Developing a two-stage model for a sustainable closed-loop supply chain with pricing and advertising decisions
در حال توسعه یک مدل دو مرحله ای برای یک زنجیره تامین حلقه بسته پایدار با تصمیمات قیمت گذاری و تبلیغات-2021
Closed-Loop Supply Chain (CLSC) has become a critical problem due to its effects on various factors including economic motivations, environmental concerns, and social impacts. Moreover, there are coordination tools, such as pricing and advertising, which impact its performance. In this paper, we offer a two-stage approach to model and solve a sustainable CLSC, taking into account pricing, green quality, and advertising. In the first stage, optimal decisions on pricing, greening, and advertising are made, while in the second stage, a fuzzy multi- objective Mixed Integer Linear Programming (MILP) model is used to maximize the total profit, reduce CO2 emissions, and improve social impacts. Suitable solution methods are introduced according to the scale of the problem. For small-scale instances, an augmented ϵ-constraint method is used to solve the problem. For large-scale instances, approximations are required, and a Lagrangian relaxation algorithm solves the problem in polynomial time. The performance of the proposed model is evaluated through various numerical examples. The results illustrate the applicability and efficiency of the model, while confirming significant improvements in sustainable objectives under optimal pricing, green quality, and advertising. Besides, the proposed Lagrangian relaxation method significantly reduces the computational time for large-scale instances, with only a 2.308% deviation from the optimal results.
Keywords: Sustainable closed-loop supply chain | Multi-objective programming | Supply chain pricing | Augmented ϵ-constraint | Lagrangian relaxation | CO2 emissions
Efficient and sustainable closed-loop supply chain network design: A two-stage stochastic formulation with a hybrid solution methodology
طراحی شبکه زنجیره تامین حلقه بسته کارآمد و پایدار: یک فرمول تصادفی دو مرحله ای با روش راه حل ترکیبی-2021
In recent years, consumers and legislators have pushed companies to design their supply chain networks to consider environmental and social impacts as an important performance outcome. Due to the role of resource utilization as a key component of logistics network design, another primary goal of design is ensuring available scarce resources are used as efficiently as possible across all facilities. To address efficiency issues in a sustainable closed-loop supply chain network, a stochastic integrated multi-objective mixed integer nonlinear programming model is developed in this paper, in which sustainability outcomes as well as efficiency of facility resource utilization are considered in the design of a sustainable supply chain network. In doing so, efficiency is assessed using a bi-objective output-oriented data envelopment analysis model. A hybrid three-step solution methodology is presented that creates a linear form of the original mixed integer nonlinear programming problem using piecewise McCormick envelopes approach. In the second step, an aggregated single objective programming model is derived by exploiting the multi-choice goal programming. Finally, a Lagrangian relaxation algorithm is developed to effectively solve the latter stochastic single objective mixed integer linear programming problem. The application of the proposed approach is investigated with data drawn from a case study in the electronics industry. This case study illustrates how firms may balance sustainability and efficiency in the supply chain network design problem. Further, it demonstrates the integration of efficiency results in improving economic aspects of sustainability as well as social responsibility outcomes, but also highlights the trade-offs that exist between efficiency and environmental impacts.
Keywords: Closed-loop supply chain network | Sustainability | Data envelopment analysis | Stochastic programming | Multi-choice goal programming | Lagrangian relaxation
A multi-objective fuzzy robust stochastic model for designing a sustainable-resilient-responsive supply chain network
یک مدل تصادفی محکم فازی چند هدفه برای طراحی یک شبکه زنجیره تأمین پایدار ، قابل انعطاف و پاسخگو-2021
This study proposes a multi-objective mixed-integer programming model to configure a sustainable supply chain network while considering resilience and responsiveness measures. The model aims at minimizing the total costs and environmental damages while maximizing the social impacts, as well as the responsiveness and resilience levels of the supply chain network. An improved version of the fuzzy robust stochastic optimization approach is proposed to tackle the uncertain data arising in the dynamic business environment. Furthermore, a new version of meta-goal programming named the multi-choice meta-goal programming associated with a utility function is developed to solve the resulting multi-objective model. A case study in the water heater industry is investigated to illustrate the application of the proposed model and its solution approach. The numerical results validate the proposed model and the developed solution method. Finally, interactions between the sustainability, responsiveness, and resilience dimensions are investigated and several sensitivity analyses are performed on critical parameters by which useful managerial insights are provided.
Keywords: Supply chain network design | Sustainability | Resilience | Responsiveness | Fuzzy robust stochastic optimization | Multi-choice meta-goal programming
Conceptual MINLP approach to the development of a CO2 supply chain network – Simultaneous consideration of capture and utilization process flowsheets
رویکرد مفهومی MINLP برای توسعه یک شبکه زنجیره تامین CO2 - در نظر گرفتن همزمان صفحه های جریان فرآیند ضبط و استفاده-2021
A large fraction of anthropogenic CO2 emissions comes from large point sources such as power plants, petroleum refineries, and large industrial facilities. A significant decrease of these CO2 emissions can be achieved with CO2 capture, utilization, and storage (CCUS) technologies. This study proposes a conceptually simplified model for the optimization of combined CO2 supply networks and capture and utilization technologies by the mixed-integer non-linear programming (MINLP) approach. The objective is to maximize the profit of CCUS technologies, considering chemisorption using methyl-diethanolamine (MDEA) as a capture technology and conversion of CO2 to CH3OH as a utilization technology. Additionally, avoided tax from reduced CO2 emissions is considered as a revenue. A hypothetical case study of five larger point sources of CO2 was investigated, namely coal power plants, biogas plant, aluminium production plant and two cement plants. Two scenarios were considered: i) Scenario A considering different values of the CO2 tax, and ii) Scenario B considering different flue gas flowrates at different values of the CO2 tax. The results show the potential of model-based optimization in reducing the amount of CO2 in the atmosphere by CCUS technology. Furthermore, the results in Scenario A show that CCUStechnology is only profitable if the price of CO2 emissions is higher than 110 €/t emitted CO2. Moreover, the results in Scenario B show that both the profit and the production of CH3OH depend to a large extent on the flue gas flow.
KEYWORDS: Point sources of CO2 | Carbon capture | Storage and utilization (CCUS) | Supply network optimization | Process optimization | MINLP approach
A detailed MILP formulation for the optimal design of advanced biofuel supply chains
یک فرمول دقیق MILP برای طراحی بهینه زنجیره های پیشرفته تأمین سوخت زیستی-2021
The optimal design of a biomass supply chain is a complex problem, which must take into account multiple interrelated factors (i.e the spatial distribution of the network nodes, the efﬁcient planning of logistics activities, etc.). Mixed Integer Linear Programming has proven to be an effective mathematical tool for the optimization of the design and the management strategy of Advanced Biofuel Supply Chains (ABSC). This work presents a MILP formulation of the economical optimization of ABSC design, comprising the deﬁnition of the associated weekly management plan. A general modeling approach is proposed with a network structure comprising two intermediate echelons (storage and conversion facilities) and accounts for train and truck freight transport. The model is declined for the case of a multi- feedstock ABSC for green methanol production tested on the Italian case study. Residual biomass feed- stocks considered are woodchips from primary forestry residues, grape pomace, and exhausted olive pomace. The calculated cost of methanol is equal to 418.7 V/t with conversion facility cost accounting for 50% of the fuel cost share while transportation and storage costs for around 15%. When considering only woodchips the price of methanol increases to 433.4 V/t outlining the advantages of multi-feedstock approach.© 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-NDlicense (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords: Residual biomass | Advanced biofuels | Supply chain design | Logistics network | MILP | Optimization
A multi-objective robust optimization model for upstream hydrocarbon supply chain
یک مدل بهینه سازی قوی چند هدفه برای زنجیره تأمین هیدروکربن بالادست-2021
The hydrocarbon supply chain (HCSC) is a significant part of the world’s energy sector. The energy market has experienced erratic behavior over the last few years results in financial risks such as exceeding certain limits of the budget or not achieving the desired levels of cash in-flow, i.e., revenue. In this work, robust optimization and multi-objective mathematical programming are used to develop a model that eliminates or at least mitigates the impact of uncertain market behavior. Robust optimization provides tactical plans that are feasible and robust over market scenarios. The model assesses the trade-offs between alternatives and guides the decision-maker towards the effective management of the HCSC. The economic objectives are to minimize total cost and maximize revenue, while the non-economic objective is to minimize the depletion rate. The model considers the environmental aspect by limiting the emission of CO2 and the sustainability aspect by reducing the depletion rate of natural resources. Uncertain behavior of the oil market is modeled on scenario representation. A case study based on real data from Saudi Arabia HCSC is provided to demonstrate the model’s practicality, and a sensitivity analysis is conducted to provide some managerial insights. The results indicate that Saudi Arabia can cover its entire expenditure, break-evenpoint, by producing oil at 7.18 MMbbld and gas at 3,543.48 MMcftd. Besides, the robust approach provides a preferred plan with the highest cash inflow and the lowest sustainability over other approaches, e.g., deterministic, stochastic, and risk-based. The differences show that the robust model increases oil production to compensate for the variability of the scenario.
KEYWORDS: Hydrocarbon supply chain | Multi-objective optimization | Robust optimization | Scenario-Based Optimization | Tactical planning
Multi-objective optimization modelling of sustainable green supply chain in inventory and production management
مدلسازی چند هدفه بهینه سازی زنجیره تأمین سبز پایدار در مدیریت موجودی و تولید-2021
The ever increasing pressure to conserve the environment from global warming cannot be overemphasized. Emission from the inventory and production process contributes immensely to global warming and hence, the need to device a sustainable green inventory by the operational managers. In this paper, a multi-item multi-objective inventory model with back-ordered quantity incorporating green investment in order to save the environment is proposed. The model is formulated as a multi-objective fractional programming problem with four objectives: maximizing profit ratio to total back-ordered quantity, minimizing the holding cost in the system, minimizing total waste produced by the inventory system per cycle and minimizing the total penalty cost due to green investment. The constraints are included with budget limitation, space restrictions, a constraint on cost of ordering each item, environmental waste disposal restriction, cost of pollution control, electricity consumption cost during production and cost of greenhouse gas emission in the production process. The model effectiveness is illustrated numerically, and the solution obtained to give a useful suggestion to the decision-markers in the manufacturing sectors.
KEYWORDS: Multi-objective fractional programming | Fuzzy goal programming | Sustainable green supply chain | Inventory and production management
The optimal recovery-fund based strategy for uncertain supply chain disruptions: A risk-averse two-stage stochastic programming approach
استراتژی مبتنی بر صندوق بازیابی بهینه برای اختلالات نامشخص زنجیره تأمین: رویکرد برنامه ریزی تصادفی دو مرحله ای ریسک پذیر-2021
For a supply chain subject to uncertain production disruptions, the joint optimization of invest- ment intervention on recovery speed and duration of disrupted production capacity and location and inventory management has not been well studied. In this paper, a novel recovery strategy is introduced and studied, which uses investment to adjust the recovery speed and duration of production capacity, and two recovery behaviors responding to different types of disruptions are modeled. Considering uncertain disruption scenarios and their ripple effects over the supply chain, a risk-averse two-stage stochastic programming model (RTSPM) is established to study the integrated supply chain management of selection of distribution centers, multi-period inventory, transportation flows, and recovery-fund based mitigation policy. The RTSPM incorporates the risk preference of managers in decision making. We propose a trust-region-based decomposition method to solve the RTSPM and demonstrate its efficiency by benchmarking on state-of-the-art commercial solvers. Through numerical examples, we deeply analyze the effectiveness of RTSPM and the relations of optimal recovery investment decisions with the uncertain disruption factors. Finally, we provide implications and suggestions induced from the models and findings to aid the decisions on renting of distribution centers and the emergency investment and operational decisions when suffering the disruptions.
Keywords: Supply chain disruption management | Recovery-fund based mitigation strategy | Location-inventory-transportation model | Risk-averse two-stage stochastic programming | Trust-region-based decomposition method
Supply chain design to tackle coronavirus pandemic crisis by tourism management
طراحی زنجیره تامین برای مقابله با بحران همه گیری ویروس کرونا توسط مدیریت گردشگری-2021
The rapid growth of the COVID-19 pandemic in the world and the importance of controlling it in all regions have made managing this crisis a great challenge for all countries. In addition to imposing various monetary costs on countries, this pandemic has left many serious damages and casualties. Proper control of this crisis will provide better medical services. Controlling travel and tourists in this crisis is also an effective factor. Hence, the proposed model wants to control the crisis by controlling the volume of incoming tourists to each city and region by closing the entry points of that region, which reduces the inpatients. The proposed multi-objective model is designed to aim at minimizing total costs, minimizing the tourist patients, and maximizing the number of city patients. The Improved Multi-choice Goal programming (IMCGP) method has been used to solve the multi-objective problem. The model examines the results by considering a case study. Sensitivity analyses and managerial insight are also provided. According to the results obtained from the model and case study, two medical centers with the capacity of 300 and 700 should be opened if the entry points are not closed.© 2021 Elsevier B.V. All rights reserved.
Keywords: Pandemic control | COVID-19 | Multi-objective supply chain optimization | IMCGP | Tourism management
Biomass supply chain coordination for remote communities: A game-theoretic modeling and analysis approach
هماهنگی زنجیره تأمین زیست توده برای جوامع از راه دور: رویکرد مدل سازی و تحلیل نظری بازی-2021
Biomass, as one of the most available renewable energies, could reduce dependency on fossil fuels and the consequent environmental impacts. There is a need for biomass supply chain management, which is managing bioenergy production from harvesting feedstock to energy conversion facilities. In case of remote communities, bioenergy adoption requires dealing with dispersed geographies of suppliers and places of consumption with small scales of energy demand. As such, coordination plays a key role in increasing the efficiency of the biomass supply chain network through bundling of demand and thus improving the economy of scale. This paper employs a game-theoretic approach to formulate a coordinated biomass supply chain with three echelons including suppliers, hubs, and energy convertors. To investigate the strategic interactions of participants, three decision making structure scenarios have been considered under Stackelberg game providing insights into the impact of power distribution, the role of side payments in enforcing the flow of decisions, and the resulting efficiency and performance improvements. In doing so, a case study bioenergy supply chain for three northern Canadian communities is explored to demonstrate the application of the proposed formulation, solution methods, and the practicality and significance of the adopted approach and outcomes for remote communities.
Keywords: Bioenergy | Supply chains | Coordination | Remote communities | Game theory | Mathematical Program with Equilibrium | Constraints (MPEC)