A Comparative Representation Approach to Modern Heuristic Search Methods in a Job Shop

The job shop problem is among the class of NP- hard combinatorial problems. This Research paper addresses the problem of static job shop scheduling on the job-based representation and the rule based representations. The popular search techniques like the genetic algorithm and simulated annealing are used for the determination of the objectives like minimizations of the makespan time and mean flow time. Various rules like the SPT, LPT, MWKR, and LWKR are used for the objective function to attain the results. The summary of results from this paper gives a conclusion that the genetic algorithm gives better results in the makespan time determination on both the job based representation and the rule based representation and the simulated annealing algorithm gives the better results in the mean flow time in both the representations

The evolution of cell formation problem methodologies based on recent studies (1997-2008): review and directions for future research

This paper presents a literature review of the cell formation (CF) problem concentrating on formulations proposed in the last decade. It refers to a number of solution approaches that have been employed for CF such as mathematical programming, heuristic and metaheuristic methodologies and artificial intelligence strategies. A comparison and evaluation of all methodologies is attempted and some shortcomings are highlighted. Finally, suggestions for future research are proposed useful for CF researchers

SHORT TERM HYDRO THERMAL SCHEDULING PROBLEM: A REVIEW

Operation of a system having both hydro and thermal plants is far more complex and is of much more importance in a modern interconnected power system. The objective of the STHS problem is to optimize the electricity production, considering a short-term planning horizon. This paper presents an extensive review of a short term hydro thermal scheduling problem. The paper demonstrates results of various evolutionary and analytical methods applied on a short term hydro thermal scheduling problem .All the assumptions made and a brief description of the solution methods is presented in the paper. The paper provides helpful information and resources for the future studies for researchers those interested in the problem or intending to do additional research in this area

Handbook of Computational Intelligence in Manufacturing and Production Management

Artificial intelligence (AI) is simply a way of providing a computer or a machine to think intelligently like human beings. Since human intelligence is a complex abstraction, scientists have only recently began to understand and make certain assumptions on how people think and to apply these assumptions in order to design AI programs. It is a vast knowledge base discipline that covers reasoning, machine learning, planning, intelligent search, and perception building. Traditional AI had the limitations to meet the increasing demand of search, optimization, and machine learning in the areas of large, biological, and commercial database information systems and management of factory automation for different industries such as power, automobile, aerospace, and chemical plants. The drawbacks of classical AI became more pronounced due to successive failures of the decade long Japanese project on fifth generation computing machines. The limitation of traditional AI gave rise to development of new computational methods in various applications of engineering and management problems. As a result, these computational techniques emerged as a new discipline called computational intelligence (CI)

Facility layout planning. An extended literature review

[EN] Facility layout planning (FLP) involves a set of design problems related to the arrangement of the elements that shape industrial production systems in a physical space. The fact that they are considered one of the most important design decisions as part of business operation strategies, and their proven repercussion on production systems' operation costs, efficiency and productivity, mean that this theme has been widely addressed in science. In this context, the present article offers a scientific literature review about FLP from the operations management perspective. The 232 reviewed articles were classified as a large taxonomy based on type of problem, approach and planning stage and characteristics of production facilities by configuring the material handling system and methods to generate and assess layout alternatives. We stress that the generation of layout alternatives was done mainly using mathematical optimisation models, specifically discrete quadratic programming models for similar sized departments, or continuous linear and non-linear mixed integer programming models for different sized departments. Other approaches followed to generate layout alternatives were expert's knowledge and specialised software packages. Generally speaking, the most frequent solution algorithms were metaheuristics.The research leading to these results received funding from the European Union H2020 Program under grant agreement No 958205 `Industrial Data Services for Quality Control in Smart Manufacturing (i4Q)'and from the Spanish Ministry of Science, Innovation and Universities under grant agreement RTI2018-101344-B-I00 `Optimisation of zerodefectsproduction technologies enabling supply chains 4.0 (CADS4.0)'Pérez-Gosende, P.; Mula, J.; Díaz-Madroñero Boluda, FM. (2021). Facility layout planning. An extended literature review. International Journal of Production Research. 59(12):3777-3816. https://doi.org/10.1080/00207543.2021.189717637773816591

Operation and planning of distribution networks with integration of renewable distributed generators considering uncertainties: a review

YesDistributed generators (DGs) are a reliable solution to supply economic and reliable electricity to customers. It is the last stage in delivery of electric power which can be defined as an electric power source connected directly to the distribution network or on the customer site. It is necessary to allocate DGs optimally (size, placement and the type) to obtain commercial, technical, environmental and regulatory advantages of power systems. In this context, a comprehensive literature review of uncertainty modeling methods used for modeling uncertain parameters related to renewable DGs as well as methodologies used for the planning and operation of DGs integration into distribution network.This work was supported in part by the SITARA project funded by the British Council and the Department for Business, Innovation and Skills, UK and in part by the University of Bradford, UK under the CCIP grant 66052/000000

Simulation-based Flexible Layout Planning Considering Stochastic Effects

Layout planning is an important practical problem for manufacturing companies. In today's market conditions - characterized with continuously changing product portfolio and shortening product lifecycles - frequent reconfiguration is requested if the primary goal for the company is to remain competitive. The key to win customers is to widen the product portfolio and customize the products, however, this leads to the problem that the manufacturing system has to be re-organized several times during its life cycle that requires solving design problems frequently. In the paper, a novel layout planning method is introduced that can be applied efficiently to solve real industrial problems. The method applies automated simulation model building to create the different layouts. It focuses on minimizing the objective function that is specified according to the predefined key performance indicators (KPI). The solution is a hybrid optimization method, in which evaluation of the lay out alternatives is done by simulation and the improvement of the solution is performed by a near-to-optimal search algorithm. The optimization is separated from the simulation model in order to boost the computations. Important advantage of the solution is the efficiency consideration of stochastic parameters that improve the applicability of the results

The design and applications of the african buffalo algorithm for general optimization problems

Optimization, basically, is the economics of science. It is concerned with the need to maximize profit and minimize cost in terms of time and resources needed to execute a given project in any field of human endeavor. There have been several scientific investigations in the past several decades on discovering effective and efficient algorithms to providing solutions to the optimization needs of mankind leading to the development of deterministic algorithms that provide exact solutions to optimization problems. In the past five decades, however, the attention of scientists has shifted from the deterministic algorithms to the stochastic ones since the latter have proven to be more robust and efficient, even though they do not guarantee exact solutions. Some of the successfully designed stochastic algorithms include Simulated Annealing, Genetic Algorithm, Ant Colony Optimization, Particle Swarm Optimization, Bee Colony Optimization, Artificial Bee Colony Optimization, Firefly Optimization etc. A critical look at these ‘efficient’ stochastic algorithms reveals the need for improvements in the areas of effectiveness, the number of several parameters used, premature convergence, ability to search diverse landscapes and complex implementation strategies. The African Buffalo Optimization (ABO), which is inspired by the herd management, communication and successful grazing cultures of the African buffalos, is designed to attempt solutions to the observed shortcomings of the existing stochastic optimization algorithms. Through several experimental procedures, the ABO was used to successfully solve benchmark optimization problems in mono-modal and multimodal, constrained and unconstrained, separable and non-separable search landscapes with competitive outcomes. Moreover, the ABO algorithm was applied to solve over 100 out of the 118 benchmark symmetric and all the asymmetric travelling salesman’s problems available in TSPLIB95. Based on the successful experimentation with the novel algorithm, it is safe to conclude that the ABO is a worthy contribution to the scientific literature