Green supply chains with carbon trading and environmental sourcing: Formulation and life cycle assessment

Industry practitioners and policy makers are under increasing pressure to continuously reduce the negative environmental impact of their supply chains. An original equipment manufacturer that is concerned with minimizing the environmental impact of its activities should choose its suppliers based on the trade-off between costs and respective emissions. This decision requires the manufacturer to coordinate closely with its suppliers in order to achieve the required level of emissions. In this paper, we develop a mixed integer program for the carbon-sensitive supply chain that minimizes emissions throughout the supply chain by taking into consideration green procurement also known as environmental sourcing. A sample case study is presented with a life cycle assessment of three scenarios based on different carbon emissions costs.     

Approximate analysis and optimization of batch ordering policies in capacitated supply chains

Devising manufacturing/distribution strategies for supply chains and determining their parameter values have been challenging problems. Linking production management to stock keeping processes improves the planning of the supply chain activities, including material management, culminating in improved customer service levels. In this study, we investigate a multi-echelon supply chain consisting of a supplier, a plant, a distribution center and a retailer. Material flow between stages is driven by reorder point/order quantity inventory control policies. We develop a model to analyze supply chain behavior using some key performance metrics such as the time averages of inventory and backorder levels, as well as customer service levels at each echelon. The model is validated against simulation, yielding good agreement of robust performance metrics. The metrics are then used within an optimization framework to design the supply chain so as to minimize expected total system costs. The outcome of the optimization framework specifies how to move inventory throughout the supply chain and how to set inventory control parameters, i.e., reorder levels and replenishment batch sizes.     

供应链链际竞争的研究与发展

本文试图从供应链的结构、竞争的数学经济模型,以及工业应用的领域等三个方向, 概括和介绍国际和国内有关供应链链际竞争的研究工作。其中绝大多数的数学模型是建立 在竞争的各方都是具有相同性质和相同结构的供应链的前提假设下,因此是对称的链际竞 争模型,而且主要局限于二层或者三层的供应链网络。但是我们也特别介绍了一些非对称的模型可以用来研究不同性质和结构的供应链之间的竞争问题。另一个有意义的方向是研 究链内的合作契约在链际竞争中所起到的作用。     

Robust environmental closed-loop supply chain design under uncertainty

With the fast developments in product remanufacturing to improve economic and environmental performance, an environmental closed-loop supply (ECLSC) chain is important for enterprises' competitiveness. In this paper, a robust ECLSC network is investigated which includes multiple plants, collection centers, demand zones, and products, and consists of both forward and reverse supply chains. First, a robust multi-objective mixed integer nonlinear programming model is proposed to deal with ECLSC considering two conflicting objectives simultaneously, as well as the uncertain nature of the supply chain. Cost parameters of the supply chain and demand fluctuations are subject to uncertainty. The first objective function aims to minimize the economical cost and the second objective function is to minimize the environmental influence. Then, the proposed model is solved as a single-objective mixed integer programming model applying the LP-metrics method. Finally, numerical example has been presented to test the model. The results indicate that the proposed model is applicable in practice.     

Synchronization in supply chains: implications for design and management

Supply chain management literature calls for coordination between the different members of the chain. Materials should be moved from one supplier to the next according to a just-in-time schedule. In this paper, we show that for many supply chain configurations, complete synchronization will result in some members of the chain being ‘losers’ in terms of cost. We develop an algorithm for optimal synchronization of supply chains and provide some guidelines for incentive alignment along the supply chain. In developing the model, we use the economic delivery and scheduling problem model and analyze supply chains dealing with single and multiple components. For single-component supply chains, we derive a closed-form expression for the optimal synchronized cycle time. For multi-component supply chains, we develop an algorithm for finding the optimal synchronized cycle time. We test the performance of the algorithm and show that it provides optimal solutions for a wide range of problems. We illustrate the models with numerical examples.     

Supply chain operations in the presence of a spot market: a review with discussion

We survey the recent literature on the use of spot market operations to manage procurement in supply chains. We present results in two categories: work that deals with optimal procurement strategies and work related to the valuation of procurement contracts. As an example of the latter, we provide new results on valuation of a supply contract with abandonment option. Based on our review, we also discuss the scope for doing further work.     

Designing a sustainable closed-loop supply chain network based on triple bottom line approach: A comparison of metaheuristics hybridization techniques

Recently, there is a growing concern about the environmental and social footprint of business operations. While most of the papers in the field of supply chain network design focus on economic performance, recently, some studies have considered environmental dimensions.     

Supply chain network operations management of a blood banking system with cost and risk minimization

Blood service operations are a key component of the healthcare system all over the world and yet the modeling and the analysis of such systems from a complete supply chain network optimization perspective have been lacking due to their associated unique challenges. In this paper, we develop a generalized network optimization model for the complex supply chain of human blood, which is a life-saving, perishable product. In particular, we consider a regionalized blood banking system consisting of collection sites, testing and processing facilities, storage facilities, distribution centers, as well as points of demand, which, typically, include hospitals. Our multicriteria system-optimization approach on generalized networks with arc multipliers captures many of the critical issues associated with blood supply chains such as the determination of the optimal allocations, and the induced supply-side risk, as well as the induced cost of discarding the waste, while satisfying the uncertain demands as closely as possible. The framework that we present is also applicable, with appropriate modifications, to the optimization of other supply chains of perishable products.     

Strategic capacity planning for light emitting diode (LED) supply chains across Taiwan and China

This paper describes a mixed-integer programming (MIP) model formulated for strategic capacity planning for light emitting diode (LED) makers of Taiwan, major companies in the global LED market. These firms have complex supply chains across Taiwan and China, and the region’s unique political and economic environment has created not only competitive advantages but also challenges in supply chain management: government regulations require that customer orders be accepted from Taiwan or China according to customer attributes; when conducting manufacturing, Taiwanese firms may need to transfer orders across national borders for reasons such as manufacturing technology (the required technology is available only at certain manufacturing facilities) or more efficient capacity utilization; and there are operations to be performed with specific processing requirements to follow, posing substantial challenges for planners. Motivated by the significance of these firms in the global market, we develop a MIP model with novel features to support their strategic capacity planning, covering demand and manufacturing-related decisions, including order acceptance and transfer, manufacturing starts, capacity expansion, and logistics. We illustrate the model’s performance using modified industry data in a numerical example; we also describe the potential impacts the model may create in industry applications.     

Equilibrium analysis of supply chain structures under power imbalance

This paper investigates the implications of channel power on supply chain stability in a setting where multiple suppliers sell substitutable products through a common retailer. Such supply chains have been traditionally analyzed as one- or two-stage Stackelberg non-cooperative games with all suppliers sharing balanced (equal) decision-making power. In this paper, we relax this assumption and formulate game-theoretic models to examine scenarios where one supplier can act as the Stackelberg leader. Consequently, we analyze new supply chain structures and introduce the notion of structure dominance, a novel approach to analyze the performance of supply chains that has practical implications. Thus, a decision maker can employ the concepts of structure dominance to determine whether there exist supply chain scenarios that are more stable than others, i.e., less prone to power reconfigurations, at both agent and group level. We find that power imbalance causes significant declines in supply chain profits, and the more balanced the agents are the higher their profits when demand is linear, regardless of product competition. It develops that neither the Manufacturer Stackelberg nor the Retailer Stackelberg supply chains are stable structures in our generalized setting, but that structures where power is equally split between agents provide for best stability and performance.     

A multi-structural framework for adaptive supply chain planning and operations control with structure dynamics considerations

A trend in up-to-date developments in supply chain management (SCM) is to make supply chains more agile, flexible, and responsive. In supply chains, different structures (functional, organizational, informational, financial, etc.) are (re)formed. These structures interrelate with each other and change in dynamics. The paper introduces a new conceptual framework for multi-structural planning and operations of adaptive supply chains with structure dynamics considerations. We elaborate a vision of adaptive supply chain management (A-SCM), a new dynamic model and tools for the planning and control of adaptive supply chains. SCM is addressed from perspectives of execution dynamics under uncertainty. Supply chains are modelled in terms of dynamic multi-structural macro-states, based on simultaneous consideration of the management as a function of both states and structures. The research approach is theoretically based on the combined application of control theory, operations research, and agent-based modelling. The findings suggest constructive ways to implement multi-structural supply chain management and to transit from a “one-way” partial optimization to the feedback-based, closed-loop adaptive supply chain optimization and execution management for value chain adaptability, stability and crisis-resistance. The proposed methodology enhances managerial insight into advanced supply chain management.     

Environmentally conscious optimization of supply chain networks

The optimization of supply chain structures considering both economic and environmental performances is nowadays an important research topic. However, enterprises are commonly faced with the competing issues of reduced cost, improved customer service and increased environmental factors as a multi-faceted trade-off problem when designing supply chains. Hence, this paper proposes an environmentally conscious optimization model of a supply chain network with a broader and more comprehensive objective function that considers not just the transportation costs, but also the costs for the amount of greenhouse gas emissions, fuel consumption, transportation times, noise and road roughness. The paper sheds light on the trade-offs between various parameters such as vehicle speed, fuel, time, emissions, noise and their total cost, and offers managerial insights on economies of environmentally conscious supply chain optimization. An integer non-linear programming model is developed to help decision makers find the optimal solution under mentioned considerations. The proposed model is validated through the solution of an example, where its applicability to supply chain problems is demonstrated for managerial insights.     

Timing and eco(nomic) efficiency of climate-friendly investments in supply chains

Emission trading schemes such as the European Union Emissions Trading System (EUETS) attempt to reconcile economic efficiency with ecological efficiency by creating financial incentives for companies to invest in climate-friendly innovations. Using real options methodology, we demonstrate that under uncertainty, economic and ecological efficiency continue to be mutually exclusive. This problem is even worse if a climate-friendly project depends on investing in of a whole supply chain. We model a sequential bargaining game in a supply chain where the parties negotiate over implementation of a carbon dioxide (CO₂) saving investment project. We show that the outcome of their bargaining is not economically efficient and even less ecologically efficient. Furthermore, we show that a supply chain becomes less economically efficient and less ecologically efficient with every additional chain link. Finally, we make recommendations for how managers or politicians can improve the situation and thereby increase economic as well as ecological efficiency and thus also the eco-efficiency of supply chains.     

Finding short and implementation-friendly addition chains with evolutionary algorithms

Finding the shortest addition chain for a given exponent is a significant problem in cryptography. In this work, we present a genetic algorithm with a novel encoding of solutions and new crossover and mutation operators to minimize the length of the addition chains corresponding to a given exponent. We also develop a repair strategy that significantly enhances the performance of our approach. The results are compared with respect to those generated by other metaheuristics for exponents of moderate size, but we also investigate values up to \(2^{255} - 21\). For numbers of such size, we were unable to find any results produced by other metaheuristics which could be used for comparison purposes. Therefore, we decided to add three additional strategies to serve as benchmarks. Our results indicate that the proposed approach is a very promising alternative to deal with this problem. We also consider a more practical perspective by taking into account the implementation cost of the chains: we optimize the addition chains with regards to the type of operations as well as the number of instructions required for the implementation.     

Evaluating green supplier development programs with a grey-analytical network process-based methodology

Under circumstances of increasing environmental pressures from markets and regulators, focal companies in supply chains have recognized the importance of greening their supply chain through green supplier development programs. Various studies have started to explore the inter-relationships between green supply chain management and supplier performance. Much of this performance can be achieved only with suppliers’ involvement in green supplier development programs. But, the literature focusing on green supplier development programs and supplier involvement propensity is very limited. In addition, formal tools and models for focal companies to evaluate these inter-relationships, especially considering propensity of suppliers’ involvement, are even rarer. To help address this gap in the literature, we introduce a grey analytical network process-based (grey ANP-based) model to identify green supplier development programs that will effectively improve suppliers’ performance. We further comprehensively evaluate green supplier development programs with explicit consideration of suppliers’ involvement propensity levels. A real world example is introduced to demonstrate the effectiveness of the model. We end with a discussion of managerial implications and present some directions for further research.     

An airline scheduling model and solution algorithms under stochastic demands

When setting a good flight schedule airlines not only have to consider their fleet supply and related operations, as well as market share, but also stochastic variations caused by daily passenger demands in actual operations. Most of the past research on short-term flight scheduling has used the average passenger demand as input to produce the final timetable and schedule, which means that daily passenger variations that occur in actual operations are neglected. To consider such stochastic disturbances we developed a stochastic-demand scheduling model. We employed arc-based and route-based strategies to develop two heuristic algorithms that can be used to solve the model. The test results, based on a major Taiwan airline’s operation, show the good performance of the model and the solution algorithms.     

Negotiation-based collaborative planning between supply chains partners

It is often proposed that operations planning in supply chains can be organized in terms of a hierarchical planning system. However, the hierarchical approach assumes a single, centralized planning task for synchronizing operations across the supply chain. As central coordination can usually be realized only for isolated parts of an overall supply chain, the question arises whether there are alternative ways of coordination.In this paper we propose a non-hierarchical, negotiation-based scheme which can be used to synchronize plans between two independent supply chain partners linked by material flows. Assuming that plans are generated based upon mathematical programming models, we show how modified versions of these models can be utilized for evaluating material orders or supplies proposed by the supply chain partner and for generating counter-proposals. Resulting is an iterative, negotiation-like process which establishes and subsequently improves a consistent overall plan. Computational tests suggest that the scheme comes close to optimal results as obtained by central coordination.     

Knowledge-based framework for automated dynamic supply chain configuration

Supply chain management has gained renewed interest among researchers in recent years. This is primarily due to the availability of timely information across the various stages of the supply chain, and therefore the need to effectively utilize the information for improved performance. Although information plays a major role in effective functioning of supply chains, there is a paucity of studies that deal specifically with the dynamics of supply chains and how data collected in these systems can be used to improve their performance. In this paper I develop a framework, with machine learning, for automated supply chain configuration. Supply chain configuration used to be mostly a one-shot problem. Once a supply chain is configured, researchers and practitioners were more interested in means to improve performance given that initial configuration. However, recent developments in e-commerce applications and faster communication over the Internet in general necessitates dynamic (re)configuration of supply chains over time to take advantage of better configurations. Using examples, I show performance improvements of the proposed adaptive supply chain configuration framework over static configurations.     

The joint replenishment problem with resource restriction

There are many resource restrictions in real production/inventory systems (for example, budget, storage, transportation capacity, etc.). But unlike other research areas, there is very little research to handle the joint replenishment problem (JRP) with resource restriction. The purpose of this paper is to develop two efficient algorithms for solving these problems. Firstly, we modify the existing RAND algorithm to be applicable to the JRP with resource restriction. Secondly, we develop a genetic algorithm for the JRP with resource restriction. Extensive computational experiments are performed to test the performances of the algorithms.