Supply Chain Design Research Articles

Optimal design of a biofuel supply chain using an augmented multi-objective and TOPSIS method

A mixed-integer linear programming (MILP) model for designing a multi-objective biofuel supply chain is developed to minimize total annual cost and emissions by use of an augmented ε-constraint method.

An optimization model for a sustainable closed-loop supply chain considering efficient supplier selection and total quantity discount policies

This paper addresses the sustainable closed-loop supply chain (SCLSC) design problem regarding selecting a supplier under total quantity discount with demand uncertainty and logistic flow uncertainty. The proposed model considers the three pillars of sustainability: the economic, environmental, and social realms. The model deals with the costs incurred by products-related manufacturing and minimizes the carbon dioxide emissions resulting from different manufacturing processes, as well as the attendant rate of injuries among the workers. Python edition 2019-07 software with the SCIPY solver was used to solve the model, using a sequential least squares programming algorithm (SLSQP) to obtain optimal solutions. A numerical study was conducted to validate the model. A sensitivity analysis was conducted to address the effects of both types of uncertainty on the optimal solution. It was found that the effect of a high rate of demand uncertainty is more severe than the effect of the uncertainty of the flow logistics in the reverse direction since the former generated a lower value of the optimal solution than the worst-case scenario generated by the uncertainty budget. Moreover, the higher the weight of environmental and social objectives, the higher the proportion of recycled products from the total production. This study proposes a robust optimization model for an SCLSC that considers two types of uncertainty: the uncertainty budget that is used for the logistics flow in the reverse direction for refurbished and redesigned products and the box of uncertainty that is used to address the demand uncertainty.

Hybrid higher education supply chain and value stream mapping design

Hybrid higher education supply chain and value stream mapping design

A bi-objective integrated mathematical model for blood supply chain: Case of Turkish red crescent

<p style='text-indent:20px;'>Various criteria feature in blood supply chain (BSC) designs, where cost-based and time-based are the most commonly found in the literature. In the current study, total annual cost is used together with a new time-based objective. The total time spent in the transportation of blood products is considered as time lost, and weight is given to that time according to the product amount and then normalized with respect to shelf life. In using cost and time objectives, we developed a bi-objective mixed-integer mathematical programming model for the BSC of Turkish Red Crescent (TRC, the singular authority controlling BSC throughout Turkey), including collection, production, and distribution echelons, and also considering bag-type decisions for whole-blood collection. The objective of the study was to propose a BSC design model and solution approach. With all real-life TRC instances resolved optimally, a linear programming relaxation-based heuristic was developed for large-scale problem sizes. Real-life data were obtained from the TRC and the remainder from open-to-public sources. The study's main finding is that cost and time objectives alone produce significantly different designs, whilst using them together to form efficient-frontier solutions for decision-makers adds practical value.</p>

Competition model and contract design for supply chain with green products under yield uncertainty

This paper considers a supply chain (SC) in which the manufacturer produces and sells green products to consumers through two competing retailers. We assume that the manufacturer's yield is uncertain, and then conduct the integrated and the decentralized SC models. A comparison analysis of these two SC models is further performed. The results show that the profit under the integrated SC model is higher than that under the decentralized SC model, which means that there exists double marginalization under the decentralized SC. Then, the cost-sharing contract and the revenue-sharing contract are introduced to improve the decentralized SC. We find that both contracts can not only enable the manufacturer to improve the green degree, but also enable the retailers to order more green products. The main difference between these two contracts is dug out: Compared the situation without any contract, the manufacturer charges a higher wholesale price under the cost-sharing contract, but charges a lower wholesale price under the revenue-sharing contract. In addition, the profits of all SC members can be improved after reasonable designs of these two contracts, thus achieving the Pareto improvements. Finally, we introduce numerical analysis to illustrate and exhibit theoretical conclusions.

Exploring the effect of blockchain technology on supply chain resilience and transparency: Evidence from the healthcare industry

Blockchain technology is revolutionizing all sectors of industries including the healthcare one. Blockchain technology applications have been realized in healthcare as they have the potential to revolutionize healthcare systems. Integrating blockchain into the design of supply chain design leads to decentralizing the management of the supply chain, and, in turn, improves workflow efficiency and reduces the various security threats. Therefore, this study aimed at examining the impact of integrating blockchain technology, into supply chain practices, on supply chain transparency and resilience. By adopting the organizational information processing theory, this study developed a research model that explored the impact of blockchain technology features including data quality, smart contracts, and traceability on supply chain resilience and transparency. Furthermore, this study examined the effect of supply chain transparency on supply chain resilience. The data was collected, from healthcare industry personnel, in Jordan, using an electronic survey. In total, 215 participants responded to the questionnaire. RStudio- 2022.07.1 was used to conduct the data analysis. Results revealed that data quality significantly and positively affects supply chain transparency and resilience. In addition to that, results indicated that while smart contracts are positively related to supply chain transparency, they do not affect supply chain resilience. Also, traceability was found to be positively related supply chain transparency and resilience. Finally, it was found that blockchain-driven supply chain transparency positively impacts blockchain-driven supply chain resilience. The results imply that integrating blockchain technology into the supply chain can enhance both supply chain transparency and resilience, and, in turn, provide the supply chain the capability of recovering to its original state should it encounter disruptive events. The outcomes of this study can help supply chain managers and other stakeholders to develop strategies and tactics to best utilize blockchain technology to enhance blockchain-driven supply chain transparency and resilience.

Life cycle optimisation tool development for process systems and centralised supply chain design

The research introduces a life cycle assessment optimisation model with software featuring a graphical interface. The toolbox enables integrating LCA and multi-objective optimisation, providing insights on emissions and economic benefits.

A multiobjective robustness methodology for a closed cycle supply chain design considering volume discount and economic benefits by reducing CO2 emissions of transportation modes

A multiobjective robustness methodology for a closed cycle supply chain design considering volume discount and economic benefits by reducing CO2 emissions of transportation modes

Sustainability Fruit Supply Chain Design in the Context of Decentralized Smallholders: Modelling Framework and Sensitivity Analysis

Abstract This paper aims to examine the sustainability of smallholders’ citrus supply chain characterized by low coordination and the need to reinforce producers’ welfare, through optimization-based decision support method in the field where logistics specialization and global sustainability are required by agricultural producers. More specifically, a mixed-objective and multi-objective linear programming model is developed to achieve a sustainable balance: at the economic level, to reduce the logistics costs associated with the production chain as well as to reduce the environmental impacts in the form of carbon dioxide emissions generated during transport, while at the social level, integration through job creation into a three-echelons fruit supply chain including producers, intermediaries and retailers. The model is applied to four scenarios, whose analyses determine the importance of supply chain costs that cannot be neglected but which lead to an optimum that also reduces environmental burden and increases social welfare. In conclusion, the integration of opposing approaches represented by this model enables the improvement of the performance of the supply chain studied.

Development of a robust multi-objective model for green capacitated location-routing under crisis conditions

Location-Routing Problem (LRP) is a strategic supply chain design problem aimed at meeting customer demands. LRPs involve selecting one or more depot sites from a set of potential locations and determining the best routes to connect them to demand points. With the rising awareness about the environmental impacts of transportation over the past years, the use of green logistics to mitigate these impacts has become increasingly important. To compensate for a gap in the literature, this paper presents a robust bi-objective mixed-integer linear programming (MILP) model for the green capacitated location-routing problem (G-CLRP) with demand uncertainty and the possibility of failure in depots and routes. The final result of this Robust Multi-Objective Model is to set up the depots and select the routes that offer the highest reliability (Maximizing network service) while imposing the lowest cost and environmental pollution. A Nondominated Sorting Genetic Algorithm (NSGA-II) is used to solve the large-sized instances of the modeled problem. The paper also provides a numerical analysis and a sensitivity analysis of the solutions of the model.

A new supply chain design to solve supplier selection based on internet of things and delivery reliability

In this study, an attempt is made to examine the planning for supply, production, and distribution with an integrated approach to reduce waste and returned products in such a way that the supplier and manufacturer exchange information in supply chain. In this paper, manufacturer share information with suppliers through IoT and blockchain. For best implementation of supply chain based on these technologies, a manufacturer must chose the optimal combination of suppliers that can better meet the criteria of technologies implementation. In this paper, IoT and blockchain are examined to supply chain management by considering the impactful criteria of implementation in suppliers' selection and optimization. For this purpose, this paper applies the multi criteria decision making (MCDM) methods for prioritizing the suppliers. Then, the obtained weights are plugged into the model as the inputs of the designed model. In this study chance constraint programming (CCP) is leveraged to develop optimization models that take the uncertainties and randomness into account. Since the developed model is a non-differentiable and non-convex, genetic algorithm (GA) and particle swarm optimization (PSO) are leveraged to solve the formulated model. Finally, the efficiency of the presented method is verified through a case study from the food supply chain.

Economic and environmental analysis of composite recycling techniques for the design of future waste management supply chains

Economic and environmental analysis of composite recycling techniques for the design of future waste management supply chains

Integrated Production and Distribution Planning using Mathematical Model for Maximizing Profit

In three-dimensional concurrent engineering context, product, process, and supply chain designs are approached by the imperative of concurrency principle. The imperative of concurrency proposes product architecture and supply chain architecture conducted simultaneously. The detailed product design and production process design are conducted simultaneously. While manufacturing system design is conducted simultaneously with logistic system design. This study concerns on integrating production planning with distribution planning in a new plastic bags plant. Since the plant is relatively new, the number of orders is still scarce. Therefore, production planning becomes critical. Producing more products than required creates inventories while producing as many as the order required creates an intermittent production schedule. A mathematical is developed to simultaneously schedule production and distribution to maximize profit. The model provides solutions on the number of materials to be delivered, the number of products produced and delivered within its period for the planning horizon.

A spatial superstructure approach to the optimal design of modular processes and supply chains

Modularity is a design principle that aims to provide flexibility for spatio-temporal assembly/disassembly and reconfiguration of systems. This design principle can be applied to multiscale (hierarchical) manufacturing systems that connect units, processes, facilities, and entire supply chains. Designing modular systems is challenging because of the need to capture spatial interdependencies that arise between system components due to product exchange/transport between components and due to product transformation in such components. In this work, we propose an optimization framework to facilitate the design of modular manufacturing systems. Central to our approach is the concept of a spatial superstructure, which is a graph that captures all possible system configurations and interdependencies between components. The spatial superstructure is a generalization of the notion of a superstructure and of a p-graph used in process design, in that it encodes spatial (geographical) context of the system components. We show that this generalization facilitates the simultaneous design and analysis of processes, facilities, and of supply chains. Our framework aims to select the system topology from the spatial superstructure that minimizes design cost and that maximizes design modularity. We show that this design problem can be cast as a mixed-integer, multi-objective optimization formulation. We demonstrate these capabilities using a case study arising in the design of a plastic waste upcycling supply chain.

A sustainable network design of a hybrid biomass supply chain by considering the water–energy–carbon nexus

AbstractNowadays, with population growth, rising global energy demand, increasing water consumption, carbon emissions, and excessive use of fossil fuels, the world faces a major challenge. Biomass is one of the most attractive sources of energy production, with positive effects on the economy, environment, and society, which can decrease the world's reliance on fossil fuels and deal with this universal challenge. Consequently, the biomass supply chain network design has received more attention in recent decades. Since energy and water are two significant sources for society's sustainable development and carbon emissions are essential for environmental health, the study of the water–energy–carbon (WEC) nexus is essential. In this study, a multiobjective multiperiod model for designing a sustainable supply chain network based on hybrid second‐generation (i.e., Jatropha, agricultural waste, and animal waste) and third‐generation (i.e., microalgae) biomasses is presented. The proposed multiobjective model consists of five objective functions that maximize the total energy produced and the number of jobs created and minimize the total water consumed, carbon emitted, and total costs. Case study results demonstrate the performance of the model utilizing the MINMAX goal programming approach. Based on the results, energy production from Jatropha is more appropriate in comparison with energy production from microalgae, agriculture waste, and animal waste. The findings also show that the proposed model in this research considering the WEC nexus performs significantly better than the classical model without considering the WEC nexus.

A robust fuzzy stochastic multi-objective model for stone paper closed-loop supply chain design considering the flexibility of soft constraints based on Me measure

The Closed-Loop Supply Chain Network Design (CLSCND) encourages attention to the economic and environmental issues concerning returned products. CLSCND is a significant decision issue and it is accompanied by uncertainty. Hybrid uncertainties and the flexibility of soft constraints are some of the key issues in CLSCND. There is a research gap surrounding the utilization of the uncertainties of randomness, epistemic, and soft constraints simultaneously with a robust procedure. This study focuses on the Me measure and proposes a novel flexible, probabilistic, and stochastic programming based on robust optimization. In this approach, a convex mixture of the pessimistic–optimistic views based on the Me measure is implemented. The least level of satisfaction with soft constraints is determined Optimally that the need for repetitive reviews by Decision-Maker (DM) is obviated in the proposed model. Additionally, the model controls violations of soft constraints as well as stochastic deviations, probabilistic deviations, unfulfilled demand, and capacity. A case study is conducted on the development of a stone paper considering economic, environmental, and responsive objectives by minimizing total cost and carbon emissions as well as transportation and processing time. In addition, to estimate the weight of the objectives and handle the multi-objective model, the Best–Worst method (BWM) and the interactive fuzzy programming approach are employed, respectively. Robustness analysis and sensitivity analysis for the case study are then performed and the results are simulated and evaluated using the realization model. The findings revealed that the suggested model performed well.

Agile and Lean Supply Chain: Research Trends (2017-2021)

Today, the two terms - agility and leanness are frequently encountered in the literature due to their effects on industrial sectors. They are the two basic paradigms of the businesses that help them to build strategies and to gain a competitive advantage. This study aimed to explore the emerging research topics and the main research trends within agile and lean supply chain (SC) paradigms in recent years and to explore the interrelationships between them. A word co-occurrence analysis technique was adopted in this study. A data set of 526 publications, obtained from the Web of Science database, had been analyzed. The findings show that many topics such as SC strategy, green SC, and SC agility are present since 2018. Besides, the topics such as SC design, collaboration, cold SC, lean and agile SC, and SC risk management were emerging topics. The most common research areas revealed are; environmental risks in the agile SC, combining lean SC with agile SC, leagile SC strategies, SC risk management, and agility in SC integration.

Location management for the supply of PD fluid via large neighborhood search based virus optimization algorithm

The facility location problem is extended by a new two-stage zero-one programming system (2S-ZOPS). It is a type of design optimization issue that exists in logistics implementations such as supply chain planning in healthcare or agriculture. Along with concerns regarding PD delivery time manner for connecting logistics centers and customers, recent studies have considered the zero-one location design model. This research discussed a route selection model for the 2S-ZOPS that did not exist in the published studies by taking into account the level of risk associated with physical appearance. The mathematical models were developed in response to a PD supply chain design that occurred in Thailand’s National Health Insurance Program. By combining the virus optimization algorithm (VOA) with a large neighborhood search (LNS), we created a hybrid metaheuristic method for solving the 2S-ZOPS. Experiments with real-world data demonstrated that the hybrid algorithm was efficient in terms of time consumption and solution quality, saving approximately 6% on total costs. The presented practice benefits not only the healthcare industry but also various other businesses.

Assessing Readiness ‌of Khorramabad University Hospitals to Respond to COVID--19 Epidemic based‌ on WHO-EMRO Checklist

Background: The spread and epidemic of COVID-19, as a new disease, challenged many health achievements. In hospitals, as the center of crisis management activities, it is very important to develop a crisis preparedness plan. In this study, the response-readiness of Khorram Abad hospitals in the crisis of COVID-19 was discussed. Materials and Methods: This study is a descriptive and applied research that was conducted cross-sectionally in the summer of 1400 in two stages. In the first stage, the condition of 3 Khorram Abad University Hospitals was based on a checklist WHO-EMRO evaluation of the hospital's response readiness to deal with the covid disease was done. The data were analyzed after being recorded in Excel. Based on the obtained results, the weak points of each hospital were extracted and examined through the management panel and individual interviews, and finally implementation solutions were presented. Results: Hospital "A" is relatively prepared in terms of human resources, rapid identification and infection control; Hospital "B" was relatively prepared in the fields of continuity of services, rapid identification, isolation and management of patients; Hospital "C" was also relatively prepared in the fields of information, human resources, rapid identification and infection control; They were fully prepared in other areas. Two hospitals "B and C" are at the level of full readiness and hospital "A" is at the border of full readiness and the overall level of readiness with 81% was estimated at the optimal level. Conclusion: Considering the optimal preparation of Khorram Abad hospitals in dealing with the crisis of the COVID-19 epidemic, it is necessary to train and allocate sufficient manpower to face future crises, and allocate budget to the program. Crisis preparedness, standard supply chain design and evaluation of crisis management programs should be given more attention.

A sustainable-circular citrus closed-loop supply chain configuration: Pareto-based algorithms

Configuration of sustainable supply chains for agricultural products has been a well-known research field recently which is continuing to evolve and grow. It is a complex network design problem, and despite the abundant literature in the field, there are still few models offered to integrate social impacts and environmental effects to support network design decision-making to support the configuration of the citrus supply chain. In this work, the citrus supply chain design problem is investigated by integrating the production, distribution, inventory control, recycling and locational decisions in which the triple bottom lines of sustainability, as well as circularity strategy, are addressed. Accordingly, a novel multi-objective Mixed-Integer Linear Programming (MILP) model is proposed to formulate a multi-period multi-echelon problem to design the sustainable citrus Closed-Loop Supply Chain (CLSC) network. To solve the developed model, the ε-constraint approach is employed in small-sized problems. Furthermore, Strength Pareto Evolutionary Algorithm II (SPEA-II) and Pareto Envelope-based Selection Algorithm II (PESA-II) algorithms are used in medium- and large-sized problems. Taguchi design technique is then utilized to adjust the parameters of the algorithms efficiently. Three well-known assessment metrics and convergence analysis are regarded to test the efficiency of the suggested algorithms. The numerical results demonstrate that the SPEA-II algorithm has a superior efficiency over PESA-II. Moreover, to validate the applicability of the developed methodology, a real case study in Mazandaran/Iran is investigated with the help of a set of sensitivity analyses.