A multi-objective mathematical programming for sustainable reverse logistics network design. Part II: Model application and analysis

Source at https://www.witpress.com/books/978-1-78466-169-4. Reverse logistics has received more and more attention during the past decade due to the increasing public awareness of sustainable development. Because of the fluctuation in both quantity and quality of the reverse material flow, design and planning of reverse logistics network is much more complicated compared with the forward ones. Therefore, it is important to develop decision support tools for designing reverse logistics network in an economically efficient and environmental-friendly manner. This research proposes a novel multi-objective mixed integer programming model in order to justify the relationship between the cost and sustainability of reverse logistics system, and the weighted sum utility method is employed for combining the two objective functions. This research is presented in a series of two papers. Part I formulates the conceptual framework of reverse logistics network and the mathematical programming for the minimization of the overall system cost and environmental influence. Part II introduces the weighted sum utility method for combining the two objective functions, and the application and analysis are also given in this part

An improved multi-objective programming with augmented ε-constraint method for hazardous waste location-routing problem

Source at https://doi.org/10.3390/ijerph13060548. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Hazardous waste location-routing problems are of importance due to the potential risk for nearby residents and the environment. In this paper, an improved mathematical formulation is developed based upon a multi-objective mixed integer programming approach. The model aims at assisting decision makers in selecting locations for different facilities including treatment plants, recycling plants and disposal sites, providing appropriate technologies for hazardous waste treatment, and routing transportation. In the model, two critical factors are taken into account: system operating costs and risk imposed on local residents, and a compensation factor is introduced to the risk objective function in order to account for the fact that the risk level imposed by one type of hazardous waste or treatment technology may significantly vary from that of other types. Besides, the policy instruments for promoting waste recycling are considered, and their influence on the costs and risk of hazardous waste management is also discussed. The model is coded and calculated in Lingo optimization solver, and the augmented ε-constraint method is employed to generate the Pareto optimal curve of the multi-objective optimization problem. The trade-off between different objectives is illustrated in the numerical experiment

Enhancing the competitiveness of manufacturers through Small-scale Intelligent Manufacturing System (SIMS): A supply chain perspective

An electronic version of the final, peer-reviewed manuscript upon acceptance for publication made publicly after an IEEE embargo period of 24 months from the date of publication. Link to publishers version:http://doi.org/10.1109/ICITM.2017.7917904In order to survive in this competitive and ever changing market, manufacturers have to improve and enhance the competitiveness, flexibility, responsiveness and sustainability with the application of the cutting-edge technologies and innovative management methods. New concepts, i.e., Intelligent manufacturing, flexible manufacturing, agile manufacturing, network manufacturing, green manufacturing and Industry 4.0, etc., have been proposed and developed in recent years based upon the newest and most advanced manufacturing technologies and Information and Communication technologies (ICT). This paper presents a new concept: Small-scale Intelligent Manufacturing System (SIMS), and the comparison with previous concepts and the benefits of SIMS are discussed in this paper. Different from the previous research works which mainly emphasize the technological integration for improving the flexibility and intelligence of an individual manufacturing system, this paper, however, focuses on and discusses the supply chain problems arisen from a holistic perspective. The features of the supply chain for realizing small-scale intelligent production and responsive distribution are discussed, and the limitation and future works are also discussed and suggested latter in this paper

A New Two-Stage Stochastic Model for Reverse Logistics Network Design under Government Subsidy and Low-Carbon Emission Requirement

Embargoed Access, etter IEEEs generelle retningslinjer (manuscript version for OA after 24 mnths embargo from publication date) Link to publisher's version: http://doi.org/10.1109/IEEM.2017.8289857Nowadays, increasing number of companies incorporates the reverse logistics decisions into their supply chain design in order to cope with the enforced international and national legislation and improve the resource efficiency and public image. This paper investigates a new stochastic optimization model for designing a single-period multiproduct multi-level reverse logistics system under government subsidy and low-carbon emission requirement. In order to resolve the stochastic optimization problem, a modified multi-criteria scenario-based approach is proposed to maximize the profit generation while simultaneously improve the stability of the decision-making under uncertainty. The model and solution method are tested with several numerical experiments, and managerial insights are obtained with respect to the carbon emission requirement, governmental subsidy, economy of scale, and system flexibility

A green supply chain network design model for enhancing competitiveness and sustainability of companies in high north arctic regions

To survive in today's competitive and ever-changing marketplace, companies need not only to engage in their products and/or services, but also to focus on the management of the whole supply chain. Effectively managing and balancing the profitability and interconnection of each player in the supply chain will improve the overall supply chain surplus as well as individual profit. However, it is extremely difficult to simultaneously optimize several objectives in design and planning of a supply chain, i.e., cost-minimization, risk-minimization, responsiveness-maximization, etc., which are somehow conflict with one another. Furthermore, the natural and infrastructural challenges in high north arctic regions make it become much more difficult and complicated to design and develop cost-efficient, highly responsive, environmentally friendly, and sustainable supply chain network. In order to provide companies in high north arctic regions with decision support tool for the design and planning of theirs supply chain networks, a green supply chain network design (GrSCND) model is formulated in this study based on multi-objective mixed integer programming (MIP). The optimal trade-off among several conflicting objectives is the focus of this GrSCND model aiming to enhance both competitive competence and sustainability of companies and supply chains operated in high north regions. In addition, a numerical experiment is also given to present a deep insight of the GrSCND model. Copyright © 2014 International Energy and Environment Foundation -All rights reserved

A Value Chain Analysis for Bioenergy Production from Biomass and Biodegradable Waste: A Case Study in Northern Norway

During the past decades, the concerns of the depletion of fossil fuels and global warming caused by excess GHG emissions have become the most important driving force for the development and utilization of renewable energy resources. The successful experiences from the EU-28 have proved that bioenergy production from biomass and biodegradable waste is the most reliable and promising solution in today’s renewable energy market. This chapter presents a general model for value chain analysis of bioenergy production from biomass and biodegradable waste. In addition, a feasibility study for establishing a bioenergy plant in the northern part of Norway is given to discuss the opportunities and challenges of bioenergy production. The feedstock of the planned bioenergy plant is from local agriculture, waste management sector, fishery and livestock industry. Value chain analysis is used to balance the economic and environmental influences of the bioenergy production in the area. Furthermore, suggestions for resolving the challenges and minimizing the potential risks of bioenergy production are also discussed in this chapter

An introduction of small-scale intelligent manufacturing system

Embargoed OA, manuscript version after 24 months from publishing date. Link to publishers version: http://doi.org/10.1109/SIMS.2016.7802896Manufacturing companies in Northern Peripheral and Arctic region are predominately small and medium-sized and face considerable challenges like geographical isolation and a lack of benefits offered by industrial clusters. For the ultimate goal of enhancing their competitiveness in a global market, it is imperative for companies to innovate or adopt innovations in order to quickly response to changes in market, meet customer demands, reduce time-to-market and lower cost. A novel concept for small-scale intelligent manufacturing systems (SIMS) is introduced, in which diverse methods and innovative technologies can be applied and integrated. This paper gives an introduction of SIMS, defines its design objectives, and summarizes major relevant tools, techniques and paradigms for the development of SIMS, to generate a facilitative environment for small and medium-scale manufacturing enterprises to embrace new and innovative technologies

The Application of Industry 4.0 Technologies in Sustainable Logistics: A Systematic Literature Review (2012-2020) to Explore Future Research Opportunities

Nowadays, the market competition becomes increasingly fierce due to diversified customer needs, stringent environmental requirements, and global competitors. One of the most important factors for companies to not only survive but also thrive in today’s competitive market is their logistics performance. This paper aims, through a systematic literature analysis of 115 papers from 2012 to 2020, at presenting quantitative insights and comprehensive overviews of the current and future research landscapes of sustainable logistics in the Industry 4.0 era. The results show that Industry 4.0 technologies provide opportunities for improving the economic efficiency, environmental performance, and social impact of logistics sectors. However, several challenges arise with this technological transformation, i.e., trade-offs among different sustainability indicators, unclear benefits, lifecycle environmental impact, inequity issues, and technology maturity. Thus, to better tackle the current research gaps, future suggestions are given to focus on the balance among different sustainability indicators through the entire lifecycle, human-centric technological transformation, system integration and digital twin, semi-autonomous transportation solutions, smart reverse logistics, and so forth

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

A multi-objective mathematical programming for sustainable reverse logistics network design. Part I: Model formulation

Reverse logistics has received more and more attention during the past decade due to the increasing public awareness of sustainable development. Because of the fluctuation in both quantity and quality of the reverse material flow, design and planning of reverse logistics network is much more complicated comparing with the forward ones. Therefore, it is important to develop decision support tools for designing reverse logistics network in an economically efficient and environment-friendly manner. This research proposes a novel multi-objective mixed integer programming model in order to justify the relationship between the cost and sustainability of reverse logistics system, and the weighted sum utility method is employed for combining the two objective functions. This research is presented in a series of two papers. Part I formulates the conceptual framework of reverse logistics network and the mathematical programming for the minimization of the overall system cost and environmental influence. Part II introduces the weighted sum utility method for combining the two objective functions, and the application and analysis are also given in this part