Supply Chain Design Model Research Articles

Strategic and tactical planning model for the design of perishable product supply chain network in Ethiopia

This article discusses the challenges associated with ensuring food security for a growing global population and emphasizes the critical role of the Agri Fresh Food Supply Chain in addressing these challenges such as mitigating food insecurity and reducing wastes. The economic repercussions of inefficiencies in perishable supply chains impact businesses' profit margins and consumers' access to essential goods. The focus is on the effective management of the complex perishable product supply chains, considering the economic significance of this sector and the need for advanced strategies to optimize the flow of time-sensitive products. The study discusses the limited consideration of product intrinsic characteristics in strategic and tactical planning decisions. The research aims to contribute to supply chain management by proposing a mathematical model (multi echelon, multi product, and multi period) for designing a perishable product supply chain network, considering factors like perishability, quantity discount policy, souring strategies and business continuity. The study explores the impact of these factors on the Agri Fresh Food Supply Chain design and its overall performances. Although the proposed model is designed to be applicable to any country or region, but its capabilities are shown through a real case study in Ethiopia. Results show that a agri-food supply chain design model that considers the intrinsic characteristics of the product, processing capacity level, and quantity discount leads to improved configurations of the food supply chain regardless of adding the associated cost.

Comparing resilience strategies for a multistage green supply chain to mitigate disruptions: A two-stage stochastic optimization model

Supply chain disruptions, notably exemplified by the COVID-19 pandemic, have substantial impacts, requiring effective resilience strategies. This study introduces a comprehensive multi-stage, multi-period green supply chain design model, incorporating six resilience strategies to tackle downstream and upstream disruptions. A rigorous two-stage stochastic optimization approach is employed to manage parameter uncertainties and disruptions. The objectives include minimizing disruption costs and CO2 emissions, for a chain that operates under cap-and-trade emission regulation. The findings, derived from a numerical experiment and sensitivity analysis, offer the optimal supply chain structure and effective resilience strategies to mitigate disruptions. The demand structure for essential and non-essential products during disruptions are explored, emphasizing proactive resource allocation. In particular, the impact of facility capacity reductions underscores the importance of capacity management. Sensitivity analyses reflect the trade-offs involved in capacity management strategies, emphasizing the critical need to maintain an optimal level of capacity to prevent service level degradation. Additionally, examination of carbon abatement regulations reveals the intricate balance between environmental responsibility and economic efficiency. In summary, this study addresses supply chain disruptions and offers actionable insights for supply chain managers and policy makers. The research highlights the importance of specific and data-driven resilience strategies to optimize supply chain performance in face of disruptions.

A data-driven optimization model for renewable electricity supply chain design

The rising electricity demand, driven by technological progress and population growth, has made the design of the electricity supply chain a major challenge. The transition to renewable energy sources has led to a substantial reduction in the costs and greenhouse gases emissions of electricity production. This study proposes a hybrid approach consisting of a recurrent neural network and an optimization model for designing a resilient electricity supply chain based on wind and solar energies. First, the electricity demand is forecast using the long short-term memory algorithm. Second, based on the forecast demand, a two-stage stochastic programming model is developed for designing an electricity supply chain under disruption risks. Partial and complete disruptions in power lines and facilities, including power plants, distribution and subtransmission substations, and low-power solar panel farms are considered. The objective of the proposed model is to minimize the total cost of the supply chain by making optimal decisions on facility location; technology selection for power plants; setting up transmission lines; and the quantity of power generation, power transmission, and power shortage in low- and high-voltage energy consumption systems. Three strategies are adopted to enhance the supply chain's resilience: multiple renewable energy sources, multiple power transmission lines, and lateral power lines. Lastly, the Sistan and Baluchestan Electric Power Distribution Company in Iran is used as a case study for this work to demonstrate the applicability of the proposed approach, analyze the findings, and derive relevant managerial insights.

Integrating virtual power plants for sustainable supply chain operations in production-climate nexus

ABSTRACT This paper aims to design and integrate virtual power plant (VPP) into a geographically dispersed production-distribution network for attaining environmental sustainability goal. The VPP is comprised wind turbines, photovoltaics, combined heat and power, and energy storage units. The proposed VPP-based supply chain design model makes strategic allocations on renewable capacity as well as scheduling production, inventory, transportation, and energy generation and trading decisions. The model represents a first-of-its-kind in planning a multi-layer supply chain network in which renewable-based electric-thermal cogeneration is coordinated in production-climate nexus. The study yields the following findings. First, products with larger energy use opt to be made in locations with stronger sunshine or wind profile despite higher transportation costs. Second, the time-of-use rate greatly stimulates the installation of onsite photovoltaics and energy storage. Third, under flat utility rate, reducing energy storage cost has limited benefit to the cost savings of the supply chain operations.

Design of a supply chain network for chemicals from biomass using green electrochemistry

Increasing concern about the environmental impact of industrial activities has prompted a shift to renewable energy sources and the development of environmentally conscious supply chains. In this regard, electrochemistry has shown promise for converting biomass into specialty chemicals in distributed facilities that exploit renewable energy resources. To examine the impact of electrochemistry technology on optimal supply chain configuration, we formulate a mixed-integer linear programming model to optimize the locations and capacities of distributed facilities for converting biomass to chemicals. The economic objective of the supply chain design model is to minimize the totalannualcost of producing chemicals from biomass-derived glucose and delivering them to market. To analyze the trade-off between environmental andeconomicconsiderations, we also consider an environmental objective of minimizing greenhouse gas (GHG) emissions. The results of a US case study indicate that, while cost is minimized by constructing one large facility, GHG emissions are lowered by a distributed configuration. Varying the setting of a process design parameter expands the Pareto frontier along which decision-makers can choose a configuration according to their preferences between economic and environmental criteria.

Robust Optimization Model for Sustainable Supply Chain Design Integrating LCA

Supply chain management is the basis for the operations in an organization. The development of realistic supply chain designs that work effectively in the presence of disturbances in a stochastic environment and incorporate sustainability factors, is a complex challenge being investigated in recent years. However, the inclusion of a methodological structured framework to evaluate environmental impacts constitutes a knowledge gap in the literature on supply chain design. This study developed a model for sustainable supply chain design, integrating Life Cycle Assessment and based on a robust optimization approach. The study follows a 4-stage methodology beginning with data collection and the execution of a Life Cycle Assessment. Then, the deterministic modeling is proposed, concluding with a robust model. A bi-objective model is proposed to maximize utility and minimize environmental impact based on demand scenarios. The model was validated with real data from a medium-sized enterprise that produces antibacterial gel, generating as a result, different configuration alternatives for the supply chain to transport the products and raw materials between its elements. The conclusions of this work highlight the importance of including sustainability factors during supply chain design, the consequences and costs of its inclusion, as well as the priority actions that promote sustainable designs.

A novel two-stage multi-objective optimization model for sustainable soybean supply chain design under uncertainty

Soybean is one of the most important agricultural products in international markets due to its high global consumption. However, there are limited studies considering sustainability issues and global economic aspects of the soybean supply chain design and optimization. Moreover, it is challenging to integrate many sustainability criteria into a single-stage optimization model for a soybean supply chain configuration. This research proposes a novel two-stage approach, integrating a multi-criteria decision-making technique and a new multi-objective optimization model to design and optimize a soybean supply chain network in Canada, considering sustainability and global factors. In Stage 1, several qualitative and quantitative sustainability criteria are considered to calculate the sustainability scores for the potential suppliers, using the Interval Type 2 Trapezoidal Best Worst Method. Then, the scores are used as input in Stage 2, where a new optimization model with four objective functions is formulated to design and optimize a soybean supply chain. The objective functions include maximization of total profit, created job opportunities, and suppliers' sustainability, and minimization of CO2 emissions. Then, the Pareto frontier is generated, using the augmented ε-constraint method which helps policymakers to make appropriate decisions. Furthermore, four cases are proposed and analyzed to assess the impacts of the objective functions on strategic and tactical decisions. The results show the importance of the presented method as an integrated approach, considering sustainability pillars in agri-food supply chains. In addition, the results indicate that the proposed stochastic multi-objective model can handle fluctuations in uncertain parameters.

Optimising location, inventory and transportation in a sustainable closed-loop supply chain

Operations management researchers and practitioners have shown increasing interest in incorporating sustainability into supply chain (SC) design models. This means that sustainability must be considered in all aspects of the SC, including location, inventory and transportation (LIT) decisions. Hence the aim of this article is to propose an optimisation model that incorporates: (i) LIT decisions in an integrated manner; (ii) the three sustainability (3S) aspects, i.e. economic, environmental and social, into each named decisions; and (iii) a closed-loop supply chain (CLSC) structure. The proposed formulation is a multi-objective mixed integer non-linear programming (MO-MINLP) model whose objectives consider minimisation of economic and social costs (economic aspect) and carbon emissions (environmental aspect), and maximisation of the social impact of SC operations (social aspect). A transformation technique is applied to one of the objective functions, which results in an MO-MILP model solved by the lexicographic method. This article focuses on commodity industries where only one finished product is manufactured. Hence the 3S-LIT model is validated with a randomly generated dataset and against a recently published alternative model applied to the copper mining industry.

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.

Robust supply chain design with suppliers as system integrators: an aerospace case study

ABSTRACT Original Equipment Manufacturers (OEMs) have sought new supply chain paradigms that allowed them to focus on core activities, i.e. overall product design and commercialisation. This pursuit led to partnerships with a new generation of tier-1 strategic suppliers acting as integrators. Integrators are not only responsible for system supply, but also for system design. However, critical integrators were not able to live up to their new roles, which led to costly delays in development and production. These failures highlight the ineptitude of current risk management practices employed by OEMs. To support OEMs in implementing a more differentiated and suitable approach to the use of integrators, this paper proposes a mathematical programming model for Supply Chain Design (SCD). Instead of looking at the introduction of integrators as a dichotomous decision, the model suggests the optimal number of integrators, i.e. systems, and individual part suppliers. We propose new measures for integration risk, which build upon current risk assessment practices. Robust optimisation is used to study the effect of uncertainty over baseline risk values. All approaches were tested using both randomly generated instances and real data from a large European OEM in the aerospace industry.

Multi-objective sustainable supply chain network design and planning considering transportation and energy source selection using a lexicographic procedure

In this paper, a multi-objective integrated production and energy model for sustainable supply chain design, planning, and energy source selection has been proposed. The model considers economic, environmental, and social concerns. The lexicographic procedure was applied to compromise the conflict objectives with different percentages of deviations. It aims to maximize both profit and overall customer service level while minimizing the gas emissions generated from both transportation and production operations.In addition, the model helps to select the optimal source of energy from three potential sources of energy considering both capital and operating costs, in addition to the selection of the transportation mode (heavy or light trucks) to satisfy customer demand during the planning horizon with six periods of all products.The efficacy of the model has been evaluated using a comprehensive case study in addition to the analysis of several scenarios with various conditions. Six different scenarios have been carried out to validate the effectiveness of the proposed model and to study the effect of changing objective prioritization and the maximum allowable deviation on the performance of the supply.

A multi-objective optimization approach for supply chain design of alum sludge-derived supplementary cementitious material

Most alum sludge is disposed of in landfill sites in Australia, resulting in significant environmental pollution and management cost. Effective management of sludge in a more sustainable manner remains a critical social, environmental, and economic concern. To simultaneously solve the dilemma of sludge management, economic expenditure of disposal, greenhouse gas emissions, and social impacts, this study proposed a multi-objective model for supply chain design of alum sludge-derived supplementary cementitious material as a viable solution. This study identified and quantified the potential social benefit by considering different impacts of new working opportunities based on their type and location, optimized the greenhouse gas emission saving for the potential supply chain, and assessed the economic benefit of the optimal untapped supplementary cementitious material value chain. The viability and applicability of the model are demonstrated through a case study in Australia, where potential utilization paths for available alum sludge are valued. A set of Pareto-optimal solutions was generated using the ɛ-constraint method, and three compromise Pareto-optimal solutions showed a clear trade-off among three objectives. The result analysis of each single-objective optimization indicates that the maximum working opportunity brought by the proposed supply chain was over 3.6 million working hours. Besides, up to 5.5 billion tonnes of greenhouse gas emissions saving was reported when maximizing the environmental objective, and the maximum economic benefit in the form of net present value was over 200 million Australian dollars in simulated 20 years. The Pareto-optimal solutions report a trade-off among three objectives, and two compromised solutions are proposed based on the level of objective values.

Solution of large-scale supply chain models using graph sampling & coarsening

We present a graph sampling and coarsening scheme (gSC) for computing lower and upper bounds for large-scale supply chain models. An edge sampling scheme is used to build a low-complexity problem that is used to finding an approximate (but feasible) solution for the original model and to compute a lower bound (for a maximization problem). This scheme is similar in spirit to the so-called sample average approximation scheme, which is widely used for the solution of stochastic programs. A graph coarsening (aggregation) scheme is used to compute an upper bound and to estimate the optimality gap of the approximate solution. The coarsening scheme uses node sampling to select a small set of support nodes that are used to guide node/edge aggregation and we show that the coarsened model provides a relaxation of the original model and a valid upper bound. We provide evidence that gSC can yield significant improvements in solution time and memory usage over state-of-the-art solvers. Specifically, we study a supply chain design model (a mixed-integer linear program) that contains over 38 million variables and show that gSC finds a solution with an optimality gap of <0.5% in less than 22 minutes.

Probability, adaptability and time: some research-practice paradoxes in supply chain resilience and viability modelling

Modelling of resilience and viability became crucial and visible research avenues for decision-making support in case of supply chain disruptions. This article discusses some research-practice paradoxes stemming from typical problem settings and assumptions in supply chain resilience and viability modelling. We show that the categories of probability, adaptability, and time are major determinants of resilience and viability modelling in supply chains. As an example, we discuss how probability-based supply chain design models can be enhanced through inclusion of adaptability and connectivity characteristics of the network elements with consideration of time. We deduce some open questions the elaboration on which can provide novel and substantial contributions in future. In particular, we stress the importance of furthering our understanding of reliable suppliers, disruption probabilities, disruption time and ripple effect estimation, value-creation perspective of resilience, and viability of intertwined supply networks.

Optimization of the Location, Inventory and Routing of Capacity Vehicles with Interval Uncertainty

In today’s industrial world, manufacturing units are trying to reduce their costs by properly locating the warehouses they need as well as routing vehicles to transport manufactured goods to these warehouses. In fact, the inventory-routing location model is an integrated supply chain design model that simultaneously optimizes location, inventory, and routing decisions. The purpose of this paper is to provide an integrated model for locating warehouses, allocating stores to warehouses, and finding inventory at the end of the course and routing decisions such as determining the routes of vehicles starting from a distribution center opened to serve customers. Eventually return to the same distribution center; in such a way that the total system costs are minimized. The model is formulated as a mixed integer linear programming model (MILP) and a robust optimization approach has been used to optimize the problem under uncertainty conditions. To solve the model and demonstrate its feasibility, GAMS software and to compare the results of the software MATLAB is used.

A bi-objective robust possibilistic programming model for blood supply chain design in the mass casualty event response phase: a M/M/1/K queuing model with real world application

A bi-objective robust possibilistic programming model for blood supply chain design in the mass casualty event response phase: a M/M/1/K queuing model with real world application

A robust multi-objective optimisation model for natural gas supply chain design under uncertainty: a case study

The appropriate design of a complex natural gas supply chain becomes a critical issue for both academics and practitioners. To deal with the design of complicated natural gas supply chain, a mixed integer nonlinear programming model is developed in this paper. Two conflicting objectives including the minimisation of the total cost and the maximum unfulfilled demands are considered subject to technical and logical pipeline and compressor constraints. Since a number of critical parameters are tainted with imprecision in the proposed model, a robust possibilistic programming approach is applied to handle the inherent uncertainty. A natural gas network in Iran is used as a case study to verify and validate the developed model. [Received: October 08, 2019; Accepted: May 27, 2020]

A robust multi-objective optimisation model for natural gas supply chain design under uncertainty: a case study

A robust multi-objective optimisation model for natural gas supply chain design under uncertainty: a case study

A multi-objective stochastic model for a reverse logistics supply chain design with environmental considerations

Some electronic devices have a short lifetime, and variety-seeking and consumerism are increasingly growing in today’s societies. Moreover, electronic wastes contain precious substances such as gold, silver, copper, and aluminum. The proper disposal and processing of them by recycling offer considerable advantages to the environment, given the hazardous natures of such devices’ substances. The proposed reverse logistics with waste electrical and electronic equipment (WEEE) is an important task considered by researchers, the use of which offers economic benefits and reduces the environmental impacts of wastes. The present study models the electrical and electronic equipment (EEE) reverse logistics process as a bi-objective mixed-integer programming model under uncertainties. The mathematical model investigates two objectives: an economic objective and an environmental objective. The first is minimizing cost, while the second is maximizing the environmental score by reverse logistics processes in recovering and recycling. The parameters of demand and WEEE return rate which is obtained from the customer were considered as two uncertain parameters. A scenario-based stochastic programming (SSP) approach is applied to deal with the uncertainties. A case study of an electronic equipment manufacturer in Esfahan, Iran was included. The model was solved by a nominal approach and an SSP approach via the epsilon-constraint (EC) and augmented epsilon-constraint (AEC) methods to obtain optimal Pareto solutions and compare the methods. Finally, the optimal results of the two approaches were evaluated. The results indicated that the SSP approach using the AEC method had better outcomes.

A bi-objective robust possibilistic programming model for blood supply chain design in the mass casualty event response phase: A M/M/1/K queuing model with real world application

This paper presents a bi-objective blood supply chain design model for disaster relief. The model aims to simultaneously minimise the total costs of the supply chain, the trade-off between the total expected waiting times of patients in hospitals and the average hospital idle-time probability, unsatisfied demands and the average delivery time from mobile blood facilities to healthcare centres as the first, second, third and fourth objective functions. Since the critical parameters are tainted with great degree of epistemic uncertainty, basic chance constraint programming (BCCP) and robust fuzzy chance constraint programming (RFCCP) are utilised to deal with the uncertain nature of the supply chain. In order to solve the proposed model, two different multiple objective decision making approaches are used. The applicability of the proposed model for earthquake response phase is demonstrated via a real case study in a region of Iran. Useful managerial insights are also provided through conducting some sensitivity analyses.