Research on Remanufacturing Closed-Loop Logistics Network Design under Low-Carbon Restriction

Due to tremendous losses of resources in modern food supply chains, higher priority should be given to reducing food waste and environmental impacts of food production. In practice, multiple production options are available, but must be quantitatively assessed with respect to economic and environmental performances before they are adopted in food supply chains. The objective of this paper is to develop a mathematical model that can be used for such a quantitative assessment of alternative production options that are associated with different ways to deal with waste in food supply chains, i.e. prevention, recycling, and disposal of food waste. We develop a multi-objective mixed integer linear programming model to derive the set of eco-efficient solutions corresponding to production planning decisions. Environmental performance of the chain is expressed with an indicator based on exergy analysis, which has the potential to capture other commonly used indicators, such as energy consumption, fuel consumption, and waste generation, and express them in a single value. This simplifies the calculation of the eco-efficient frontier, and enables its intuitive graphical representation, which is much easier to communicate to the involved decision makers. The applicability of the developed model is demonstrated on a real-life industrial bread supply chain in the Netherlands. Results confirm the findings from literature that prevention is the best waste management strategy from environmental perspective. The advantages of using exergy as an indicator to capture the environmental performance is demonstrated by comparing the outcomes to other commonly used indicators of environmental performance. We illustrate the potential of studying food production planning decision problems in a multi-objective context, and demonstrate the applicability of the model in the assessment of alternative production options.

Not only greenhouse gases but also other air pollutant emissions from transportation have direct impacts on the environment and human health. The challenge of solving the conflict between profit and environmental consequences in logistics has motivated many studies on the vehicle routing problem. In this paper, a multiobjective mixed integer linear programming model is proposed to minimize transportation expenses and pollutant emissions for the multi-depot heterogeneous vehicle routing problem. A metaheuristic is adapted to obtain the Pareto optimal solutions. After a search procedure, decision makers are able to choose among best transportation plans that balance many objectives at once including economic benefits and environmental impacts. Computational experiments are performed on seven well-known benchmark problem sets. The results demonstrate the existence of greener transportation plans, which are illustrated alongside the best solutions previously reported. The study shows that, in return for a minimal economic tradeoff, a substantial amount of pollution could be avoided.

This paper proposes a comprehensive framework for strategic power system decarbonization planning that integrates the William Newman method (diagnosis–options–forecast–decision) with a multi-objective Mixed-Integer Linear Programming (MILP) model. The approach simultaneously minimizes (i) generation cost, (ii) expected cost of energy not supplied (Value of Lost Load, VoLL), (iii) demand response cost, and (iv) CO₂ emissions, subject to power balance, technical limits, and binary unit commitment decisions. The methodology is validated on the IEEE RTS 24-bus system with increasing demand profiles and representative cost and emission parameters by technology. Three transition pathways are analyzed: baseline scenario (no environmental restrictions), gradual transition (−50% target in 20 years), and accelerated transition (−75% target in 10 years). In the baseline case, the oil- and coal-dominated mix concentrates emissions (≈14 ktCO₂ and ≈12 ktCO₂, respectively). Under gradual transition, progressive substitution with wind and hydro reduces emissions by 15.38%, falling short of the target, showing that renewable expansion alone is insufficient without storage and demand-side management. In the accelerated transition, the model achieves −75% by year 10 while maintaining supply, with a cost–emissions trade-off highly sensitive to the carbon price. Results demonstrate that decarbonization is technically feasible and economically manageable when three enablers are combined: higher renewable penetration, storage capacity, and policy instruments that both accelerate fossil phase-out and valorize demand-side flexibility. The proposed framework is replicable and valuable for outlining realistic, verifiable transition pathways in power system planning.

Owing to its interconnections and open characteristics, Physical Internet (PI) has the potential to help companies meet the sustainability challenges. In this paper, to investigate the sustainability performance of the PI in an integrated production-inventory-distribution system, we propose a multi-objective mixed integer linear programming model (MOMILP) that encompasses all three dimensions of sustainability. The economic and environmental objectives are measured through the total cost and greenhouse gas (GHG) emissions, respectively. The social objective is captured by the impact of accident risk. Then, we solve the model by using the augmented ϵ-constraint method and compare the sustainability performance of the PI-enabled model with that of models enabled by the traditional (TR) and horizontal collaboration (HC) networks. The results show that the PI can not only improve the economic and environmental performance by increasing vehicle utilisation, but also alleviate the impacts of accident risks by avoiding risky routes. More interestingly, good compromise solutions featuring the PI can further improve the sustainability performance while guaranteeing the performance of the TR at the ideal point—something unachievable for the HC and TR. Moreover, sensitivity analyses demonstrate that the PI can still guarantee significant sustainability performance advantages under different common retailer quantities and network structures.

Multi-objective optimisation of a carbon capture and sequestration supply chain under seismic risk constraints. A case study considering industrial emissions in Italy

Cycle-tourism which is a type of niche and sustainable tourism is increasingly promoted by transport and tourism agencies in recent years, but there are still few researches on the cycle-tourist route planning. This paper aims to formulate the cycle-tourist route-planning problem, in order to maximize cycle-tourists’ utility of visiting points of interest (POIs), minimize the total travel time, maximize the Bicycle Level of Service (BLOS) and minimize the number of intersections on the cycle route taken, subject to monetary and time budget, etc. A multi-objective mixed integer linear programming model is formulated to optimize the cycle-tourist route plan. The problem is solved by augmented -constraint method (AUGMECON) whereby a set of nondominated solutions are generated to address the trade-offs among multiple objectives. The proposed model can help local government and agencies promote cycle-tourism and help cycle-tourists better plan the route to satisfy their personal requirements in a sustainable transportation system.

Biofuels production has been promoted in the attempt to address global warming and oil dependence concerns. However, the environmental impact of biofuels is a very complex issue and cannot be represented by GHG (greenhouse gas) emissions only (carbon footprint). In particular, water consumption (water footprint) has been recognized as a key issue in renewable fuels production. This paper proposes a multiobjective Mixed Integer Linear Programming modeling framework to optimize the environmental (i.e., the carbon and water footprints) and economic performances of bioethanol supply chains. Multiechelon, multiperiod, and spatially explicit features are embedded within the formulation to steer decisions and investments through a global approach. The strategic design and planning of corn- and stover-based bioethanol production networks is taken into account. A case study is presented referring to the emerging Italian ethanol production. Results show the effectiveness of mathematical programming-based tools to p...

Sustainable supply chain networks have attracted considerable attention in recent years as a means of dealing with a broad range of environmental and social issues. This paper reports a multi-objective mixed-integer linear programming (MILP) model for use in the design of a sustainable closed loop supply chain network under uncertain conditions. The proposed model aims to minimize total cost, optimize environmental impacts of establishment of facilities, processing and transportation between each level as well as social impacts including customer satisfaction. Due to changes in business environment the uncertainty existed in the research problem, in this paper the chance constrained fuzzy programming approach applied to cope with uncertainties in parameter of the proposed model. Then the proposed multi-objective model solves as single-objective model using LP-metric method.

Today, serious environmental problems arise with overconsumption. The most effective way of protecting the environment is recycling. To obtain maximum benefit from the recycling activities depends on the effective and robust design of the disassembly lines. In addition, the distribution plan of products to be recycled is as important as the disassembly process. Therefore, this study includes the integrated optimization of the partial disassembly line balancing problem and the multi-objective green vehicle routing problem for the first time. The integrated problem is formulated as a Multi-Objective Mixed Integer Linear Programming. The model’s objectives consist of minimization of total CO2 emission and cost. The Pareto optimal solution set is found by solving this problem with the Augmented ℇ-Constraint Method. The model respectively determines (i) the total number of opened workstations in the disassembly center, (ii) optimal task assignments to workstations in disassembly centers and non-disassembled tasks, (iii) total workstation times and idle times of each workstation, (iv) optimal distribution routes for all vehicles. This study examines a case study in Turkey. To determine the best Pareto optimal solution for homogeneous and heterogeneous fleets, the integrated multi-criteria decision-making method consisting of EWM (Entropy Weight Method) and COPRAS (Complex Proportional Assessment) is used.

Passive Optical Network (PON) deployments have recently been aiming to combine the capacity of metro and access networks in the last mile of the Internet service provisioning. Deployment of PONs by running fiber to the premises introduces the advantage of huge capacity but at the same time, it calls for a robust design in order to avoid long service outage durations in case of network failures where survivable network design is mostly limited to the deployment budget. In this paper, we propose three mixed integer linear programming (MILP) models for various survivability policies to deploy reliable long-reach PONs under the budget limitations. Each MILP model aims to place the ONUs in optimal locations so that the covered area is maximized while availability requirements of the users are satisfied within the deployment budget. We solve the MILP models under the uniform and heterogeneous availability requirement scenarios and show that service availability and coverage introduce a trade-off so as the coverage and deployment cost do. Two out of the three survivability policies can guarantee 99.99% service availability while the third one is able to guarantee 99.999% by running the proposed MILP models. However, the first two schemes are able to cover larger area when compared to the third scheme which is the most reliable protection policy.

Pharmaceutical industry supply chains: How to sustainably improve access to vaccines?

Multi-objective optimization of a distributed energy network integrated with heating interchange

The increasing level of customer awareness and application of environmental laws by governments, on the one hand, and the increasing number of competitors, on the other hand, has obliged industry owners to include green activities in the design of the supply chain network. Green activities include any type of action that reduces environmental degradation. Hence, this study seeks to develop a bi-objective, mixed integer linear programming (MILP) model for designing a green supply chain network. In the proposed model, the minimisation of costs and detrimental environmental effects are discussed. LP metric method is used to solve the bi-objective model and the proposed model is run by using simulated data in small and medium sizes in GAMS software. Finally, the model sensitivity analysis is measured in order to evaluate the validity and performance of the proposed model by using demand-driven scenarios. The results of this model indicate the effectiveness of the proposed model.

This research uses multi-objective optimisation to determine the optimal mixture of energy and transportation technologies, while optimising economic and environmental impacts. We demonstrate the added value of using multi-objective mixed integer linear programming (MOMILP) considering economies of scale versus using continuous multi-objective linear programming assuming average cost intervals. This paper uses an improved version to solve MOMILPs exactly. To differentiate optimal solutions with and without subsidies, the impact of policy on the Pareto frontier is assessed. We distinguish between minimising economic life cycle costs (complete rationality) and required investments (bounded rationality). The approach is illustrated using a Belgian company with demands for electricity and transport. Electricity technologies are solar photovoltaics and the grid; transportation includes internal combustion engine vehicles, grid powered battery electric vehicles (BEVs), and solar-powered BEVs. The impact of grid powered BEVs to reduce GHG emissions is limited, yet they are less costly than solar panels to decrease emissions. Current policy measures are found to be properly targeting rational investors who consider life cycle costs, while private (potentially bounded rational) investors often focus on required investments only.

Analytic network process and multi-period goal programming integration in purchasing decisions