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The purpose of this paper is to minimize the mean flow time in open shop scheduling problem (OSSP). The scheduling problems consist of n jobs and m machines, in which each job has O operations. The processing time for 50 OSSP was generated using a linear congruential random number.

Different evolutionary algorithms are used to minimize the mean flow time of OSSP. This research study used simulated annealing (SA), Discrete Firefly Algorithm and a Hybrid Firefly Algorithm with SA. These methods are referred as A1, A2 and A3, respectively.

A comparison of the results obtained from the three methods shows that the Hybrid Firefly Algorithm with SA (A3) gives the best mean flow time for 76 percent instances. Also, it has been observed that as the number of jobs increases, the chances of getting better results also increased. Among the first 25 problems (i.e. job ranging from 3 to 7), A3 gave the best results for 13 instances, i.e., for 52 percent of the first 25 instances. While for the last 25 problems (i.e. Job ranging from 8 to 12), A3 gave the best results for all 25 instances, i.e. for 100 percent of the problems.

From the literature it has been observed that no researchers have attempted to solve OOSPs using Firefly Algorithm (FA). In this research work an attempt has been made to apply the FA and its hybridization to solve OSSP. Also the research work carried out in this paper can also be applied for a real-time Industrial problem.

This study aims to address the hybrid open shop problem (HOSP) with respect to the minimization of the overall finishing time or makespan. In the HOSP, we have to process n jobs in stages without preemption. Each job must be processed once in every stage, there is a set of m_k identical machines in stage k and the production flow is immaterial.

Computational experiments carried out on a set of randomly generated instances showed that the minimal idleness heuristic (MIH) priority rule outperforms the longest processing time (LPT) rule proposed in the literature and the other proposed constructive methods on most instances.

The proposed mathematical model outperformed the existing model in the literature with respect to computing time, for small-sized instances, and solution quality within a time limit, for medium- and large-sized instances. The authors’ hybrid iterated local search (ILS) improved the solutions of the MIH rule, drastically outperforming the models on large-sized instances with respect to solution quality.

The authors formalize the HOSP, as well as argue its NP-hardness, and propose a mixed integer linear programming model to solve it. The authors propose several priority rules – constructive heuristics based on priority measures – for finding feasible solutions for the problem, consisting of adaptations of classical priority rules for scheduling problems. The authors also propose a hybrid ILS for improving the priority rules solutions.

The disjunctive graph is a network representation of the job‐shop scheduling problem, while the longest path problem (LPP) is one of the most important subjects in this research field. This paper aims to study the special topological structure of the disjunctive graph, and proposes a suite of quick value‐setting algorithms for solving the LPPs commonly encountered in job‐shop scheduling.

The topological structure of the disjunctive graph is analyzed, and some properties and propositions regarding LPPs are presented. Subsequently, algorithms are proposed for solving LPPs encountered in job‐shop scheduling.

The proposed algorithms significantly improve the efficiency of the shifting‐bottleneck procedure, making it practicable to realise real‐time scheduling and hence effective operations of modern manufacturing systems.

The paper demonstrates that it is possible to develop very efficient algorithms by imposing a special topological structure on the network.

In the current competitive environment, each company faces a number of challenges: quick response to customers’ demands, high quality of products or services, customers’ satisfaction, reliable delivery dates, high efficiency, and others. As a result, during the last five years many firms have proceeded to the adoption of enterprise resource planning (ERP) solutions. ERP is a packaged software system, which enables the integration of operations, business processes and functions, through common data‐processing and communications protocols. However, the majority, if not all, of these systems do not support the production scheduling process that is of crucial importance in today’s manufacturing and service industries. In this paper, the authors propose a knowledge‐based system for production‐scheduling that could be incorporated as a custom module in an ERP system. This system uses the prevailing conditions in the industrial environment in order to select dynamically and propose the most appropriate scheduling algorithm from a library of many candidate algorithms.

In the context of systems and cybernetics theory, we present a new general stochastic method of search and optimization of solutions of problems that we have named Prototyped Genetic Search. Our new method is based mainly on prototype and learning concepts, although it uses concepts of population and evolution just as Evolutionary Algorithms. Moreover, and in order to show the interest of this method and to demonstrate its real potential, we have chosen to apply it on the Job‐Shop Scheduling Problem in the context of the flexible production. This paper is also the opportunity for us to present an other new kind of genetic algorithms, resulting from the integration of the recursivity in the basis functioning of genetic algorithms, and that we have named Recursive Genetic Algorithm.

The current study aims to propose a new analytical approach by considering energy consumption (EC), maximum tardiness and completion time as the primary objective functions to assess the performance of parallel, non-bottleneck and multitasking machines operating in dynamic job shops.

An analytical and iterative method is presented to optimize a novel dynamic job shop under technical constraints. The machine’s performance is analyzed by considering the setup energy. An optimization model from initial processing until scheduling and planning is proposed, and data sets consisting of design parameters are fed into the model.

Significant variations of EC and tardiness are observed. The minimum EC was calculated to be 141.5 hp.s when the defined decision variables were constantly increasing. Analysis of the optimum completion time has shown that among all studied methods, first come first served (FCFS), earliest due date (EDD) and shortest processing time (SPT) have resulted in the least completion time with a value of 20 s.

Considerable amount of energy can be dissipated when parallel, non-bottleneck and multitasking machines operate in lower-power modes. Additionally, in a dynamic job shop, adjusting the trend and arrangement of decision variables plays a crucial role in enhancing the system’s reliability. Such issues have never caught the attention of scientists for addressing the aforementioned problems. Therefore, with these underlying goals, this paper presents a new approach for evaluating and optimizing the system’s performance, considering different objective functions and technical constraints.

In this paper, the authors propose the application of an intelligent engine to develop a set of computational schedules for the maintenance of vehicles to cover all scheduled flights. The aim of the paper is to maximize the utilization of ground support vehicles and enhance the logistics of aircraft maintenance activities.

A mathematical model is formulated and the solution is obtained using genetic algorithms (GA). Simulation is used to verify the method using an Excel GA generator. The model is illustrated with a numerical case study, and the experience of this project is summarized.

The results indicate that this approach provides an effective and efficient schedule for deploying the maintenance equipment resources of the company, China Aircraft Service Limited.

The proposed model using the GA generator provides an effective and efficient schedule for the aircraft maintenance services industry.

Operating theaters are considered as the most sensitive health department within hospital centers due to their significant cost/necessity for patients and their economic benefits for hospitals. In this paper, the authors consider patients that may require more than one surgery on the same day in the surgery scheduling problem which is a major technology enhancement in the health industry.

The surgery scheduling includes both the preoperative and the postoperative units of the operative stage. Two objectives are considered in a lexicographic way: the minimization of the makespan while prioritizing the patients having two surgeries and the total completion time to perform. An adapted tabu-search algorithm is used to tackles this NP-hard scheduling problem.

The proposed schedule is more relevant for operating theaters as it integrates all stages of the surgical procedure and considers patients with more than one operation during the same day.

This paper is original as it considers patients who need more than one operation, which responds to real challenge faced by decision-makers' in hospitals. The application of the time lags between stages of the surgical procedure generates a good utilization of the hospital resources and makes the scheduling task more flexible.

Minimizing penalty cost is one of the major objectives in FMS decision making when job‐due dates cannot be met. The study focuses on minimization of penalty cost when neither increase in system capacity nor diversification of jobs to other plants or production lines is possible. A mathematical programming model combining both part type selection and scheduling decision making is proposed for small problems. For large problems, a simple control parameter is identified. Seven despatching rules are applied in conjunction with the control parameter and their differences in performance are reported. Not only proves that performance can be improved by a filtration process applied before despatching rules are applied, but also finds that there is no significant difference in performance between different despatching rules after the appropriate filtration process is applied. The effectiveness of the proposed control parameter is further illustrated by comparing the results with a continuous flow model.

This paper aims to provide a comprehensive analysis of the state of the art in energy efficiency for autonomous mobile robots (AMRs), focusing on energy sources, consumption models, energy-efficient locomotion, hardware energy consumption, optimization in path planning and scheduling methods, and to suggest future research directions.

The systematic literature review (SLR) identified 244 papers for analysis. Research articles published from 2010 onwards were searched in databases including Google Scholar, ScienceDirect and Scopus using keywords and search criteria related to energy and power management in various robotic systems.

The review highlights the following key findings: batteries are the primary energy source for AMRs, with advances in battery management systems enhancing efficiency; hybrid models offer superior accuracy and robustness; locomotion contributes over 50% of a mobile robot’s total energy consumption, emphasizing the need for optimized control methods; factors such as the center of mass impact AMR energy consumption; path planning algorithms and scheduling methods are essential for energy optimization, with algorithm choice depending on specific requirements and constraints.

The review concentrates on wheeled robots, excluding walking ones. Future work should improve consumption models, explore optimization methods, examine artificial intelligence/machine learning roles and assess energy efficiency trade-offs.

This paper provides a comprehensive analysis of energy efficiency in AMRs, highlighting the key findings from the SLR and suggests future research directions for further advancements in this field.