Supply Chain Network Planning Research Articles

An Improved Migratory Birds Optimization Algorithm for Closed- Loop Supply Chain Network Planning in a Fuzzy Environment

An Improved Migratory Birds Optimization Algorithm for Closed- Loop Supply Chain Network Planning in a Fuzzy Environment

A Proposal of Resilient Supply Chain Network Planning Method with Two-stage Stochastic Programming and Latin Hypercube Sampling

A Proposal of Resilient Supply Chain Network Planning Method with Two-stage Stochastic Programming and Latin Hypercube Sampling

Multi-Objective Sustainable Supply Chain Network Planning Based on Proximity Optimization With Hybrid Genetic Algorithm Variable Neighborhood Search Strategy

Multi-Objective Sustainable Supply Chain Network Planning Based on Proximity Optimization With Hybrid Genetic Algorithm Variable Neighborhood Search Strategy

Regional bioethanol supply chain optimization with the integration of GIS-MCDM method and quantile-based scenario analysis

This study presents a novel decision-support framework for the bioethanol supply chain network planning and management under uncertainties. Under the holistic framework, the most suitable sites for biorefineries are first screened out by adopting a GIS-based multi-criteria decision-making approach. Then, a mixed-integer linear programming model combined with quantile-based scenario analysis is developed to determine the strategic planning (i.e. locations and size of biorefineries) and tactical management (i.e. biomass purchasing, feedstock transportation, bioethanol production, and product delivery) under uncertainties. The model can effectively search for reliable solutions under uncertainties and achieve tradeoff solutions with the consideration of decision makers’ risk tolerance. The proposed framework is demonstrated through a case study in China. It is suggested to build seven biorefineries with a capacity of 100 million liters in Zhumadian city. Utilizing 41% of local agricultural residues could satisfy the bioethanol requirement in the transportation sector under the E20 policy. However, the estimated production cost of bioethanol in Zhumadian is very high, about 1.11 $/L, which makes it lose cost advantage in the fuel market. Thus, currently, effective subsidies, mandatory energy substitution policies, along other environmental regulatory measures are desired to promote the bioethanol industry development.

A Bilevel Model for Carbon Pricing in a Green Supply Chain Considering Price and Carbon-Sensitive Demand

In today’s industrial landscape, there is a mounting urgency to mitigate the adverse environmental impacts of emissions stemming from supply chain operations. On one front, policy-makers impose increasingly stringent emission reduction targets for supply chains, while on another front, consumers express a heightened preference for products and services with reduced carbon footprints. This study addresses the challenge of determining an optimal carbon pricing strategy by integrating the imperatives of a green supply chain with carbon taxation policies. To this end, we introduce a bi-level mixed-integer linear programming model for supply chain network planning, encompassing considerations of carbon taxation policies and the responsiveness of demand to the final product’s price and associated carbon emissions. Findings from a case study underscore that an escalation in carbon pricing prompts the supply chain to prioritize emissions reduction through the utilization of environmentally conscious approaches. The results reveal the need for a USD 0.9/kg carbon price to achieve a 10% emission reduction, resulting in an 80% profit decline. Notably, a 10% reduction has profound impacts, which leads to the suggestion of a gradual approach. Furthermore, as carbon prices reach higher levels, the supply chain tends toward curtailing production, thereby fostering an environment conducive to emission abatement. Consequently, policy formulators must judiciously calibrate a fitting carbon pricing mechanism to strike a harmonious equilibrium between emission reduction targets and the financial outlays of the supply chain.

Integrated simulation-optimization modeling framework of resilient design and planning of supply chain networks

Optimal control is a convenient way to develop both supply chain process optimization models and describe the dynamics of process fulfillment. A rich diversity of knowledge has been developed for the integration of optimization and simulation methods with applications to supply chain management at conceptual, informational, and computational levels. At the same time, model-algorithmic integration and alignment frameworks have received less attention. The importance of this level should not be underestimated since synthesis and analysis problems in supply chains imply tight intersections between and within the models (e.g., objective functions and constraint systems). This paper seeks to bring the discussion forward by carefully elaborating on the issues of optimization and simulation model and algorithm integration and providing implementation guidance. Conventionally, optimization has pre-dominantly been used at the planning level while dynamic system control was frequently investigated using simulation models. This study develops an integrated optimization-simulation framework at the model-algorithmic level for the given domain. We offer insights on how to describe planning and control in a unified model-algorithmic complex with consideration of uncertainty factors which are anticipated at the planning and confronted at the control stages. The developed theoretical framework was exemplified by a combined optimization-simulation modelling of the SC design and planning problem with disruption risks consideration in anyLogistix.

Multiobjective Optimization of Sustainable WCO for Biodiesel Supply Chain Network Design

Bioenergy is attracting more attention worldwide due to its environmental and economic benefits. The design of a feasible biodiesel supply chain network can effectively improve the production and use of biodiesel and then further promote the development of the biodiesel industry. As an easy recyclable material with high yield, kitchen waste has a good prospect and can solve public health and safety problems. This paper takes the kitchen waste producing biodiesel as the object to design and optimize the biodiesel supply chain in order to improve the sustainable development of biodiesel industry and the operational efficiency of the biodiesel supply chain. By designing a sustainable biodiesel supply chain model under defined conditions, it proposes strategic and tactical decisions related to location, production, inventory, and distribution within multiple planning cycles. In order to effectively solve the model, a Pareto optimal NSGAII heuristic algorithm is proposed and applied to a practical case study of restaurants in Jiangsu Province. The efficiency of the method and the optimal solution are verified by a case study. The overall optimization of biodiesel supply can effectively improve the efficiency of supply chain, reduce system cost, improve the profit of biodiesel operators, and promote the sustainable development of biodiesel industry, which has important guiding significance and reference value for the practice of biodiesel supply chain network planning.

System modelling and empirical study on 3PL location in a multi-nodes supply chain network

The multi-nodes supply chain network composed of suppliers, logistics centres, transfer stations, retailers and other different types of enterprise nodes was taken as the research object in this paper. Starting from the demand-driven of retailers, the mathematical model of third-party logistics centre location problem was established in a multi-nodes supply chain network according to the logical design of bottom-up supply chain network planning. The improved genetic algorithm was adopted to solve the problem and carried out the analysis of an actual case. The empirical analysis results demonstrate that the modelling and the algorithm in a multi-nodes supply chain network are reasonable and effective. The optimal location of different logistics nodes can be obtained, and the corresponding total system cost is the lowest. This paper provides an improved algorithm and technological innovation in the field of logistics location for the multi-nodes supply chain network.

Supply Chain Network Planning in Diversification of Tourism Products and Community Culture Creativity Efforts to Overcome Unemployment in Ubud Village Bali

Supply Chain Network Planning in Diversification of Tourism Products and Community Culture Creativity Efforts to Overcome Unemployment in Ubud Village Bali

Assessing site integration into semi-continuous production, distribution and liquidity planning of supply chain networks

Abstract In order to strengthen competitiveness, the pool of available network sites in supply chains of processing industries with 24/7 operations should be adjusted permanently. However, an integration of additional sites is to be assessed thoroughly by the network managers as an exact coordination with existing partners must be ensured within multi-day planning horizons. On the one hand, continuous-time scheduling of network-wide operations exclusively allows for using time-based assessment criteria such as the minimum production time to be provided by the integrated sites. On the other hand, the assessment needs to be based on the calculation of network-wide liquidity, which must not fall below its previous value after the realization of an integration. A realistic consideration of financial planning needs to include both credits and financial investments not being based on given interest rates, but on cash flow series characterized by arbitrary compositions of payments and repayments occurring after any intervals of time periods. Thus, it is to be implemented by discrete-time modeling. As a result of merging both methods of assigning decisions to appropriate points on the time axis, a semi-continuous mixed-integer linear model is developed in the paper on hand. By interrelating three different shapes of this model, the minimum and maximum scope of an integration can be determined. The approach is validated for an illustrative example of a three-stage supply chain network, which is extended by two or three additional production sites. The computability of the problem on a high-performance cluster is evaluated in a scenario analysis including 50 test instances with randomly generated data.

Integrated supply chain network planning and financial planning respecting the imperfection of the capital market

Within supply chain networks, the integration of decisions regarding configuration, operations and financing is important to balance overall liquidity and to prevent insolvency. This applies in particular in case of a business take-over by an institutional investor, as the latter may formulate return expectations that require complete restructuring of processes. Motivated by the real-life case of a sanitary company in Germany that had to deal with the aforementioned situation, we develop a mixed-integer linear programming model that is able to coordinate planning during the investor’s engagement (limited to a specific number of years) with payouts that are structured according to his/her consumption preferences. As the investor’s scope of action is characterized by alternatives that additionally include financial investments and financings with different transaction-specific interest rates, it stands to base modeling on the premises of imperfect capital markets. The underlying coherent theory is applied to the integrated supply chain network planning and financial planning after a business take-over for the first time. Besides the exemplary analysis of a four-stage supply chain with three suppliers, plants, warehouses and markets, a numerical analysis with other randomly generated network structures is used for the validation of the model formulation. As a result, all considered instances could be solved to optimality in acceptable computation times using high-performance hardware and software.

A flow-to-equity approach to coordinate supply chain network planning and financial planning with annual cash outflows to an institutional investor

AbstractA common side effect of cross-linked global economies is that well-positioned middle class companies are acquired by institutional investors, which formulate unreasonable return expectations in many cases. As a consequence, the resulting payouts are often not in line with business operations so that even world market leaders get into trouble or close down. In this context, we consider the case of a sanitary company, which had to manage the described situation after a business takeover. In order to coordinate the annual cash outflows to the investor with intra-organizational supply chain planning and financial planning, we propose a mixed-integer non-linear programming model that is based on the flow-to-equity discounted cash flow method. The objective is to maximize the present value of equity while determining annual cash outflows to the institutional investor during his engagement. As the decisions of the investor during his engagement influence possible operations of the company after his engagement, the residual value of equity (that influences the selling price) is taken into account. The modeling is based on cash flow series, which result from supply chain operations and restructuring on the one hand, and from financial transactions on the other. Financing is characterized by interest rates depending on the time period the credit starts, the credit period, the debt limit of the company and the current total debt. As the latter is a result of the optimization, non-linearity arises. Nevertheless, both the expected demand scenario and further randomly generated demand scenarios of the sanitary company could be solved to the optimum with the commercial optimization package GAMS 23.8/SCIP 2.1.1 within acceptable computation times, if capacity profiles are assigned to the locations to depict feasible and/or preferred capacity developments.

The planning and optimisation of a supply chain network under conditions of uncertainty

This paper discusses the planning and optimisation of a supply chain network under uncertainty, and which is followed by a case study. Research revealed that the nature of demand uncertainty was the key differentiator between the various supply chain optimisation techniques analysed and therefore this development is based on a survey of existing multi-uncertainty supply chain optimisation techniques, followed by an analysis, extraction and collation of the core components contained therein, and thereby creating the desired 'supply chain network planning and optimisation methodology under conditions of uncertainty'. In the example, the objective is to maximise the production and distribution of a range of NPK fertiliser in accordance with market demand whilst simultaneously minimising the discharge of effluent. The optimum operating results achieved were very good, i.e., throughput of NPK fertiliser was 99.3% of maximum possible capacity and effluent discharge was 94.1% of minimum possible.

The planning and optimisation of a supply chain network under conditions of uncertainty

This paper discusses the planning and optimisation of a supply chain network under uncertainty, and which is followed by a case study. Research revealed that the nature of demand uncertainty was the key differentiator between the various supply chain optimisation techniques analysed and therefore this development is based on a survey of existing multi-uncertainty supply chain optimisation techniques, followed by an analysis, extraction and collation of the core components contained therein, and thereby creating the desired 'supply chain network planning and optimisation methodology under conditions of uncertainty'. In the example, the objective is to maximise the production and distribution of a range of NPK fertiliser in accordance with market demand whilst simultaneously minimising the discharge of effluent. The optimum operating results achieved were very good, i.e., throughput of NPK fertiliser was 99.3% of maximum possible capacity and effluent discharge was 94.1% of minimum possible.

A literature review on green supply chain modelling for optimising CO<SUB align="right">2 emission

Global warming impacts are becoming more visible in our daily life. Supply chain activities and many logistics activities are the leading sources of carbon dioxide (CO2) emission and environmental pollutions. These issues have raised concerns to reduce CO2 emissions amount through design and planning of supply chain networks. Operations research has been recognised by many studies as an effective tool to deal with CO2 emission in design and planning of green supply chains. To date, a number of literature reviews have highlighted the contribution of operations research to green supply chain management with broader areas of focus. In this paper, we present a review which highlights the operations research contribution to recent green supply chain and logistics literature which specifically focuses on planning and control of supply chain activities with respect to CO2 emission. Finally, we propose some possible areas for further developments of current studies and directions for future research.

An Endosymbiotic Evolutionary Algorithm for the Hub Location-Routing Problem

We consider a capacitated hub location-routing problem (HLRP) which combines the hub location problem and multihub vehicle routing decisions. The HLRP not only determines the locations of the capacitatedp-hubs within a set of potential hubs but also deals with the routes of the vehicles to meet the demands of customers. This problem is formulated as a 0-1 mixed integer programming model with the objective of the minimum total cost including routing cost, fixed hub cost, and fixed vehicle cost. As the HLRP has impractically demanding for the large sized problems, we develop a solution method based on the endosymbiotic evolutionary algorithm (EEA) which solves hub location and vehicle routing problem simultaneously. The performance of the proposed algorithm is examined through a comparative study. The experimental results show that the proposed EEA can be a viable solution method for the supply chain network planning.

Design of Simulator for Reverse Supply Chain Network

Recently, many countries have introduced various regulations and steps to reduce pollution and investigate to reuse energy. Thus, environmental conscious research has become important and has been widely studied in today's industries. Reverse supply chain network (RSCN) problem has been widely investigated in the research area. The purpose of this study is to design the simulator in the reverse supply chain network. Real world reverse supply chain network problem has many factors and constraints. Considering the complexity of solving such model, this study had applied the simulation technology. This study shows basic design concept and architecture of simulator for the reverse supply chain network problem. The simulator can be used to decide a realistic reverse supply chain network planning in the area.

Integrated Planning of Supply Chain Networks and Multimodal Transportation Infrastructure Expansion: Model Development and Application to the Biofuel Industry

Abstract: As the biofuel industry continues to expand, the construction of new biorefinery facilities induces a huge amount of biomass feedstock shipment from supply points to the refineries and biofuel shipment to the consumption locations, which increases traffic demand in the transportation network and contributes to additional congestion (especially in the neighborhood of the refineries). Hence, it is beneficial to form public‐private partnerships to simultaneously consider transportation network expansion and biofuel supply chain design to mitigate congestion. This article presents an integrated mathematical model for biofuel supply chain design where the near‐optimum number and location of biorefinery facilities, the near‐optimal routing of biomass and biofuel shipments, and possible highway/railroad capacity expansion are determined. The objective is to minimize the total cost for biorefinery construction, transportation infrastructure expansion, and transportation delay (for both biomass/biofuel shipment and public travel) under congestion. A genetic algorithm framework (with embedded Lagrangian relaxation and traffic assignment algorithms) is developed to solve the optimization model, and an empirical case study for the state of Illinois is conducted with realistic biofuel production data. The computational results show that the proposed solution approach is able to solve the problem efficiently. Various managerial insights are also drawn. It shall be noted that although this article focuses on the booming biofuel industry, the model and solution techniques are suitable for a number of application contexts that simultaneously involve network traffic equilibrium, infrastructure expansion, and facility location choices (which determine the origin/destination of multi‐commodity flow).

A Tax Integrated Approach for Global Supply Chain Network Planning

Globalization is here to stay. Companies source, manufacture, and sell across borders. There are several destinations available for undertaking these activities offering varying degrees of incentives and at each destination the company incurs a different delivery cost. Multinational companies need to make more realistic decisions about where to make, source, locate, move, and store products to minimize the total cost of delivery keeping in mind the incentives offered by the governments and the logistics costs at and from the location. Current literature on supply chain optimization does not emphasize on tax. To attract foreign investment, many developing economies have included tax-holidays in their export-import (EXIM) policy for companies operating in free trade zones (FTZs). In this paper, we propose a tax integrated mixed integer model, for optimally deciding the foreign direct investment (FDI)-outsourcing (the choice of establishing captive production centers versus complete outsourcing) alternatives at the various stages of a global supply chain. For a general acyclic supply chain, this decision problem is NP-hard and obtaining analytical results on optimal FDI-outsourcing strategy may be difficult. We linearize the tax integrated model by introducing exactly one hub at each stage. In this case, termed hub-based sourcing-single hub case, we prove that the greedy strategy is an optimal FDI-outsourcing strategy. However, by associating multiple hubs at each stage the decision problem remains NP-hard. Finally, we empirically analyze the tax integrated model (for the general case) on a use-case scenario in which some locations in the choice have free trade zones offering tax incentives.

Environmentally conscious long-range planning and design of supply chain networks

In this paper, a mathematical programming-based methodology is presented for the explicit inclusion of life cycle assessment (LCA) criteria as part of the strategic investment decisions related to the design and planning of supply chain networks. By considering the multiple environmental concerns together with the traditional economic criteria, the planning task is formulated as a multi-objective optimization problem. Over a long-range planning horizon, the methodology utilizes mixed integer modelling techniques to address strategic decisions involving the selection, allocation and capacity expansion of processing technologies and assignment of transportation links required to satisfy the demands at the markets. At the operational level, optimal production profiles and flows of material between various components within the supply chain are determined. As such, the formulation presented here combines the elements of the classical plant location and capacity expansion problems with the principles of LCA to develop a quantitative decision-support tool for environmentally conscious strategic investment planning.