Robust Supply Chain Network Design Research Articles

Collaborative supply chain network design under demand uncertainty: A robust optimization approach

This paper studies a collaborative robust supply chain network design (CRSCND) problem aimed at maximizing economic and social benefits by enabling enterprises to jointly address demand uncertainties. Through strategies including joint inventory replenishment, shared distribution centers (DCs), and pooled transportation resources, the CRSCND problem seeks to optimize plant and DC locations and the allocation of DCs to customers under a collaborative framework. To address this, we develop two robust optimization models incorporating a budget uncertainty set, each model representing a distinct risk-pooling policy. These models are then reformulated into solvable linear programming structures. Results from numerical experiments confirm the cost-reduction benefits of collaboration and robust optimization. Sensitivity analysis reveals that factors like violated probability and high demand volatility minimally impact cost savings enabled by collaboration and robustness. Moreover, each robust model shows distinct suitability depending on specific scenario parameters. Finally, we test three cost-saving allocation mechanisms, finding that only the Shapley value method yields best allocations in cases involving overlapping demand.

Get Us PPE: A Self-Organizing Platform Ecosystem for Supply Chain Optimization during COVID-19

The COVID-19 pandemic caused a global health emergency that triggered an acute shortage of Personal Protective Equipment (PPE), putting essential healthcare workers at risk. Starting March 2020, given the skyrocketing prices of PPE in the open market, healthcare institutions were confronted with the dire need to reshape their PPE procurement strategy. One avenue that financially constrained healthcare institutions pursued were donation platforms that offered access to donated PPE by individuals and organizations. We document a real-life case study of one of the most prominent donor platforms that emerged during this period: Get Us PPE. From 20 March 2020 to 2 July 2021, Get Us PPE received 23,001 total individual requests for PPE from every US state and some US territories. In response to these 23,001 requests, Get Us PPE delivered 17,540,571 units of PPE. The number of PPE units delivered was still less than 25% of the total units of PPE requested, indicating the enormity of the PPE shortage. Using this case study, we identify key supply chain issues that manifest during emergencies and highlight the formation of a platform ecosystem to resolve these issues. We also observed a robust supply chain network design that addresses key supply chain sustainability metrics such as minimizing material waste and transportation miles, while balancing other stakeholder factors such as donor and recipient characteristics as well as shipment complexity. We argue that the roadmap followed by Get Us PPE could serve as a template for organizations that emerge in the context of future humanitarian crises.

Sustainable sugarcane-to-bioethanol supply chain network design: A robust possibilistic programming model

Issues such as high fossil-fuel price fluctuations, considerable air pollution, and the realization that fossil sources are not unlimited threaten the global economy. Hence, researchers have been attracted by biomass, especially sugarcane, as a source of renewable energy to produce bioethanol and other biofuels. This research proposes a three-phase robust supply chain network design optimization model to produce bioethanol from sugarcane. Fuzzy integrated data envelopment analysis method is employed to select suitable cultivation lands as supply potential points. The model considers sustainability in its implementation so that the objectives are to maximize the profit, minimize environmental effects, and maximize social performance. Since some parameters are naturally uncertain, a robust possibilistic programming model is proposed, considering the possibility of transportation disruptions. The model performance has been illustrated through a case study in Iran. Finally, the results of the mathematical model have shown that the recommended supply chain design is justifiable. Practical strategic insights include recommendations for locating refineries in seven provinces (Gilan, Kermanshah, Hamedan, Semnan, Khorasan Razavi, Khouzestan, Kihkooliye). These results have been validated using a simulation-based approach, which demonstrates that the proposed model recommends decisions that more suitable than the deterministic model in terms of average and standard deviation of objective values.

Carbon market sensitive robust optimization model for closed loop supply chain network design under uncertainty

The adoption of closed-loop supply chain (CLSC) network is one of the effective approaches to reduce carbon emissions. In current globalization, inherent uncertainty exists in business environment so there is a need to be design robust supply chains. This paper proposes a deterministic mixed integer linear programming (MILP) model integrating economics and carbon emission considerations including selection of production technologies and transportation mode as a part of CLSC network strategic and tactical decisions. The robust counterpart of the proposed deterministic model is developed based on three alternative uncertainty sets to represent the imprecise input parameters. The robust counterpart is used to study the supply chain performance by considering the two most globally practiced carbon regulatory policies; carbon tax policy and carbon trading policy. Numerical results show that total cost of the proposed robust optimization model under each uncertainty set is greater than the total cost of deterministic model. The additional cost is due to solution space of each uncertainty set to accommodate any uncertainty level. As uncertainty level increases the overall supply chain cost worsen. Moreover, the results suggest that carbon tax rate has direct relation with overall supply chain cost whereas having carbon market trading flexibility in carbon trading policy, this policy is more efficient policy as compared to carbon tax policy. Furthermore, the proposed robust optimization model is useful for mangers to achieve not only a robust supply chain network design which can withstand any possible uncertainty level but also significant reduction in carbon emissions by choosing suitable carbon-efficient policy.

Robust design and planning for a multi-mode multi-product supply network: a dairy industry case study

As a salient matter of decision, supply chain design and planning has been a point of attraction for both researchers and practitioners. In real-world problems, the data based on which the decision is made are subject to uncertainty. Robust optimization is a well-known approach developed for modeling the uncertainty in such cases. In this research, a robust supply chain network design (RSCND) problem considering multiple products, multiple transportation modes, monetary value of time and uncertainty in transportation costs, demand and supply is studied. To endorse applicability of the proposed model, a case study of dairy products packaging and distribution network is studied and comprehensive analyses are provided. In addition, through using the proposed linearization technique, the model can be solved within a reasonable amount of time by utilizing conventional exact methods for small- and medium-size problems.

Robust supply chain network design with multi-products for a company in the food sector

The paper aims to solve a problem faced by a company competing in the snacks market in Turkey. In line with the growth in this market, the company needs to make important decisions over the next few years about the timing and location of a new plant, its initial capacity, the timing and amount of additional capacity to be installed at the new and existing plants, the assignment of demand points to plants and the amount of raw materials to be shipped from suppliers to the plants in each period. The objective is to minimize the total cost of various components. The problem is formulated as a multi-period supply chain network design model with multi products. The resulting mixed-integer linear programming model is solved by the commercial solver CPLEX. This model enables us to carry out all analyses requested by the company in an efficient way. After this deterministic model is solved on the basis of a 9% annual increase in demand, it is extended to a minimax regret model to deal with uncertainty in demand quantities. The results suggest that opening the new plant in the city of İzmir is indeed a robust solution that is unaffected in different scenarios that are based on three distinct demand increase rates. Even though the location of the new plant remains unchanged with respect to scenarios, the optimal robust solution differs from the optimal solution of each scenario in terms of the capacity expansion decisions. After all obtained results had been communicated to the company managers and executives, the new plant construction was started in 2016 very close to the city that the mathematical model had determined. The new plant is expected to start operating in 2018.

A Robust Supply Chain Network Design Considering Capacity, Supply and Delivery Distance Limits Under Uncertain Demand

A Robust Supply Chain Network Design Considering Capacity, Supply and Delivery Distance Limits Under Uncertain Demand

Robust supply chain network design: an optimization model with real world application

This paper presents a robust optimization model for the design of a supply chain facing uncertainty in demand, supply capacity and major cost data including transportation and shortage cost parameters. We first present a base model that aims to determine the strategic ‘location’ and tactical ‘allocation’ decisions for a deterministic four-tier supply chain. The model is then extended to incorporate uncertainty in key input parameters using a robust optimization approach that can overcome the limitations of scenario-based solution methods in a tractable way, i.e. without excessive changes in complexity of the underlying base deterministic model. The application of the approach is investigated in an actual case study where real data is utilized to design a bread supply chain network. Numerical results obtained from model implementation and sensitivity analysis experiments arrive at important managerial insights and practical implications.

Robust optimization of supply chain network design in fuzzy decision system

This paper presents a new robust optimization method for supply chain network design problem by employing variable possibility distributions. Due to the variability of market conditions and demands, there exist some impreciseness and ambiguousness in developing procurement and distribution plans. The proposed optimization method incorporates the uncertainties encountered in the manufacturing industry. The main motivation for building this optimization model is to make tools available for producers to develop robust supply chain network design. The modeling approach selected is a fuzzy value-at-risk (VaR) optimization model, in which the uncertain demands and transportation costs are characterized by variable possibility distributions. The variable possibility distributions are obtained by using the method of possibility critical value reduction to the secondary possibility distributions of uncertain demands and costs. We also discuss the equivalent parametric representation of credibility constraints and VaR objective function. Furthermore, we take the advantage of structural characteristics of the equivalent optimization model to design a parameter-based domain decomposition method. Using the proposed method, the original optimization problem is decomposed to two equivalent mixed-integer parametric programming sub-models so that we can solve the original optimization problem indirectly by solving its sub-models. Finally, we present an application example about a food processing company with four suppliers, five plants, five distribution centers and five customer zones. We formulate our application example as parametric optimization models and conduct our numerical experiments in the cases when the input data (demands and costs) are deterministic, have fixed possibility distributions and have variable possibility distributions. Experimental results show that our parametric optimization method can provide an effective and flexible way for decision makers to design a supply chain network.

An Effective Heuristic Algorithm for Robust Supply Chain Network Design under Uncertainty

This paper addressing a study on robust supply chain network design under uncertainty environment. We use decomposition and coordination strategy to decompose the model as two parties: the first part is facility location decision which only concludes 0 1 decision variable, tabu search algorithm is used to determine the 0 1 decision variables, and then regard the 0 1 variables as known input parameters. The second part is flow decision, all-or-nothing meth od is proposed to design the capacity of the facility. From the numerical example, we can see that, the model and algorithm is valid and effective, the robust optimization model not only reduces the risk of market, but avoids the error from the shortage cost.

Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case

We have developed a stochastic mathematical formulation for designing a network of multi-product supply chains comprising several capacitated production facilities, distribution centres and retailers in markets under uncertainty. This model considers demand-side and supply-side uncertainties simultaneously, which makes it more realistic in comparison to models in the existing literature. In this model, we consider a discrete set as potential locations of distribution centres and retailing outlets and investigate the impact of strategic facility location decisions on the operational inventory and shipment decisions of the supply chain. We use a path-based formulation that helps us to consider supply-side uncertainties that are possible disruptions in manufacturers, distribution centres and their connecting links. The resultant model, which incorporates the cut-set concept in reliability theory and also the robust optimisation concept, is a mixed integer nonlinear problem. To solve the model to attain global optimality, we have created a transformation based on the piecewise linearisation method. Finally, we illustrate the model outputs and discuss the results through several numerical examples, including a real-life case study from the agri-food industry.