A Novel Matheuristic based on bi-level optimization for the multi-Objective design of hydrogen supply chains

This paper presents an optimization modeling approach to support strategic planning for designing hydrogen supply chain (HSC) networks. The energy source for hydrogen production is proposed to be electricity generated at Mexican sugar factories. This study considers the utilization of existing infrastructure in strategic areas of the country, which brings several advantages in terms of possible solutions. This study aims to evaluate the economic and environmental implications of using biomass wastes for energy generation, and its integration to the national energy grid, where the problem is addressed as a mixed-integer linear program (MILP), adopting maximization of annual profit, and minimization of greenhouse gas emissions as optimization criteria. Input data is provided by sugar companies and the national transport and energy information platform, and were represented by probability distributions to consider variability in key parameters. Independent solutions show similarities in terms of resource utilization, while also significant differences regarding economic and environmental indicators. Multi-objective optimization was performed by a genetic algorithm (GA). The optimal HSC network configuration is selected using a multi-criteria decision technique, i.e., TOPSIS. An uncertainty analysis is performed, and main economic indicators are estimated by investment assessment. Main results show the trade-off interactions between the HSC elements and optimization criteria. The average internal rate of return (IRR) is estimated to be 21.5% and average payback period is 5.02 years.

Hydrogen supply chain optimization for deployment scenarios in the Midi-Pyrénées region, France

One of the main critical steps that should be taken during natural disasters is the assignment and distribution of resources among affected people. In such situations, this can save many lives. Determining the demands for critical items (i.e., the number of injured people) is very important. Accordingly, a number of casualties and injured people have to be known during a disaster. Obtaining an acceptable estimation of the number of casualties adds to the complexity of the problem. In this paper, a location-routing problem is discussed for urgent therapeutic services during disasters. The problem is formulated as a bi-objective Mixed-Integer Linear Programming (MILP) model. The objectives are to concurrently minimize the time of offering relief items to the affected people and minimize the total costs. The costs include those related to locations and transportation means (e.g., ambulances and helicopters) that are used to carry medical personnel and patients. To address the bi-objectiveness and verify the efficiency and applicability of the proposed model, the ε-constraint method is employed to solve several randomly-generated problems with CLEPX solver in GAMS. The obtained results include the objective functions, the number of the required facility, and the trade-offs between objectives. Then, the parameter of demands (i.e., number of casualties), which has the most important role, is examined using a sensitivity analysis and the managerial insights are discussed.

The role of sustainability in the function of a company and more specifically a food company is pivotal for its financial performance. The environmental issues as well as the potential economic gains from the implementation of its principles ask for the use of multiple instruments that have been developed to green supply chains. Moreover, social issues also arise and involve the food companies social responsibility, as this can be realized through the supply of fresh products that meet consumption security standards. On this basis, the strategic design of these companies’ supply chains can assists them towards meeting their sustainability objectives as it may lead to the selection of transportation modes, location of entry points and distribution centers, and flows between the nodes of the networks under cost, environmental and social impact minimization criteria. Under this context the purpose of this manuscript is to develop and employ a multi-objective (namely cost, social-time and emission minimization) mixed integer linear programming decision-making model for the network design of sustainable supply chains of perishable food products. The specific model was implemented in the case of a fruits importer in the North-Eastern European region considering its geographical settings. To synopsize and according to our findings the suggested model is an easy to use decision-making tool that leads to a whole set of possible solutions incorporating trade-offs between three aspects of sustainability.

Renewable hydrogen supply chain for transport application in Corsica island

Multi-objective optimization of a hydrogen supply chain network: Wind and solid biomass as primary energy sources for the public transport in Sicily

Chapter 11 - Multiobjective Life Cycle Optimization of Hydrogen Supply Chains

Design and operation of a future hydrogen supply chain: Multi-period model

We present the first (criterion space search) algorithm for optimizing a linear function over the set of efficient solutions of biobjective mixed integer linear programs. The proposed algorithm is developed based on the triangle splitting method [Boland N, Charkhgard H, Savelsbergh M (2015) A criterion space search algorithm for biobjective mixed integer programming: The triangle splitting method. INFORMS J. Comput. 27(4):597–618.], which can find a full representation of the nondominated frontier of any biobjective mixed integer linear program. The proposed algorithm is easy to implement and converges quickly to an optimal solution. An extensive computational study shows the efficacy of the algorithm. We numerically show that the proposed algorithm can be used to quickly generate a provably high-quality approximate solution because it maintains a lower and an upper bound on the optimal value of the linear function at any point in time.

Design of a future hydrogen supply chain: A multi period model for Turkey

Simultaneous product family and supply chain design: An optimization approach

Bioethanol supply chain network design considering land characteristics

Renewable Energies play crucial role in modern distribution systems. An active distribution system consists of several Micro-Grids (MGs) each of which has kinds of Distributed Generations (DGs) and Demand Response (DR) resources. Hence MG optimal operation is bi-level optimization problem whose upper level seeks for micro-grid owner profit and the lower level corresponds to distribution companies (DC) profit. In such a system MGs can exchange power between each other as well as distribution company. Therefore, upper level problem determines DGs generation, curtailed load, power exchange with other MGs subject to load balance constraint. Also Distribution Company (DISCO) clears retail market. On the other hand, renewable energies uncertainty and variably makes the MGs operation more complicated. In this paper, renewable energy variability handled by shift able load management and their uncertainty has been managed by curtailable load. The developed model has been solved by Particle Swarm Optimization (PSO) for upper level and then with given population in each iteration, lower level problem has been solved completely by other PSO package. This process ends when upper level PSO converge. Numerical studies have been shown that renewable management by proposed model leads to more optimal solutions.

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.

Ensemble of metaheuristics for energy-efficient hybrid flowshops: Makespan versus total energy consumption