Linear Programming Research Articles

Orbit-finite linear programming

An infinite set is orbit-finite if, up to permutations of atoms, it has only finitely many elements. We study a generalisation of linear programming where constraints are expressed by an orbit-finite system of linear inequalities. As our principal contribution we provide a decision procedure for checking if such a system has a real solution, and for computing the minimal/maximal value of a linear objective function over the solution set. We also show undecidability of these problems in case when only integer solutions are considered. Therefore orbit-finite linear programming is decidable, while orbit-finite integer linear programming is not.

Nested benders decomposition for a deterministic biomass feedstock logistics problem

AbstractIn this paper, we address a biomass feedstock logistics problem to supply biomass from production fields to satellite storage locations (SSLs) and from there to bioenergy plants (BePs) and then to a biorefinery. It entails a new problem feature of routing load-out equipment sets among the SSLs to perform loading/unloading of biomass and/or its pre-processing operations. The ownership of the loading equipment is a very capital-intensive link of the ethanol production supply chain, which when loaded onto trucks and routed along the logistics chain significantly brings down the ethanol production costs. This will make ethanol a cost-competitive alternative to fossil fuels, lead to sustainable use of fossil fuels and add to the overall relevance of the bioenergy sector. In this regard, the objective of our problem is to minimize the total cost incurred due to the ownership of equipment sets, fixed setups, and land rental cost, as well as the cost of transporting biomass from the fields to the BePs and biocrude oil from the BePs to the refinery. A mixed-integer mathematical model of the problem is presented, and a nested Benders decomposition-based solution approach is developed which involves decomposing this large problem into three stages. Stage 1 deals with the selection of fields, BePs, and SSLs, and assignment of fields to the SSLs. The remaining model consists of multiple Capacitated Vehicle Routing Problems (CVRPs) that are separable over individual BePs. For each BeP, the CVRP is further decomposed into Stage 2 and Stage 3 sub-problems where the Stage 2 problem is an allocation problem that assigns SSLs to tours associated to each BeP, and the Stage 3 problem is a variant of Traveling Salesman Problem (TSP) that determines the sequence in which equipment is routed over the predesignated set of SSLs for each tour. These sub-problems are integer programs rather than linear programs. First novelty of our proposed approach is to effectively handle the integrality of variables arising due to the consideration of the routing of load-out equipment. Second is solution methodology and in the use of proposed multi-cut version of optimality cuts that capture the solution value at an integer solution for the sub-problems. These cuts aid in faster convergence and are shown to be stronger than those proposed in the literature. The applicability of the proposed methodology is demonstrated by applying it to a real-life problem that utilizes available GIS data for the catchment area of regions around Gretna and Bedford in Virginia. We then solved a set of varying problem size instances using the state-of-the-art CPLEX® Branch-and-Bound and Benders Strategy methods. The CPLEX® algorithms struggled to solve instances even 10 times smaller than the real-life problem size instances; with MIP optimality gaps ranging from 5.85% to 82.79% in the allowed time limit of 10,000 s. On the other hand, our proposed nested Benders decomposition algorithm was able to achieve faster convergence and provided optimal solutions for all the considered problem instances with an average CPU run-time of around 3,700 s. This validates the efficacy and superiority of our solution approach. Lastly, we summarize our work and point out some interesting potential future research opportunities.

Heuristic Approaches for Using an Exact Model to Obtain the Placement of a Set of Rectangles on a Half-Strip

There is considered the problem of the compact orthogonal placement, which consists in placing of the set of rectangles on the half-strip and its solution by constructing an optimization model that reduces to solving a high-dimensional linear partial Boolean programming problem. The main method of solving the problem is the algorithm of branches and boundaries, providing consistent fiXation of the Boolean variables. The direct use of this algorithm represents significant computational difficulties and is used only for the small size of the initial set of rectangles. In this paper are proposes approaches that reduce the computational complexity of the solution of this optimization model by using heuristic approaches to fix individual Boolean variables. Most of the variables responsible for the relative arrangement of all pairs of rectangles relative to each other are fixed on the basis of generating the permutation of rectangles and special code that determines the direction by which the intersection of the location of the rectangles should be monitored. Thus, the direct computing complexity of the method of branches and the boundaries of solving the problem becomes insignificant. To generate pre-fixation of the Boolean variables, genetic algorithms and local search algorithms are used. Computational experiments showed that the used techniques of fixing the Boolean variables allowed to reduce the volume of calculations and the quality of the resulting approximate solution, in general, it turns out acceptable.

Maximizing the number of allowable approximation errors when building a linear regression model

Subject of research: errors in approximation of a linear regression model within the framework of a logical-algebraic approach to data analysis. Purpose of the study: to develop an algorithmic method for solving the problem of maximizing the number of permissible approximation errors using a linear-Boolean programming computer. Methods and objects of research: the object of research is a linear regression model, the methods are linear regression analysis and mathematical programming apparatus. Main results of the study: an algorithmic method is proposed for maximizing the number of permissible absolute and relative errors in approximation of a linear regression equation, which reduces to solving linear-Boolean programming problems of a dimension acceptable for practical situations. Solving generated problems of this type should not cause computational problems due to a significant number of effective software tools, for example, the LPsolve program, which is freely available on the Internet.

Dynamic pricing and demand forecasting: Integrating time-series analysis, regression models, machine learning, and competitive analysis

Abstract. Dynamic pricing is a critical strategy for businesses seeking to optimize revenue and stay competitive in fluctuating markets. This paper explores the integration of various demand forecasting techniques, including time-series analysis, regression models, and machine learning algorithms, with competitive analysis methodologies to enhance dynamic pricing strategies. Time-series analysis focuses on decomposing data into trend, seasonality, and random fluctuations, using ARIMA models for accurate demand prediction. Regression models delve into the complexities of variable interactions, extending beyond linear relationships to include advanced techniques like Ridge, Lasso, and Elastic Net regression. Machine learning algorithms, such as decision trees, random forests, gradient boosting, and neural networks, revolutionize demand forecasting by uncovering complex patterns in large datasets. Competitive analysis incorporates market scanning, price elasticity estimation, and competitor behavior modeling to inform dynamic pricing decisions. Optimization algorithms, including linear programming, genetic algorithms, and simulated annealing, are employed to refine pricing strategies, while revenue management techniques like yield management and overbooking ensure maximum revenue from perishable goods and services. This comprehensive approach enables businesses to dynamically adjust prices in real-time, maintaining competitiveness and maximizing profitability in an ever-evolving market landscape.

APPLICATION OF OPTIMIZATION METHOD IN MULTIDIMENSIONAL SPACE TO THE PROBLEM OF DETERMINING FULLERENE CONCENTRATION IN FULLERENE-CONTAINING MIXTURE

The work proposes a method for the quantitative processing of experimental data from ultraviolet spectroscopy of substituted fullerene mixtures, taking into account errors in the experimental data. This task can be written as a system of Vierordt equations (a system of linear equations). The coefficients of the system are the extinction coefficients of individual substituted fullerenes, which are difficult to measure accurately. Therefore, the mathematical model of the problem contains significant errors that affect the reliability and stability of the solution. To minimize the influence of errors, the work [1] proposes a method, which involves reducing the considered system of linear equations to a linear programming problem. This study generalizes the work [1] and proposes to use linear programming problems to find interval values of correction factors for parameters containing errors. Since decreasing the discrepancy reduces the solution error only for well-conditioned coefficient matrices, the values of correction coefficients that minimize the condition number of the coefficient matrix are found in the obtained intervals. The found correction values allowed to significantly improve the conditionality of the system coefficient matrix and obtain a more accurate solution. By using the refined extinction coefficients, the distribution of molar fractions of fullerene fragments and fullerene-containing compounds in the mixture of products formed by the attachment of cyanoisopropyl radicals was obtained, as well as the distribution of molar fractions of fullerene fragments of different degrees of substitution for samples of fullerene-containing polymethyl methacrylate and the distribution of molar fractions of fullerene fragments of different degrees of substitution for samples of fullerene-containing polystyrene. The results are correct and consistent with the theory, both for low-molecular-weight mixtures and for fullerene-containing polymers.

Digital Garment Alteration

AbstractGarment alteration is a practical technique to adapt an existing garment to fit a target body shape. Typically executed by skilled tailors, this process involves a series of strategic fabric operations—removing or adding material—to achieve the desired fit on a target body. We propose an innovative approach to automate this process by computing a set of practically feasible modifications that adapt an existing garment to fit a different body shape. We first assess the garment's fit on a reference body; then, we replicate this fit on the target by deriving a set of pattern modifications via a linear program. We compute these alterations by employing an iterative process that alternates between global geometric optimization and physical simulation. Our method utilizes geometry‐based simulation of woven fabric's anisotropic behavior, accounts for tailoring details like seam matching, and incorporates elements such as darts or gussets. We validate our technique by producing digital and physical garments, demonstrating practical and achievable alterations.

Linear Programming with Unitary-Equivariant Constraints

Unitary equivariance is a natural symmetry that occurs in many contexts in physics and mathematics. Optimization problems with such symmetry can often be formulated as semidefinite programs for a dp+q\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$d^{p+q}$$\\end{document}-dimensional matrix variable that commutes with U⊗p⊗U¯⊗q\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$U^{\\otimes p} \\otimes {\\bar{U}}^{\\otimes q}$$\\end{document}, for all U∈U(d)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$U \\in \ extrm{U}(d)$$\\end{document}. Solving such problems naively can be prohibitively expensive even if p+q\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p+q$$\\end{document} is small but the local dimension d is large. We show that, under additional symmetry assumptions, this problem reduces to a linear program that can be solved in time that does not scale in d, and we provide a general framework to execute this reduction under different types of symmetries. The key ingredient of our method is a compact parametrization of the solution space by linear combinations of walled Brauer algebra diagrams. This parametrization requires the idempotents of a Gelfand–Tsetlin basis, which we obtain by adapting a general method inspired by the Okounkov–Vershik approach. To illustrate potential applications of our framework, we use several examples from quantum information: deciding the principal eigenvalue of a quantum state, quantum majority vote, asymmetric cloning and transformation of a black-box unitary. We also outline a possible route for extending our method to general unitary-equivariant semidefinite programs.

AI-Driven Numerical Optimization for Carbon Footprint Reduction and Sustainable Supply Chain Management in the Fashion Industry

This study explores how AI-driven optimization can help lower the carbon footprint of the fashion industry's supply chain. By using advanced AI models, the research delves into sustainable strategies across core areas—sourcing, production, logistics, and retail—focusing on reducing emissions, energy use, water consumption, and waste. Techniques like linear programming, genetic algorithms, and reinforcement learning showcase how AI can bring about real, measurable environmental benefits. The findings reveal that these optimized processes boost energy efficiency, save water, and cut down waste, making them valuable tools for sustainable supply chain management. The study wraps up with practical recommendations for adopting AI-based models to help the fashion industry embrace sustainability more effectively.

A prospective approach to the optimal deployment of a hydrogen supply chain for sustainable mobility in island territories: Application to Corsica

This study develops a framework for designing hydrogen supply chains (HSC) in island territories using Mixed Integer Linear Programming (MILP) with a multi-period approach. The framework minimizes system costs, greenhouse gas emissions, and a risk-based index. Corsica is used as a case study, with a Geographic Information System (GIS) identifying hydrogen demand regions and potential sites for production, storage, and distribution. The results provide an optimal HSC configuration for 2050, specifying the size, location, and technology while accounting for techno-economic factors. This work integrates the unique geographical characteristics of islands using a GIS-based approach, incorporates technology readiness levels, and utilizes renewable electricity from neighboring regions. The model proposes decentralized configurations that avoid hydrogen transport between grids, achieving a levelized cost of hydrogen (LCOH) of €8.54/kg. This approach offers insight into future options and incentive mechanisms to support the development of hydrogen economies in isolated territories.

A novel branch and bound algorithm for solving the linear multiplicative programming problems

In this paper, we propose a novel branch and bound algorithm for solving the linear multiplicative programming (LMP) problem. A new quadratic relaxation is proposed. We first show that the Hessian matrix of the multiplication of two linear functions has a decomposition form, given by the difference of two rank-one matrices, resulting in a decomposition of each quadratic term of the objective function. And then the minus term is relaxed into a linear one. To find the global optimal solutions, branch and bound method is adopted. Theoretical analysis is provided that guarantees the convergence of our algorithm. Numerical experiments show that the new algorithm runs faster than several typical algorithms for certain types of LMP.

Optimization of Maintenance Strategies in an Oil and Gas Production Facility to Minimize Maintenance Cost

Abstract: In this study, maintenance strategies in an oil and gas production facility were optimized to minimize maintenance costs. The centrifugal pump system in Port Harcourt Refinery was selected for this study because it significantly impacts on the productivity of the refining plant. The pump system’s failure mode effects and criticality analysis (FMECA) were examined, and an optimized maintenance task was developed for the system. The exponential reliability method was employed to analyse the pump’s failure data to determine the pump systems' failure rate and reliability while reliability centred maintenance (RCM) and linear programming (LP) model were employed to optimize the pump’s maintenance strategies and the pump’s maintenance labour force respectively. Broad-based results of the optimized maintenance strategies consisted of on-condition-directed (CD) maintenance, preventive maintenance (PreM), and Proactive maintenance (ProM) strategies to be carried out monthly. The optimized maintenance labour force result revealed that approximately three engineers and two technicians should be employed for the pump’s maintenance task with a minimum of N2, 585, 700 as cost of salaries for the labour force monthly as against the current maintenance labour force requiring fourteen engineers and thirteen technicians monthly. The results showed that the labour cost decreased from N180,000,000 per year to N31,028,400 per year (approximately 82.76% reduction) using the proposed optimized maintenance task compared to the current maintenance plan. Conclusion and recommendation were made that the maintenance optimization technique presented in this work should be adopted by the oil and gas processing and production companies in order to minimize maintenance cost.

Optimal power dispatch in microgrids using mixed-integer linear programming

Abstract As greenhouse gases emissions continue to rise, society is actively seeking methods to reduce them. Microgrids (MGs), which predominantly consist of renewable energy sources, play a significant role in achieving this objective. This paper proposes an optimized methodology for power dispatch in MGs using mixed-integer linear programming (MILP). The MGs include photovoltaic systems, wind turbines, biogas (BG) generators, battery energy storage systems (BESS), electric vehicles (EV), and loads. The model features an objective function focused on cost minimization, power balance, and the necessary limits and constraints for the system’s safe operation. Real-time pricing is employed for energy transactions between the MGs and the main grid. The results demonstrate a cost-efficient operation for the proposed system comprising two MGs and the main grid. During periods of negative power balance, the demand was met by discharging the BESS, EV’s battery, or purchasing energy from the grid. The BESS was charged when energy prices were low and discharged during peak demand periods and high energy prices. The intermittent nature of renewable sources necessitates an efficient management system to ensure reliable operation. Additionally, storage systems help mitigate the variability in generation. The BG generator was another crucial component for power supply due to its flexibility. Integrating these components into the system improved reliability and ensured a secure and balanced operation.

Home Healthcare Staffing, Routing, and Scheduling Problem With Multiple Shifts and Emergency Considerations

ABSTRACTEffective planning of human resources is critical in designing an efficient home healthcare system. In this study, we present a novel home healthcare staffing, routing, and scheduling problem inspired by a real‐world application. The proposed problem addresses a set of patients, with varying daily visit requirements, being served by a set of caregivers with different qualification levels over a multi‐day multi‐shift planning horizon. The problem aims to minimize the number of extra shifts for caregivers, maximize the allocation of caregivers to emergencies, and minimize the sum of route durations over the planning horizon. These objectives are optimized hierarchically while considering a set of restrictions, including time windows, skill matching, synchronization, care continuity, and labor regulations. To tackle the problem, we introduce a mixed‐integer linear programming model. The model is then extended and two sets of valid inequalities are incorporated to enhance its tightness. Computational experiments are conducted on a set of 20 instances. The results highlight the efficiency of the proposed extension in increasing both the number of instances that can be solved to optimality and the number of instances for which a feasible solution is found.

PangeBlocks: customized construction of pangenome graphs via maximal blocks

BackgroundThe construction of a pangenome graph is a fundamental task in pangenomics. A natural theoretical question is how to formalize the computational problem of building an optimal pangenome graph, making explicit the underlying optimization criterion and the set of feasible solutions. Current approaches build a pangenome graph with some heuristics, without assuming some explicit optimization criteria. Thus it is unclear how a specific optimization criterion affects the graph topology and downstream analysis, like read mapping and variant calling.ResultsIn this paper, by leveraging the notion of maximal block in a Multiple Sequence Alignment (MSA), we reframe the pangenome graph construction problem as an exact cover problem on blocks called Minimum Weighted Block Cover (MWBC). Then we propose an Integer Linear Programming (ILP) formulation for the MWBC problem that allows us to study the most natural objective functions for building a graph. We provide an implementation of the ILP approach for solving the MWBC and we evaluate it on SARS-CoV-2 complete genomes, showing how different objective functions lead to pangenome graphs that have different properties, hinting that the specific downstream task can drive the graph construction phase.ConclusionWe show that a customized construction of a pangenome graph based on selecting objective functions has a direct impact on the resulting graphs. In particular, our formalization of the MWBC problem, based on finding an optimal subset of blocks covering an MSA, paves the way to novel practical approaches to graph representations of an MSA where the user can guide the construction.

A New Artificial Intelligence-Based System for Optimal Electricity Arbitrage of a Second-Life Battery Station in Day-Ahead Markets

Electric vehicles (EVs) are widely regarded as a crucial tool for carbon reduction due to the gradual increase in their numbers. However, these vehicles are equipped with batteries that have a limited lifespan. It is commonly stated that when the battery capacity falls below 70%, it needs to be replaced, and these discarded batteries are typically sent for recycling. Nevertheless, there is an opportunity to repurpose these worn-out batteries for a second life in electric power systems. This study focuses on the arbitrage situation of a second-life battery (SLB) facility in day-ahead electricity markets. This approach not only contributes to balancing supply and demand in the electric power system but also allows the battery facility to achieve significant gains. We propose an artificial intelligence system that integrates optimized deep learning algorithms for market price predictions with a mixed-integer linear programming (MILP) model for market participation and arbitrage decisions. Our system predicts prices for the next 24 h using Neural Hierarchical Interpolation for Time Series (N-HiTS) and decides when to enter the market using the MILP model and incorporating the predicted data and the statuses of the batteries. We compare the accuracy of our trained deep learning model with other deep learning models such as recurrent neural networks (RNNs), Long Short-Term Memory (LSTM), and Neural Basis Expansion Analysis for Interpretable Time-Series Forecasting (N-BEATS). We test the efficiency of the proposed system using real-world Turkish day-ahead market data. According to the results obtained, this study concludes that substantial gains can be achieved with the predicted prices and the optimal operating model. A facility with a total battery energy capacity of 5.133 MWh can generate a profit of USD 539 in one day, showcasing the potential of our study. Our new system’s approach provides proof of concept of new research opportunities for the participation of SLB facilities in day-ahead markets.

Assessing the Energy Efficiency of Saudi Arabia’s Relatively Middle Eastern Countries in the Context of Sustainable Development Goals

This paper employs a non-parametric approach to assess and compare the energy efficiency of Saudi Arabia with that of other Middle Eastern countries. An approach for evaluating the performance of each unit that utilizes optimization and linear programming is data envelopment analysis (DEA). A reference set for an inefficient unit is created to facilitate the comparison of various units against the efficiency boundary and enhance the efficiency of each unit. The research employs data envelopment analysis, incorporating three input and two output variables. This approach enables Middle Eastern countries to identify ineffective measures and recall essential actions necessary for enhancement. This study's findings suggest recommendations for the effective utilization of resources. This analysis indicates that Saudi Arabia, along with certain Middle Eastern nations, exhibits greater energy efficiency than most countries in the region. When analyzing the Middle East, Saudi Arabia, United Arab Emirates, Bahrain, Cyprus, Qatar, and Turkiye are notable examples of sustainable practices requiring further investigation. Furthermore, Yemen, Lebanon, and Syria require additional scrutiny to enhance effectiveness. The proposed study presents noteworthy conclusions concerning energy efficiency. The findings are anticipated to aid government agencies in pinpointing the factors influencing development and facilitating policymaking in Middle Eastern countries.

Circuit and graver walks and linear and integer programming

We show that a circuit walk from a given feasible point of a given linear program to an optimal point can be computed in polynomial time using only linear algebra operations and the solution of the single given linear program. We also show that a Graver walk from a given feasible point of a given integer program to an optimal point is polynomial time computable using an integer programming oracle, but without such an oracle, it is hard to compute such a walk even if an optimal solution to the given program is given as well. Combining our oracle algorithm with recent results on sparse integer programming, we also show that Graver walks from any point are polynomial time computable over matrices of bounded tree-depth and subdeterminants.

The Effect of Multi-Ingredient Protein versus Collagen Supplementation on Satellite Cell Properties in Males and Females

ABSTRACT Introduction Skeletal muscle satellite cells (SC) contribute to the adaptive process of resistance exercise training (RET) and may be influenced by nutritional supplementation. However, little research exists on the impact of multi-ingredient supplementation on the SC response to RET. Purpose We tested the effect of a multi-ingredient supplement (MIS) including whey protein, creatine, leucine, calcium citrate, and vitamin D on SC content and activity as well as myonuclear accretion, SC and myonuclear domain compared with a collagen control (COL) throughout a 10-wk RET program. Methods Twenty-six participants underwent a 10-wk linear RET program while consuming either the MIS or COL supplement twice daily. Muscle biopsies were taken from the vastus lateralis at baseline and 48 h after a bout of damaging exercise, before and after RET. Muscle tissue was analyzed for SC and myonuclear content, domain, acute SC activation, and fiber cross-sectional area (fCSA). Results MIS resulted in a greater increase in type II fCSA following 10 wk of RET (effect size (ES) = 0.89) but not myonuclear accretion or SC content. Change in myonuclei per fiber was positively correlated with type I and II and total fiber hypertrophy in the COL group only, indicating a robust independent effect of MIS on fCSA. Myonuclear domain increased similarly in both groups, whereas SC domain remained unchanged following RET. SC activation was similar between groups for all fiber types in the untrained state but showed a trend toward greater increases with MIS after RET (ES = 0.70). Conclusions SC responses to acute damaging exercise and long-term RET are predominantly similar in MIS and COL groups. However, MIS can induce greater increases in type II fCSA with RET and potentially SC activation following damage in the trained state.

A GRASP-based multi-objective approach for the tuna purse seine fishing fleet routing problem

Nowadays, the world’s fishing fleet uses 20% more fuel to catch the same amount of fish compared to 30 years ago. Addressing this negative environmental and economic performance is crucial due to stricter emission regulations, rising fuel costs, and predicted declines in fish biomass and body sizes due to climate change. Investment in more efficient engines, larger ships and better fuel has been the main response, but this is only feasible in the long term at high infrastructure cost. An alternative is to optimize operations such as the routing of a fleet, which is an extremely complex problem due to its dynamic (time-dependent) moving target characteristics. To date, no other scientific work has approached this problem in its full complexity, i.e., as a dynamic vehicle routing problem with multiple time windows and moving targets. In this paper, two bi-objective mixed linear integer programming (MIP) models are presented, one for the static variant and another for the time-dependent variant. The bi-objective approaches allow to trade off the economic (e.g., probability of high catches) and environmental (e.g., fuel consumption) objectives. To overcome the limitations of exact solutions of the MIP models, a greedy randomized adaptive search procedure for the multi-objective problem (MO-GRASP) is proposed. The computational experiments demonstrate the good performance of the MO-GRASP algorithm with clearly different results when the importance of each objective is varied. In addition, computational experiments conducted on historical data prove the feasibility of applying the MO-GRASP algorithm in a real context and explore the benefits of joint planning (collaborative approach) compared to a non-collaborative strategy. Collaborative approaches enable the definition of better routes that may select slightly worse fishing and planting areas (2.9%), but in exchange for a significant reduction in fuel consumption (17.3%) and time at sea (10.1%) compared to non-collaborative strategies. The final experiment examines the importance of the collaborative approach when the number of available drifting fishing aggregation devices (dFADs) per vessel is reduced.