Modelling and optimisation of biomass supply chains: a review

A growing number of researchers are attracted by the domain of bioenergies, due to the problems induced by greenhouse gas emissions and increasing energy demand. One possible way of producing biofuels in a renewable way is to use biomass, however the economic viability of a biorefinery system depends critically on the cost of its supplies. As biomass is not very expensive, logistics is responsible of an important fraction of this cost. Models and methods to optimise biomass supply chains are surveyed in this article, after introducing some technical terms and describing the main activities in these chains. This review aims to give a comprehensive overview of the research in this field with a focus on optimisation modelling issues and solution approaches. Recent advances in the current research and possible new directions are sketched.     

An integrated decision support system for strategic supply chain optimisation in process industries: the case of a zinc company

We introduce a menu-driven user-friendly decision support system (DSS) for supply chain planning based on optimisation. The DSS is based on a multi-source (supplier), multi-destination (warehouse) network having multiple manufacturing facilities, with multiple materials and multiple storage areas. This integrated supply chain model performs multiple period planning. The use of this DSS requires little knowledge of management sciences tools. We discuss the need for an integrated approach towards supply chain modelling for the process industry. We present the integrated model in the form of a database structure. We validate the model with the real data of a zinc company and demonstrate the impact of optimisation in terms of percentage improvement. The result shows that it is possible to improve unit contribution to profit from 1.89 to 4.66%.     

Dynamic optimisation for highly agile supply chains in e-procurement context

In the conditions of an increased worldwide competition, supply chains are struggling to respond to an increasingly volatile and complex environment. With technological advances, current practices to build efficient supply chains have changed. Companies are seeking to use internet in order to cope with the flexible and dynamic nature of logistics networks. The purpose of this article is to address the flexible dynamic e-procurement context under asynchronous and repetitive variations over time. The supply chain considered is composed of two levels (buyer–suppliers) operating in highly agile environment. The questions facing the buyer is how many units of product should be purchased and from which supplier in response to variation in term of price and capacity. Because of this highly changing environment characterised by frequent changes in a short time, most of the classical optimisation approaches seem inadequate to address these problems. Recently, dynamic optimisation has been proposed to deal with such problems. However, we have no knowledge of its application in a supply chain context. We suggest a dynamic genetic approach which is applied to an e-procurement context in aim to optimise the procurement process during time.     

Solving closed-loop supply chain problems using game theoretic particle swarm optimisation

In this paper, we propose a closed-loop supply chain network configuration model and a solution methodology that aim to address several research gaps in the literature. The proposed solution methodology employs a novel metaheuristic algorithm, along with the popular gradient descent search method, to aid location-allocation and pricing-inventory decisions in a two-stage process. In the first stage, we use an improved version of the particle swarm optimisation (PSO) algorithm, which we call improved PSO (IPSO), to solve the location-allocation problem (LAP). The IPSO algorithm is developed by introducing mutation to avoid premature convergence and embedding an evolutionary game-based procedure known as replicator dynamics to increase the rate of convergence. The results obtained through the application of IPSO are used as input in the second stage to solve the inventory-pricing problem. In this stage, we use the gradient descent search method to determine the selling price of new products and the buy-back price of returned products, as well as inventory cycle times for both product types. Numerical evaluations undertaken using problem instances of different scales confirm that the proposed IPSO algorithm performs better than the comparable traditional PSO, simulated annealing (SA) and genetic algorithm (GA) methods.     

Reverse supply chain optimisation with disassembly line balancing

Due to responding environmental issues, conforming governmental legislations and providing economic benefits, there has been a growing interest in recycling activities through the supply chains. Reverse supply chain (RSC) optimisation problem has a great potential as an efficient tactic to achieve this goal. While disassembly, one of the main activities in RSC, enables reuse and recycling of products and prevents the overuse, disassembly line balancing problem involves determination of a line design in which used products are partially/completely disassembled to obtain available components. The aim of this study is to optimise a RSC, involving customers, collection/disassembly centres and plants, that minimises the transportation costs while balancing the disassembly lines, which minimises the total fixed costs of opened workstations, simultaneously. A non-linear mixed-integer programming model, which simultaneously determines: (i) optimal distribution between the facilities with minimum cost, (ii) the number of disassembly workstations that will be opened with minimum cost, (iii) the cycle time in each disassembly centre and (iv) optimal assignment of tasks to workstations, is developed. A numerical example is given to illustrate the applicability of the proposed model. Different scenarios have been conducted to show the effects of sensitivity analyses on the performance measures of the problem.     

Optimal Scheduling of Track Maintenance on a Railway Network

To ensure the safety and continued operation of the railway network system, many maintenance and renewal activities are performed on the track every month. Unplanned maintenance activities are expensive and would cause low service quality. Therefore, the track condition should be monitored, and when it has degraded beyond some acceptable limit, it should be scheduled for maintenance before failure. An optimal timetable of the maintenance activities is needed to be scheduled, planning the monthly workload, to reduce the effect on the transportation service and to reduce the potential costs. Considering the uncertainties of the deterioration process, the safety of transportation service, the lifetime loss of the replaced track, the maintenance cost and the travel cost, this article advances an optimisation model for the maintenance scheduling of a regional railway network. An enhanced genetic algorithm approach is proposed to search for a solution producing maintenance schedule such that the overall cost is minimised in a finite planning horizon. A case study is given to demonstrate the application of the method. The case study results were derived by using an enhanced genetic algorithm method, which is specifically developed to deal with the characteristics of the railway maintenance problem. Copyright © 2012 John Wiley & Sons, Ltd.     

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.     

Ant colony optimisation algorithm for distribution-allocation problem in a two-stage supply chain with a fixed transportation charge

In a fixed charge transportation problem, each route is associated with a fixed charge (or a fixed cost) and a transportation cost per unit transported. The presence of the fixed cost makes the problem difficult to solve, thereby requiring the use of heuristic methods. In this paper, an algorithm based on ant colony optimisation is proposed to solve the distribution-allocation problem in a two-stage supply chain with a fixed transportation cost for a route. A numerical study on benchmark problem instances has been carried out. The results obtained for the proposed algorithm have been compared with that for the genetic algorithm-based heuristic currently available in the literature. It is statistically confirmed that the proposed algorithm provides significantly better solutions.     

Energy-oriented bi-objective optimisation for a multi-module reconfigurable manufacturing system

This paper investigates a multi-module reconfigurable manufacturing system for multi-product manufacturing. The system consists of a rotary table and multiple machining modules (turrets and spindles). The production plan of the system is divided into the system design phase and the manufacturing phase, where the installation cost and the energy consumption cost correspond to the two phases, respectively. A mixed-integer programming model for a more general problem is presented. The objectives are to minimise the total cost and minimise the cycle time simultaneously. To solve the optimisation problem, the ε-constraint method is adopted to obtain the Pareto front for small size problems. Since the ε-constraint method is time consuming when problem size increases, we develop a multi-objective simulated annealing algorithm for practical size problems. To demonstrate the efficiency of the proposed algorithm, we compare it with a classic non-dominated sorting genetic algorithm. Experimental results demonstrate the efficiency of the multi-objective simulated annealing algorithm in terms of solution quality and computation time.     

Stochastic optimisation model for integrated decisions on relief supply chains: preparedness for disaster response

This paper proposes a stochastic linear mixed-integer programming model for integrated decisions in the preparedness and response stages in pre- and post-disaster operations, respectively. We develop a model for integrated decisions that considers three key areas of emergency logistics: facility and stock prepositioning, evacuation planning and relief vehicle planning. To develop a framework for effective relief operations, we consider not only a cost-based but also an equity-based solution approach in our multiple objectives model. Then a normalised weighted sum method is used to parameterise our multiple objective programming model. This paper suggests a compromise between the cost, and the equity of relief victims. The experiments also demonstrate how time restrictions and the availability of relief vehicles impact the two objective functions.     

Fundamental performance of novel power supply for HTS magnet using solar energy system

A technical fusion is an important option to establish renewal development in the mutual fields. We have proposed a novel superconducting power supply that is combined with superconducting power supply and solar energy system. An eternal electric energy can be converted by solar energy system, which contains solar panel, photovoltaic (PV) controller and energy-storing battery, can be utilized in the utility power of superconducting power applications. The novel power supply could operate without external utility power to charge the HTS load magnet due to the solar energy. We can improve the operating efficiency and install it in remote locations where utility power is not available. In this paper, as a first step of this work, we showed the possibility of technical fusion between a superconducting power supply and a solar energy system.     

A combined supply chain optimisation model for the installation phase of offshore wind projects

This paper proposes a combined model for port selection and supply chain optimisation for the installation phase of an offshore wind farm. Two strategic models are proposed where the first model, based on Analytical Hierarchy Process (AHP), aims to select the most suitable installation port. The second model is developed using Integer Linear Programming (ILP) in order to determine the optimal transportation schedule of the components from suppliers to the chosen installation port. The proposed models are evaluated for the West Gabbard (UK) offshore wind farm located in southern part of the North Sea. According to the computational results, the AHP model chooses port of Oostende, Belgium as the most suitable installation port for this offshore wind farm whereas the proposed supply chain model shows that the total transportation cost makes up 9% of total supply chain cost.     

采用微机分布式控制的阳极化电源系统的研究

阳极化电源是军工产品在进行硬质极化时频繁使用的关键设备之一，过去硬质阳极化过程中使用单机电源，工作人员需要经常进入非常恶劣的工作环境之中长时间工作，这对他们的身体健康是不小的威胁，而且效率十分低下。本文提出了一种采用微机分布式控制的阳极化电源系统解决了上述问题，详细介绍了该系统的三个主要组成部分的功能及其特点，分别论述了三者的设计方案。实际应用表明，该系统对提高工作效率、降低工作人员的劳动强度和增加系统可靠性具有重要作用。     

Biogeography-based optimisation for flexible manufacturing system scheduling problem

Biogeography-based optimisation (BBO) algorithm is a new evolutionary optimisation algorithm based on geographic distribution of biological organisms. With probabilistic operators, this algorithm is able to share more information from good solutions to poor ones. BBO prevents the good solutions to be demolished during the evolution. This feature leads to find the better solutions in a short time rather than other metaheuristics. This paper provides a mathematical model which integrates machine loading, part routing, sequencing and scheduling decision in flexible manufacturing systems (FMS). Moreover, it tackles the scheduling problem when various constraints are imposed on the system. Since this problem is considered to be NP-hard, BBO algorithm is developed to find the optimum /near optimum solution based on various constraints. In the proposed algorithm, different types of mutation operators are employed to enhance the diversity among the population. The proposed BBO has been applied to the instances with different size and degrees of complexity of problem adopted from the FMS literature. The experimental results demonstrate the effectiveness of the proposed algorithm to find optimum /near optimum solutions within reasonable time. Therefore, BBO algorithm can be used as a useful solution for optimisation in various industrial applications within a reasonable computation time.     

上海港岸基船用供电系统研究与实践

描述的一种移动式岸基船用变频变压供电系统利用集装箱码头前沿为桥吊运行配备10 kV/50 Hz/2 000 kV ·A的高压电源,经过变压和变频为靠港船舶提供450 V/60 Hz电源。该方案灵活性强,节能减排效果明显,且无需对码头进行土木改造,为我国作业繁忙的集装箱码头实现岸基供电提供新方案。     

随钻高效电源的管理系统优化设计

根据随钻仪器的工作特点,提供了一种对随钻电源进行管理的高效且易于实现的解决方案,该方案从工作方式上采用间歇供电,使得仪器在非工作状态时电能的消耗降到最低;在工作原理上,采用Buck型开关电源,使得电能的转化效率达到最大。而根据不同电源的需求指标,数字电源与模拟电源采用不同的芯片。对于数字电源,利用了TPS40200芯片设计简单的特点,并且在高温下可实现较大功率;对于模拟电源,利用了UC3842芯片适用性广泛的特点,在其后添加合适变压器,同时输出正负两路模拟电源。经过实际验证,该方式很大程度上提高了电能的利用效率和电池的工作时间,符合随钻高效电源的设计要求。     

An extended uniform-price auction mechanism of homogeneous divisible goods: supply optimisation and non-strategic bidding

In an extended uniform-price auction mechanism of homogeneous divisible goods, strategic buyers and non-strategic buyers are allowed to participate in the auction simultaneously, and the optimal supply to strategic buyers is determined by a seller after having observed the bids. For both nonlinear bids and linear bids given by symmetric buyers, pure strategy symmetric Nash equilibriums are analysed, and then the dominant relationship is discussed deeply between the two kinds of equilibrium bids. Some conclusions are drawn about this extended uniform-price auction mechanism, i.e. nonlinear equilibrium bids dominate linear ones, and the supply optimisation can effectively reduce the buyers’ market power and thus eliminate many underpricing equilibriums. Moreover, several suggestions are given on how to choose optimal bidding strategies for buyers and how to design an optimal mechanism for a seller.     

Selective maintenance optimisation for series-parallel systems alternating missions and scheduled breaks with stochastic durations

This paper deals with the selective maintenance problem for a multi-component system performing consecutive missions separated by scheduled breaks. To increase the probability of successfully completing its next mission, the system components are maintained during the break. A list of potential imperfect maintenance actions on each component, ranging from minimal repair to replacement is available. The general hybrid hazard rate approach is used to model the reliability improvement of the system components. Durations of the maintenance actions, the mission and the breaks are stochastic with known probability distributions. The resulting optimisation problem is modelled as a non-linear stochastic programme. Its objective is to determine a cost-optimal subset of maintenance actions to be performed on the components given the limited stochastic duration of the break and the minimum system reliability level required to complete the next mission. The fundamental concepts and relevant parameters of this decision-making problem are developed and discussed. Numerical experiments are provided to demonstrate the added value of solving this selective maintenance problem as a stochastic optimisation programme.     

LCC resonant converter with power factor correction for power supply units

This article presents a power supply using an LCC resonant converter having power factor correction with burst mode operation. In order to improve the performance from no load to full load, a microcontroller with an active control has been introduced. The light load efficiency is increased using burst mode operation. The proposed controller provides zero voltage switching. Mathematical analysis is done, and steady state characteristics are drawn. A simple design example is given based on the equations. The proposed converter has good efficiency with good power factor at all loading conditions. This is shown by the simulation and experimental results obtained through testing the prototype.     

An improved ant colony optimisation algorithm for three-tier supply chain scheduling based on networked manufacturing

This paper presents a study on supply chain scheduling from the perspective of networked manufacturing (NM). According to feature analysis of supply chain scheduling based on NM, we comprehensively consider the combined benefits of cost, time, and satisfaction level for customised services. In order to derive a scheduling strategy among supply chain members based on NM, we formulate a three-tier supply chain scheduling model composed of manufacturer, collaborative design enterprise and customer. Three objective functions – time function, cost function and delay punishment function – are employed for model development. We also take into account multi-objective optimisation under the constraint of product capacity. By using an improved ant colony optimisation algorithm, we add different pheromone concentrations to selected nodes that are obtained from feasible solutions and we confine pheromone concentrations τ within the minimum value τ _min and the maximum value τ _max, thus obtaining optimal results. The results obtained by applying the proposed algorithm to a real-life example show that the presented scheduling optimisation algorithm has better convergence, efficiency, and stability than conventional ant colony optimisation. In addition, by comparing with other methods, the output results indicate that the proposed algorithm also has better solutions.