An evolutionary approach for solving the job shop scheduling problem in a service industry

In this paper, an evolutionary-based approach based on the discrete particle swarm optimization (DPSO) algorithm is developed for finding the optimum schedule of a registration problem in a university. Minimizing the makespan, which is the total length of the schedule, in a real-world case study is considered as the target function. Since the selected case study has the characteristics of job shop scheduling problem (JSSP), it is categorized as a NP-hard problem which makes it difficult to be solved by conventional mathematical approaches in relatively short computation time

An evolutionary approach for solving the job shop scheduling problem in a service industry

In this paper, an evolutionary-based approach based on the discrete particle swarm optimization (DPSO) algorithm is developed for finding the optimum schedule of a registration problem in a university. Minimizing the makespan, which is the total length of the schedule, in a real-world case study is considered as the target function. Since the selected case study has the characteristics of job shop scheduling problem (JSSP), it is categorized as a NP-hard problem which makes it difficult to be solved by conventional mathematical approaches in relatively short computation time

Multiobjective Optimisation of Job Shop Scheduling of Renewable Powered Machinery

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Job shop scheduling with makespan objective: A heuristic approach

Job shop has been considered as one of the most challenging scheduling problems and there are literally tremendous efforts on reducing the complexity of solution procedure for solving job shop problem. This paper presents a heuristic method to minimize makespan for different jobs in a job shop scheduling. The proposed model is based on a constructive procedure to obtain good quality schedules, very quickly. The performance of the proposed model of this paper is examined on standard benchmarks from the literature in order to evaluate its performance. Computational results show that, despite its simplicity, the proposed heuristic is computationally efficient and practical approach for the problem

Optimization of Job Shop Scheduling Problem using Tabu Search Optimization Technique

ABSTRACT-The Job shop scheduling (JSS) problem consists of "n" jobs and "m" operations on each of the jobs and it is hardest combinatorial optimization problems for which it is extremely difficult to find optimal solutions. Past two decades, much attention has been made to general heuristics such as Genetic algorithm, Ant Colony Optimization, Tabu Search and Simulated Annealing to solve this type of combinatorial optimization problems. In this paper we present how the adaptive search algorithms namely Tabu search is applied to solve Job shop scheduling (JSS) problem. The method uses dispatching rules to obtain an initial solution and searches for new solutions in a neighborhood based on the critical paths of the jobs. Several benchmark problems are tested using this algorithm for the best makespan and the obtained results are encouraging when compared with benchmark values

Composite Differential Evolution for Constrained Evolutionary Optimization

When solving constrained optimization problems (COPs) by evolutionary algorithms, the search algorithm plays a crucial role. In general, we expect that the search algorithm has the capability to balance not only diversity and convergence but also constraints and objective function during the evolution. For this purpose, this paper proposes a composite differential evolution (DE) for constrained optimization, which includes three different trial vector generation strategies with distinct advantages. In order to strike a balance between diversity and convergence, one of these three trial vector generation strategies is able to increase diversity, and the other two exhibit the property of convergence. In addition, to accomplish the tradeoff between constraints and objective function, one of the two trial vector generation strategies for convergence is guided by the individual with the least degree of constraint violation in the population, and the other is guided by the individual with the best objective function value in the population. After producing offspring by the proposed composite DE, the feasibility rule and the ϵ constrained method are combined elaborately for selection in this paper. Moreover, a restart scheme is proposed to help the population jump out of a local optimum in the infeasible region for some extremely complicated COPs. By assembling the above techniques together, a constrained composite DE is proposed. The experiments on two sets of benchmark test functions with various features, i.e., 24 test functions from IEEE CEC2006 and 18 test functions with 10 dimensions and 30 dimensions from IEEE CEC2010, have demonstrated that the proposed method shows better or at least competitive performance against other state-of-the-art methods

Determining efficient scheduling approach of doctors for operating rooms: An analysis on Al-Shahid Ghazi Al-Hariri hospital in Baghdad

Government hospitals in Iraq have long been suffering from overcrowded patients, and shortages of doctors and nurses. Unstable environment with occurrences of random warrelated incidents has put further burden on hospitals’ limited resources particularly the surgical department. Large number of pre-scheduled elective surgeries has occasionally been interrupted by the incoming war-related incidents patients. This in turn has put tremendous pressure on the hospital management to maximize utilization of its operating rooms’ resources including surgeons and nurses, whilst simultaneously minimizing idle time. Al-Shahid Ghazi Al-Hariri hospital in Baghdad is presently experiencing these issues. Therefore, this study has been undertaken with the aims to identify efficient scheduling approach for elective surgeries for operating rooms in Al-Shahid Ghazi Al-Hariri hospital while considering interruptions from non-elective surgery (incoming patients from warrelated incidents). Specifically, this study intends to develop a Mixed Integer Linear Programming (MILP) model to maximize the utilizations of operating rooms, availability of surgeons as well as to minimize potential idle time. A meta-heuristic approach in the form of a Tabu Search is then employed to generate an acceptable solution and utilizing time more efficiently. Real data was collected from the hospital in the form of interviews, observations and secondary reports. The initial MILP computational results show that the proposed model has successfully produced optimal solutions by improving the utilization of operating rooms. Notwithstanding, the difficulty to produce results in reasonable time for larger problem instances has led to the application of a more efficient meta-heuristic approach. The Tabu Search results indicated better performance of the model with good quality solutions in fewer computation times. The finding is important as it determines the feasibility of the proposed model and its potential benefit to all relevant stakeholders

Intelligent algorithm for the optimization of assembly and handling systems and processes for in-line production

Povečanje konkurenčnosti podjetij je v veliki meri odvisno od učinkovitosti montažnih in strežnih sistemov ter procesov (MiSSP). Njihovo učinkovitost lahko povečamo z različnimi metodami optimizacije, predvsem z vidika zmanjševanja stroškov, skrajševanja pretočnih časov, dobavnih terminov, povečanja izkoriščenosti opreme itd. Ena najbolj učinkovitih metod optimiranja takšnih sistemov je optimiranje s sprotno simulacijo. Takšen pristop zahteva podrobne raziskave, študije in analizo vseh gradnikov ter parametrov, ki so potrebni, da se postavi ekspertni sistem sprotne oziroma "On-line" simulacije MiSSP linijske proizvodnje. V doktorski disertaciji je zato podrobno obravnavan razvoj inteligentnih algoritmov in uporaba le-teh za razvoj digitalnih agentov ter ekspertnih sistemov MiSSP linijske proizvodnje. Ekspertni sistem, razvit v okviru doktorske naloge, v povezavi z digitalnim dvojčkom in digitalnimi agenti nenehno nadzoruje in sproti optimira MiSSP linijske proizvodnje. Prav tako je v doktorskem delu razvit inteligentni algoritem, imenovan "premešaj in vstavi", ki npr. samodejno predlaga najboljše rešitve razporejanja naročil, strojev itd. v krajšem času kot uveljavljeni primerljivi algoritmi. Za potrebe validacije ekspertnega sistema z inteligentnim algoritmom je bil v laboratorijskem okolju zgrajen realni montažni in strežni sistem linijske proizvodnje. Digitalni MiSSP smo združili z realnim sistemom preko oblaka in s tem postavili vse potrebne okvire sprotne ali "On-line" simulacije in tako razvili ekspertni sistem, ki je v nenehni povezavi z realnim sistemom in ga sproti nadzoruje ter optimira. Postavljena metodologija zasnove inteligentnega algoritma, digitalnih agentov in digitalnih dvojčkov omogoča okvir za njihovo praktično uporabo v realnem proizvodnem okolju.Successful improvement of the competitiveness of enterprises depends to a large extent on the efficiency of assembly and handling systems and processes (AHSP). Their efficiency can be enhanced through various optimization methods, in particular in terms of the cost reduction, reduction of the throughput times, delivery times, increased utilization of equipment, etc. One of the most effective methods for optimizing such systems is optimization with on-line simulation. Such approach requires detailed research, study and analysis of all the building blocks and parameters needed to set up an expert system of on-line simulation of AHSP of the production line. Therefore, in the doctoral thesis, the development of intelligent algorithms and the use of them for the development of digital agents and expert systems of AHSP production line is discussed in detail. The expert system, developed in the doctoral thesis, in connection with the digital twin and digital agents, constantly monitors and continuously optimizes AHSP of the production line. In the doctoral thesis, an intelligent algorithm, called "flip and insert" is developed that can automatically suggest a very competitive schedule of orders, machines, etc. in a shorter time than well-established comparable algorithms. For the needs of validating the expert system with an intelligent algorithm, a real system of production line has been built in the laboratory environment. We combined the digital AHSP with the real system over the cloud, and thus set up all the necessary frameworks of the on-line simulation and thus develop an expert system that is in constant connection with the real system and is constantly monitoring and optimizing it. The methodology for intelligent algorithm, digital agents and digital twins provides a framework for their practical application in a real production environment

Metodología multiobjetivo basada en un comportamiento evolutivo para programar sistemas de producción flexible job shop. Aplicaciones en la industria metalmecánica

El objeto de estudio de la presente tesis es el taller de trabajo flexible en el sector metalmecánico. El problema de investigación se derivó a partir de la búsqueda sistemática de metodologías y algoritmos para programar sistemas productivos; se identificaron configuraciones de variables de proceso no abordadas en la literatura, lo que se considera un vacío en el conocimiento. Consecuente con lo anterior, se diseñó una metodología basada en un algoritmo evolutivo para programar los pedidos en un taller de trabajo flexible, con restricciones de tiempo, secuencia, mantenimiento, liberación de pedidos, disponibilidad, consumo y costo de recurso que varía en el tiempo, con el fin de minimizar tiempo de proceso y costo de producción; incluyó un proceso de ponderación para escoger la mejor secuencia de programación. Como aporte principal se propone una metodología novedosa que al compararla con otras metodologías encontradas en la bibliografía, demostró mejoras mayores al 10% en makespan y costo total del recurso consumidoAbstract: The study object of the present thesis is the flexible job shop in the metal mechanic sector. The research problem was derived from the systematic search of methodologies and algorithms to schedule production systems; configurations of process variables not addressed in the literature were identified, which is considered an empty in knowledge. Consequent with previous, a methodology was designed based on an evolutionary algorithm to schedule orders in a flexible job shop, with time restrictions, sequence, maintenance, liberation of orders, availability, consumption and cost of resource that varies in time, in order to minimize processing time and cost of production; it includes a weighting process to choose the best programming sequence. As main contribution a novel methodology was proposed which, compared with other methodologies found in the literature, it demonstrated greater improvements to 10% in Makespan and total cost of consumed resourceDoctorad