Method and Approach Mapping of Fair and Balanced Risk and Value-added Distribution in Supply Chains: A Review and Future Agenda

This paper proposes a fair and balanced risk and value-added distribution as a novel approach for collaborative supply chain. The objective of this article is to analyze the existing methods and approaches for risk management, value-adding, risk and revenue sharing to develop a new framework for balancing risk and value-adding in collaborative supply chains. The authors reviewed and synthesized 162 scientific articles which were published between 2001 and 2017 and. The reviewed articles were categorized into supply chain management and performance, risk management, value-added, fair risk and value-added distribution and supply chain negotiation. The potentials identified for future research were the importance of decision-making and sustainability for effectiveness of supply chain risk management. Most previous authors have applied an approach of revenue and risk-- sharing with both decentralized and centralized supply chains to achieve the fair risk and value-added distribution. The dominant methods we found in literature were game theory and complex mathematical formulation. Most literature focused on operation research techniques. We identified a lack of discussion of the intelligent system approach and a potential for future exploration. This paper guide future research and application agenda of fair risk and value-added distribution in supply chain collaboration. We developed a new framework for a fair and balanced risk and value-added distribution model. For a future agenda, we point towards the development of a systematic intelligent system applying soft-computing techniques and knowledge transfer for maintaining sustainable supply chains.Keywords Supply chain collaboration, Fair risk and value-added distribution, Revenue sharing, Risk management, Risk sharin

Substitution Effects in Supply Chains with Asymmetric Information Distribution and Upstream Competition

Inventory management in markets with substituting customers is extremely challenging, not only for a downstream wholesaler, but also for upstream manufacturers. Motivated by the structures in the agrochemical market, we analyze the optimal production and stocking quantities of a manufacturer and a wholesaler, respectively, in a two-stage supply chain with upstream competition and vertical information asymmetries. We characterize a monopolistic wholesaler's optimal stocking quantities and show that these quantities are not necessarily monotonic, neither in the available production quantities nor in the customers' substitution rates. We further derive the optimal production quantities of a monopolistic and a competitive manufacturer when they are incompletely informed about the wholesaler's stocking quantities. We find that the introduction of competition may lead to decreasing production quantities for some products. Furthermore, a product's end-of-season inventories at the manufacturer which arise due to information asymmetries may decrease even when initial production levels increase. Key words: customer substitution; supply chain; asymmetric information; competition; inventory managemen

A novel optimization method on logistics operation for warehouse & port enterprises based on game theory

Purpose: The following investigation aims to deal with the competitive relationship among different warehouses & ports in the same company. Design/methodology/approach: In this paper, Game Theory is used in carrying out the optimization model. Genetic Algorithm is used to solve the model. Findings: Unnecessary competition will rise up if there is little internal communication among different warehouses & ports in one company. This paper carries out a novel optimization method on warehouse & port logistics operation model. Originality/value: Warehouse logistics business is a combination of warehousing services and terminal services which is provided by port logistics through the existing port infrastructure on the basis of a port. The newly proposed method can help to optimize logistics operation model for warehouse & port enterprises effectively. We set Sinotrans Guangdong Company as an example to illustrate the newly proposed method. Finally, according to the case study, this paper gives some responses and suggestions on logistics operation in Sinotrans Guangdong warehouse & port for its future development.Peer Reviewe

Construction of Equilibria in Strategic Stackelberg Games in Multi-Period Supply Chain Contracts

Almost every supplier faces uncertain and time-varying demand. E-commerce and online shopping have given suppliers unprecedented access to data on customers’ behavior, which sheds light on demand uncertainty. The main purpose of this research project is to provide an analytic tool for decentralized supply channel members to devise optimal long-term (multi-period) supply, pricing, and timing strategies while catering to stochastic demand in a diverse set of market scenarios. Despite its ubiquity in potential applications, the time-dependent channel optimization problem in its general form has received limited attention in the literature due to its complexity and the highly nested structure of its ensuing equilibrium problems. However, there are many scenarios where a single-period channel optimization solution may turn out to be myopic as it does not consider the after-effects of current pricing on future demand. To remedy this typical shortcoming, using general memory functions, we include the strategic customers’ cognitive bias toward pricing history in the supply channel equilibrium problem. In the form of two constructive theorems, we provide explicit solution algorithms for the ensuing Nash–Stackelberg equilibrium problems. In particular, we prove that our recursive solution algorithm can find equilibria in the multi-periodic variation of many standard supply channel contracts such as wholesale, buyback, and revenue-sharing contracts.publishedVersio

Noncooperative game theory to ensure the marketability of organic fertilizers within a sustainable circular economy

To optimize the environmental performance and the conflicting economic interests of the main stakeholders that interact within circular integrated waste management systems (CIWMSs), life cycle analysis and a game-theoretical model-based on the Stackelberg equilibrium-were integrated into a multiobjective optimization framework. The framework was used to determine the operational decisions and the configuration of a CIWMS that simultaneously minimize the total global warming impacts (GWIs) and maximize the profits of (i) the waste managers that valorize the municipal organic waste generated in the Spanish region of Cantabria and (ii) the regional farmers that purchase the resulting organic fertilizers. A bilevel optimization problem was formulated and solved by replacing the lower-level problem with its equivalent Karush-Kuhn-Tucker conditions. The balance between the stakeholders' objectives is reflected in the low prices set for the organic fertilizers (0-2 €·metric ton-1 of compost and 0-1 €·metric ton-1 of digestate). Although the minimal GWIs are constrained by the waste managers' profits, it is possible to improve the values of the objective functions by increasing the waste management tax. The proposed framework proved to be useful to plan for a sustainable circular economy, warranting the profitability of organic fertilizers for both ends of the supply chain.The authors acknowledge the financial support from the Spanish Ministry of Education 567 (EST18/00007 and FPU15/01771

Integration of environmental aspects in modelling and optimisation of water supply chains

Climate change becomes increasingly more relevant in the context of water systems planning. Tools are necessary to provide the most economic investment option considering the reliability of the infrastructure from technical and environmental perspectives. Accordingly, in this work, an optimisation approach, formulated as a spatially-explicit multi-period Mixed Integer Linear Programming (MILP) model, is proposed for the design of water supply chains at regional and national scales. The optimisation framework encompasses decisions such as installation of new purification plants, capacity expansion, and raw water trading schemes. The objective is to minimise the total cost incurring from capital and operating expenditures. Assessment of available resources for withdrawal is performed based on hydrological balances, governmental rules and sustainable limits. In the light of the increasing importance of reliability of water supply, a second objective, seeking to maximise the reliability of the supply chains, is introduced. The epsilon-constraint method is used as a solution procedure for the multi-objective formulation. Nash bargaining approach is applied to investigate the fair trade-offs between the two objectives and find the Pareto optimality. The models' capability is addressed through a case study based on Australia. The impact of variability in key input parameters is tackled through the implementation of a rigorous global sensitivity analysis (GSA). The findings suggest that variations in water demand can be more disruptive for the water supply chain than scenarios in which rainfalls are reduced. The frameworks can facilitate governmental multi-aspect decision making processes for the adequate and strategic investments of regional water supply infrastructure

Design of a Cooperative Sustainable Three-Echelon Supply Chain under Uncertainty in CO2 Allowance

Driven by the growing concern regarding greenhouse gas emissions, in this work, we provide a robust stochastic model for the design of a cooperative supply chain (SC) under uncertainty in CO2 allowance prices from the European Union Emissions Trading System (EU ETS). During the last years, CO2 allowance prices have undergone unexpected changes, having strong impact on the design and management of optimal SC. The consideration of uncertainty in the allowance prices has therefore become more important. We use an autoregressive integrated moving average (ARIMA) model to predict future allowance prices. A full discretization of the underlying probability space leads to a number of scenarios far too large to be handled, so we compare two approaches to reduce the number of scenarios to a feasible maximum, the ScenRed algorithm and K-means clustering. The obtained results are compared with a deterministic approach that is widely studied in the literature, showing an increase in the benefits and a reduction of emissions.The authors gratefully acknowledge financial support to the Conselleria de Innovacion, Universidades, Ciencia y Sociedad Digital of the Generalitat Valenciana, Spain, under project PROMETEO/2020/064

Fuzzy Bi-level Decision-Making Techniques: A Survey

© 2016 the authors. Bi-level decision-making techniques aim to deal with decentralized management problems that feature interactive decision entities distributed throughout a bi-level hierarchy. A challenge in handling bi-level decision problems is that various uncertainties naturally appear in decision-making process. Significant efforts have been devoted that fuzzy set techniques can be used to effectively deal with uncertain issues in bi-level decision-making, known as fuzzy bi-level decision-making techniques, and researchers have successfully gained experience in this area. It is thus vital that an instructive review of current trends in this area should be conducted, not only of the theoretical research but also the practical developments. This paper systematically reviews up-to-date fuzzy bi-level decisionmaking techniques, including models, approaches, algorithms and systems. It also clusters related technique developments into four main categories: basic fuzzy bi-level decision-making, fuzzy bi-level decision-making with multiple optima, fuzzy random bi-level decision-making, and the applications of bi-level decision-making techniques in different domains. By providing state-of-the-art knowledge, this survey paper will directly support researchers and practitioners in their understanding of developments in theoretical research results and applications in relation to fuzzy bi-level decision-making techniques