An outbreak of deadly COVID-19 virus has not only taken the lives of people but also severely crippled the economy. Due to strict lockdown, the manufacturing and logistics activities have been suspended, and it has affected the demand and supply of various products as a result of restrictions imposed on shopkeepers and retailers. Impacts of COVID-19 are observed ubiquitously in every type of units from different sectors. In this study, a simulation model of the public distribution system (PDS) network is developed with three different scenarios to demonstrate disruptions in the food supply chain. Difficulties have been increased in matching supply and demand in a vast network of PDS because of changing scenarios with the growth of infected cases and recovery. This paper also highlights the importance of a resilient supply chain during a pandemic. Our proposed simulation model can help in developing a resilient and responsive food supply chain to match the varying demand, and then further assist in providing decision-making support for rerouting the vehicles as per travel restrictions in areas. Paper has been summarised with significant highlights and including future research scope for developing a more robust food supply chain network.

In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas, including product and process design, assembly, materials planning, quality control, scheduling, maintenance, fault detection etc. Data mining has emerged as an important tool for knowledge acquisition from the manufacturing databases. This paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with a special emphasis on the type of functions to be performed on the data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. The papers reviewed have therefore been categorized in these five categories. It has been shown that there is a rapid growth in the application of data mining in the context of manufacturing processes and enterprises in the last 3 years. This review reveals the progressive applications and existing gaps identified in the context of data mining in manufacturing. A novel text mining approach has also been used on the abstracts and keywords of 150 papers to identify the research gaps and find the linkages between knowledge area, knowledge type and the applied data mining tools and techniques.

The use of Unmanned Aerial Vehicles (UAVs) in delivery logistics has become an efficient solution with the advancement of autonomous robotics. This paper proposes a novel mechanism that synchronizes drones and delivery trucks; particularly the case where trucks can work as mobile launching and retrieval sites. The problem is a Vehicle Routing Problem with Time Windows and Synchronized Drones. A multi-objective optimization model is developed with two conflicting objectives, minimizing the travel costs and maximizing the customer service level in terms of timely deliveries. A novel Collaborative Pareto Ant Colony Optimization algorithm is proposed to solve the model and Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used to compare and validate the proposed algorithm. The experimental results indicate that the proposed mechanism is an efficient solution to parcel delivery logistics.

Disassembly operations are inevitable elements of product recovery with the disassembly line as the best choice to carry out the same. In the light of different structures of returned products (models) and variations in task completion times, the process of disassembly could not be efficiently mapped on a simple straight line. Another important issue that needs consideration is the task-time variability pertaining to human factor. In order to resolve these complexities a Mixed-Model U-shaped Disassembly Line with Stochastic Task Times has been proposed in this article. A novel approach, Collaborative Ant Colony Optimization (CACO), has been utilized that simultaneously tackles the interrelated problem of line balancing and model sequencing. The distinguishing feature of the proposed approach is that it maintains bilateral colonies of ants which independently identifies the two sequences, but utilizes the information obtained by their collaboration to guide the future path. The approach is tested on benchmark instances that were generated using Design of Experiment techniques and Analysis of Variance is performed to determine the impact of various factors on the objective. The robustness of proposed algorithm is authenticated against Ant Colony Optimization over which it always demonstrated better results thereby proving its superiority on the concerned problem.

Research and development (R&D) project selection is a complex decision-making process. It involves a search of the environment of opportunities, the generation of project options, and the evaluation by different stakeholders of multiple attributes, both qualitative and quantitative. Qualitative attributes are often accompanied by certain ambiguities and vagueness because of the dissimilar perceptions of organizational goals among pluralistic stakeholders, bureaucracy and the functional specialization of organizational members. Such differences in perceptions often hinder the attainment of consensus and coordination. Therefore, failures are frequent in R&D investment planning. To perceive the preferences of the various stakeholders and to map them into an analytical decision-making framework are challenging tasks. Further, risks and uncertainties are also associated with the investments and returns of R&D projects. This paper illustrates an application of fuzzy ANP (analytic network process) along with fuzzy cost analysis in selecting R&D projects. Fuzzy set theory is incorporated to overcome the vagueness in the preferences. The method adopted uses triangular fuzzy numbers for pair-wise comparison and applies extent analysis followed by defuzzification to determine the weights for various attributes.