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The introduction of modern technologies in manufacturing is contributing to the emergence of smart (and data-driven) manufacturing systems, known as Industry 4.0. The benefits of adopting such technologies can be fully utilized by presenting optimization models in every step of the decision-making process. This includes the optimization of maintenance plans and production schedules, which are two essential aspects of any manufacturing process. In this paper, we consider the real-time joint optimization of maintenance planning and production scheduling in smart manufacturing systems. We have considered a flexible job shop production layout and addressed several issues that usually take place in practice. The addressed issues are: new job arrivals, unexpected due date changes, machine degradation, random breakdowns, minimal repairs, and condition-based maintenance (CBM). We have proposed a real-time optimization-based system that utilizes a modified hybrid genetic algorithm, an integrated proactive-reactive optimization model, and hybrid rescheduling policies. A set of modified benchmark problems is used to test the proposed system by comparing its performance to several other optimization algorithms and methods used in practice. The results show the superiority of the proposed system for solving the problem under study. The results also emphasize the importance of the quality of the generated baseline plans (i.e., initial integrated plans), the use of hybrid rescheduling policies, and the importance of rescheduling times (i.e., reaction times) for cost savings.

This paper presents a model for parallel replacement and improvement for a fleet of assets to minimize both the economic costs and greenhouse gas (GHG) emissions where the emissions are limited by ...

Research on human multitasking suggests several measures to evaluate performance. However, the suggested measures evaluate performance either when tasks are performed sequentially, or when tasks are performed in a parallel manner. There is a lack of models with performance measures that consider concurrently sequential and parallel task execution. This paper aims to develop a measure of human performance that considers both sequential and parallel execution of tasks in multitasking conditions. First, the literature is reviewed to select a taxonomy to model the features and execution of tasks during multitasking. Task features include a list of tasks, task demands (in terms of physical, psychological, and emotional loads), and coordination between tasks (in terms of priorities, similarities, dependence, and time constraints). Task execution is represented as a network of sequential and overlapping tasks. Second, a set of measures are identified to evaluate human performance in multitasking conditions. The analysis of literature suggests a task switching cost model for sequential task execution and a task interference ratio when tasks are executed in parallel. To enable combining switching cost (i.e., sequential execution) and interference ratio (i.e., parallel execution) in multitasking conditions, a classification scheme based on tasks’ modalities is utilized. Finally, the developed model is applied to different scenarios.

In practice, production lines are dynamic and subject to several disruptions, unforeseen events, and requirements. Examples of such disruptions and events include random machine breakdowns, new order arrivals, order cancellations, due date changes, and shortage of material. Production schedules are adapted to such events by conducting rescheduling continuously, using real-time information about the current status of work-in-progress, machines, and resources on a shop-floor. This level of connectivity and real-time information sharing is achieved with the help of advanced initiatives in manufacturing technologies and industrial informatics such as Industry 4.0. Industry 4.0, driven by many emerging technologies, such as cyber-physical systems (CPS), internet of things (IoT), and internet of services (IoS), delivers real-time actionable data for smart decision-making in manufacturing. Several optimization approaches have been proposed to take advantage of such technology by incorporating the use of real-time information in the optimization process. Recently, with the increasing power of new machine learning (ML) algorithms in solving real-world problems, several ML approaches have been introduced to production planning and scheduling.In this paper, to achieve the Industry 4.0 vision in production control, we apply a reinforcement learning (RL) approach to real-time scheduling (RTS). The proposed RL based RTS uses a multiple dispatching rules (MDRs) strategy to enhance the production performance. A case study of a smart manufacturing firm is considered to apply the proposed approach. The firm is located in Ontario (Canada) and specializes in thermoplastic injection molding of various components and assemblies.The production schedules on the shop floor are sensitive to the changes resulting from random breakdowns and their associated maintenance activities. The production managers are using the data from the continuous monitoring system to update production schedules. The updating process is conducted manually based on their knowledge and a single dispatching rule (SDR) strategy. We believe that the proposed RTS system will help the company utilize the installed Industry 4.0 concepts and achieve the Industry 4.0 vision in the production control.The performance of the proposed RTS system is compared to the current strategy applied in the company. Results show the efficiency of the proposed RTS system compared to the current strategy.

Hybrid flowshop scheduling problems have attracted much attention owing to their wide applications in a variety of real-world problems. In some industries, products cannot be allowed to wait between any consecutive productions stages, which illustrates the importance of studying the no-wait constraint. One of the reasons for such a constraint is that, for some products, the waiting time could cause permanent damage. However, the no-wait constraint was neglected in many prior studies, which in some production environments may not be allowed. Minimizing the total tardiness plays a key role in making scheduling decisions to meet customers' due dates. In this study, we solve the no-wait two-stage hybrid flowshop scheduling problem with total tardiness minimization as an optimizing criterion. We formulated the problem mathematically and proposed two discrete versions of the particle swarm optimization (PSO) to solve it. Moreover, three discrete versions of PSO are adopted from previous studies and used as benchmarks to test the effectiveness of the two proposed algorithms. Compared to the benchmark algorithms, the results showed that the two newly proposed algorithms were effective and performed better than the benchmark algorithms in terms of the average relative error. The current study represents one of the few attempts to investigate the considered problem with total tardiness minimization, as well as introducing new and effective discrete versions of the PSO algorithms to solve the problem under investigation.

In this paper, an integrated academic accreditation program (named IAAP) is developed. The main focus of this study was engineering education, and a case study of the Faculty of Engineering and Information Technology at Taiz University (TU), Yemen was considered. The IAAP is developed by integrating two academic accreditation programs, the national program (Council of Accreditation and Quality Assurance: CAQA) and the Accreditation Board of Engineering and Technology (ABET). A clause-by-clause comparison of the criteria of the two programs (CAQA and ABET) was applied to construct the IAAP. The developed program is applicable to any other engineering faculty in Yemen. An investigation of the capability of implementing the IAAP was conducted. The investigation process involved the six academic departments of the faculty under study. A weighing and scoring scheme was obtained for each element of the IAAP using the analytical hierarchy process (AHP).

The studies about the effect of the graphene reinforcement ratio and machining parameters to improve the machining performance of Ti6Al4V alloy are still rare and incomplete to meet the Industry 4.0 manufacturing criteria. In this study, a hybrid adaptive neuro-fuzzy inference system (ANFIS) with a multi-objective particle swarm optimization method is developed to obtain the optimal combination of milling parameters and reinforcement ratio that lead to minimize the feed force, depth force, and surface roughness. For achieving this, Ti6Al4V matrix nanocomposites reinforced with 0 wt.%, 0.6 wt.%, and 1.2 wt.% graphene nanoplatelets (GNPs) are produced. Afterward, a full factorial approach was used to design experiments to investigate the effect of cutting speed, feed rate, and graphene nanoplatelets ratio on machining behaviour. After that, artificial intelligence based on ANFIS is used to develop prediction models as the fitness function of the multi-objective particle swarm optimization method. The experimental results showed that the developed models can obtain an accurate estimation of depth force, feed force, and surface roughness with a mean absolute percentage error of 3.87%, 8.56%, and 2.21%, respectively, as compared with experimentally measured outputs. In addition, the developed artificial intelligence models showed 361.24%, 35.05%, and 276.47% less errors for depth force, feed force, and surface roughness, respectively, as compared with the traditional mathematical models. The multi-objective optimization results from the new approach indicated that a cutting speed of 62 m/min, feed rate of 139 mm/min, and GNPs reinforcement ratio of 1.145 wt.% lead to the improved machining characteristics of GNPs reinforced Ti6Al4V matrix nanocomposites. Henceforth, the hybrid method as a novel artificial intelligent method can be used for optimizing the machining processes with complex relationships between the output responses.

This study analyzes a stochastic continuous review inventory system (Q,r) using a simulation-based optimization model. The lead time depends on lot size, unit production time, setup time, and a shop floor factor that represents moving, waiting, and lot size inspection times. A simulation-based model is proposed for optimizing order quantity (Q) and reorder point (r) that minimize the total inventory costs (holding, backlogging, and ordering costs) in a two-echelon supply chain, which consists of two identical retailers, a distributor, and a supplier. The simulation model is created with Arena software and validated using an analytical model. The model is interfaced with the OptQuest optimization tool, which is embedded in the Arena software, to search for the least cost lot sizes and reorder points. The proposed model is designed for general demand distributions that are too complex to be solved analytically. Hence, for the first time, the present study considers the stochastic inventory continuous review policy (Q,r) in a two-echelon supply chain system with lot size-dependent lead time L(Q). An experimental study is conducted, and results are provided to assess the developed model. Results show that the optimized Q and r for different distributions of daily demand are not the same even if the associated total inventory costs are close to each other.

This paper proposes a genetic algorithm (GA) for scheduling two identical parallel machines subjected to release times and delivery times, where the machines are periodically unavailable. To make the problem more practical, we assumed that the machines are undergoing periodic maintenance rather than making them always available. The objective is to minimize the makespan (Cmax). A lower bound (LB) of the makespan for the considered problem was proposed. The GA performance was evaluated in terms of the relative percentage deviation (RPD) (the relative distance to the LB) and central processing unit (CPU) time. Response surface methodology (RSM) was used to optimize the GA parameters, namely, population size, crossover probability, mutation probability, mutation ratio, and pressure selection, which simultaneously minimize the RPD and CPU time. The optimized settings of the GA parameters were used to further analyze the scheduling problem. Factorial design of the scheduling problem input variables, namely, processing times, release times, delivery times, availability and unavailability periods, and number of jobs, was used to evaluate their effects on the RPD and CPU time. The results showed that increasing the release time intervals, decreasing the availability periods, and increasing the number of jobs increase the RPD and CPU time and make the problem very difficult to reach the LB.

This paper investigates integrated maintenance and production scheduling in an unrelated parallel-machine production environment. We address three different preventive maintenance (PM) policies, namely (1) PM at fixed predefined time intervals, (2) optimal PM period that maximizes machines’ availability, and (3) optimal PM period that maintains a minimum reliability threshold for a given production period t. We incorporate the three PM policies into a mixed-integer mathematical model that jointly optimizes maintenance and production scheduling. The objective is to minimize maximum completion time and maintenance costs. The weighted-sum method is adopted to joint these two objectives as they have different units. Due to the complexity of the proposed model, a problem-specific designed simulated annealing (SA) algorithm is used to solve it. The effect of the three adopted maintenance policies on the delivery times of products and cost of maintenance activities is illustrated through a small case study.

Interruptions are unexpected breaks that introduce new tasks on top of ongoing activities. In work environments, interruptions occur when operators and decision-makers have to deal simultaneously with several stimuli and information sources and have to make decisions so as to maintain the flow of activities at a satisfactory level of performance or quality of service. The causes and effects of interruptions and their subsequent management strategies in workplace environments have been researched in the past, however, only a few review articles are available to report on current advances in this area, to analyze contributions, and to highlight open research directions. This paper offers an up-to-date review and a framework for interruptions and interruption management strategies. The current approaches to identify, report, and manage interruptions in a variety of workplace environments are reviewed and a description of environmental characteristics that favor the occurrence of interruptions and influence interruption management in workplace environments is provided. Various approaches to classify and model the different types of interruptions and their cause-consequence relationships are discussed and the strategies to manage interruptions and approaches to measure human performance when dealing with interruptions are analyzed. Based on these insights, several guidelines to manage interruptions in workplace environments are provided, and future research directions are highlighted.

The adaption of state-of-the-art inventions in information technology and industrial informatics in manufacturing has led to the advent of Industry 4.0, commonly known as the fourth industrial revolution. Industry 4.0 will take manufacturing productivity and quality to new levels and create enormous opportunities for business and revenue growth. Unlike classical manufacturing systems, Industry 4.0-based manufacturing systems are supported by several advanced technologies (known as Industry 4.0 concepts), which include cyber-physical systems (CPS) and internet of things (IoT), among other Industry 4.0 concepts. The adoption of such technology (i.e., Industry 4.0) allows for the delivery of real-time actionable data for smart decision-making. In order to fully realize the potential of such technologies, real-time decision making should be present in all aspects of the manufacturing process. This includes two core components of manufacturing: maintenance and production scheduling.

In biomedical applications, micro-channels are one of the important features and are often fabricated by laser ablation process. However, laser-processed micro-channels exhibit crucial issues of heat-affected zone (HAZ), dimensional errors, and poor surface quality. This paper investigates the influence of key laser process parameters on the dimensional accuracy and the surface quality of the micro-channels fabricated on zirconia dental material. A full factorial based on design of experiment (DOE) methodology was adopted for carrying out experiments in a pulsed Nd:YAG laser micro-milling. The process parameters considered during machining were laser scanning speed, pulse frequency, and pulse intensity with four levels for each parameters resulting into 64 individual experiments. Channel dimensional accuracy (depth error, top width error and taper angle), surface roughness (Ra), and HAZ were considered as the performance criteria for the present study. The experimental results show that the scanning speed and the pulse intensity and their interaction have the most influential effects on the depth error and HAZ. Moreover, it was found that the pulse intensity controls Ra, and the top width error is mainly affected by the scanning speed. The pulse frequency has the main effect on the channel taper angle, while its effects on other responses are negligible. The multi-objective genetic algorithm (MOGA-II) was employed to determine the optimal parametric conditions for minimizing the ablated depth errors and HAZ while putting the constraints on the minimum acceptable levels of the surface roughness, top width error, and taper angle. The optimized solutions were obtained at the moderate levels of scanning speed and pulse intensities and low levels of pulse frequencies.

In production lines, several factors contribute to the manufacturing of final products. Among these factors, the production time, machine status, and energy consumption, before and during production, need to be investigated further. In this paper, we present a mathematical model which jointly optimizes production scheduling and maintenance planning in a single-machine production environment. The performance of the machine deteriorates with time, and the machine is subject to stochastic deterioration-based failures. We assume that the transitions between the machine’s deterioration states follow an exponential distribution. We consider that processing times and energy consumption are affected by machine deterioration and failures. The main contribution of the paper is that maintenance and scheduling decisions are made based on the machine’s degradation level (i.e., the machine’s condition). We address the machine’s deterioration as a discrete multi-state degradation process; and model the effects of the machine’s deterioration and failures on the duration of job processing and the machine’s energy consumption. Then, we develop a stochastic mixed-integer programming model that integrates decisions about maintenance and production scheduling. The model generates the optimal maintenance action for each degradation state, as well as the optimal inspection policy and job sequence, with the overall aim being to minimize the total cost, including: inspection costs, repair costs, machine energy consumption costs, and the makespan penalty for exceeding a predetermined threshold. Due to the complexity of the developed model, an effective genetic algorithm (GA) based on the properties of the considered problem is proposed. Finally, through a comparative numerical study, we show that making decisions according to the deterioration level of the machine results in more integrated and cost-effective plans compared to the current method of repairing the machine only once it has reached its failure state.

The airport gate assignment problem (AGAP) is one of the most important problems operations managers face daily. Many researches have been done to solve this problem and tackle its complexity. The objective of the task is assigning each flight (aircraft) to an available gate while maximizing both conveniences to passengers and the operational efficiency of airport. This objective requires a solution that provides the ability to change and update the gate assignment data on a real time basis. In this paper, we survey the state of the art of these problems and the various methods to obtain the solution. Our survey covers both theoretical and real AGAP with the description of mathematical formulations and resolution methods such as exact algorithms, heuristic algorithms, and metaheuristic algorithms. We also provide a research trend that can inspire researchers about new problems in this area.

The flexible flow-shop scheduling problem (FFSSP) is an important branch of production scheduling, the flexible flow shop is a combination of two well-known machine environments, which are flow shops and parallel machines, and it is well known that this problem is NP-hard. The no-wait requirement is a phenomenon that may occur in flow shops, and it's prevent the jobs to wait between two successive machines (or stages), jobs must be processed from the start to finish, without any interruption on machines (or stages) or between them. Many different approaches have been applied to FFSSP with no-wait. In this research, two-stage no-wait flexible flow shop scheduling problem (NWFFSSP) has been solved using two meta-heuristics, which are Tabu Search (TS) and Particle Swarm Optimization (PSO). We solved the NWFFSSP with minimum makespan as a performance measure. The performance of the proposed algorithms are studied and compared with previous research results using the same problem data. The results of the study proposes the effective algorithm.

Scheduling of jobs has been a challenging task in manufacturing and the most real life scheduling problems, which involves multi-objectives and multi-machine environments. This paper presents tabu search and simulated annealing approaches for scheduling jobs on identical parallel machines. The identical parallel machine scheduling problem has been considered to minimize the mean tardiness for the jobs. Initially, an initial solution has been obtained using EDD dispatching rule then, simulated annealing and tabu search have been applied to reach a near optimal solution. Computational experiments are performed on problems with up to 10 machines and 150 jobs. The computational results indicate that the two proposed approaches are capable of obtaining better solutions for the given scheduling problem. Moreover, the tabu search approach provides better solution then simulated annealing approach.

This paper includes a simulation model for KSU Main Student Restaurant that built using Arena simulation software. We proposed some performance measures to be evaluated for our case study, which is the average waiting time in system and the average number of students in queues. The system faced a big pressure in the first hour of serving the lunch, a slightly pressure in the second hour and we can say there is no pressure on the system in the last hour. We used Arena simulation software to build a simulation model and after that, we analyzed the output from the simulation program results and applying “what if” analysis to produce a group of alternatives (scenarios). We ranked these alternatives to choose the best alternative to improve the efficiency of our system to get better service quality during rush hours. We used Arena Process Analyzer to rank and select the best scenario beside the D&D procedures for ranking and selecting the best alternative, which we will explain it latter in details. At the end, we describe some recommendations and future work as the last part of our study.