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.

Abstract:The main aim of this research is to demonstrate strategic supplier performance evaluation of a UK-based manufacturing organisation using an integrated analytical framework. Developing long term relationship with strategic suppliers is common in today's industry. However, monitoring suppliers' performance all through the contractual period is important in order to ensure overall supply chain performance. Therefore, client organisations need to measure suppliers' performance dynamically and inform them on improvement measures. Although there are many studies introducing innovative supplier performance evaluation frameworks and empirical researches on identifying criteria for supplier evaluation, little has been reported on detailed application of strategic supplier performance evaluation and its implication on overall performance of organisation. Additionally, majority of the prior studies emphasise on lagging factors (quality, delivery schedule and value / cost) for supplier selection and evaluation. This research proposes both leading (organisational practices, risk management, environmental and social practices) and lagging factors for supplier evaluation and demonstrates a systematic method for identifying those factors with the involvement of relevant stakeholders and process mapping. The contribution of this article is a real-life casebased action research utilizing an integrated analytical model that combines Quality Function Deployment and the Analytic Hierarchy Process method for suppliers' performance evaluation. The effectiveness of the method has been demonstrated through number of validations (e.g. focus group, business results, and statistical analysis). Additionally, the study reveals that enhanced supplier performance results positive impact on operational and business performance of client organisation.

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This article contributes to location-routing literature on three inter-linked aspects viz., formulation of a novel integrated low-carbon/green location-routing model for the demand side of a Supply Chain (SC) with a single product and multiple consumers, i.e., Drop-off Points (DoPs), 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 location-routing solutions (i.e., prioritisation, ranking and scenario analysis). The total costs, CO 2 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 (AHP). 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 location-routing 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.