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“Technological intelligence” is the capacity to appreciate and adapt technological advancements, and “artificial intelligence” is the key to achieve persuasive operational transformations in majority of contemporary organizational set-ups. Implicitly, artificial intelligence (the philosophies of machines to think, behave and perform either same or similar to humans) has knocked the doors of business organizations as an imperative activity. Artificial intelligence, as a discipline, initiated by scientist John McCarthy and formally publicized at Dartmouth Conference in 1956, now occupies a central stage for many organizations. Implementation of artificial intelligence provides competitive edge to an organization with a definite augmentation in its social and corporate status. Mere application of a concept will not furnish real output until and unless its performance is reviewed systematically. Technological changes are dynamic and advancing at a rapid rate. Subsequently, it becomes highly crucial to understand that where have the people reached with respect to artificial intelligence research. The present article aims to review significant work by eminent researchers towards artificial intelligence in the form of top contributing universities, authors, keywords, funding sources, journals and citation statistics.

As rightly remarked by past researchers that reviewing is learning from experience, research team has reviewed (by applying systematic literature review through bibliometric analysis) the concept of artificial intelligence in this article. A sum of 1,854 articles extracted from Scopus database for the year 2018–2019 (31st of May) with selected keywords (artificial intelligence, genetic algorithms, agent-based systems, expert systems, big data analytics and operations management) along with certain filters (subject–business, management and accounting; language-English; document–article, article in press, review articles and source-journals).

Results obtained from cluster analysis focus on predominant themes for present as well as future researchers in the area of artificial intelligence. Emerged clusters include Cluster 1: Artificial Intelligence and Optimization; Cluster 2: Industrial Engineering/Research and Automation; Cluster 3: Operational Performance and Machine Learning; Cluster 4: Sustainable Supply Chains and Sustainable Development; Cluster 5: Technology Adoption and Green Supply Chain Management and Cluster 6: Internet of Things and Reverse Logistics.

The result of review of selected studies is in itself a unique contribution and a food for thought for operations managers and policy makers.

Manufacturing industries these days gives importance to reduce the energy consumption due to the increase in energy prices and to create an environmental friendly industry. Robotic assembly lines (RALs) are used in an industry for assembling different types of products in an assembly line due to the flexibility it offers to the production system. Since different types of robots are available with different specialization and capabilities, there is a requirement of efficiently balancing the assembly line by allocating equal amount of tasks to the workstations and allocate the best fit robot to perform the allocated tasks. The goal of this paper is to maximize the line efficiency by minimizing the total energy consumption in a U-shaped robotic assembly line.

Particle swarm optimization (PSO) and Differential evolution (DE) are the two evolutionary algorithms used as the optimization tool to solve this problem. Performance of these proposed algorithm are tested on a set of randomly generated problems which are generated using the benchmark problems available in the open literature and the results are reported.

The proposed algorithm are found to be useful to reduce the total energy consumption on an assembly line which maximizes the line efficiency. It is found that DE algorithm could improve the line efficiency than PSO algorithm. Computational time taken by the two algorithm are also reported.

Till date, no research has been reported on optimizing the line efficiency by minimizing the total energy consumption in a U-shaped robotic assembly line systems. Particle swarm optimization (PSO) and Differential evolution (DE) are the two evolutionary algorithms used as the optimization tool to solve this problem.

Mechanical properties such as tensile, yield, impact strengths, and development of residual stresses play an important role intooling applications. The objective of this paper is to investigate the effect of layer thickness – one of the influential process parameters in stereolithography (SL) process, on mechanical properties of SL components.

Test specimens are constructed as per the ASTM standards for different layer thicknesses in SL 5000 machine, using epoxy resin CIBA tool ^® SL5530, a high temperature resistant SL material that is suitable for rapid tooling applications. Tensile, yield and impact tests are carried out with suitable equipments. Residual stress is analysed using hole drill method.

The analysis reveals that when the layer thickness is smaller, the strength of the part is higher.

Conclusion of this research is drawn based on the analysis of the most widely used three layer thicknesses of 100, 125 and 150 μm. X‐ray diffraction or molecular resonance analysis may be useful to understand the reason for the variation in mechanical properties.

This experimental study provides the useful information to the SL machine users in the selection of layer thickness to manufacture rapid tools.

In this paper, the joint replenishment problem is modeled for a two-level supply chain consisting of a single supplier and multiple retailers that use the vendor-managed inventory (VMI) policy for several products. This paper aims to find the optimal number of products to order in both policies, the optimal times at which each retailer orders the products in the traditional policy and the optimal times at which the supplier orders the product in the VMI policy.

The problem is first formulated into the framework of a constrained integer nonlinear programming model; then, the problem is solved using a teacher-learner based optimization algorithm. As there are no benchmarks available in the literature, a genetic algorithm is used as well to validate the results obtained.

The solutions obtained using both the algorithms for several numerical examples are compared to the ones of a random search procedure for further validation. A real case is solved at the end to demonstrate the applicability of the proposed methodology and to compare both the policies.

The paper does not have any special limitations.

The study has significant practical implications for the sellers and for the suppliers who have to get the most profit. Also, satisfying the constraints make decision more complicated.

This paper has two main originalities. The authors have developed the model of the joint replenishment problem and have contributed in the problem-solving process. They have used a new meta-heuristic and then compared it to a classic one.

The purpose of this paper is to define the “fast evolving industry” (FEI) and its supply chain management (SCM) challenges. The authors review and structure the literature regarding integration strategies and implementation methods to develop a strategic decision-making framework for SCM in the FEI.

The authors conduct a review of SCM literature, including supply chain strategy, supply chain integration (SCI), agile and responsive supply chain and SCM for innovative and fast-changing industries. The authors develop a conceptual model and a decision-making framework and use four mini cases to provide support for the model and framework.

The FEI, characterised by a high level of innovation and differentiation, short products/services lifecycle and high variety, is yet to be fully defined. Inherent uncertainty in FEI supply systems makes SCM in these industries a complex but strategic task for their managers. The framework and the model offered in this study, which employ a core competency concept and provide risk management strategies, offer a strategic tool for managers and scholars in the field to optimise their integration strategies and to operationalise integration decisions.

Little research has been published on transferable and cross-industrial SCM in FEIs. This paper defines the FEI and its resource-related concerns and then offers a conceptual model and a strategic decision-making framework for SCI in FEIs.

Uncertainty can arise for a manipulator because its motion can deviate unpredictably from the assumed dynamical model and because sensors might provide information regarding the system state that is imperfect because of noise and imprecise measurement. This paper aims to propose a method to estimate the probable error ranges of the entire trajectory for a manipulator with motion and sensor uncertainties. The aims are to evaluate whether a manipulator can safely avoid all obstacles under uncertain conditions and to determine the probability that the end effector arrives at its goal area.

An effective, analytical method is presented to evaluate the trajectory error correctly, and a motion plan was executed using Gaussian models by considering sensor and motion uncertainties. The method used an integrated algorithm that combined a Gaussian error model with an extended Kalman filter and a linear–quadratic regulator. Iterative linearization of the nonlinear dynamics was used around every section of the trajectory to derive all of the prior probability distributions before execution.

Simulation and experimental results indicate that the proposed trajectory planning method based on the motion and sensor uncertainties is indeed highly convenient and efficient.

The proposed approach is applicable to manipulators with motion and sensor uncertainties. It helps determine the error distribution of the predefined trajectory. Based on the evaluation results, the most appropriate trajectory can be selected among many predefined trajectories according to the error ranges and the probability of arriving at the goal area. The method has been successfully applied to a manipulator operating on the Chinese Space Station.

RICHARD DE BURY'S prayer that war, the great enemy of the book and therefore of the library, be averted must have risen to the minds of some librarians recently. As we write these lines international relations seem to have reached a boding complexity unrivalled since 1939 and with potentialities for ill as great or even greater. By the time these words appear we hope sanity and a calmer spirit will prevail and that the Christmas we face as librarians may indeed be a happy one. However that may be, the many frustrations all development, including library development, have suffered in the past year, are not likely to be overcome soon. The 35 to 50 millions our interruption for good or ill in the Israel‐Egyptian affair has cost—a relatively small matter financially against our national annual spendings of thousands of millions—are not likely to make for library progress. Yet, paradoxically, our greater advances in modern times have been the outcome of conditions created it would seem by war. The Great World War showed the naked need of the public library service in a way that the previous seventy years of peaceful advocacy had failed to do. Even greater progress came out of the Second World War. What was lost in each of these catastrophes no one has been able to calculate.

In this paper, the uncertainties important components and the structure status are obtained by using the condition monitoring, expert groups and multiple membership functions by creating a fuzzy system in MATLAB software.

In the form of fuzzy type, the average structural safety must be followed to replace the damages or to absolutely control the decision-making. Uncertainty in the functionality of hydraulic automated guided vehicles (AGVs), without knowing the reliability of pieces, can cause failure in the manufacturing process. It can be controlled by the condition monitoring pieces done by measurement errors and ambiguous boundaries.

As a result, this monitoring could increase productivity with higher quality in delivery in flexible manufacturing systems with an increase of 70% reliability mutilation for the hydraulic AGV parts.

Hydraulic AGVs play a vital role in flexible manufacturing in recent years. Lately, several strategies for maintenance and repairing of hydraulic AGVs exist in the industry but are still confronted with many uncertainties. The hydraulic AGV is faced with uncertainty after 10 years of working in terms of reliability. Reconstruction of the old parts with the new parts may not have the quality and durability.

The production-distribution (P-D) problems are two critical problems in many industries, in particular, in manufacturing systems and the supply chain management. In previous researches on P-D planning, the demands of the retailers and their inventory levels have less been controlled. This may lead into huge challenges for a P-D plan such as the bullwhip effects. Therefore, to remove this challenge, the purpose of this paper is to integrate a P-D planning and the vendor-managed inventory (VMI) as a strong strategy to manage the bullwhip effects in supply chains. The proposed P-D-VMI aims to minimize the total cost of the manufacturer, the total cost of the retailers, and the total distribution time simultaneously.

This paper presents a multi-objective non-linear model for a P-D planning in a three-level supply chain including several external suppliers at the first level, a single manufacturer at the second level, and multi-retailer at the third level. A non-dominated sorting genetic algorithm and a non-dominated ranking genetic algorithm are designed and tuned to solve the proposed problem. Then, their performances are statistically analyzed and ranked by the TOPSIS method.

The applicability of the proposed model and solution methodologies are demonstrated under several problems. A sensitivity analysis indicates the market scale and demand elasticity have a substantial impact on the total cost of the manufacturer in the proposed P-D-VMI.

Although the P-D planning is a popular approach, there has been little discussion about the P-D planning based on VMI so far. The novelty comes from developing a practical and new approach that integrates the P-D planning and VMI.

The purpose of this paper is of two‐fold. First, the authors propose the application of genetic algorithm (GA)‐based heuristic for solving a distribution allocation problem for a three‐stage supply chain with fixed cost. Second, a methodology for parameter design in GA is discussed which can lead to better performance of the algorithm.

A mathematical model is formulated as an integer‐programming problem. The model is solved using GA‐based heuristic and illustrated with a numerical example. An investigation is made for determining the best combination of the parameters of GA using factorial design procedure.

The optimum population size for the selected problem size is found to be 100. The mutation probability for a better solution is 0.30. The objective function value at the above mentioned levels is better than that obtained at the other combinations.

This work provides a good insight about the fixed cost transportation problem (FCTP) in a three‐stage supply chain and design of numerical parameters for GA. The model developed assumes a single product environment in a single period. Hence, the present study can be extended to a multi‐product, multi‐period, and varying demand environment. In the parameter design, three distinct numerical parameters are considered. The parameters, population size and mutation probability are set at four levels and the parameter, crossover probability is set at three levels. More levels can be selected so that more combinations can be experimented.

The paper presents the formulation and solution of a distribution‐allocation problem in a three‐stage supply chain with fixed cost for a transportation route.