Abstract
Bioenergy is a new source of energy that accounts for a substantial portion of the renewable energy production in many countries. The production of bioenergy is expected to increase due to its unique advantages, such as no harmful emissions and abundance. Supply-related problems are the main obstacles precluding the increase of use of biomass (which is bulky and has low energy density) to produce bioenergy. To overcome this challenge, large-scale optimization models are needed to be solved to enable decision makers to plan, design, and manage bioenergy supply chains. Therefore, the use of effective optimization approaches is of great importance. The traditional mathematical methods (such as linear, integer, and mixed-integer programming) frequently fail to find optimal solutions for non-convex and/or large-scale models whereas metaheuristics are efficient approaches for finding near-optimal solutions that use less computational resources. This paper presents a comprehensive review by studying and analyzing the application of metaheuristics to solve bioenergy supply chain models as well as the exclusive challenges of the mathematical problems applied in the bioenergy supply chain field. The reviewed metaheuristics include: (1) population approaches, such as ant colony optimization (ACO), the genetic algorithm (GA), particle swarm optimization (PSO), and bee colony algorithm (BCA); and (2) trajectory approaches, such as the tabu search (TS) and simulated annealing (SA). Based on the outcomes of this literature review, the integrated design and planning of bioenergy supply chains problem has been solved primarily by implementing the GA. The production process optimization was addressed primarily by using both the GA and PSO. The supply chain network design problem was treated by utilizing the GA and ACO. The truck and task scheduling problem was solved using the SA and the TS, where the trajectory-based methods proved to outperform the population-based methods.
Highlights
Bioenergy is a crucial renewable source of energy that provides benefits, in terms of the economical and the environmental perspectives, by diminishing the dependence on oil as a source of energy [1]. the production of bioenergy is expected to increase in the near future [2,3,4], logistics costs are a major obstacle in the enhancement of the bioenergy production [5], primarily because biomass is bulky, has low energy density, and its availability is seasonal.Mathematical optimization of logistics problems within the bioenergy industry includes complex constraints related to transportation, feedstock supply sources, operational and logistics costs and power plant location and capacity
This review summarized the latest research regarding the development of metaheuristics including population-based algorithms: ant colony optimization (ACO), genetic algorithm (GA), particle swarm optimization (PSO), and bee colony algorithm (BCA) as well as two trajectory-based methods: tabu search (TS) and simulated annealing (SA) for solving bioenergy supply chain problems and provided insights for researchers and practitioners
This review illustrates that bioconversion and production process optimization, scheduling, and integrated supply chain planning are remarkable areas of inquiry and application of metaheuristics
Summary
Bioenergy is a crucial renewable source of energy that provides benefits, in terms of the economical and the environmental perspectives, by diminishing the dependence on oil as a source of energy [1]. the production of bioenergy (defined as the biomass exploitation for the production of energy and bio-based chemical products) is expected to increase in the near future [2,3,4], logistics costs are a major obstacle in the enhancement of the bioenergy production [5], primarily because biomass is bulky, has low energy density, and its availability is seasonal.Mathematical optimization of logistics problems within the bioenergy industry includes complex constraints related to transportation, feedstock supply sources, operational and logistics costs and power plant location and capacity. Deterministic multi-criteria programming models can optimize two conflicting objectives by providing a Pareto front with non-dominated feasible solutions. Classical optimization approaches are based upon gradient methods to obtain the optimal solution of convex, continuous, and differentiable functions. Classical optimization algorithms cannot provide a global solution for large-scale stochastic non-convex non-linear problems with multiple objective functions and constraints. To address this problem, metaheuristics, which are higher-level strategies that guide and modify other heuristics (experience-based techniques for problem solving) to produce solutions beyond local optimal solutions and obtain a near-optimal solution for large-scale complex problems [6,7,8], can be employed. Kurka and Blackwood [11] selected and reviewed the Multi-Criteria
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