Multi-objective approach for sustainable ship routing and scheduling with draft restrictions

We propose a multi-objective optimization model, which integrates different shipping operations to address the environmental sustainability and safety challenges associated with complex, practical and real-time maritime transportation problems. We formulate a mixed integer non-linear programming (MINLP) model, considering routing and scheduling of ships, time window concept regarding ports’ high tidal conditions, discrete planning horizon, loading/unloading operations, carbon emissions, and draft restrictions to maintain vessel’s safety in ports. The novelty of our research lies in (1) incorporating environmental sustainability in the optimization model by defining the relationship between fuel consumption and vessel speed to estimate the fuel consumption and carbon emissions from each vessel; (2) considering the time window to improve port’s service level by imposing penalty charges for early arrivals of vessels and for time window violation; (3) depicting the relationship between the number of containers carried by a ship with its maximum allowable draft restriction and tonnage of containerized cargo on a ship per centimetre of the draft; (4) applying two algorithms - Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) - to solve the mathematical model. Computational experiments are performed based on the practical problems of an international shipping company. Sensitivity analysis is carried out by varying the tonnage of containerized cargo loaded on the vessel per centimetre of the draft for different ports. Results associated with ship route, vessel speed, fuel consumption (tons per day), and carbon emissions rate are presented to provide an idea about the output of the mathematical model

An integrated fuzzy intuitionistic sustainability assessment framework for manufacturing supply chain: a study of UK based firms

The increasing importance of sustainability has put pressure on organisations to assess their supply chain sustainability performance, which requires a holistic set of key performance indicators (KPIs) related to strategic, tactical and operational decision making of firms. This paper presents a comprehensive set of KPIs for sustainable supply chain management using a mixed method approach including analysing data from the literature survey, content analysis of sustainability reports of manufacturing firms and expert interviews. A 3-level hierarchical model is developed by classifying the identified KPIs into key sustainability dimensions as well as key supply chain decision-making areas including strategic, tactical and operational.A novel multi-attribute decision-making (MADM) based sustainability assessment framework is proposed. The proposed framework integrates value focussed thinking (VFT), intuitionistic fuzzy (IF) Analytic Hierarchy Process (AHP) and IF Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods. The novelty of the research lies in (1) using a rigorous mixed method approach for KPIs identification and industrial validation (2) the development of a novel integrated intuitionistic sustainability assessment framework for decision making and (3) the innovative application of the proposed methodologies in the context not explored before.The practical data on the performance ratings of various KPIs were obtained from the experts and a novel intuitionistic fuzzy TOPSIS was applied to benchmark the organisations for their sustainability performance. Furthermore, the case study aims to evaluate and identify the problem areas of the organisations and yield guidance on KPIs by recognising the most significant areas requiring improvement. This research contributes to the practical implication by providing a innovative sustainability assessment framework for supply chain managers to evaluate and manage sustainability performance by making informed decisions related to KPIs. <br

Bunkering policies for a fuel bunker management problem for liner shipping networks

This paper investigates the problem of bunker fuel management for liner shipping networks under different fuel pricing scenarios and taking into consideration different fuel bunkering policies. The fuel consumption of a vessel on a sailing leg may fluctuate as the real vessel speed deviates from the planned vessel speed. Furthermore, fluctuation of fuel prices at various ports increases the complexity of bunkering decisions related to the selection of the bunkering ports and the estimation of bunkered fuel cost. We have developed a mixed integer non-linear programming model to minimise the total expected cost consisting of inventory cost related to container transportation, operating cost associated with ship hiring, as well as bunkering cost and fuel consumption cost at the port. The novelty of our research lies in its consideration of stochastic fuel consumption for different sailing legs, stochastic fuel prices at each port and different fuel bunkering policies to determine optimal bunker fuel management strategies for the selection of bunkering ports and for the estimation of the amount of bunkered fuel required. We have proposed a novel approximate algorithm based on mathematical formulation and the fuel bunkering policies to calculate the total expected cost; the fuel inventory while arriving at and departing from the port; the number of vessels hired for weekly service; the arrival and departure time of the ship; and the amount of fuel bunkered at a port. We have performed extensive computational experiments on the practical routes to demonstrate the applicability, efficacy and robustness of the proposed novel methodology

Highly Efficient One-Pot Multienzyme Cascades for the Stereoselective Synthesis of Natural Naphthalenones

Herein, a biocatalytic cascade containing an ene-reductase (NostocER) and naphthol reductase (tetrahydroxynaphthalene or trihydroxynaphthalene reductase) of Magnaporthe grisea and NADPH is developed. The optimized multienzyme cascade is applied for the one-pot reduction of plumbagin to obtain biologically active cis-(3R,4R)-isoshinanolone, with drcis:trans 98:2 and >99% ee in 96% yield. Furthermore, naturally occurring (+)-isosclerone, (+)-shinanolone, (−)-shinanolone, and (S)-4-hydroxy-3,4-dihydronaphthalen-1­(2H)-one were also synthesized with excellent stereoselectivity and high yields (71–89%) using the enzymatic cascades. The investigation of NostocER–T4HNR-cascade reduction of menadione, plumbagin, and 5-methoxymenadione revealed specificity of tetrahydroxynaphthalene reductase toward these substrates. In addition, the kinetic studies showed a high catalytic efficiency of NostocER and T4HNR toward plumbagin and dihydroplumbagin, respectively, compared to other enzymes