Discrete-event process simulation now has a long and praiseworthy history of providing a path to higher productivity and efficiency to a variety of enterprises – the history thereof begins with manufacturing – now extending to warehousing operations, transportation hubs, service industries, retail stores, and health-care operations such as hospitals, clinics, dentists’ offices, and pharmacies. Use of the analytical techniques of simulation, in tight conjunction with statistical analysis methods applied to both input data and outputs from the simulation model, empowers engineers, process experts, and managers to experiment on a computer model of a system, evaluating various proposed improvements or adaptations to changing conditions quickly, economically, and (very significantly) without disruption to the current system during the analysis. In this paper, we present the application of discrete-event simulation to the study and improvement of a small, independently owned restaurant. Challenges presented to and successfully addressed by this simulation study include effects of seasonal variation and the imperative to increase customer-service capacity quickly and at minimal expense.

Manufacturing requires various machining processes. Nowadays, machining implies advanced technologies in order to meet more exacting process performance criteria. This paper addresses the optimization of four conventional and nonconventional machining processes: drilling, grinding, water jet machining (WJM), and wire electrical discharge machining (EDM). The input process parameters are: cutting speed, feed rate, cutting environment, depth of cut, grit size, water jet pressure, diameter of water jet nozzle, traverse rate of the nozzle, stand-off-distance, ignition pulse current, pulse-off time, pulse duration, servo reference mean voltage, servo speed variation, wire speed, wire tension, and injection pressure. The multi-objective EDM optimization problem is converted to a single-objective problem using the weighted-sum method. Two nature-inspired algorithms of artificial intelligence (AI) are implemented for solving these problems, namely the particle swarm optimization (PSO) and the flower pollination algorithm (FPA). Penalty functions are introduced to handle the constraints and to enhance the algorithms for better results. The machining outputs, required number of function evaluations, CPU time, and standard deviations are the performance metrics. The results obtained are compared and show better performance than that already documented in the literature.

When, some decades ago now, discrete-event process simulation first expanded from academic research into the commercial arena, its first and very enthusiastic users were manufacturing enterprises. From there, simulation has deservedly expanded into other realms: Health care, warehousing, supply chain and transshipment improvement, public transport (improvements to airports, highway networks, and railroad operations), and service industries. We provide here an example of simulation applied to a service industry – the detailing of privately owned vehicles. Such detailing, properly and thoroughly done, can make a vehicle “like new” – shining headlights, restored upholstery, “squeaky clean” inside and out, and all surfaces vigorously polished. In the simulation analysis examined in this paper, a recently established (2021) auto detailing service in the United Arab Emirates, experienced significant increase in customer demand. The entrepreneur, having established an excellent reputation for service quality, sought the most cost-effective ways to accommodate the increased demand with no degradation of (indeed, even improvement to) its service: Short waiting times and “delivery when promised.”

Tuning the PID (proportional-integral-derivative) controller is one of the most important tasks to achieve accurate control. Many methods have been developed to tune the values of P, I, and D; however, classical methods showed limitations. During the last decade, many works addressed the problem as an optimization problem and showed that nature-inspired optimization techniques are prevalent, such as genetic algorithms and particle swarm optimization. This chapter provides a brief overview of applying nature-inspired optimization techniques to PID control optimization.

The design of any system contemplates the elaboration of a prototype of the entire system or some parts, before the manufacturing phase. Nowadays, rapid prototyping (RP) is widely used by the designers. Achieving good manufacturing performances needs to handle various process parameters. Most works deal with single objective process parameters. The reality is quite different and the processes involve conflicting objectives. This paper addresses the multi-objective factors optimization of the fused deposition modelling (FDM) technology. The problem is converted into a single one using the weighted-sum method and then solved by resorting to two nature-inspired computing techniques, namely particle swarm optimization (PSO) and differential evolution (DE). The results obtained are compared.

Robots presently occupy a large place in diverse activities. The robots currently in service are manipulator-type robots. They are often used in modern manufacturing processes to increase the production volume as well as to improve the quality of the product. This type of robot consists of a base, a carrier composed of segments, mobile joints expressed in the degree of freedom (rotoid and prismatic), actuator, sensor, and terminal organ. It is known that these systems have defects, such as insufficient accuracy, very slow reaction time, and instability. Furthermore, to adjust them, control systems are used: classic, dynamic, and adaptive. These commands need geometric, kinematic, and dynamic modeling of the studied systems. Most often, non-linear systems are transformed into linear systems using linearization methods. This chapter presents a short overview of robot manipulator control.

Discrete-event process simulation historically began its now long and distinguished "career" in support of manufacturing operations, ranging from assembly lines to make-to-order operations.From than auspicious beginning, it has expanded its usage to many other fields, such as warehousing, public transport (e.g., airports, bus terminals, railroads…), health care delivery (e.g., hospitals, clinics, urgent care centers, dental practices…), government functions (e.g., welfare administration, timing of traffic lights, courthouses…), and the service industry.Service industry applications of simulation have included restaurants, retail stores, hotels, and drive-through oil change centers.In this paper, we describe the important and productive role of simulation in improving the service performance metrics and resource allocation within an automotive service center.

We present new Spitzer transit observations of four K2 transiting sub-Neptunes: K2-36c, K2-79b, K2-167b, and K2-212b. We derive updated orbital ephemerides and radii for these planets based on a joint analysis of the Spitzer, TESS, and K2 photometry. We use the EVEREST pipeline to provide improved K2 photometry, by detrending instrumental noise and K2's pointing jitter. We used a pixel level decorrelation method on the Spitzer observations to reduce instrumental systematic effects. We modeled the effect of possible blended eclipsing binaries, seeking to validate these planets via the achromaticity of the transits (K2 versus Spitzer). However, we find that Spitzer's signal-to-noise ratio for these small planets is insufficient to validate them via achromaticity. Nevertheless, by jointly fitting radii between K2 and Spitzer observations, we were able to independently confirm the K2 radius measurements. Due to the long time baseline between the K2 and Spitzer observations, we were also able to increase the precision of the orbital periods compared to K2 observations alone. The improvement is a factor of 3 for K2-36c, and more than an order of magnitude for the remaining planets. Considering possible JWST observations in 1/2023, previous 1 sigma uncertainties in transit times for these planets range from 74 to 434 minutes, but we have reduced them to the range of 8 to 23 minutes.

The study proposes a geographic information systems (GIS)-based slope stability analysis method assuming a normal stress distribution acting on the slip surface. Compared with traditional methods, the three-dimensional (3D) safety factor acquired through this method will more closely approximate the actual value. First, a 3D slope stability analysis model is developed using grid column units, and the spatial expression of calculation parameters based on the grid column is given by the spatial analysis capability of GIS. Then, four equilibrium equations are derived under the limit equilibrium condition. The normal stress distribution acting on the slip surface is analyzed to construct a reasonable normal stress distribution approximation function. The 3D safety factor is obtained through the approximation function and the Mohr-Coulomb strength criterion. Moreover, we develop a GIS-based extension module which combines the grid-based data with the 3D slope stability analysis model. The accuracy and feasibility of the module are verified by three typical cases.

This paper is related to a solution approach for the nonlinear and nonconvex combined heat and power economic dispatch problem (CHPED). It combines the cuckoo optimization algorithm with penalty function (PFCOA) published in “Mellal and Williams (2015)” and the binary approach published in “Geem and Cho (2012).” The binary approach discretizes the nonconvex operating feasible region into two convex regions in order to explore the whole operating region. A numerical case study involving four units is investigated and the superiority of the mixed method, i.e, the PFCOA with the binary approach is proved.

Competitiveness and rapid technological advances lead to the obsolescence of household items and industrial components. The challenge to people and industrial firms is to be equipped with the latest items, but at the lowest cost. During the last decade, several works have investigated various replacement strategies in order to optimally replace the obsolete industrial components. This article introduces the first work of the literature addressing a replacement strategy in case of fuzzy data on the components. The approach is based on fuzzy logic and the cuckoo optimization algorithm. Two case studies are illustrated for highlighting the applicability of the approach proposed.

Stability of permeable soils near large-scale water reservoirs for paved and unpaved road pavements is all too frequently compromised due to excessive seepage and the climatic conditions of that area. In this research, a multilevel research approach was adopted by conducting a comparative study of the microspectroscopy through Fourier transform infrared (FTIR) spectra to investigate the maximum absorbance correlation along with mechanical investigations (such as the compressive strength, modified proctor test, California bearing ratio test, and swell percentage test). The native low plastic soil sample (CL) was blended with varying percentages of petroleum additives (bitumen and used motor oil) independently at 0%, 4%, 8%, 12%, 16%, and 20%. A comparison of results in the case of bitumen and used motor oil revealed that a decrease in Atterberg’s limits occurred accompanied by an increase of bitumen blending percentage, while used motor oil (UMO) increased the plastic limit. Maximum dry density (MDD) increases while optimum moisture content (OMC) decreases with the increase in bitumen. Used motor oil (UMO) initially (up to 4%) increased the MDD and subsequently decreased it. Investigative reports show that bitumen causes a decrease in swell percentage and increases California bearing ratio (CBR), whereas UMO causes a continuous increase in percentage swell and decrease in CBR. The addition of bitumen in soil resulted in a decrease in the coefficient of permeability (k), while UMO has a significant result of up to 4%. Regarding the control sample, spectrum analysis through FTIR effectively supports the laboratory results as the intensity of peaks increases with the oil, and bitumen concentration reveals that oil and bitumen impart cementitious property to the soil. Moreover, this research work by experiment supported and strengthened the idea of soil pavement stabilization through bitumen, which gives antiwater stability, and facilitates low-cost construction by obtaining raw material on the spot. UMO adversely affects soil properties beyond 4% addition by weight.

During the last decade, system reliability optimization has been widely investigated. New strategies have been introduced recently to improve the overall system reliability, such as the standby and heterogeneous redundant components. However, the problem formulations of these strategies are more complex. This paper addresses the system reliability-redundancy allocation problem (RRAP) with heterogeneous components. A new solution approach, called hosted cuckoo optimization algorithm (HO-COA), is proposed to effectively solve the problem. It is based on the latest researches on the cuckoos. The egg-laying recognition used in this paper is more realistic than the simple cuckoo optimization algorithm (COA). The effectiveness of the proposed approach is verified on five case studies and the application results are compared to those obtained in the literature, the simple cuckoo optimization algorithm (COA), the differential evolution method (DE), and the flower pollination algorithm (FPA). The fifth case study represents a large-scale system highlighting the superiority of the HO-COA.

Over the last half-century or more, simulation has established a splendid record of helping to improve complex systems. This fine record began historically with improvements to manufacturing processes, and in due course expanded to many other fields, including warehousing, transportation systems, health-care systems such as clinics and hospitals, and general customer-service systems such as banks, hotels, retail stores, and other venues where customer service is highly important. In this work, the application of simulation to improvement of customer service at an amusement park in Southeast Asia is documented, along with the contributions it made and indications for further work. The management of the park was justifiably concerned with operating costs, long customer waiting lines, and loss of potential customers via balking. Simulation pointed the way to significant process improvements and hence customer-service improvements with negligible increases in operating costs.

The internal logistics for warehouses of many industrial applications, based on the movement of heavy goods, is commonly solved by the installment of a multi-crane system. The job scheduling of a multi-crane system is an interesting problem of optimization, solved in many ways in the past. This paper describes a comparison between the optimization by the use of Genetic Algorithms (GA) and introduce a framework for the solution of the problem using machine learning driven by Neural Networks (NN). Even though this last approach is not implemented in this paper, performances very close to GA ones are expected with NN. A case-study for steel coil production is proposed as a test frame for two different simulation software tools, one based on a heuristic solution and one on machine learning; performances and data achieved from reviews and simulations are compared.

System availability is a key element for any industry. System designers and operators try to do their best to maintain the required availability of the systems to avoid production stoppages. They set up and undertake different maintenances, and these interventions imply cost. Therefore, the goal is to minimize the cost, but considering the constraint of the availability requirement. The problem involves three main aspects: redundancy allocation, component failure rates, and repair rates. In this paper, a novel solution approach is proposed based on an efficient cuckoo optimization algorithm (EF-COA). Two numerical case studies are solved, and the results confirm the effectiveness of the approach proposed.

Welding is a well-known process in manufacturing industries due to its importance. Several process parameters should be tuned in order to perform a high-quality welding. Usually, the problem is described as an optimization one and the challenge is to reconcile conflicting objectives. This paper deals with a multi-objective welding process namely the submerged arc welding process, involving five objectives. The weighted sum approach is used to handle it. An accelerated cuckoo optimization algorithm is implemented for this process model and applied to a practical instance of it. On this practical example, the superiority of the proposed optimization technique has been demonstrated in terms of better solutions and fewer required generations of the cuckoos relative to the basic COA and four other optimization algorithms.

This research examines the criteria to assess Smart Urban Mobility. The research goal is to define the criteria related to smart urban mobility and to investigate their mutual influences. The study proposes six criteria applied to the analysis of transportation in Beijing city: urban planning, mobility, connectivity, environment, governance, infrastructure and economy. The DEMATEL method of multi-criteria analysis has been applied to analyse the relationship, influence and impact of criteria on each other. Two groups of experts have been evaluated the criteria. The first group consists of six experts from academia; the second group includes five experts from the city administration. All experts have long experience in transport planning. The assessment of criteria has been made individually for each expert from both groups. The results show that the scores of criteria by both groups are close. It was found that the criteria mobility (18.16%), connectivity (16.75%) and environment (16.69%) have a major impact on smart urban mobility. The criteria in the cause group are urban planning, governance, infrastructure, and economy. The criteria in the effect group are mobility, connectivity and environment. They are influenced by other factors. The AHP method has been used to validate the results given by the DEMATEL method. The results of both methods are close. The novelty in this study regards the defined criteria, determined weights and their mutual influences, and the analysis for the situation of Beijing. The proposed methodology can be used in future studies to assess various megalopolises in terms of Smart Urban Mobility.

Discrete-event process simulation now has a long and distinguished history of supporting the improvement of manufacturing processes. From those origins, it has expanded its applicability to supply chains, service industries, health care, and public transport. In manufacturing contexts, simulation modeling and analysis regularly helps fine-tune the trade-off between high inventory versus danger of stockout, improve and balance machine utilization, schedule workers more effectively, and improve performance metrics such as average and maximum times in queue and average and maximum length of queues. In the present work, the authors describe a successful application of simulation to the manufacture of footwear. The original manufacturing process was beset by problems including low throughput, high headcount, overly high or low machine utilization, unduly large rejection rates, and ergonomic concerns. The simulation and analysis project described in this paper guided significant improvements, including doubling the output while reducing worker headcount to two-thirds of its initial value.

Slow-moving landslides are one of the most widely distributed natural hazards in the world, with severe effects on the stability of structures. However, it is hard to be detected without monitoring method. In this paper, the Differential Interferometric Aperture Radar (DInSAR) technique is used to monitor the slow-moving landslides. But, the standardised procedures for the DInSAR technique are difficult to find the boundaries of landslides. The new segmentation method of slope units with the Digital Elevation Model is proposed. Moreover, the credible zone analysis is established based on slope units to filter the error value and improve the precision of monitoring results. Finally, according to the features of slow-moving landslides, the outcomes of DInSAR technique for slow-moving landslides inventory map are available. The methodology is tested at Wudongde valley area in the North-west China, where the SAR data and natural hazards inventory maps are available. The correctness of monitoring results will be verified.