A Keyword, Taxonomy and Cartographic Research Review of Sustainability Concepts for Production Scheduling in Manufacturing Systems

The concept of sustainability is defined as composed of three pillars: social, environmental, and economic. Social sustainability implies a commitment to equity in terms of several “interrelated and mutually supportive” principles of a “sustainable society”; this concept includes attitude change, the Earth’s vitality and diversity conservation, and a global alliance to achieve sustainability. The social and environmental aspects of sustainability are related in the way sustainability indicators are related to “quality of life” and “ecological sustainability”. The increasing interest in green and sustainable products and production has influenced research interests regarding sustainable scheduling problems in manufacturing systems. This study is aimed both at reducing pollutant emissions and increasing production efficiency: this topic is known as Green Scheduling. Existing literature research reviews on Green Scheduling Problems have pointed out both theoretical and practical aspects of this topic. The proposed work is a critical review of the scientific literature with a three-pronged approach based on keywords, taxonomy analysis, and research mapping. Specific research questions have been proposed to highlight the benefits and related objectives of this review: to discover the most widely used methodologies for solving SPGs in manufacturing and identify interesting development models, as well as the least studied domains and algorithms. The literature was analysed in order to define a map of the main research fields on SPG, highlight mainstream SPG research, propose an efficient view of emerging research areas, propose a taxonomy of SPG by collecting multiple keywords into semantic clusters, and analyse the literature according to a semantic knowledge approach. At the same time, GSP researchers are provided with an efficient view of emerging research areas, allowing them to avoid missing key research areas and focus on emerging ones

Scientific research trends about metaheuristics in process optimization and case study using the desirability function

This study aimed to identify the research gaps in Metaheuristics, taking into account the publications entered in a database in 2015 and to present a case study of a company in the Sul Fluminense region using the Desirability function. To achieve this goal, applied research of exploratory nature and qualitative approach was carried out, as well as another of quantitative nature. As method and technical procedures were the bibliographical research, some literature review, and an adopted case study respectively. As a contribution of this research, the holistic view of opportunities to carry out new investigations on the theme in question is pointed out. It is noteworthy that the identified study gaps after the research were prioritized and discriminated, highlighting the importance of the viability of metaheuristic algorithms, as well as their benefits for process optimization

Scheduling Algorithms: Challenges Towards Smart Manufacturing

Collecting, processing, analyzing, and driving knowledge from large-scale real-time data is now realized with the emergence of Artificial Intelligence (AI) and Deep Learning (DL). The breakthrough of Industry 4.0 lays a foundation for intelligent manufacturing. However, implementation challenges of scheduling algorithms in the context of smart manufacturing are not yet comprehensively studied. The purpose of this study is to show the scheduling No.s that need to be considered in the smart manufacturing paradigm. To attain this objective, the literature review is conducted in five stages using publish or perish tools from different sources such as Scopus, Pubmed, Crossref, and Google Scholar. As a result, the first contribution of this study is a critical analysis of existing production scheduling algorithms\u27 characteristics and limitations from the viewpoint of smart manufacturing. The other contribution is to suggest the best strategies for selecting scheduling algorithms in a real-world scenario

Assembly sequence planning using hybrid binary particle swarm optimization

Assembly Sequence Planning (ASP) is known as a large-scale, timeconsuming combinatorial problem. Therefore time is the main factor in production planning. Recently, ASP in production planning had been studied widely especially to minimize the time and consequently reduce the cost. The first objective of this research is to formulate and analyse a mathematical model of the ASP problem. The second objective is to minimize the time of the ASP problem and hence reduce the product cost. A case study of a product consists of 19 components have been used in this research, and the fitness function of the problem had been calculated using Binary Particle Swarm Optimization (BPSO), and hybrid algorithm of BPSO and Differential Evolution (DE). The novel algorithm of BPSODE has been assessed with performance-evaluated criteria (performance measure). The algorithm has been validated using 8 comprehensive benchmark problems from the literature. The results show that the BPSO algorithm has an improved performance and can reduce further the time of assembly of the 19 parts of the ASP compared to the Simulated Annealing and Genetic Algorithm. The novel hybrid BPSODE algorithm shows a superior performance when assessed via performance-evaluated criteria compared to BPSO. The BPSODE algorithm also demonstrated a good generation of the recorded optimal value for the 8 standard benchmark problems

Symbiotic Organisms Search Algorithm: theory, recent advances and applications

The symbiotic organisms search algorithm is a very promising recent metaheuristic algorithm. It has received a plethora of attention from all areas of numerical optimization research, as well as engineering design practices. it has since undergone several modifications, either in the form of hybridization or as some other improved variants of the original algorithm. However, despite all the remarkable achievements and rapidly expanding body of literature regarding the symbiotic organisms search algorithm within its short appearance in the field of swarm intelligence optimization techniques, there has been no collective and comprehensive study on the success of the various implementations of this algorithm. As a way forward, this paper provides an overview of the research conducted on symbiotic organisms search algorithms from inception to the time of writing, in the form of details of various application scenarios with variants and hybrid implementations, and suggestions for future research directions

The problem of uninterrupted hybrid flow shop scheduling with regard to the fuzzy processing time

Purpose: In this paper, an uninterrupted hybrid flow shop scheduling problem is modeled under uncertainty conditions. Due to the uncertainty of processing time in workshops, which is due to delays in receiving raw materials or machine failure, fuzzy programming method has been used to control the processing time parameter. In the proposed model, there are several jobs that must be processed by machines in sequence. The main purpose of the proposed model is to determine the correct sequence of operations and assign operations to each machine at each stage, so that the total completion time (Cmax) is minimized. Methodology: In this paper, the fuzzy programming method is used to control the uncertain parameter. Also, The GAMS software and CPLEX solver have also been used to solve the sample problems. Findings: The results of solving the problem in small and medium size show that with increasing the rate of uncertainty, the amount of processing time increases and therefore the completion time of the whole work increases. On the other hand, with the increase in the number of machines in each stage due to the high efficiency of the machines, the completion time of all works has decreased. Originality/Value: The most important innovation of this article is the design of uninterrupted hybrid flow shop scheduling with regard to the fuzzy processing time

A Novel Algorithm for Solving Structural Optimization Problems

In the past few decades, metaheuristic optimization methods have emerged as an effective approach for addressing structural design problems. Structural optimization methods are based on mathematical algorithms that are population-based techniques. Optimization methods use technology development to employ algorithms to search through complex solution space to find the minimum. In this paper, a simple algorithm inspired by hurricane chaos is proposed for solving structural optimization problems. In general, optimization algorithms use equations that employ the global best solution that might cause the algorithm to get trapped in a local minimum. Hence, this methodology is avoided in this work. The algorithm was tested on several common truss examples from the literature and proved efficient in finding lower weights for the test problems