Nishikant Mishra

A multi-agent architecture for reverse logistics in a green supply chain

Green supply chain issues have attracted a lot of attention in recent years with growing awareness of environmental concerns. This has drawn the considerable world-wide attention of academics and practitioners. Therefore, recycling has now become an integral component of the supply chain. Recycling of used products and the related logistics management pose a significant challenge to manufacturing industries. In order to resolve the complexity of the task, this study proposes a multi-agent architecture to handle recycling and reverse logistics issues, which have so far been neglected. It addresses the different aspects of recycling such as waste classification, recycling, logistics and reuse of products. Additionally, it also discusses how the agent communicates and acts autonomously to facilitate the efficient logistics of materials between different units. The proposed agent architecture can assist manufacturing industries in efficiently managing their green supply chain system and complex logistics issues.

A Multi-Agent Self Correcting Architecture for Distributed Manufacturing Supply Chain

Nowadays the supply chain for distributed manufacturing is gaining attention of the researchers worldwide. Realizing its significance this article proposes a self correcting multi-agent architecture for the supply chain for the distributed manufacturing environment. The main aim of the proposed architecture is to generate an effective manufacturing plan while exploring the algorithm portfolio concept to minimize the manufacturing and supply chain costs. This architecture focuses on automatic selection of best techniques and suppliers while making the tradeoff between the cost, availability, reliability, distance and quality of the products supplied. When the new order arrives, the proposed architecture explores the delicacy of the skill exploitation algorithm to simultaneously incorporate the new and old orders. This will help manufacturing firms to execute their manufacturing processes efficiently.

Minimizing time risk in on-line bidding: An adaptive information retrieval based approach

Knowledge is of prime importance, particularly for the individuals who are involved in e-business. A lot of energy and time is wasted by the individuals in seeking required knowledge and information. In order to facilitate the individuals with required information, an efficient technique for the proper retrieval of knowledge is must. Almost all online business activities, particularly e-auction based firms are surrounded by various risk factors pertaining to time, security, brand etc. The main focus of the present paper is to analyze all such risk factors and further to categorize the same as per their degree of influence. A nominal group technique (NGT) based approach has been utilized to do the same that ranks the risk factors using agreed criteria based approach. Further, the paper proposed an adaptive information retrieval to resolve the problems related to time risk in online bidding process, while other risk factors has been tried to resolved by using corporate memory based data warehousing. Efficient knowledge retrieval along with the knowledge development and knowledge management became a backbreaking task for any organization. A corporate memory based approach has been utilized to represent the required knowledge stored in memory warehouse for its current and future usage. In underlying retrieval model, adaptiveness is achieved using genetic algorithm based matching function adaptation, where, a total of five matching functions viz. Jaccards coefficient, Overlaps coefficient, Dice coefficient, Inclusion measure, and Cosine measures have been considered to determine the retrieval effectiveness. Later, effectiveness of information retrieval system is calculated in terms of well known parameters namely precision, recall, fallout and miss. Results of adaptive information retrieval using a weighted combination of matching functions are compared with individual matching functions.

Cloud-based multi-agent architecture for effective planning and scheduling of distributed manufacturing

In modern world, manufacturing processes have become very complex because of consistently fluctuating demand of customers. Numerous production facilities located at various geographical locations are being utilised to address the demands of their multiple clients. Often, the components manufactured at distinct locations are being assembled in a plant to develop the final product. In this complex scenario, manufacturing firms have to be responsive enough to cope with the fluctuating demand of customers. To accomplish it, there is a need to develop an integrated, dynamic and autonomous system. In this article, a self-reactive cloud-based multi-agent architecture for distributed manufacturing system is developed. The proposed architecture will assist manufacturing industry to establish real-time information exchange between the autonomous agents, clients, suppliers and manufacturing unit. The mechanism described in this study demonstrates how the autonomous agents interact with each other to rectify the internal discrepancies in manufacturing system. It can also address the external interferences like variations in client’s orders to maximise the profit of manufacturing firm in both short and long term. Execution process of proposed architecture is demonstrated using simulated case study.

An efficient approach to radiotherapy dose planning problem: a TOPSIS case-based reasoning approach

ABSTRACTDose planning of prostate cancer is a complex and time-consuming process. Usually, oncologists use past experience and spend a large amount of time to determine the optimal combination of dose in phase I and II of treatment. In this article, a novel TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) case-based reasoning (CBR) approach is proposed to capture the past experience and expertise of oncologists. Initially, cases that resemble new case are extracted from database. Thereafter, inferred cases are evaluated using TOPSIS, a multi-criteria decision-making approach to prescribe an optimal dose plan. Robustness of the proposed method is validated on data sets collected from the City Hospital Campus, Nottingham University Hospitals, NHS, UK, using leave-one-out strategy. In experiment, the proposed methodology outperformed CBR approach. It also endorses the suitability of multi-criteria decision-making approach. This method will help oncologists to make a better trade-off be...

Stochastic modelling and optimisation of multi-plant capacity planning problem

In this paper, the problem of capacity planning under risk from demand and price/cost uncertainty of the finished products is addressed. The deterministic model is extended into a two-stage stochastic model with fixed recourse by means of various expected levels of demand as random. A recourse penalty is also included in the objective for both shortage and surplus in the finished products. The model is analysed to quantify the risk using Markowitz mean-variance model.

Interpretive structural modelling and fuzzy MICMAC approaches for customer centric beef supply chain: application of a big data technique

Abstract The food retailers have to make their supply chains more customer-driven to sustain in modern competitive environment. It is essential for them to assimilate consumer’s perception to improve their market share. The firms usually utilise customer’s opinion in the form of structured data collected from various means such as conducting market survey, customer interviews and market research to explore the interrelationships among factors influencing consumer purchasing behaviour and associated supply chain. However, there is abundance of unstructured consumer’s opinion available on social media (Twitter). Usually, retailers struggle to employ unstructured data in above decision-making process. In this paper, firstly, by the help of literature and social media Big Data, factors influencing consumer’s beef purchasing decisions are identified. Thereafter, interrelationships between these factors are established using big data supplemented with ISM and Fuzzy MICMAC analysis. Factors are divided as per their dependence and driving power. The proposed frameworks enable to enforce decree on the intricacy of the factors. Finally, recommendations are prescribed. The proposed approach will assist retailers to design consumer centric supply chain.

Priority-based scheduling in flexible system using AIS with FLC approach

In the present paper, an extensive decision-making problem of scheduling in flexible environment has been discussed. The novelty of the research is to allocate the machines to the operations on the basis of the priority. The highly prioritised machines are allocated first but least prioritised machines are also allocated for on-time delivery. The priority-based schedule provides a tradeoff approach between the utilisation and lead time. The problem has been tested on make-span as performance measure along with the sum of priority of all selected machines. To unravel the complexities of this problem, a heuristic based on a new approach, called artificial immune system (AIS) has been proposed. To strengthen AIS, a fuzzy logic controller (FLC) has been incorporated in the AIS heuristic. FLC changes the hypermutation rate adaptively at iteration. A numerical example has been taken for showing the efficacy of the proposed algorithm. The supremacy of the problem has been shown by the randomly generated data-set with increased complicacy of the problems. The results are also validated by statistical analysis using analysis of variance.

Barriers to effective circular supply chain management in a developing country context

Abstract Circular supply chain (CSC) emphasises surge in application of reuse, recycling, remanufacturing and thereby promotes transformation from linear to circular model of flow of products. Supply chains of manufacturing industries have become global over the years. Products manufactured in developing nations are being sent to developed nations for mass consumption. Developed nations have regulatory policies, technological knowhow and modern infrastructure to adopt CSC model. Their counterpart is trailing in these aspects. In literature, limited work has been performed on identifying challenges of implementing CSC in developing nations. Therefore, employing literature review and feedback received from experts, 16 important barriers were identified to CSC adoption in India. These barriers were analysed using integrated Interpretive Structural Modelling ? MICMAC approach. The findings will contribute in transforming supply chains thereby bringing economic prosperity, addressing global warming and generating employment opportunities. Finally, crucial policy measures and recommendations are proposed to assist managers and government bodies.

Integrated Decisions for Supplier Selection and Lot-Sizing Considering Different Carbon Emission Regulations in Big Data Environment

Abstract The rising concerns about carbon emissions due to drastic environmental changes globally has increased awareness of customers regarding the carbon footprint of the products they are consuming. Thus, compelled supply chain managers to reformulate strategies for controlling the carbon emissions. The various activities contributing to carbon emissions in a supply chain are procurement, transportation, ordering and holding of inventory. Operational decisions like selection of the right supplier of right lot-sizes can play a vital role in reducing the overall carbon footprint of a supply chain. This paper proposes a mixed-integer nonlinear program (MINLP) for supplier selection along with determining the right lot-sizes in a dynamic setting having multi-periods, multi-products and multi-suppliers with a view of overall reduction in the supply chain cost as well as associated cost of carbon emissions. The model requires a range of real time parameters from both the buyer’s and supplier’s perspectives such as costs, capacities and carbon caps. These parameters have been mapped with the different dimensions of Big Data viz. volume, velocity and variety. The model provides an optimal supplier selection and lot-sizing policy along with the carbon emissions. For the purpose of evaluating the carbon emissions, three different carbon regulating policies viz., carbon cap-and-trade, strict cap on carbon emission and carbon tax on emissions, have been considered and insights are drawn. The validation of the proposed MINLP has been done using a randomly generated dataset having the essential parameters of Big Data, i.e. volume, velocity, and variety.