving climate targets, including greenhouse gas neutrality. Germany has made significant progress in increasing renewable energy generation. However, feed-in management actions have led to losses of renewable electricity in the past years, primarily from wind energy. These actions aim to maintain grid stability but result in excess renewable energy that goes unused. The lost electricity could have powered a multitude of households and saved CO2 emissions. Moreover, feed-in management actions incurred compensation claims of around 807 million Euros in 2021. Wind-abundant regions like Schleswig-Holstein are particularly affected by these actions, resulting in substantial losses of renewable electricity production. Expanding the power grid infrastructure is a costly and time-consuming solution to avoid feed-in management actions. An alternative approach is to increase local electricity consumption during peak renewable generation periods, which can help balance electricity supply and demand and reduce feed-in management actions. The dissertation focuses on energy-aware manufacturing decision-making, exploring ways to counteract feed-in management actions by increasing local industrial consumption during renewable generation peaks. The research proposes to guide production management decisions, synchronizing a company's energy consumption profile with renewable energy availability for more environmentally friendly production and improved grid stability

Energy Efficient Policies, Scheduling, and Design for Sustainable Manufacturing Systems

Climate mitigation, more stringent regulations, rising energy costs, and sustainable manufacturing are pushing researchers to focus on energy efficiency, energy flexibility, and implementation of renewable energy sources in manufacturing systems. This thesis aims to analyze the main works proposed regarding these hot topics, and to fill the gaps in the literature. First, a detailed literature review is proposed. Works regarding energy efficiency in different manufacturing levels, in the assembly line, energy saving policies, and the implementation of renewable energy sources are analyzed. Then, trying to fill the gaps in the literature, different topics are analyzed more in depth. In the single machine context, a mathematical model aiming to align the manufacturing power required to a renewable energy supply in order to obtain the maximum profit is developed. The model is applied to a single work center powered by the electric grid and by a photovoltaic system; afterwards, energy storage is also added to the power system. Analyzing the job shop context, switch off policies implementing workload approach and scheduling considering variable speed of the machines and power constraints are proposed. The direct and indirect workloads of the machines are considered to support the switch on/off decisions. A simulation model is developed to test the proposed policies compared to others presented in the literature. Regarding the job shop scheduling, a fixed and variable power constraints are considered, assuming the minimization of the makespan as the objective function. Studying the factory level, a mathematical model to design a flow line considering the possibility of using switch-off policies is developed. The design model for production lines includes a targeted imbalance among the workstations to allow for defined idle time. Finally, the main findings, results, and the future directions and challenges are presented

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

Analysis of no-wait flow shop scheduling problems and solving with hybrid scatter search method

Beklemesiz Akış Tipi Çizelgeleme (BATÇ), pratik uygulamalarından dolayı kapsamlı bir araştırma alanıdır. BATÇ problemlerinde işler, makinelerde kesintisiz olarak işlem görmek zorundadır. Bir işin tüm makinelerde işlenme süresi boyunca, makineler bekleyebilir fakat işler kesintisiz olarak işlenmelidir. Amaç ise makinelerin boşta bekleme süresini en aza indirmektir. BATÇ problemlerinin çoğunluğunda toplam gecikmenin ve maksimum tamamlanma zamanının minimizasyonu olmak üzere, iki performans ölçüsü göz önünde bulundurulur. Literatürde, son yirmi beş yılda BATÇ ile ilgili yapılan çalışmalar analiz edilmiştir. BATÇ problemlerinin çözümü ile ilgili geliştirilen kesin ve yaklaşık çözüm veren yöntemler incelenmiştir. Literatürde 1 ve 2 makineli problemler için optimum çözüm veren matematiksel yöntemler bulunurken, 3 ve daha fazla makineli problemler için standart zamanda optimum çözüm veren bir yöntem bulunmamaktadır. Kabul edilebilir bir süre içerisinde m makine içeren problemlere optimum ya da optimuma yakın çözümler üretebilmek için sezgisel ve meta sezgisel yöntemler geliştirilmektedir. Bu çalışmada, BATÇ problemlerinin çözümü için Hibrit Dağınık Arama (HDA) yöntemi önerilmiştir. Önerilen yöntem, literatürde iyi bilinen kıyaslama problemleri yardımı ile test edilmiştir. Elde edilen sonuçlar, Hibrit Uyarlanabilir Öğrenme Yaklaşım (HUÖY) algoritması ve Hibrit Karınca Kolonileri Optimizasyon (HKKO) algoritması ile kıyaslanmıştır. Amaç fonksiyonu olarak maksimum tamamlanma zamanının minimizasyonu seçilmiştir. Elde edilen çözüm sonuçları, önerilen HDA yönteminin BATÇ problemlerinin çözümünde etkili olduğunu göstermiştir.No-wait flow shop (NWFS) is extensively research area due to its practical applications. In NWFS, jobs are processed in machines without interruption. During the schedule period, machines can wait, but jobs cannot wait. The aim is to minimize the idle time for machines. The majority of NWFS, two performance measures are consid-ered: minimization of total delay and minimization of the makespan. The researches on the NWFS in the last twenty-five years have been analysed from the literature. The methods developed for the solution of the NWFS, which give exact and approximate solutions, have been examined. While there are mathematical methods that give optimum solutions for 1 and 2 machine problems in the literature, there is no method that provides optimum solutions in standard time for problems with 3 or more machines. The difference methods are developed in order to produce optimum or near-optimum solutions to m-machine problems in an acceptable time. A Hybrid Scatter Search Method (HSSM) is proposed for solving the NWFS. The developed HSSM tested with the well-known benchmarking instances in the literature. The results obtained were compared with the Hybrid Adaptive Learning Approach algorithm and the Hybrid Ant Colonies Optimization algorithm. The objective function is makespan minimization. According to solutions, the proposed HSSM is an effective metaheuristic to solve NWFS

Particle Swarm Optimization

Particle swarm optimization (PSO) is a population based stochastic optimization technique influenced by the social behavior of bird flocking or fish schooling.PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithms (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. This book represents the contributions of the top researchers in this field and will serve as a valuable tool for professionals in this interdisciplinary field

Bounded dynamic programming approach to minimize makespan in the blocking flowshop problem with sequence dependent setup times

This paper aims at presenting an algorithm for solving the blocking flow shop problem with sequence dependent setup times (BFSP-SDST) with minimization of the makespan. In order to do so, we propose an adapted Bounded Dynamic Programming (BDP-SN) algorithm as solution method, since the problem itself does not present a significant number of sources in the state-of-art references and also because Dynamic Programming and its variants have been resurfacing in the flowshop literature. Therefore, we apply the modified method to two sets of problems and compare the results computationally and statistically for instances with a MILP and a B&B method for at most 20 jobs and 20 machines. The results show that BDP-SN is promising and outperforms both MILP and B&B within the established time limit. In addition, some suggestions are made in order to improve the method and employ it in parallel research regarding other branches of machine scheduling

Planning and Scheduling Optimization

Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development