Blockchain in supply chain management: a review, bibliometric, and network analysis.

Blockchain is a distributed ledger technology that has attracted both practitioners and academics attention in recent years. Several conceptual and few empirical studies have been published focusing on addressing current issues and recommending the future research directions of supply chain management. To identify how blockchain can contribute to supply chain management, this paper conducts a systematic review through bibliometric and network analysis. We determined the key authors, significant studies, and the collaboration patterns that were not considered by the previous publications on this angel of supply chain management. Using citation and co-citation analysis, key supply chain areas that blockchain could contribute are pinpointed as supply chain management, finance, logistics, and security. Furthermore, it revealed that Internet of Things (IoT) and smart contracts are the leading emerging technologies in this field. The results of highly cited and co-cited articles demonstrate that blockchain could enhance transparency, traceability, efficiency, and information security in supply chain management. The analysis also revealed that empirical research is scarce in this field. Therefore, implementing blockchain in the real-world supply chain is a considerable future research opportunity

Sustainable vehicle routing problem for coordinated solid waste management

The quick growth of urbanization and population, as well as the transformation of industrial and materials, have pushed the management of municipal solid waste into a crisis especially for developing markets based on the grand challenge of sustainable development. The compounding complexity of the multiple objectives and dynamic problem constraints required to represent coordinated solid waste management (CSWM) problem in practice is a hugely significant issue for vehicle routing problem studies. The purpose is to introduce a new coordinated framework for a practical and efficient vehicle routing problem considering the triple bottom line of sustainability. The CSWM multiple objective functions applied in this study incorporate financial, environmental and social considerations to develop a sustainable vehicle routing problem considering heterogeneous vehicle fleets operating across a multi-echelon logistics network with the optimization goals. An entirely novel development and application of the adaptive memory social engineering optimizer (AMSEO) is introduced and is shown to perform significantly better than the simulated annealing (SA) as well as the social engineering optimizer (SEO) itself. Finally, the potential overall waste disposal cost savings achievable through increased recycling (revealed by framing the logistics problem across several echelons) is of particular significance. The main findings are the practical solutions with the use of sustainability goals for the CSWM and further application and development of the AMSEO in the routing optimization

Multi-objective robust-stochastic optimisation of relief goods distribution under uncertainty: a real-life case study

This paper proposes a multi-objective robust-stochastic humanitarian logistics model to assist disaster management officials in making optimal pre- and post-disaster decisions. This model identifies the location of temporary facilities, determines the amount of commodity to be pre-positioned, and provides a detailed schedule for the distribution of commodities and the dispatch of vehicles. Uncertainties in demand, node reachability by a particular mode of transportation, and condition of pre-positioned supplies after a disaster are considered. Another supposition of this paper is the equity in the distribution of commodities. This paper contributes to the existing literature by adding vehicle flow and multi-periodicity into a robust-stochastic optimisation model. A real-life case study of a flood in Bangladesh shows the applicability of our model. Finally, the findings show that the proposed model can aid decision-makers in allocating resources optimally

Hybrid meta-heuristic algorithms for a supply chain network considering different carbon emission regulations using big data characteristics

Big data (BD) approach has significantly impacted on the development and expansion of supply chain network management and design. The available problems in the supply chain network (SCN) include production, distribution, transportation, ordering, and inventory holding problems. These problems under the BD environment are challenging and considerably affect the efficiency of the SCN. The drastic environmental and regulatory changes around the world and the rising concerns about carbon emissions have increased the awareness of customers regarding the carbon footprint of the products they are consuming. This has enforced supply chain managers to change strategies to reframe carbon emissions. The decisions such as an optimization of the suitable network of the proper lot sizes can play a crucial role in minimizing the whole carbon emissions in the SCN. In this paper, a new integrated production–transportation–ordering–inventory holding problem for SCN is developed. In this regard, a mixed-integer nonlinear programming (MINLP) model in the multi-product, multi-level, and multi-period SCN is formulated based on the minimization of the total costs and the related cost of carbon emissions. The research also uses a chance-constrained programming approach. The proposed model needs a range of real-time parameters from capacities, carbon caps, and costs. These parameters along with the various sizes of BD, namely velocity, variety, and volume, have been illustrated. A lot-sizing policy along with carbon emissions is also provided in the proposed model. One of the important contributions of this paper is the three various carbon regulation policies that include carbon capacity-and-trade, the strict capacity on emission, and the carbon tax on emissions in order to assess the carbon emissions. As there is no benchmark available in the literature, this study contributes toward this aspect by proposing two hybrid novel meta-heuristics (H-1) and (H-2) to optimize the large-scale problems with the complex structure containing BD. Hence, a generated random dataset possessing the necessary parameters of BD, namely velocity, variety, and volume, is provided to validate and solve the suggested model. The parameters of the proposed algorithms are calibrated and controlled using the Taguchi approach. In order to evaluate hybrid algorithms and find optimal solutions, the study uses 15 randomly generated data examples having necessary features of BD and T test significance. Finally, the effectiveness and performance of the presented model are analyzed by a set of sensitivity analyses. The outcome of our study shows that H-2 is of higher efficiency