In the rapidly evolving landscape of distributed computing, the integration of fog computing, cloud paradigms, and blockchain technology has emerged as an innovative approach to enhance the efficiency and security of task scheduling. This article introduces a novel method using the Sparrow Search Algorithm (SSA), along with its decomposition variant SSA/D, to optimize task scheduling within this integrated system. This paper presents a comprehensive framework that outlines the unique characteristics of fog, cloud, and blockchain environments, addressing their individual constraints while leveraging their combined strengths. The adaptability and robustness of SSA/D are employed to address the multi-objective nature of the scheduling problem, focusing on minimizing latency, energy consumption, and financial cost while ensuring data integrity through the blockchain's immutable ledger. We have employed the Sparrow Search Algorithm (SSA), a recent optimization method proven effective in other applications. Our main contribution is the integration of a decomposition strategy to enhance the exploration of the search space and enable parallel execution of the SSA process. Experimental results show that the proposed approach outperforms existing algorithms in terms of both efficiency and effectiveness, providing significant improvements over popular task scheduling metaheuristics. This paper not only offers a new algorithmic solution but also sets a precedent for future research in integrated fog-cloud-blockchain systems, with a view towards integrating machine learning techniques to further optimize these problems.

Flow Shop Scheduling Problem (FSSP) is one of the most known problems in the production domain. Flow Shop Scheduling Problem with Worker flexibility (FSSPW) is an extension of the classical FSSP consisting of two sub-problems: (i) operation sequencing and (ii) assigning workers to the selected machines in order to optimize the performance measure. In this paper, we present a state-of-the-art review of the different works proposed for the FSSPW, where we propose a new classification schema according to five criteria such as the used method, the variant of the FSSPW, the implemented approach, the optimization criteria and the worker flexibility.

Quantum computing offers an accelerated mechanism for solving combinatorial optimization problems that could be hardly solvable with classical tools, in this context, this paper highlights the utility of quantum computing in solving NP-complete problems efficiently and shows up the importance of employing quantum algorithms achieving parallel calculation in the purpose of reducing the algorithmic complexity and simplifying the development of optimization applications. Within this publication, an optimized version of quantum circuit conception for developing the oracle of the Grover's search algorithm is introduced for solving the vertex cover problem, and it was also tested using the Qiskit library and applied to a set of instances to evaluate its performance.

Due to extensive use and wear of equipment, the occurrence of failures in automated manufacturing systems (AMSs) remains inevitable. However, with maintenance policies, the reliability and availability of such systems can be increased. This paper presents different basic models based on stochastic Petri nets allowing the modeling of the integration of corrective and preventive maintenance in unreliable manufacturing systems and analysis of their effects on system's productivity. In all these models, we make sure that after having undergone a preventive or corrective operation, a resource is functioning normally. This property avoids the execution in the model of an infinite failure loop. Finally, we show how the proposed modeling of these maintenance strategies can be integrated in unreliable controlled manufacturing systems without causing failure-induced deadlocks.

The Job shop Scheduling Problem (JSP) is one of the most known problems in the domain of the production task scheduling. The Dual-Resource Constrained Job shop Scheduling Problem (DRCJSP) and the Dual-Resource Constrained Flexible Job shop Scheduling Problem (DRCFJSP) are two extensions of the classical JSP consisting of three sub-problems: (i) Assigning operations to resources of machines, (ii) Assigning operations to resources of workers; (iii) Sequencing the operations on the machines considering workers in order to optimize the performance measure. In this paper, we make a state-of-the-art review of the different works proposed for the DRCJSP and DRCFJSP, where we present a new classification schema according to six criteria such as the used method, the machine flexibility, the worker flexibility, the optimization criteria, the implemented approaches, and the structure of the approach.

The current industrial production environment is characterized by highly competitive markets, where customer requirements and expectations are becoming stronger in terms of quality, cost and time to delivery. The modern production systems are generally composed of machines that must participate in the manufacture of several types of products simultaneously and efficiently. The Flexible Job shop Scheduling Problem (FJSP) is one of the scheduling problems where each operation can be processed on different machine and its processing time depends on the used machine and this in a single factory. But in the recent years, many companies decide to move towards the decentralization of their factories which allows them to gain advantages towards their customers. So, we are now interested in Distributed and Flexible Job shop Scheduling Problem (DFJSP) where there is a set of geographically distributed factories in different locations. Each factory contains m machines on which n jobs must be processed. The Distributed scheduling problems and more specifically the DFJSP are much more complicated than standard problems because they involve not only the problem of assigning jobs to machines but also the problem of distribution of jobs in different factories. So, the DFJSP is harder than the FJSP. The DFJSP is classified, as most of scheduling problems, NP-Hard in complexity theory. In this paper, we propose a Chemical Reaction Optimization metaheuristic to solve the Distributed and Flexible Job shop Scheduling Problem in order to minimize the maximum completion time (makespan) among all factories. To evaluate the performance of our algorithm, a set of experiments are performed on well known benchmark instances in the literature.

Job shop Scheduling Problem with Generic Time Lags is defined as a job shop problem with minimal and maximal delays between starting times of operations of different jobs. It belongs to a category of problems known as NP-hard. In this paper, a multi-agent model based on hybrid genetic algorithm is proposed for solving the Job shop Scheduling Problem with Generic Time Lags in order to minimize the makespan. The hybridization of genetic algorithm consists in using the greedy algorithm for the generation of initial population and using the tabu search metaheuristic in the mutation step. The proposed model is composed of two classes of agents: Interface Agent and a set of Scheduler Agents. Good performances of the proposed model are shown through different comparisons on a set of benchmarks from the literature based on 8 Carlier's instances and 40 Lawrence's instances.

Scheduling in production systems consists in assigning operations on a set of available resources in order to achieve defined objectives. The Flexible Job shop Scheduling Problem (FJSP) is one of the scheduling problems where each operation can be processed on different machine and its processing time depends on the used machine. But in the recent years, many companies decide to move towards the decentralization of their factories which allow it to gain advantages towards its customers. In the case of the classic Flexible Job shop Scheduling Problem, we assume that there is a single factory with a set of m machines and n jobs, but in Distributed and Flexible Job shop Scheduling Problem (DFJSP), there is a set of geographically distributed factories in different locations. Each factory contains m machines on which n jobs must be processed. The Distributed scheduling problems and more specifically the DFJSP are much more complicated than standard problems because they involve not only the problem of assigning jobs to machines but also the problem of distribution of jobs in different factories. So, the DFJSP is harder than the FJSP. The DFJSP is classified, as most of scheduling problems, NP-Hard in complexity theory. In this paper, we propose a decentralized model based on tabu search to solve the Distributed and Flexible Job shop Scheduling Problem in order to minimize the maximum completion time (makespan). To evaluate the performance of our model, a set of experiments are performed on benchmark instances well known in the literature.

The Distributed Permutation Flowshop Scheduling Problem (DPFSP) is one of the most computationally complex problems. It has gained a wide attention not only in theoretical studies but also in manufacturing industry. In recent years, a lot of work has been done and many heuristics and metaheuristics have been proposed to tackle the DPFSP. Unfortunately, all the existing algorithms are centralized despite the fact that the distributed approaches are known to be more practical for the complex scheduling problems ones. Thus, we argue that distributed artificial intelligence techniques, namely Multi-Agent Systems (MAS), offer an appropriate tool to tackle problems of a distributed nature when they are properly designed and implemented. Thanks to their flexibility, adaptively and extensibility; MAS represents a promising variant to achieve a better performance. In this study, by combining the population-based evolutionary searching abilities of Chemical Reaction Optimization (CRO) metaheuristic with the capabilities of MAS in modeling hard combinatorial problems, we suggest an agent-based evolutionary algorithm called CROMAS to effectively solve the DPFSP. We tested our algorithm on well-known benchmark instances and compared its performance with respect to other recent methods. Experiments reveal that CROMAS is very effective and able to provide competitive results.

The Distributed Job shop Scheduling Problem (DJSP) deals with the assignment of jobs to factories geographically distributed and with determining a good operation schedule of each factory. The objective is to minimize the global makespan over all the factories. This paper is a first step to deal with the DJSP using three versions of a bio-inspired algorithm, namely the Ant Colony Optimization (ACO) which are the Ant System (AS), the Ant Colony System (ACS) and a Modified Ant Colony Optimization (MACO) aiming to explore more search space and thus guarantee better resolution of the problem. Comprehensive experiments are conducted to evaluate the performance of the three algorithms and the results show that the MACO is effective for the problem and AS and ACS algorithms in resolving the DJSP.

Scheduling in production systems consists in assigning operations on a set of available resources in order to achieve defined objectives. The Flexible Job shop Scheduling Problem (FJSP) is one of the scheduling problems and also an extension of classical Job shop Scheduling Problem (JSP) such that each operation can be processed on different machine and its processing time depends on the used machine. The FJSP is classified, as most of scheduling problems, NP-Hard in complexity theory and can be decomposed into two sub-problems: a routing sub-problem, which consists of assigning each operation to a machine out of a set of alternative machines, and a scheduling sub-problem, which consists of sequencing the assigned operations on all selected machines in order to attain a feasible schedule with optimized objectives. In this paper, we propose a decentralized model named Multi Agent model based on Chemical Reaction Optimization with Greedy algorithm (MACROG-FJSP) to solve the FJSP in order to minimize the maximum completion time (Makespan). Experiments are performed on well known benchmark instances proposed in the literature which are Fattahi, Kacem, Brandimarte and Hurink instances to evaluate the performance of our model.

Solving scheduling problems is an important branch of operational research field. It consists in allocation number of jobs to machines taking into consideration a set of constraints. Recently, a new generalization of the permutation flow shop scheduling problem with multi-factory environment has been proposed. In this work, we suggest to apply a Chemical Reaction Optimization (CRO) metaheuristic to solve this problem in order to minimize makespan or the total duration of the schedule. An experimental study is carried out in order to analyze the performance of the proposed algorithm and to compare it with powerful algorithms on standard benchmarks.

In systems based robotic cells, the control of some elements such as transport robot has some difficulties when planning operations dynamically. The Job Shop scheduling Problem with Transportation times and a Single Robot (JSPT-SR) is a generalization of the classical Job Shop scheduling Problem (JSP) where a set of jobs additionally have to be transported between machines by a single transport robot. Hence, the JSPT-SR is more computationally difficult than the JSP presenting two NP-hard problems simultaneously: the job shop scheduling problem and the robot routing problem. This paper proposes a hybrid metaheuristic approach based on clustered holonic multiagent model for the JSPT-SR. Firstly, a scheduler agent applies a Neighborhood-based Genetic Algorithm (NGA) for a global exploration of the search space. Secondly, a set of cluster agents uses a tabu search technique to guide the research in promising regions. Computational results are presented using benchmark data instances from the literature of JSPT-SR. New upper bounds are found, showing the effectiveness of the presented approach.

The Flexible Job Shop scheduling Problem (FJSP) is an extension of the classical Job Shop scheduling Problem (JSP) that allows to process operations on one machine out of a set of alternative machines. It is an NP-hard problem consisting of two sub-problems which are the assignment and the scheduling problems. This paper proposes a hybridization of two metaheuristics within a holonic multiagent model for the FJSP. Firstly, a scheduler agent applies a Neighborhood-based Genetic Algorithm (NGA) for a global exploration of the search space. Secondly, a cluster agents set uses a local search technique to guide the research in promising regions. Numerical tests are made to evaluate our approach, based on two sets of benchmark instances from the literature of the FJSP, which are the Brandimarte and Hurink data. The experimental results show the efficiency of our approach in comparison to other approaches.