Robust optimisation of sustainable food grain transportation with uncertain supply and intentional disruptions

The proliferating need for sustainability intervention in food grain transportation planning is anchoring the attention of researchers in the interests of stakeholders and environment at large. Uncertainty associated with food grain supply further intensifies the problem steering the need for designing robust, cost-efficient and sustainable models. In line with this, this paper aims to develop a robust and sustainable intermodal transportation model to facilitate single type of food grain commodity shipments while considering procurement uncertainty, greenhouse gas emissions, and intentional hub disruption. The problem is designed as a mixed integer non-linear robust optimisation model on a hub and spoke network for evaluating near optimal shipment quantity, route selection and hub location decisions. The robust optimisation approach considers minimisation of total relative regret associated with total cost subject to several real-time constraints. A version of Particle Swarm Optimisation with Differential Evolution is proposed to tackle the resulting NP-hard problem. The model is tested with two other state-of the art meta-heuristics for small, medium, and large datasets subject to different procurement scenarios inspired from real time food grain operations in Indian context. Finally, the solution is evaluated with respect to total cost, model and solution robustness for all instances.     

Optimising clarification of carrot juice by bacterial crude pectinase

This study was undertaken to search for potential use of crude bacterial pectinase enzyme produced from Bacillus subtilis grown on hazelnut shell hydrolysate in clarification of carrot juice and to optimize the enzyme load, pH and time using the Box–Behnken response surface methodology (RSM). The carrot juice was treated with the crude pectinase enzyme (5.60 U mL^?1) at different concentrations (0.1–0.5%), pH (4–7), and time (2–6 h). The obtained enzyme was also compared with commercial fungal pectinase at identical conditions. RSM provided optimal clarification conditions of 0.5% (w/v) enzyme load, 7.0 pH, and 6 h of time estimating 100% clarity, whose experimental counterpart was 94.47 ± 0.01%. High values of coefficient of determination (R² = 0.9631), predicted R² (0.8989) and insignificant lack‐of‐fit (0.12) also showed that the model was successful in predicting % clarity for various combinations. This study also indicated that crude bacterial pectinase providing about 95% clarity is superior to commercial fungal pectinase, which gave 78% clarity under tested conditions, in terms of clarification ability for carrot juice.     

Relining efficiency and liner design for improved plant performance

Liners in grinding mills not only protect the mill shell from the aggressive environment inside the mill, but they also play a significant role in the efficiency of grinding. The design of mill liners dictates the charge trajectory and hence the grinding efficiency. The common approach in designing liners is designing for a longer life. However, this approach does not necessarily consider optimum performance over the liner life. It has been observed in many operations that mills under-perform over a significant portion of the liner life (10–25% of the liner life at the beginning and often 5–10% at the end). This paper extends the method proposed by Toor (2013) and Toor et al. (2013) to design liners for performance through investigating the effect of relining efficiency using such an approach in an industrial case.As indicated in the Toor et al. (2013) study, relining efficiency affects the benefits that can be realised by designing liners for efficiency. Russell Mineral Equipment’s Mill Reline Director (MRD) analyses the relining process and provides an accurate estimation of relining time for a given scenario. In this study, five different relining scenarios were simulated and compared against the reference reline (i.e. current liner design) to accurately estimate the time required for relining. This is the first study to demonstrate that incorporating relining constraints in the liner design can be used to inform liner design characteristics of a proper design that meets the requirements of an efficient relining practice.JKSimMet simulation for the industrial case predicts a liner which has same lifter face angle as the current liner design with reduced lifter height from 300 mm to 210 mm, could increase the plant throughput by 8% on average while producing a product with same P80 as the current liner. Considering relining time predictions by MRD for the proposed liner, this study predicts a 3.7% increase on average in throughput per annum. Although the proposed strategy will increase the cost of liners plus relining by 31.5% (i.e. A$ 548,000), the increase in plant throughput is estimated to yield A$ 20.1 M of additional revenue based on data for 12 months to 30 June 2014 from the plant quarterly report.     

Genetic algorithm to simultaneously optimise stope sequencing and equipment dispatching in underground short-term mine planning under time uncertainty

ABSTRACT

The main issue in short-term planning optimisation for underground mining is organising the mining process with limited resources in the form of equipment and materials to satisfy production targets and stable feed grade requirements. In this paper, an integrated optimisation model is proposed based on an individual generation algorithm and an improved Genetic Algorithm to simultaneously optimise stope extraction sequencing and timing, extracted ore grade and equipment dispatching. The model objectives are to shorten the time gap between the stope mining processes and the overall working time. When the uncertainty of equipment working time is taken into account in a short-term scheduling model, the Monte Carlo simulation is applied to evaluate the risk of not meeting the production target. A modification strategy is defined to evaluate equipment failure. Consequently, any available equipment is automatically reassigned to the mining site to replace the broken-down equipment. A case study is used to validate the model in the Sanshandao gold mine of China to formulate an optimal monthly schedule. Compared with the conventional approach, the new model could reduce the variance of ore tonnage and feed grade and improve the equipment allocation efficiency. Discussions are presented to address the uncertainty.     

Reliability of complex chemical engineering processes

This paper presents a stochastic performance modelling approach that can be used to optimise design and operational reliability of complex chemical engineering processes. The framework can be applied to processes comprising multiple units, including the cases where closed form process performance functions are unavailable or difficult to derive from first principles, which is often the case in practice. An interface that facilitates automated two-way communication between Matlab^® and process simulation environment is used to generate large process responses. The resulting constrained optimisation problem is solved using both Monte Carlo Simulation (MCS) and First Order Reliability Method (FORM); providing a wide range of stochastic process performance measures. Adding such capabilities to traditional deterministic process simulators provides a more informed basis for selecting optimum design factors; giving a simple way of enhancing overall process reliability and cost-efficiency. Two case study systems are considered to highlight the applicability and benefits of the approach.     

An energy-efficient two-stage hybrid flow shop scheduling problem in a glass production

Energy-efficient scheduling is highly necessary for energy-intensive industries, such as glass, mould or chemical production. Inspired by a real-world glass-ceramics production process, this paper investigates a bi-criteria energy-efficient two-stage hybrid flow shop scheduling problem, in which parallel machines with eligibility are at stage 1 and a batch machine is at stage 2. The performance measures considered are makespan and total energy consumption. Time-of-use (TOU) electricity prices and different states of machines (working, idle and turnoff) are integrated. To tackle this problem, a mixed integer programming (MIP) is formulated, based on which an augmented ε-constraint (AUGMECON) method is adopted to obtain the exact Pareto front. A problem-tailored constructive heuristic method with local search strategy, a bi-objective tabu search algorithm and a bi-objective ant colony optimisation algorithm are developed to deal with medium- and large-scale problems. Extensive computational experiments are conducted, and a real-world case is solved. The results show effectiveness of the proposed methods, in particular the bi-objective tabu search.     

A fast branch,bound and remember algorithm for disassembly line balancing problem

In recent years, the interests of disassembly line have increased owing to economic reasons and the increase of environmental awareness. Effective line can provide many advantages in terms of economic aspect and it facilitates competition the companies with others. This study contributes to the relevant literature by a branch, bound and remember algorithm for disassembly line balancing problem with AND/OR precedence. The proposed exact solution method employs the memory-based dominance rule to eliminate the reduplicated sub-problems by storing all the searched sub-problems and to utilise cyclic best-first search strategy to obtain high-quality complete solutions fast. In this paper, minimising the number of stations is taken as the performance measure. The proposed methodology is tested on a set of 260 instances and compared with the mathematical model using CPLEX solver and five well-known metaheuristics. Computational results show that the proposed method is capable of obtaining the optimal solutions for all the tested instances with less than 0.1?seconds on average. Additionally, comparative study demonstrates that the proposed method is the state-of-the-art algorithm and outperforms the CPLEX solver and metaheuristics in terms of both solution quality and search speed aspects.     

Optimal cost design of flow lines with reconfigurable machines for batch production

Modular reconfigurable machines offer the possibility to efficiently produce a family of different parts. This paper formalises a cost optimisation problem for flow lines equipped with reconfigurable machines which carry turrets, machining modules and single spindles. The proposed models take into account constraints related to: (i) design of machining modules, turrets, and machines, (ii) part locations, and (iii) precedence relations among operations. The goal is to minimise equipment cost while reaching a given output and satisfying all the constraints. A mixed integer programming model is developed for the considered optimisation problem. The approach is validated through an industrial case study and extensive numerical experiments.     

Development and optimisation of a defined high cell density yeast medium

Saccharomyces cerevisiae cells grown in a small volume of chemically defined media neither reach the desired cell density nor grow at a fast enough rate to scale down the volume and increase the sample number of classical biochemical assays, as the detection limit of the readout often requires a high number of cells as an input. To ameliorate this problem, we developed and optimised a new high cell density (HCD) medium for S. cerevisiae. Starting from a widely used synthetic medium composition, we systematically varied the concentrations of all components without the addition of other compounds. We used response surface methodology to develop and optimise the five components of the medium: glucose, yeast nitrogen base, amino acids, monosodium glutamate, and inositol. We monitored growth, cell number, and cell size to ensure that the optimisation was towards a greater density of cells rather than just towards an increase in biomass (i.e., larger cells). Cells grown in the final medium, HCD, exhibit growth more similar to the complex medium yeast extract peptone dextrose (YPD) than to the synthetic defined (SD) medium. Whereas the final cell density of HCD prior to the diauxic shift is increased compared with YPD and SD about threefold and tenfold, respectively. We found normal cell-cycle behaviour throughout the growth phases by monitoring DNA content and protein expression using fluorescent reporters. We also ensured that HCD media could be used with a variety of strains and that they allow selection for all common yeast auxotrophic markers.     

Enzymatic extraction of fucoxanthin from brown seaweeds

Brown seaweeds contain a number of bioactive compounds. The xanthophyll, fucoxanthin, has in vivo efficacy against disorders such as type 2 diabetes, obesity and cancer. Organic solvents are traditionally employed to extract fucoxanthin, but carry a toxic chemical and environmental burden. The aim of this study was to optimise a fucoxanthin extraction method using enzymes, water, low‐temperature dehydration and mechanical blending, to produce yields comparable to those achieved with an organic solvent (acetone). Response surface methodology was applied, using Fucus vesiculosus as a model species. A fucoxanthin yield of 0.657 mg g^?1 (dry mass) was obtained from F. vesiculosus blade using the enzymatic method, equivalent to 94% of the acetone‐extracted yield. Optimum extraction parameters were determined to be enzyme‐to‐water ratio 0.52%, seaweed‐to‐water ratio 5.37% and enzyme incubation time 3.05 h. These findings may be applied to the development of value‐added nutraceutical products from seaweed.