GASA-JOSH: a Hybrid Evolutionary-Annealing Approach for Job-Shop Scheduling Problem

The job-shop scheduling problem is well known for its complexity as an NP-hard problem. We have considered JSSPs with an objective of minimizing makespan. In this paper, we develope a hybrid approach for solving JSSPs called GASA-JOSH. In GASA-JOSH, the population is divided in non-cooperative groups. Each group must refer to a method pool and choose genetic algorithm or simulated annealing to solve the problem. The best result of each group is maintained in a solution set, and then the best solution to the whole population is chosen among the elements of the solution set and reported as outcome. The proposed approach have been compared with other algorithms for job-shop scheduling and evaluated with satisfactory results on a large set of JSSPs derived from classical job-shop scheduling benchmarks. We have solved 23 benchmark problems and compared results obtained with a number of algorithms established in the literature

GASA-JOSH: A Hybrid Evolutionary-Annealing Approach for Job-Shop Scheduling Problem

The job-shop scheduling problem is well known for its complexity as an NP-hard problem. We have considered JSSPs with an objective of minimizing makespan. In this paper, we develope a hybrid approach for solving JSSPs called GASA-JOSH. In GASA-JOSH, the population is divided in non-cooperative groups. Each group must refer to a method pool and choose genetic algorithm or simulated annealing to solve the problem. The best result of each group is maintained in a solution set, and then the best solution to the whole population is chosen among the elements of the solution set and reported as outcome. The proposed approach have been compared with other algorithms for job-shop scheduling and evaluated with satisfactory results on a large set of JSSPs derived from classical job-shop scheduling benchmarks. We have solved 23 benchmark problems and compared results obtained with a number of algorithms established in the literature

A Memetic Algorithm with Reinforcement Learning for Sociotechnical Production Scheduling

The following interdisciplinary article presents a memetic algorithm with applying deep reinforcement learning (DRL) for solving practically oriented dual resource constrained flexible job shop scheduling problems (DRC-FJSSP). From research projects in industry, we recognize the need to consider flexible machines, flexible human workers, worker capabilities, setup and processing operations, material arrival times, complex job paths with parallel tasks for bill of material (BOM) manufacturing, sequence-dependent setup times and (partially) automated tasks in human-machine-collaboration. In recent years, there has been extensive research on metaheuristics and DRL techniques but focused on simple scheduling environments. However, there are few approaches combining metaheuristics and DRL to generate schedules more reliably and efficiently. In this paper, we first formulate a DRC-FJSSP to map complex industry requirements beyond traditional job shop models. Then we propose a scheduling framework integrating a discrete event simulation (DES) for schedule evaluation, considering parallel computing and multicriteria optimization. Here, a memetic algorithm is enriched with DRL to improve sequencing and assignment decisions. Through numerical experiments with real-world production data, we confirm that the framework generates feasible schedules efficiently and reliably for a balanced optimization of makespan (MS) and total tardiness (TT). Utilizing DRL instead of random metaheuristic operations leads to better results in fewer algorithm iterations and outperforms traditional approaches in such complex environments.Comment: This article has been accepted by IEEE Access on June 30, 202

Hierarchical workflow management system for life science applications

In modern laboratories, an increasing number of automated stations and instruments are applied as standalone automated systems such as biological high throughput screening systems, chemical parallel reactors etc. At the same time, the mobile robot transportation solution becomes popular with the development of robotic technologies. In this dissertation, a new superordinate control system, called hierarchical workflow management system (HWMS) is presented to manage and to handle both, automated laboratory systems and logistics systems.In modernen Labors werden immer mehr automatisierte Stationen und Instrumente als eigenständige automatisierte Systeme eingesetzt, wie beispielsweise biologische High-Throughput-Screening-Systeme und chemische Parallelreaktoren. Mit der Entwicklung der Robotertechnologien wird gleichzeitig die mobile Robotertransportlösung populär. In der vorliegenden Arbeit wurde ein hierarchisches Verwaltungssystem für Abeitsablauf, welches auch als HWMS bekannt ist, entwickelt. Das neue übergeordnete Kontrollsystem kann sowohl automatisierte Laborsysteme als auch Logistiksysteme verwalten und behandeln

A particle swarm optimisation for the no-wait flow shop problem with due date constraints.

Peer ReviewedThis paper considers the no-wait flow shop scheduling problem with due date constraints. In the no-wait flow shop problem, waiting time is not allowed between successive operations of jobs. Moreover, a due date is associated with the completion of each job. The considered objective function is makespan. This problem is proved to be strongly NP-Hard. In this paper, a particle swarm optimisation (PSO) is developed to deal with the problem. Moreover, the effect of some dispatching rules for generating initial solutions are studied. A Taguchi-based design of experience approach has been followed to determine the effect of the different values of the parameters on the performance of the algorithm. To evaluate the performance of the proposed PSO, a large number of benchmark problems are selected from the literature and solved with different due date and penalty settings. Computational results confirm that the proposed PSO is efficient and competitive; the developed framework is able to improve many of the best-known solutions of the test problems available in the literature

Simulation and modelling of hybrid heuristics distribution algorithm on flow shop scheduling problem to optimize makespan in an Indian manufacturing industry

This study presents a heuristic formulation of the flow shop scheduling problem by the hybrid algorithm fitness function. The genetic algorithm is used to model the time-estimates such as makespan and completion time. This paper aims to optimize the sequence-independent and sequence-dependent time-estimates. The production scheduling parameters such as permutation, non-permutation, no-wait, tardiness, and several workstations are identified from a piston manufacturing industry, in Northern India. The MATLAB programming in heuristics algorithm distribution function resulted in reduction of makespan of the product by five times. The reduced completion time is 23 minutes for the piston ring product and 26 minutes in cumulative validation of the proposed model. The cumulative optimized standard error of 0.26; (n=3) simulate and synthesize the suggested model with its validation. The system efficiency through completion time optimization ranged from 70-82 percent in piston ring, and 63-89 percent in cumulative validation of the model has been worked out for each machine type. The data generated through system optimization helps the scientific world and entrepreneurs in advancement of sequence-based transportation

Simulation and Modelling of Hybrid Heuristics Distribution Algorithm for Flow Shop Scheduling Problem to Optimize Makespan in an Indian Manufacturing Industry

137-142This study presents a heuristic formulation of the flow shop scheduling problem by the hybrid algorithm fitness function. The genetic algorithm is used to model the time-estimates such as makespan and completion time. This paper aims to optimize the sequence-independent and sequence-dependent time-estimates. The production scheduling parameters such as permutation, non-permutation, no-wait, tardiness, and several workstations are identified from a piston manufacturing industry, in Northern India. Different machine operating parameters were collected from the piston manufacturing industry to work on reducing the makespan. The MATLAB programming in heuristics algorithm distribution function resulted in a reduction of makespan of the product by five times. The reduced completion time is 23 minutes for the piston ring product and 26 minutes in the cumulative validation of the proposed model. The cumulative optimized standard error of 0.26; (n=3) simulate and synthesize the suggested model with its validation. The system efficiency through completion time optimization ranged from 70–82 percent in piston ring, and 63–89 percent in cumulative validation of the model has been worked out for each machine type. The data generated through system optimization helps the scientific world and entrepreneurs in the advancement of sequence-based transportation

AN EXAMINATION OF MULTIPLE OPTIMIZATION APPROACHES TO THE SCHEDULING OF MULTI-PERIOD MIXED-BTU NATURAL GAS PRODUCTS

As worldwide production and consumption of natural gas increase, so does the importance of maximizing profit when trading this commodity in a highly competitive market. Decisions regarding the buying, storing and selling of natural gas are difficult in the face of high volatility of prices and uncertain demand. With the introduction of alternative sources of fuels with lower levels of methane, the primary component of natural gas, these decisions become more complicated. This is an issue faced by investors as well as operational planners of industrial and commercial consumers of natural gas where incorrect planning decisions can be costly.A great deal of research in the academic and commercial arenas has been accomplished regarding the problem of optimizing the scheduling of injection and withdrawal of this commodity. While various commercial products have been in use for years and research on new approaches continues, one aspect of the problem that has received less attention is that of combining gases of different heat contents. This study examines multiple approaches to maximizing profits by optimally scheduling the purchase and storage of two gas products of different energy densities and the sales of the same in combination with a product that is a blend of the two. The result provides an initial basis for planners to improve decision making and minimize the cost of natural gas consumed.This multi-product multi-period finite (twelve-month) horizon product-mix problem is NP-Hard. The first approach developed is a Branch and Bound (B&B) technique combined with a linear program (LP) solver. Heuristics are applied to limit the expansion the trinomial tree generated. In the second approach, a stochastic search algorithm-linear programming hybrid (SS-LP) is developed. The third approach implemented is a pure random search (PRS). To make each technique computationally tractable, constraints on the units of product moved in each transaction are implemented.Then, using numerical data, the three approaches are tested, analyzed and compared statistically and graphically along with computer performance information. The best approach provides a tool for optimizing profits and offers planners an advantage over approaches that are solely history-based

The Evaluated Measurement of a Combined Genetic Algorithm and Artificial Immune System

This paper demonstrates a hybrid between two optimization methods which are the Artificial Immune System (AIS) and Genetic Algorithm (GA). The novel algorithm called the immune genetic algorithm (IGA), provides improvement to the results that enable GA and AIS to work separately which is the main objective of this hybrid. Negative selection which is one of the techniques in the AIS, was employed to determine the input variables (populations) of the system. In order to illustrate the effectiveness of the IGA, the comparison with a steady-state GA, AIS, and PSO were also investigated. The testing of the performance was conducted by mathematical testing, problems were divided into single and multiple objectives. The five single objectives were then used to test the modified algorithm, the results showed that IGA performed better than all of the other methods. The DTLZ multiobjective testing functions were then used. The result also illustrated that the modified approach still had the best performance