At the planning of combined heat and power (CHP)-based micro-grid, its distributed energy resources (DER) capacity is to be selected and deployed in such a way that it becomes economically self-sufficient to cater all the loads of the system without utility's participation. Economic deployment of DERs is meant to select optimal locations, optimal sizes, and optimal technologies. Optimal locations and sizes, which are independent of CHP-based DERs types, are selected, here, by loss sensitivity index (LSI) and by loss minimization using particle swarm optimization (PSO) method, respectively. In a micro-grid, both fuel costs and NO x emissions are, mainly, dependent on types of DERs used. So the main focus of the present paper is to incorporate originality in ideas to evaluate how different optimal output sets of DER-mix, operating within their respective capacity limits, could share an electrical tracking demand, economically, among micro-turbines and diesel generators of various sizes, satisfying different heat demands, on the basis of multi-objective optimization compromising between fuel cost and emission in a 4-DER 14-bus radial micro-grid. Optimization is done using differential evolution (DE) technique under real power demand equality constraint, heat balance inequality constraint, and DER capacity limits constraint. DE results are compared with PSO.Index Terms-Diesel generator, differential evolution, economic emission load dispatch, loss sensitivity index, micro-turbine, particle swarm optimization. NOMENCLATURE DERDistributed energy resources. CHPCombined heat and power.Mt Micro-turbine. DgDiesel generator. DGDistributed generator/generation. DE Differential evolution.System electric loss (kW).

Abstract:Sustainable supply chain management is a topical area which is continuing to grow and evolve. Within supply chains, downstream distribution from producers to customers plays a significant role in the environmental performance of production supply chains. With consumer consciousness growing in the area of sustainable food supply, food distribution needs to embrace and adapt to improve its environmental performance, while still remaining economically competitive. With a particular focus on the dairy industry, a robust solution approach is presented for the design of a capacitated distribution network for a two-layer supply chain involved in the distribution of milk in Ireland. In particular the green multiobjective optimisation model minimises CO2 emissions from transportation and total costs in the distribution chain. These distribution channels are analysed to ensure the non-dominated solutions are distributed along the Pareto fronts. A multi-attribute decision-making approach, TOPSIS, has been used to rank the realistic feasible transportation routes resulting from the trade-offs between total costs and CO 2 emissions. The refined realistic solution space allows the decision-makers to geographically locate the sustainable transportation routes. In addition to geographical mapping the decision maker is also presented with a number of alternative analysed scenarios which forcibly open closed distribution routes to build resiliency into the solution approach. In terms of model performance, three separate GA based optimisers have been evaluated and reported upon. In the case presented NSGA-II was found to outperform its counterparts of MOGA-II and HYBRID.

Abstract:The purpose of this paper is to delineate a green supply-chain performance measurement framework using an intra-organisational Collaborative Decision-Making (CDM) approach. A fuzzy-Analytic Network Process (ANP) based Green Balanced Scorecard (GrBSc) has been used within the CDM approach. CDM aids in arriving at a consistent, accurate and timely data flow across all cross-functional areas of a business thereby providing real-time information for the evaluation, control and improvement of processes, products and services so as to meet both business objectives and rapidly changing customer needs. A green causal relationship is established and linked to the fuzzy-ANP approach. The causal relationship involves organisational commitment, eco-design, green supply-chain process, social performance and sustainable performance constructs. Subconstructs and sub-sub-constructs are also identified and linked to the causal relationship to form a network. The fuzzy-ANP approach suitably handles the vagueness of the linguistics information of the CDM approach. The CDM approach is implemented in a UK-based carpet manufacturing firm. The performance measurement approach, in addition to the traditional financial performance and accounting measures, aids in making decisions of the firm in regard to the overall organisational goals. The implemented approach assists the firm in identifying further requirements of the collaborative data across the supply-chain and information about customers and markets. Overall, the CDM-based GrBSc approach assists managers in deciding if the suppliers' performances meet the industry and environment standards and the human resource is effective.

In response to hypercompetition, globalisation and increasing consumer expectations, many manufacturing firms have embraced lean manufacturing (LM). The primary goal of LM is to reduce/eliminate waste (muda). There is broad consensus as to what constitutes waste, but not on LM implementation. Implementation is not prescriptive with each firm relying on a different combination of administrative, process and routine change / innovation. Lean manufacturing brings about incremental change relying on administrative, process and routine levers. It best fits mass production where process variability is low and demand is high and stable. Lean manufacturing can significantly reduce waste but not eliminate waste, and the attained benefits have not always lived up to expectations. Additive manufacturing (AM) promises to revolutionise manufacturing beyond recognition by eliminating or drastically removing the waste thereby achieving sustainability. But AM is at its formative stagethe space between the concept and growth-where many promising breakthrough technologies fail. To reach its full potential, it needs to achieve high-scale adoption. In this paper, we examine how AM can significantly reduce/eliminate waste and how it can deliver triple bottom line on an unprecedented scale. We contend that AM, if adopted deeply and widely, will take LM to its final frontier, but there are a number of impediments to this end. We identify legitimation as critical to its wide diffusion and develop a number of propositions expediting AM's legitimation. Legitimation of AM will ensure its deep and broad diffusion and should this happen, waste will be a thing of the past an important stride towards sustainable future.