Supply Chain Network Model Research Articles

Quantifying supply chain network synergy for humanitarian organizations

Both the number of disasters and the number of people affected by disasters are growing, creating a great need for resilient disaster management. In this article, we construct multiproduct supply chain network models for multiple humanitarian organizations. The models capture uncertainty associated with costs of their supply chain activities, including procurement, storage, and distribution, under multiple disaster scenarios, along with uncertainty associated with the demand for the disaster relief products at the demand points. The models reflect the organizations’ operations, without and with cooperation, with the humanitarian organizations seeking to determine the disaster relief multiproduct flows that minimize their expected total cost and risk, subject to expected demand satisfaction. We utilize a mean-variance approach to capture the risk associated with cost uncertainty and propose a synergy measure for the assessment of the potential strategic advantages of cooperation for resilient disaster management. We also identify the role of technology in helping to parameterize the models and illustrate the analytical framework with numerical examples, accompanied by managerial insights.

Multitiered blood supply chain network competition: Linking blood service organizations, hospitals, and payers

In this paper, we present a multitiered competitive supply chain network model for the blood banking industry, with a focus on the United States, that captures the economic interactions between three tiers of stakeholders; namely, the blood service organizations, the hospitals or medical centers, which transfuse blood to patients, and the payer groups that patients belong to. In addition, the supply chain framework for this life-saving product includes the competition among blood service organizations and their various supply chain activities. We model the behavior of each category of stakeholder and use the theory of variational inequalities to derive the equilibrium conditions for the entire supply chain. Illustrative examples are provided, along with qualitative properties, followed by an algorithm, accompanied by convergence results, that is used to solve simulated numerical examples. Results from these examples demonstrate that such a model can be effectively used to determine the prices and blood pathways from blood service organizations to hospitals to payers.

Plant capacity level and location as a mechanism for sustainability in biomass supply chain

Biomass is an important energy source that has the ability to reduce dependencies on fossil fuels, while providing a greener source of energy and helping achieve sustainability. Among the most commonly used biomass feedstock is corn stover, corn residue remaining in the fields after harvesting. One of the biggest challenges of using corn stover as biomass feedstock is that burning it in field is the fastest and cheapest way for many growers so as to remove it and grow new crops. This leftover corn stover could be, instead, converted to bioethanol. In this work, we propose a decision support system for expanding existing biorefineries or building new ones to help stakeholders design a supply chain network model that converts all of the available corn stover to bioethanol. Two configurations presented in this study which is the existing plant expansion (EP) configuration and the combination of existing and new plant configuration (ENP), by exploring the incentive and greenhouse gas (GHG) emission price value for the bioenergy plant to achieve the goal. The aim of converting all corn stover is successfully achieved along with the other goals of achieving sustainability by reducing the amount of GHG emissions in the supply chain. Our results reveal that we can achieve a minimum amount of GHG emissions, while maximizing profit from the supply chain, when expanding existing plants and building new plants (ENP configuration) leading to a reduction of GHG emissions by 4%.

Chance Constrained Optimization of Biodiesel Supply Chain

Biodiesel is an alternative and renewable biofuel for blending with fossil-based diesel. One of the challenges is to design the supply-chain network for biohydrodeoxygenated diesel (BHD) under uncertainty from both raw materials availability and the demand. The supply chain with four echelons; suppliers, factories, inventories and customers has been studied. The biodiesel factory has been simulated by commercial simulation program Pro/II to find the utility cost and biodiesel production capacity. In this study, the stochastic optimization is used to solve mathematical model of the BHD supply chain network. The proposed model is based on stochastic mixed-integer programming with chance constraints. The chance constrained optimization has been applied to design supply chain network under the uncertainty at different levels of confidence. Then optimized supply chain network has been investigated on stability of the supply chain network under the uncertainties, accuracy of the profit and the effect of penalty. This study shows profit and trade-off between profit and penalty cost with different levels of confidence of BHD supply chain under uncertainties in raw-material supply and biodiesel demand. The results show that the chance constrained optimization can be used to design optimum supply chain network under uncertainties within levels of confidence.

A Robust Fuzzy Optimization Model for Closed-Loop Supply Chain Networks Considering Sustainability

Supply chain network design (SCND) is an important strategic decision determining the structure of each entity in the supply chain, which has an important impact on the long-term development of a company. An efficient and effective supply chain network is of vital importance for improving customer satisfaction, optimizing the allocation of resources, and increasing profitability. The environmental concerns and social responsibility awareness of the whole society have spurred researchers and managers to design sustainable supply chains (SSCs) integrating the economic, environmental, and social factors. In addition, the innate uncertainty of the SCND problem requires an integrated method to cope. In this regard, this study develops a multi-echelon multi-objective robust fuzzy closed-loop supply chain network (CLSCN) design model under uncertainty including all three dimensions of sustainability. This model considers the total cost minimization, carbon caps, and social impact maximization concurrently to realize supply chain sustainability, and is able to make a balance between the conflicting multiple objectives. Meanwhile, the uncertainty of the parameters is divided into two categories and addressed with two approaches: the first category is missed working days related to social impact, which is solved by the fuzzy membership theory; the second category is the demand and remanufacturing rate, which is settled by a robust optimization method. To validate the ability and applicability of the model and solution approach, a numerical example is conducted and solved using ILOG CPLEX. The result shows that the supply chain network structure and the value of the optimization objectives will change when considering sustainability and different degrees of uncertainty. This will enable supply chain managers to reduce the environmental impact and enhance the social benefits of their supply chain activities, and design a more stable supply chain to better cope with the influence of uncertainty.

Modeling and optimization of a reliable blood supply chain network in crisis considering blood compatibility using MOGWO

Due to the prominent role of blood in human life, designing an efficient blood supply chain in case of an emergency situation is essential especially considering blood compatibility. This research proposes a multi-objective model for emergency blood supply chain management considering blood compatibility, routing, and location–allocation decisions. The blood supply chain network consists of donors, collection facilities, laboratories, blood centers, and hospitals. The mathematical model aims to minimize total supply chain cost and time while maximizing minimum reliability of established routes by making decisions regarding location–allocation, blood flow, inventory levels, and optimal routes. In order to solve the problem, a novel algorithm called Multi-Objective Grey Wolf Optimizer is used and compared to two classical algorithms Multi-Objective Particle Swarm Optimization and Non-dominated Sorting Genetic Algorithm-II. Performance of the algorithms is evaluated in various test problems using powerful measures. Also, the application of the proposed model is investigated in a case study in Iran’s capital, Tehran. Based on the results, important managerial insights are derived and optimal locations for facilities, inventory levels, routes and blood flow between facilities are determined.

Global supply chain networks and tariff rate quotas: equilibrium analysis with application to agricultural products

In this paper, we develop a global supply chain network model in which profit-maximizing firms engage in competition in the production and distribution of products in the presence of quantitative trade policy instruments in the form of tariff rate quotas. Tariff rate quotas are two-tiered tariffs, in which a lower in-quota tariff is applied to the units of imports until a quota or upper bound is attained and then a higher over-quota tariff is applied to all subsequent imports. They are utilized to protect domestic producers in the case of a wide range of products, from agricultural ones to fabrics and even steel, and can be challenging to formulate. We construct the governing set of novel equilibrium conditions associated with the product flows and Lagrange multipliers, which correspond to quota rent equivalents, and derive the variational inequality formulation. Qualitative properties are presented along with an effective algorithm, which is then applied to compute solutions to numerical examples comprising an agricultural product case study on avocados and global trade. This work is the first to model and solve general, competitive supply chain network problems consisting of oligopolistic firms with multiple production sites and demand markets in multiple countries subject to tariff rate quotas.

Optimizing a robust tri-objective multi-period reliable supply chain network considering queuing system and operational and disruption risks

The closer the supply chain network (SCN) modeling to the real world, the more accurate decisions can be made. In this regard, it is essential to take into account of the factors in the real world such as the formation of the queuing system in the producers of the SCN, and batch transportation and processing (BTP) approaches, as well as the operational and disruption risks. In this research, batch sizes are considered equal to the potential transportation capacity of vehicles. The present paper considers simultaneous optimization of total cost, total time and average total number of commodities dispatched from any echelon of the SCN to the next echelon on a five-echelon multi-period SCN including suppliers, manufacturing plants, assemblers, distribution centers, and customers. To deal with the disruption risk, the reliability of facilities in sending the commodities is computed based on the exponential distribution and is maximized in the third objective function. Additionally, to tackle the operational risk of the problem, we convert the proposed tri-objective mathematical model into the robust mathematical model using Ben-Tal method (ρ-Robust method). Then five multi-objective decision-making (MODM) solution methods are selected to solve the problem. The results of these five methods are compared and the best MODM solution method is selected based on the objective function values and the CPU times. Finally, a novel solution is proposed to find the dominant and dominated objective functions in multi-objective models based on the uncertainty level (⍴) of the model.

A Complex Negotiation Model for Multi-Echelon Supply Chain Networks

This paper focuses on a complex negotiation model for supply chain networks (SCNs), where multi-attribute, multi-item, multi-linked, and multi-echelon are simultaneously involved. An ideal simultaneous multi-attribute multi-item negotiation model for multi-echelon SCNs is proposed, where the decision makings of manufacturer agents (MAs) for retailer agents and the decision makings of MAs for material supplier agents (MSAs) are made simultaneously. Buyers in the proposed model are assumed build-to-order. Both concessions and preferences among attributes and items are considered during decision makings of agents. It is verified that the proposed simultaneous model based negotiation protocol is feasible and effective in solving the complex negotiation problems, and it is a “triple-win” solution for all the participants in SCNs.

On the Stability of Coalitions in Supply Chain Networks via Generalized Complementarity Conditions

In this paper, we consider a supply chain network model that consists of three layers of decision-makers, namely, suppliers, manufacturers and retailers, with prices and shipments that evolve in time. We focus on the vertical integration of the levels of the supply chain and consider the retailer as the dominant player of the coalition. We give a novel reformulation of the evolutionary variational inequality related to the equilibrium conditions underlying the model. This approach, based on complementarity conditions, allows us to analyze the Lagrange multipliers associated with production capacities to understand better market’s trend. We study the behavior of marginal profits and provide some results for the efficiency and the stability of the coalitions. We apply our theoretical achievements to a duopolistic model.

An optimization model for traceable closed-loop supply chain networks

• A closed-loop supply chain (CLSC) network comprises forward and reverse logistics. • A stochastic multi-product traceable CLSC using a radio frequency identification system is modelled. • Customer acquisition behavior when choosing internet over conventional sales is considered. • A greedy randomized adaptive search procedure (GRASP) is used to solve the problem. • A real-life case study of network marketing shows the applicability of the proposed model. In this paper, a new non-linear mixed-integer mathematical programming problem is proposed to model a stochastic multi-product closed-loop supply chain (CLSC). The radio frequency identification (RFID) system is implemented in the supply chain to decrease product losses and the overall lead time of transportation while computing the profit derived from internet and conventional sales. The resulting traceable CLSC improves upon the existing literature by allowing us to: (1) boost the incorporation of traceability assumptions in mathematical programming problems so as to enhance the efficiency and visibility of a supply chain, (2) analyze the strategic effects that different internet sale formats have on customers’ evaluations and acquisition choices, and (3) account for the environmental and socio-economical dimension by explicitly formalizing employment-based incomes as part of the profit function. Two meta-heuristic algorithms are introduced to solve the proposed optimization problem, namely, the greedy randomized adaptive search procedure (GRASP) and particle swarm optimization (PSO). Twelve test problems of different sizes are generated and solved using these algorithms. The computational results show that GRASP outperforms PSO in terms of both profit and CPU time values. Finally, a case study in the network marketing industry is presented and managerial implications outlined to show the validity of the proposed model and shed more light on its practical implications.

Managing the risk of supply chain bankruptcy in supply chain network redesign

All vital operations of a supply chain network (SCN) are inherently affected by uncertainties and risks in the economic environment. In this study, we extend an existing SCN model by elaborating on stochastic price and demand, and integrating economic uncertainty. Financial performance and credit solvency are of utmost importance when analysing the financial status of an SCN. Each of these components focuses on a different aspect of investment attractiveness under various economic conditions, and in turn, facilitates access to capital for the companies involved. We extend the SCN model with financial performance optimisation approach to a novel model to include the trade-offs between two objective functions: (1) the total equity as the financial performance of the SC, and (2) the Altman’s Z-score as the credit solvency factor of the company. The model ascertains the future profitability, leverage, and liquidity of the SC by determining the optimal levels of production and inventory in each facility of the new network design. We introduce a weighted sum method and a novel genetic algorithm (GA) solution approach to solve the resultant bi-objective mixed integer nonlinear programming problem. We illustrate the representation scheme required for the GA in this paper.

Semantic Modeling for the Knowledge Framework of Computational Experiments and Decision Making for Supply Chain Networks

The complexity of a supply chain network (SCN) is rooted in its complex structures, multiple decision-making (DM) entities, adaptive behaviors, and open environments. Due to its unique advantages, computational experiment (CE) has been increasingly adopted as one of the most important methods for SCN complexity research. Data that are generated by computational experiments must be analyzed using effective tools. Depending on this analysis, DM acts as an important analysis and selection mechanism for the optimization and design of SCNs. This optimization and design rely on the combination of CE and DM. This combination inevitably involves multiple types of knowledge in the domains of SCNs, CE, and DM, which have been less comprehensively considered in recent studies. It remains a challenge for researchers and practitioners to clarify the knowledge system of SCNs and select the most suitable research perspectives, paradigms, and methods for CEs and DM of SCNs. To confront this challenge, it is necessary to systematically model the semantics of the knowledge that is involved in CE and DM to realize the consistency and interoperability of models, methods, and processes. Therefore, this paper uses a semantic network approach to construct a semantic model to clarify the knowledge framework of CEs and DM of SCNs. This knowledge framework is composed of the important knowledge elements that are extracted from the domains of SCNs, CE, and DM. The application procedure of the semantic model is demonstrated on a four-echelon SCN case. The semantic model’s understandability, consistency, reusability, procedure, systematization, and linkage analysis capability are evaluated. The results demonstrate that the semantic model is effective in providing a consistent, procedural, and systematic perspective for SCN complexity research and supporting linkage analysis among SCN modeling, CE, and DM.

Multitiered Blood Supply Chain Network Competition: Linking Blood Service Organizations, Hospitals, and Payers

In this paper, we present a multi-tiered competitive supply chain network model for the blood banking industry, with a focus on the United States, that captures the economic interactions between three tiers of stakeholders; namely, the blood service organizations, the hospitals or medical centers, which transfuse blood to patients, and the payer groups that patients belong to. In addition, the supply chain framework for this life-saving product includes the competition among blood service organizations and their various supply chain activities. We model the behavior of each category of stakeholder and use the theory of variational inequalities to derive the equilibrium conditions for the entire supply chain. Illustrative examples are provided, along with qualitative properties, followed by an algorithm, accompanied by convergence results, that is used to solve simulated numerical examples. Results from these examples demonstrate that such a model can be effectively used to determine the prices and blood pathways from blood service organizations to hospitals to payers.

Halal Food Supply Chain: The Proposed Extension Towards the New Islamic Supply Chain Network Model Development

This study is to propose some extensions of the existing halal food supply chain model by diagnosing and reviewing the important issues of halal in Malaysia's food and beverages (F&B) products from the perspective of supply chain and logistics model. Over the years, Malaysia has gone through a series of arguments on halal and non-halal issues from the consumers' community and how it imparted the serious contamination to the halal supply chain expansively in the foods' products industries. Some issues like Cadbury and supermarket trolleys have released how seriousness the responsibilities of authorities in tackling the halalness of the entire industries and cautiously up to date the issue with highly integrity. In this study, the biggest challenge of the F&B products is to develop a model of supply chain halal integrity and unluckily not many studies have been conducted so far. Therefore, the proposed extensions are crucial and critical to be referred to by all businesses in order to support the growing halal economic development, especially in the Muslim countries. Methodology of the research information was based from the qualitative semi-structured interviews and literature reviews. The interview's objective of this research is to get a real case scenario understanding about halal F&B products, e-Halal, halal hubs, including supply chain and logistics in Malaysia. This study gets its information from the seven respondents from various halal industry backgrounds and is analyzed in order to add some new elements into the existing halal supply chain model. As a conclusion, the study is trying to propose some extensions of the halal food supply chain model into a more comprehensive model known as New Islamic Supply Chain & Logistics Network Model.

Multi-objective bi-level supply chain network order allocation problem under fuzziness

In this paper we addressed supply chain network (SCN) as bi-level programming problem in which the primary objective is to determine optimal order allocation of products where the customer’s demands and supply for the products are fuzzy. In the proposed SCN model, we suppose that the first level (leader) and second level (follower) operate two separate groups of SCN. The leader, who moves first, determines quantities shipped to retailers, and then, the follower decides his quantities rationally. The leader’s objective is to minimize the total transportation costs, and similarly, the follower’s objective is to minimize the total delivery time of the SCN and at the same time balancing the optimal order allocation from each source, plant, retailer and warehouse respectively. The fuzzy goal programming approach has been used to achieve the highest degree of the membership goals by minimizing the deviational variables so that most satisfactory or the preferred solution for both the levels to be obtained. A numerical example is given to demonstrate the proposed methodology.

An implementation of differential evolution algorithm for a single product and single period multi-echelon supply chain network model

In this paper, five variants of Differential Evolution (DE) algorithms are proposed to solve the multi-echelon supply chain network optimization problem. Supply chain network composed of different stages which involves products, services and information flow between suppliers and customers, is a value-added chain that provides customers products with the quickest delivery and the most competitive price. Hence, there is a need to optimize the supply chain by finding the optimum configuration of the network in order to get a good compromise between several objectives. The supply chain problem utilized in this study is taken from literature which incorporates demand, capacity, raw-material availability, and sequencing constraints in order to maximize total profitability. The performance of DE variants has been investigated by solving three stage multi-echelon supply chain network optimization problems for twenty demand scenarios with each supply chain settings. The objective is to find the optimal alignment of procurement, production, and distribution while aiming towards maximizing profit. The results show that the proposed DE algorithm is able to achieve better performance on a set of supply chain problem with different scenarios those obtained by well-known classical GA and PSO.

A stochastic reverse logistics production routing model with environmental considerations

Growing global concerns of environmental problems have led to the emergence of policies and regulations to control carbon emissions in the industrial sector. These regulations must be taken into consideration to obtain optimal operational decisions on production, inventory and routing in supply chain network models. In this study, we consider the reverse logistics supply chain model with a remanufacturing option to reduce carbon emissions. We aim at providing optimal production, inventory and delivery quantities along with delivery and pickup routes under a carbon cap-and-trade emissions policy. We provide a mathematical formulation of the problem that considers heterogeneous transportation fleets and allows for lost sales under the cap-and-trade carbon emissions policy. The proposed mathematical model is provided in a deterministic and a two-stage stochastic versions to account for demand uncertainty. Proposed formulations are demonstrated through a simulated reverse logistics supply chain with added sensitivity analysis to test for the effect of modeling parameters on the optimal problem solution. Simulation results indicate that carbon policies have significant effect on the supply chain performance with carbon price as the most significant parameter.

Recent advances in hybrid priority-based genetic algorithms for logistics and SCM network design

Abstract Design and optimization of logistics and supply chain management (SCM) network is very important issue, which plans, implements and controls the efficient, effective forward and reverse flow and storage of goods, services and related information between the point of origin and the point of consumption to meet customers’ requirements. A multistage based logistics or SCM network can be modeled by means of a sequence of stages, each consisting of a set of functions, i.e. the existing or potential facilities located in several countries or regions of the world where goods are transformed or manufactured or stocked and delivered: suppliers, plants, distribution centers (DCs) and customers. The structure of each stage of the supply chain is completed by a set of arcs connected to the nodes and representing the flow of goods: each arc has a weight proportional to the cost incurred in between the two nodes it connects together. In this paper, we survey a recent advance on hybrid priority-based genetic algorithms for solving a multistage logistics or SCM network problems. In particular, the following multistage based logistics or SCM network models will be introduced: (1) the sugarcane SCM network model, (2) multiobjective supply chain network model, (3) flexible multistage logistics network model, and (4) multiobjective reverse logistics network model. In each model, we summarize the background, mathematical model, hybrid priority-based genetic algorithm and numerical experiment. We also enhance a hybrid genetic algorithm (HGA) by combining a local search (LS) technique and tuning GA parameters by a fuzzy logic control (FLC) to fast the search ability of GA. Finally, numerical experiment of each case study is carried out to show the effectiveness of the proposed approach by the hybrid priority-based genetic algorithms.

Sustainable, multiperiod supply chain network model with freight carrier through reduction in pollution stock

Sustainable supply chain management is a huge issue from both managerial and eco- nomical perspectives. This study presents a multiperiod, multitier, sustainable supply chain with freight carriers network model to address such concerns. The model comprises manufacturers, retailers, and carriers engaged in a dynamic, noncooperative game. It considers the longitudinal accumulation of pollution stock, pollution absorbtion by nature, and mitigation actions. The study examines numerical examples with an analysis of the effect of different tax rates on production, transportation, sales, and levels of pollution stock and mitigation efforts. The outcome presents important strategic reactions of decision makers to tax policies.