An evaluation of three DoE-guided meta-heuristic-based solution methods for a three-echelon sustainable distribution network

This article evaluates the efficiency of three meta-heuristic optimiser (viz. MOGA-II, MOPSO and NSGA-II)-based solution methods for designing a sustainable three-echelon distribution network. The distribution network employs a bi-objective location-routing model. Due to the mathematically NP-hard nature of the model a multi-disciplinary optimisation commercial platform, modeFRONTIER®, is adopted to utilise the solution methods. The proposed Design of Experiment (DoE)-guided solution methods are of two phased that solve the NP-hard model to attain minimal total costs and total CO2 emission from transportation. Convergence of the optimisers are tested and compared. Ranking of the realistic results are examined using Pareto frontiers and the Technique for Order Preference by Similarity to Ideal Solution approach, followed by determination of the optimal transportation routes. A case of an Irish dairy processing industry’s three-echelon logistics network is considered to validate the solution methods. The results obtained through the proposed methods provide information on open/closed distribution centres (DCs), vehicle routing patterns connecting plants to DCs, open DCs to retailers and retailers to retailers, and number of trucks required in each route to transport the products. It is found that the DoE-guided NSGA-II optimiser based solution is more efficient when compared with the DoE-guided MOGA-II and MOPSO optimiser based solution methods in solving the bi-objective NP-hard three-echelon sustainable model. This efficient solution method enable managers to structure the physical distribution network on the demand side of a logistics network, minimising total cost and total CO2 emission from transportation while satisfying all operational constraints

Helicobacter pylori evasion strategies of the host innate and adaptive immune responses to survive and develop gastrointestinal diseases

Helicobacter pylori (H. pylori) is a bacterial pathogen that resides in more than half of the human population and has co-evolved with humans for more than 58,000 years. This bacterium is orally transmitted during childhood and is a key cause of chronic gastritis, peptic ulcers and two malignant cancers including MALT (mucosa-associated lymphoid tissue) lymphoma and adenocarcinoma. Despite the strong innate and adaptive immune responses, H. pylori has a long-term survival in the gastric mucosa. In addition to the virulence factors, survival of H. pylori is strongly influenced by the ability of bacteria to escape, disrupt and manipulate the host immune system. This bacterium can escape from recognition by innate immune receptors via altering its surface molecules. Moreover, H. pylori subverts adaptive immune response by modulation of effector T cell. In this review, we discuss the immune-pathogenicity of H. pylori by focusing on its ability to manipulate the innate and acquired immune responses to increase its survival in the gastric mucosa, leading up to gastrointestinal disorders. We also highlight the mechanisms that resulted to the persistence of H. pylori in gastric mucos

On the role of corticosterone in behavioral disorders, microbiota composition alteration and neuroimmune response in adult male mice subjected to maternal separation stress

Experiencing psychosocial adversities in early life such as maternal separation (MS) increases the risk of psychiatric disorders. Immune-inflammatory responses have imperative roles in the pathophysiology of psychiatric disorders. MS relatively changes the composition of intestinal microbiota leading to an overactivation of the hypothalamic-pituitary-adrenal (HPA) axis, and subsequently increases the corticosterone level. In this study, we aimed to evaluate the role of corticosterone in behavioral changes and microbiota modifications in a mouse model of MS afflicted neuroinflammatory response in the hippocampus. For this purpose, 180 min of MS stress was applied to mice at postnatal day (PND) 2-14 followed by behavioral tests including forced swimming test (FST), splash test, open field test (OFT) and elevated plus maze (EPM) at PND 50-52. For evaluating the role of corticosterone, mice were subjected to adrenalectomy. Using real-time RT-PCR, the expression of inflammatory genes was determined in the hippocampus and colon tissues. We found that MS provoked depressive- and anxiety-like behaviors in adult male mice. In addition, MS was able to active a neuroimmune response in the hippocampus, motivate inflammation and histopathologic changes in the colon tissue and modify the composition of gut microbiota as well. Interestingly, our findings showed that adrenalectomy (decline in the corticosterone level), could modulate the above-mentioned negative effects of MS. In conclusion, our results demonstrated that overactivation of HPA axis and the subsequent increased level of corticosterone could act, possibly, as the deleterious effects of MS on behavior, microbiota composition changes and activation of neuroimmune respons

Integrated low-carbon distribution system for the demand side of a product distribution supply chain: a DoE-guided MOPSO optimiser-based solution approach

This article contributes to distribution system literature on three inter-linked aspects viz. formulation of a novel integrated low-carbon/green distribution system for the demand side of a Supply Chain (SC) with a single product and multiple consumers, i.e. drop-off points, a novel and robust solution approach through a Design of Experiment (DoE)-guided Multiple-Objective Particle Swarm Optimisation (MOPSO) optimiser and exhaustive analysis of the solutions (i.e. prioritisation, ranking and scenario analysis). The total costs, CO2 emission and the traversed distances of the vehicles during transportation are optimised. The optimisation model for the strategic decision-making is formulated by effectively integrating the 0–1 mixed-integer programming with a green constraint based on Analytic Hierarchy Process. Due to the computationally NP-hard characteristic of the model, a systematic and technically robust DoE-guided solution approach is designed using a commercial solver – modeFRONTIER®. DoE guides the solution through the MOPSO optimiser in order to eliminate the un-realistic set of feasible and optimal solution sets. A popular multi-attribute decision-making approach, TOPSIS, evaluates the solutions found from the Pareto optimal solution space of the solver. Finally, decision-makers’ preferences are analysed for monitoring the changes in the controlling parameters with respect to the changes in the decisions. A scenario analysis of the events by considering alternative possible outcomes is also conducted. It is found that the implemented methodology successfully routes the vehicles with optimal costs and low-carbon emission thus contributing to greening the environment on the demand side of a SC network