Bioethanol supply chain network design considering land characteristics

Supply chain network design and operation: Systematic decision-making for centralized, distributed, and mobile biofuel production using mixed integer linear programming (MILP) under uncertainty

This study discusses the integration model of production and distribution planning in the shrimp agroindustry supply chain, consisting of four echelons: shrimp suppliers, shrimp agroindustry, logistics provider companies, and buyers. The shrimp agroindustry supply chain is an essential part of the supply chain of processed product food, which transforms raw shrimp into various processed shrimp frozen products. One form of collaboration between supply chain actors is the integration of production and distribution planning activities. A model is developed to determine the flow of goods from each echelon, the number of processed shrimp products in the agroindustry, and the supplies of processed shrimp products. The bi-objective mixed-integer linear programming is proposed to describe the characteristics of the problem to minimize the total supply chain costs and maximize service level. Non-dominated sorting genetic algorithm II (NSGA-II) is designed to solve the shrimp agroindustry supply chain problem. The sample problem from the shrimp agroindustry in East Java, Indonesia, is applied to exhibit an algorithm’s efficiency. The result shows the best solution for the total supply chain is 1.75 trillion, and the service level is 1,502,264.5.

Environmentally conscious capacity planning and technology selection for bioethanol supply chains

This paper deals with the supply chain network design and planning for a multi-commodity and multi-layer network over a planning horizon with multiple periods in which demands of customer zones are considered to be price dependent. These prices determine the demands using plausible price–demand relationships of customer zones. The net income of the supply chain is maximized, while satisfying budget constraints for investment in network design. In addition, a new approach is considered for capacity planning to make the problem more realistic. In this regard, when production plants are opened and expanded, capacity options are taken into account for manufacturing operations. Furthermore, several aspects relevant to real world applications are captured in the problem. Different interconnected time periods in the planning horizon are considered for strategic and tactical decisions in the problem and then, a mixed-integer linear programming (MILP) model is developed. The performance and applications of the model are investigated by several test problems with reasonable sizes. The numerical results illustrate that obtained solutions after solving the MILP model by using CPLEX solver are acceptable. Moreover, using an alternative pricing approach, a tight upper bound is provided for the problem. In addition, a deep sensitivity analysis is conducted to show the validity and performance of the proposed model.

Buy BookDesk / Exam Copy...MoreThe critical role that supply chain planning contributes to a firm’s financial well-being has never been greater. All too often, however, considerable managerial resources are directed toward planning activities with minimal results. In this book, we present proven, practical management frameworks used by the authors to support supply chain operations management and planning in private industry. These frameworks provide methodologies for managing critical activities such as supply chain strategic planning and project selection, manufacturing and distribution planning, performance measurement, supply chain risk management, and customer logistics and inventory deployment. The book is intended for supply chain professionals, as well as for graduate and advanced undergraduate students. Practitioners will obtain valuable new insights and examples of implementable frameworks and methods for managing their supply chain functions and organizations. Students will develop an understanding of real-world approaches for supply chain planning, decision support, and many other key activities. $(function() {$("#shortDescription").html(function() { if($("#fullDescription").html().length > 1800){ return $("#fullDescription").html().substr(0, 1800) + $("#shortDescription").html(); }else { return $("#fullDescription").html().substr(0, 1800); } });$("#showText").click(function() {$("#fullDescription").css("display", "inline");$("#shortDescription").css("display", "none");$("#showText").css("display", "none"); }); });

Supply chain management survey of Swedish manufacturing firms

A robust possibilistic programming approach for pharmaceutical supply chain network design

The effect of uncertainty on the optimal closed-loop supply chain planning under different partnerships structure

Biofuels are important alternative fuel in recent years. In this work, the bioethanol supply chain planning is performed by BioEnergy Supply Model to optimally find the best pathways using economic and environmental objective functions in Iran. Then, different sustainability criteria focusing on environmental, economic, and social concerns are assessed for possible routes of the bioethanol production from available resources. Although agriculture residues and wastes, decrease the greenhouse gas emissions but municipal solid wastes are more economic and require less water. The agricultural wastes and residues improve the sustainability. Then, criteria focusing on environmental, economic, and social concerns are assessed for available agricultural resources. The most cost-intensive bioethanol is obtained from the rice and barley wastes with 52.6 and 42.4 billion dollars over 25 years, respectively. The lowest total cost of the supply chain belongs to sugarcane and sugar beet wastes with the amounts of 10.7 and 13.56 billion dollars, respectively. The bioethanol form wastes of rice, barely, and wheat cultivation mitigates the global warming more than other resources while they have negative carbon cycles. Similarly, the highest and lowest number of created jobs of bioethanol supply chain belong to the rice wastes and the sugar beet and corn residues.

PurposeIt is worth mentioning that in supply chain management (SCM), managerial decisions are often based on optimization of resources. Till the early 2000s, supply chain optimization problems were being addressed by conventional programming approaches such as Linear Programming, Mixed-Integer Linear Programming and Branch-and-Bound methods. However, the solution convergence in such approaches was slow. But with the advent of Swarm Intelligence (SI)-based algorithms like particle swarm optimization and ant colony optimization, a significant improvement in solution of these problems has been observed. The purpose of this paper is to present and analyze the application of SI algorithms in SCM. The analysis will eventually lead to development of a generalized SI implementation framework for optimization problems in SCM.Design/methodology/approachA structured state-of-the-art literature review is presented, which explores the applications of SI algorithms in SCM. It reviews 56 articles published in peer-reviewed journals since 1999 and uses several classification schemes which are critical in designing and solving a supply chain optimization problem using SI algorithms.FindingsThe paper revels growth of swarm-based algorithms and seems to be dominant among all nature-inspired algorithms. The SI algorithms have been used extensively in most of the realms of supply chain network design because of the flexibility in their design and rapid convergence. Large size problems, difficult to manage using exact algorithms could be efficiently handled using SI algorithms. A generalized framework for SI implementation in SCM is proposed which is beneficial to industry practitioners and researchers.Originality/valueThe paper proposes a generic formulation of optimization problems in distribution network design, vehicle routing, resource allocation, inventory management and supplier management areas of SCM which could be solved using SI algorithms. This review also provides a generic framework for SI implementation in supply chain network design and identifies promising emerging issues for further study in this area.

We observe that between the competitors of technology products, companies such as HP and Xerox or Apple and Google, one competitor (HP and Apple) manages the reverse supply chain to extract value profitably, while the other competitor (Xerox and Google) uses a third-party reverse logistics company to simply recycle consumer returns. In this chapter, we show that, under a given set of supply chain levers, the OEM’s approach to supply chain network design can significantly impact the profitability in the reverse supply chain over the product lifecycle. We propose an integrated multi-period optimization model called the product life cycle optimization model (PLCOM) to design the closed loop supply chain (CLSC) for the OEMs. The PLCOM is a mixed-integer linear program that consists of a demand model based on the extended-Bass’s diffusion model and a pricing model based on a realistic customer willingness to pay (WTP). The PLCOM is applied to a realistic case study using Apple’s iPhone 7 for a product life cycle of 8 years. We show that an integrated approach to designing the supply chain network by the OEMs is more profitable compared to a sequential approach.

Supply chain optimization of residual forestry biomass for bioenergy production: The case study of Portugal

There are several supply chain-related problems facing the construction industry, such as poor construction site logistics, lack of communication and trust. These problems can jeopardize construction projects through delays and cost overruns. Supply chain planning, a part of supply chain management (SCM), can be used as a tool to deal with these problems. The purpose of this paper is to study how linkages between common supply chain-related problems in construction can be illustrated and to demonstrate how they could be resolved through supply chain planning. Firstly, we identify how the linkages between common problems can be illustrated, and secondly the role of supply chain planning in resolving these problems. A conceptual model is developed that was verified using three cases with Swedish gypsum and kitchen supply chains. The model is shown to be useful in illustrating how supply chain problems occurring at executional level on-site are related to problems originating in lack of planning at company/pre-construction level. The study thus demonstrates how supply chain planning can aid in resolving supply chain problems. The paper contributes by bringing planning back into the picture and by showing how supply chain planning can help to adopt SCM in construction.

Currently, concerns with sustainable development lead organizations to improve their production processes in order to reduce greenhouse gases emission and energy consumption. Since the bioethanol supply chain is a CO2 emitter and depends on several energy sources, it becomes important to analyze how to improve this chain regarding environmental issues. Thus, this paper presents a comparative study of scenarios with bioethanol supply chain configurations which use different modal alternatives and renewable energy in all its mid-stages. The analysis was based on LCA (life cycle analysis) concepts and in a partial application of LCI (life cycle inventory), so that we can identify which of these scenarios is most appropriate in terms of lower total energy consumption, greater share of renewable energy use and lower CO2 emissions. Based on concepts found in the bibliographic research, the methodology used and the data collected from documental research, this paper analyzes the supply chain that begins with sugarcane plantation and bioethanol production in the south central region of Brazil with destination to export. Based on the results, we concluded that it is possible to improve the performance of the supply chain in environmental terms with a combination of renewable energy sources and modes of transport that are more suitable to the product studied.

This research proposes a novel location allocation framework that utilizes agent-based simulation for the efficient production of corn stover-based bioethanol, which requires dedicated pretreatment facilities for the feedstock. The framework comprises two main modules: (1) a Pretreatment Facility Module that assesses the performance of the corn stover-based bioethanol supply chain based on the interactions among three types of agents, namely order agent, pretreatment agent, and transport agent, and (2) an Optimization Module designed to determine the optimal supply chain configuration by selecting the most suitable number and locations for pretreatment facilities to achieve the lowest total operational cost. The framework is implemented in a case study for South Korea, which aims to raise the bioethanol blending ratio from 4% in 2025 to 8% by 2030. Experimental results reveal that, within the bioethanol supply chain comprising eight farms and four refineries, a 1% increase in bioethanol blending ratio leads to an increase in the demand for approximately 2229 kL of ethanol (10,225 tons of corn stover), and the proposed framework enables to identify the optimal location of pretreatment facilities in the subject supply chain according to the change in ethanol demand.