Supply Chain Network Design Problem Research Articles

Determining nodal capacities for military distribution problems

Purpose This paper aims to introduce a new mixed integer programming formulation and associated heuristic algorithm to solve the Military Nodal Capacity Problem, which is a type of supply chain network design problem that involves determining the amount of capacity expansion required at theater nodes to ensure the on-time delivery of military cargo. Design/methodology/approach Supply chain network design, mixed integer programs, heuristics and regression are used in this paper. Findings This work helps analysts at the United States Transportation Command identify what levels of throughput capacities, such as daily processing rates of trucks and railcars, are needed at theater distribution nodes to meet warfighter cargo delivery requirements. Research limitations/implications This research assumes all problem data are deterministic, and so it does not capture the variations in cargo requirements, transit times or asset payloads. Practical implications This work gives military analysts and decision makers prescriptive details about nodal capacities needed to meet demands. Prior to this work, insights for this type of problem were generated using multiple time-consuming simulations often involving trial-and-error to explore the trade space. Originality/value This work merges research of supply chain network design with military theater distribution problems to prescribe the optimal, or near-optimal, throughput capacities at theater nodes. The capacity levels must meet delivery requirements while adhering to constraints on the proportion of cargo transported by mode and the expected payloads for assets.

An accelerated benders decomposition algorithm for a bi-objective green closed loop supply chain network design problem

Recently, social awareness, governmental legislations and competitive business environment have spurred researchers to pay much attention to closed-loop supply chain network design. In order to support the arising trend, this paper presents a comprehensive mathematical model for a multi-period, multi-product, multi-modal and bi-objective green closed-loop supply chain. The objective of the model is to minimize the total cost and environmental emissions through making the best decisions on facility location, transportation amounts and inventory balances. According to the inherent complexity of the problem and considering multi-product, multi-period and multi- modality assumptions makes it hard to handle, and as for the solution approach, an effective accelerated benders decomposition algorithm is implemented. Then, computational results for a set of numerical example are discussed. Besides, the model and solution approach are applied on a wire-and-cable industry. Then, a sensitivity analysis is implemented in an effort to validate the model. Results reveal applicability of the proposed mathematical model and presented solution approach. Following the obtained results, it can be validly concluded that the suggested solution approach leads to more than 13 percent reduction in total cost for the studied case, and can be even employed for larger and more complex real-world industrial applications.

A set of calibrated metaheuristics to address a closed-loop supply chain network design problem under uncertainty

Nowadays, there is a great deal of interest in developing supply chain networks with the goal of optimisation. This paper addresses a new stochastic optimisation model by using a two-stage stochastic programming for a closed-loop supply chain network design problem. To control the uncertainty circumstances of manufacturing of products, customers’ demand, prices of products and its returned rates, the financial risk is considered as a distinguished objective function to find a robust solution. To address the developed model, a new recent nature-inspired algorithm, namely, Whale Optimisation Algorithm (WOA) and also Particle Swarm Optimisation (PSO) as a popular method in swarm intelligence algorithms are utilised in this study. In addition to this development, Genetic Algorithm (GA) and Simulated Annealing (SA) as the well-known metaheuristics used repeatedly among the literature are employed to have a comprehensive comparison. In order to be faired, a popular method for calibration called Response Surface Method (RSM) is applied to tune the algorithms’ parameters. Finally, some evaluation metrics to assess the Pareto optimal fronts of algorithms’ quality are considered to conduct a comparative study using statistical analyses and some sensitivity analyses are performed through an industrial example.

Global supply chain network design problem with rules of origin and limited import quotas

PurposeThis paper aims to develop an uncertain global supply chain network design (GSCND) model with rules of origin (RoOs) and limited import quotas, and to discuss the international factors’ effects on location decisions.Design/methodology/approachThe authors establish an uncertain GSCND model with the international factors. The transportation costs and customers’ demands are characterized as random variables. To deal with the risk of uncertainty, the authors introduce the customers’ demand service level. A sample approximation approach (SAA) is used to deal with the service level constraint and turn the proposed model into a mixed integer programming. On the basis of the properties of the proposed model, a hybrid memetic algorithm (MA) is designed to solve it.FindingsThe authors find that the proposed MA is efficient to the real supply chain network design problem. Besides, the RoOs and limited import quotas can affect the optimal choices of plant and distribution center locations.Originality/valueThe authors propose an uncertain GSCND model with RoOs and limited import quotas. An MA with SAA is designed to solve the proposed model. The authors apply the proposed model into a real global supply chain of an apparel corporation in East Asia, and give some managerial insights.

Power supply chain network design problem for smart grid considering differential pricing and buy-back policies

With the rising sense of environmental consciousness, the development of renewable energy and rapid technological innovation have become drivers, as well as posed challenges, for the power supply chain network. This study addresses the smart power supply chain network design problem considering two players: the electric power company and the users. Using distributed generations (DGs) such as solar, wind, and biomass, among others, users can generate their own renewable energy. Here, differential pricing and buy-back policies maximize benefits for both the companies and users. Under the buy-back contract, users who own DGs can generate renewable electricity, determine the electricity they need, and buy from or sell their share to the electric company. The continuous approximation approach is used to model resolutions for smart power supply chain network problems. Algorithms based on non-linear optimization are proposed to solve the smart power supply chain network design problems for two cases: centralized and decentralized models. Finally, a numerical analysis illustrates the solution procedures and examines the effects of dynamic parameters on decision-making. The results show that the centralized model obtains a higher profit than the decentralized model. Further, the results of the numerical analysis can serve as references for business managers or administrators.

A decomposition-based matheuristic for supply chain network design with assembly line balancing

This paper addresses a supply chain network design problem with assembly line balancing, which considers a three-layer supply chain including manufacturers, assemblers, and customers. By analyzing the characteristics and complexities of the problem, we decompose it into an upper-level problem and two lower-level problems. The upper level problem is to determine the assignment amount of each assembler. The two lower-level problems include the assembly line balancing problem (ALBP) inside each assembler and the transportation problem (TP) between different layers. To solve the problem effectively, a decomposition-based matheuristic (DMAT) is proposed by fusing metaheuristic and model-based approaches. The ALBP is solved by using branch and bound, and a lookup table method is proposed to speed up the computation time. The TP is solved by using mathematical programming. By solving the lower-level problems, the cost function of the upper-level problem can be evaluated. To optimize the upper-level problem, a metaheuristic based on the differential evolution is presented. In population initialization, the problem specific heuristics are designed. To repair and improve the infeasible solutions, reassignment with local intensification is designed. Numerical tests with extensive instances demonstrate the effectiveness of the proposed DMAT.

A stochastic risk-averse sustainable supply chain network design problem with quantity discount considering multiple sources of uncertainty

Sustainability in supply chain management is an inescapable and controversial issue due to government legislation and social responsibilities of the organizations. For that reason, a sustainable supply chain network design (SSND) has been recently developed as a means of dealing with environmental and social issues along with the economic aspect of a supply chain. On the other hand, considering uncertainty and risk aversion in the supply chain network design due to the volatility of the markets and the unavailability of sufficient information is inevitable. Thus, in this paper, a risk-averse sustainable multi-objective mathematical model is proposed in order to design and plan a network of the supply chain under uncertainty by incorporating Conditional Value at Risk (CVaR) into the basic configuration of the two-stage stochastic programming. In some other cases, many organizations offer some sort of discount to their customers so that they can compete with their rivals and enhance their accounts receivable. So, it seems ineluctable for companies to contemplate considering discount policies while making such strategic decisions for sustainability. Therefore, in this paper, we propose a mixed integer non-linear programming (MINLP) problem in order to consider discounts in the proposed model. Sensitivity analyses are also conducted on some important risk-aversion parameters in order to evaluate how these parameters affect the proposed mathematical model and the obtained Pareto solutions.

A Pareto-based optimization algorithm for a multi-objective integrated production-distribution planning problem of a multi-echelon supply chain network design

This paper addresses a multi-periodic supply chain network design (SCND) problem involving suppliers, manufacturers, distribution centres (DCs), and customer zones (CZs). Logistic decisions made in each time period have a tactical nature. Location/strategic decisions are made at the beginning of the time horizon and remain unchanged until the last period. While both backorders and lost sales are considered, the aim is to design the supply chain network (SCN) under three minimisation objectives including total costs, the transfer time of products to CZs, and backorder level and lost sale of products. A multi-objective mixed-integer linear programming (MILP) model is developed and a meta-heuristic algorithm named multi-objective vibration damping optimisation (MOVDO) with tuned parameters is proposed to find non-dominated solutions. The performance of this method is compared with two popular existing algorithms called NSGA-II and NRGA when they solve some randomly generated problems.

A multi-stage supply chain network design problem with in-house production and partial product outsourcing

We propose a mixed-integer linear programming model for a novel multi-stage supply chain network design problem. Our model integrates location and capacity choices for plants and warehouses with supplier and transportation mode selection, and the distribution of multiple products through the network. The aim is to identify the network configuration with the least total cost subject to side constraints related to resource availability, technological conditions, and customer service level requirements. In addition to in-house manufacturing, end products may also be purchased from external sources and consolidated in warehouses. Therefore, our model identifies the best mix between in-house production and product outsourcing. To measure the impact of this strategy, we further present two additional formulations for alternative network design approaches that do not include partial product outsourcing. Several classes of valid inequalities tailored to the problems at hand are also proposed. We test our models on randomly generated instances and analyze the trade-offs achieved by integrating partial outsourcing into the design of a supply chain network against a pure in-house manufacturing strategy, and the extent to which it may not be economically attractive to provide full demand coverage.

Land use and public health impact assessment in a supply chain network design problem: A case study

The establishment of freight transport facilities and logistics operations in urban areas tends to create substantial public health and land use burdens, due to the high concentration of people and the limited availability of land. This paper proposes a tri-objective supply chain network design model, to address the economic, public health, and land use impacts, associated with an urban freight transport network. The capability of the proposed model and methodologies are illustrated using a case study of the freight transport network in Can Tho city, which involves the selection of raw material sourcing and product distribution locations across rural and urban areas. The metric of disability-adjusted life years (DALYs) is used to represent the overall public health impact of the transport emissions exposure in people living within 5000 m of the transportation routes. Geographical information system (GIS) tools are used to explore the population density distribution across transport network areas and to estimate the number of exposed people. The land use impact due to the presence of warehouses and plants is taken into account, using the life cycle impact assessment (LCIA) method. The obtained design solutions under single- and multi-objective optimization are presented. The trade-offs among the contradictory objectives are also analyzed to obtain various alternative solutions, allowing geographers to understand how to design environmentally benign, urban freight transport networks, based on sustainable development preferences.

Integration of multi-product supply chain network design and assembly line balancing

In the supply chain design problems, optimization is usually done without any attention to performance and efficiency of each part of chain. On the other hand, in Assembly Line Balancing (ALB) problems, the objective of the problems is to optimize with regard to the situation of assemblers, while in this set of problems, concentration on some factors such as how to provide raw materials and also sending final products to customers are very important. Since optimal decisions in each problem will affect the other decisions, it is required to study these two matters at the same time; otherwise, the validity of the results will be reduced. In this paper, the problem of integration of multi-product Supply Chain Network designing and ALB are addressed. A Mixed Integer Nonlinear Programming model is proposed to formulate the studied problem. The model is solved by using GAMS in the form of a numerical example given in small size. Finally, Imperialist Competitive Algorithm (ICA) and Genetic Algorithm (GA) as two well-known meta-heuristic algorithms are applied to solve the model with larger dimensions. The computational results show efficiency of the ICA compared with GA. Moreover, after decomposing the integrated mode into two sub problems (ALB problem and Supply Chain Network Design problem), it is shown that optimum cost value of integrated mode is better than total cost functions of two sub-problems separately.

A Capacitated Location-Inventory Model with Demand Selection

In this paper, we consider an integrated supply chain network design problem, which incorporates inventory and pricing decisions into the capacitated facility location model. We assume that each warehouse has a capacity limitation that limits the average demand flowing through the warehouse and that the supplier can choose whether to satisfy each potential retailer’s demand. We formulate the problem as a nonlinear integer programming model and solve the model via a Lagrangian relaxation based approach. We develop an efficient algorithm to solve the subproblem that arises from the Lagrangian relaxation procedure. Finally, we conduct extensive computational experiments to test the performance of the algorithms proposed in this paper and provide the managerial insights based on the computational results.

Integrated optimization of location, inventory and routing in supply chain network design

We study the integrated optimization of location, inventory and routing in supply chain network design (SCDN) problem. The SCND decision has significant impact on the performance of the supply chain. The major obstacles of the integrated optimization are the high non-linearity and the huge amount of variables and constraints caused by routing. In this paper, we introduce real-world constraints into the integrated model to characterize the model much more precisely, which simultaneously reduce the dimension of the problem. We propose an exact algorithm based on the Generalized Benders Decomposition (GBD) method to solve the model. We can obtain the Benders Cuts (BC) explicitly. We verify the effectiveness of the model and method by a real-life case: A passenger car supply chain network design case.

Fuzzy Bi-Objective Model for a Supply Chain Network Design Problem Considering Stochastic Transportation Leadtime

In this paper, a fuzzy bi-objective integer programming for a supply chain network design (SCND) problem considering stochastic transportation leadtime is proposed. The conflicting objectives considered in this study are maximizing the total net profit related to the supply chain network design and minimizing the probability of tardiness associated with transportation leadtime between manufacturing plant and distribution centers, and distribution centers and customers. It is assumed that possible risk associated with transportation leadtime follows a normal distribution with the established mean and standard deviation. The model was tested by varying risk levels in order to achieve the maximum satisfaction level in the network. By considering the acceptable satisfaction levels, analyzing the results of the proposed model will be providing different scenarios, which helps decision makers to have better insight to design the desired supply chain network. Results indicate that by decreasing the risk level, probability of earliness to deliver products to customers increases while profit decreases.

Strategic Decision-Making in Global Supply Chain Network Design – How a Decision Support System Combining MILP and AHP on a Pareto Front Can Alleviate Decision-Making

Integrating quantitative and qualitative data is a challenge in global supply chain decision making. Decision support tools should be easier to use and focus on providing transparency and supporting human judgement. A strong emphasis on multi-objective models and trade-offs is needed. We investigate methods to solve strategic global supply chain network design problems and develop a solution framework, comprised of mixed-integer linear programming, the Analytical Hierarchy Process and the Pareto front. It is applied in a case study in the med-tech industry. Results show that the framework is easy to use for practitioners, accommodates qualitative as well as quantitative criteria and provides transparency over the entire range of efficient solutions. Managers can use it to discuss and select the configuration best fitting to their overall strategy. We believe the framework and application results can contribute to the development of more effective and user-friendly decision support tools for strategic global network design.

Mô hình tối ưu việc lựa chọn vị trí nhà xưởng trong chuỗi cung ứng để lắp đặt hệ thống pin năng lượng mặt trời

Utilization of solar energy is an effective solution for a company to reduce dependence on fossil energy and CO2 emissions. Therefore, this study presents a green supply chain network design problem considering solar rooftop photovoltaic systems (PVs) to minimize total cost and CO2 emissions occurred during production and transportation activities. Net present value (NPV) is used to evaluate and verify the feasibility of installing PVs for different facility sizes. Following this analysis, a mixed integer linear programming model (MILP) is proposed to determine facility location, the amount of transportation between components in supply chain (SC). The capability of the model and the methodology are then applied for a case study of garment mannufacturer located in the Mekong Delta region, Viet Nam. Results showed that adopting PVs creates huge benefits in energy cost savings and CO2 emissions reducion for the whole supply chain.

Humanitarian supply chain network design using data envelopment analysis and multi-objective programming models

Emergency events such as natural disasters or terrorist attacks seem to occur anywhere and tend to increase. This paper studies a humanitarian supply chain network (HTSCN) design problem in a pre-disaster scenario, which consists of finding the optimal emergency response facility (ERF) locations and allocation scheme of humanitarian supplies through ERFs, where all ERFs are under the risk of disruptions. Naturally, this type of design problem should deal with multiple goals. An innovative two-step framework of designing efficient HTSCN by combining multi-objective programming (MOP) models with data envelopment analysis (DEA) is proposed. A case study using the historical data on the disasters in South Carolina, USA is presented to illustrate the effectiveness and efficiency of the proposed combining framework. The case study demonstrates that the proposed procedure would help practitioners and researchers generate a finer evaluation of efficiency and would provide a benchmarking methodology for designing HTSCN system. [Received: 22 October 2017; Revised: 9 April 2018; Revised: 15 October 2018; Revised: 22 December 2018; Accepted: 26 January 2019]

A distributed approximation approach for solving the sustainable supply chain network design problem

This paper introduces a comprehensive Mixed Integer Linear Programming (MILP) model for a sustainable supply chain network design problem, and an efficient Distributed Approximation Approach (DAA) to solve it approximately. We study a multi-echelon, multi-product and multi-modal supply chain with different transportation modes. Besides relevant costs in the supply chain such as procurement, production and distribution cost, we also explicitly consider the environmental footprint, represented by carbon emissions and water consumption from production and transportation. The approximation approach is a decomposition-based method. First, the original problem is divided into a partner selection sub-problem and a transportation planning sub-problem. Then multiple filter mechanisms are used to remove potentially infeasible solutions, and an approximate value of the objective function is calculated for each of the remaining solutions to perform a further selection. The one with the lowest approximation is chosen to be applied with a branch-and-bound method. Finally, the algorithm is paralleled and implemented in Apache Spark distributed computing framework to further improve efficiency. Experimental results show that the proposed DAA can provide high quality solutions compared to the optimal solutions of the MILP model with mostly a negligible relative gap and solve large instances in much shorter time than CPLEX. Moreover, in our numerical study, we also compare the results of our model with another version of the model that does not take the environmental footprint into consideration. The results show that explicitly incorporating environmental footprint results in a substantial decrease of CO2 emissions and water consumption at a negligible cost increase. This insight may be of interest to managers and other decision makers and policy makers.

Multi-objective Fuzzy Programming of Closed-Loop Supply Chain Considering Sustainable Measures

The substantial impact of environmental and social issues on business performance highlights the significant role of sustainability in supply chain network design problems. The innate uncertainty of the problem factors such as customer demand and return rates requires an integrated approach to deal with the problem. This paper aims to design a closed-loop supply chain (CLSC) network considering both sustainability and uncertainty issues. Fuzzy programming is used to address the uncertainty in this study. A fuzzy multi-objective mixed-integer linear programming is developed to simultaneously optimize a sustainable CLSC network. The objectives are considered to be the total costs and the environmental impacts minimization and social benefits maximization. Three pillars of sustainability (cost, environmental, and social) are taken into account in network design. A three-phased fuzzy solution approach is developed to solve this model. To examine the significance of the proposed model and the solution approach, a computational experiment is conducted. The results approve the applicability of the proposed approach and the feasibility of the solution methodology. Results also show that the proposed model presents a systematic framework that enables the management to obtain a satisfactory solution by adjusting the search direction.

Multi-Period Configuration of Forward and Reverse Integrated Supply Chain Networks with the Choice of Transport Mode

Today’s competitive business environment has resulted in increasing attention to social responsibilities and customer’s attitudes. Buying and returning have become a common practice for different reasons, including incompleteness or immature failure of the product or its failure to meet the customer’s satisfaction. Before the buying and returning cycle can be handled appropriately, companies need a proper logistics network designed following a proper design strategic. In the present research, a forward and reverse logistics network is proposed for product distribution and collection. The contribution of this paper to the literature is the proposal of a multi-period, multi-echelon, integrated forward and reverse supply chain network design problem with transportation mode selection considered. Different kinds of decisions including the determination of optimum number and locations of facilities, facilities opening time and transportation mode selection among different facilities have been considered in this paper. Due to multi-period nature of the problem, the problem is flexible for future periods. A new mixed integer nonlinear programming model was proposed for the introduced problem considering different levels of facility capacities with the maximum profit objective function. As another contribution, a genetic algorithm was developed to cope with problem’s complexity when the problem size goes large.