INTEGRATING INVENTORY AND TRANSPORT CAPACITY PLANNING IN A FOOD SUPPLY CHAIN

[EN] The general objective of this paper is to simulate a supply chain to assess the effects that different inventory management policies and transport capacity systems have on costs (transport) and service levels (stockouts). This paper specifically aimed to facilitate the decision-making process about planning distribution capacities, particularly when contracting a transport fleet in a supply chain under uncertainty with a 1-year time horizon by evaluating different types of scenarios, which vary depending on availability of vehicles and obtaining vehicles. The system dynamics simulation model was applied to a real-world food supply chain and can be adopted by chains related to diversified cropping systems. The results provide the best decision alternative in terms of costs and inventory levels by considering the transport capacity life cycle, the time to acquire additional transport capacity, the reorder point in days of stock and the target inventory.This work was supported by the European Commission Horizon 2020 project entitled 'Crop diversification and low-input farming cross Europe: from practitioners' engagement and ecosystems services to increased revenues and value chain organisation' (Diverfarming), grant agreement 728003.Freile, A.; Mula, J.; Campuzano Bolarin, F. (2020). INTEGRATING INVENTORY AND TRANSPORT CAPACITY PLANNING IN A FOOD SUPPLY CHAIN. International Journal of Simulation Modelling. 19(3):434-445. https://doi.org/10.2507/IJSIMM19-3-52343444519

GNBG: A Generalized and Configurable Benchmark Generator for Continuous Numerical Optimization

As optimization challenges continue to evolve, so too must our tools and understanding. To effectively assess, validate, and compare optimization algorithms, it is crucial to use a benchmark test suite that encompasses a diverse range of problem instances with various characteristics. Traditional benchmark suites often consist of numerous fixed test functions, making it challenging to align these with specific research objectives, such as the systematic evaluation of algorithms under controllable conditions. This paper introduces the Generalized Numerical Benchmark Generator (GNBG) for single-objective, box-constrained, continuous numerical optimization. Unlike existing approaches that rely on multiple baseline functions and transformations, GNBG utilizes a single, parametric, and configurable baseline function. This design allows for control over various problem characteristics. Researchers using GNBG can generate instances that cover a broad array of morphological features, from unimodal to highly multimodal functions, various local optima patterns, and symmetric to highly asymmetric structures. The generated problems can also vary in separability, variable interaction structures, dimensionality, conditioning, and basin shapes. These customizable features enable the systematic evaluation and comparison of optimization algorithms, allowing researchers to probe their strengths and weaknesses under diverse and controllable conditions

Scientific Research about Climate Change Mitigation in Transport : A critical review

Peer reviewedPostprin

Genetic Algorithms For Flow-shop Scheduling Optimization Of An Automated Assembly Line

Manufacturing process is a process of producing and creating a product with the use of technologies and machinery resources. In manufacturing process there are three dimensions, which are important in improving the system. These are cost, quality, and speed that can be considered as basics of every process. In this thesis speed of the manufacturing process is enhanced, which leads to reduction in cost as well. Assembly lines are the part of manufacturing process to convert raw materials into finished products. Considering optimization problems in assembly lines, applying genetic algorithms to the established model could lead to efficient manufacturing. Genetic algorithm is a programming search technique for maximizing productivity, minimizing inefficiency and reducing production time. This work presents an approach for developing simulation models used for optimization of production lines. The results are demonstrated using the assembly line which is located in FAST-Lab. at Tampere University of Technology. The simulation of the line is created to assess cycle times and utilization of workstations using MATLAB and SimEvents library. The optimization, in the context of presented work, is the process of locating and scheduling the products in the line achieving best timing to fulfil production orders. The workstations can be first balanced for better performance and then products are scheduled based on reduction of the production time

A multi-agent optimisation model for solving supply network configuration problems

Supply chain literature highlights the increasing importance of effective supply network configuration decisions that take into account such realities as market turbulence and demand volatility, as well as ever-expanding global production networks. These realities have been extensively discussed in the supply network literature under the structural (i.e., physical characteristics), spatial (i.e., geographical positions), and temporal (i.e., changing supply network conditions) dimensions. Supply network configuration decisions that account for these contingencies are expected to meet the evolving needs of consumers while delivering better outcomes for all parties involved and enhancing supply network performance against the key metrics of efficiency, speed and responsiveness. However, making supply network configuration decisions in the situations described above is an ongoing challenge. Taking a systems perspective, supply networks are typically viewed as socio-technical systems where SN entities (e.g., suppliers, manufacturers) are autonomous individuals with distinct goals, practices and policies, physically inter-connected transferring goods (e.g., raw materials, finished products), as well as socially connected with formal and informal interactions and information sharing. Since the structure and behaviour of such social and technical sub-systems of a supply network, as well as the interactions between those subsystems, determine the overall behaviour of the supply network, both systems should be considered in analysing the overall system

A unified race algorithm for offline parameter tuning

This paper proposes uRace, a unified race algorithm for efficient offline parameter tuning of deterministic algorithms. We build on the similarity between a stochastic simulation environment and offline tuning of deterministic algorithms, where the stochastic element in the latter is the unknown problem instance given to the algorithm. Inspired by techniques from the simulation optimization literature, uRace enforces fair comparisons among parameter configurations by evaluating their performance on the same training instances. It relies on rapid statistical elimination of inferior parameter configurations and an increasingly localized search of the parameter space to quickly identify good parameter settings. We empirically evaluate uRace by applying it to a parameterized algorithmic framework for loading problems at ORTEC, a global provider of software solutions for complex decision-making problems, and obtain competitive results on a set of practical problem instances from one of the world's largest multinationals in consumer packaged goods

Green supply chain network design under uncertainty conditions with the mathematical model and solving it with a NSGA II algorithm

Purpose: In this paper a mathematical model for the green supply chain network problem is designed. In this research, we seek to optimize two inconsistent and conflicting goals of the problem which are as follows: 1.Minimization of costs 2.Minimization of environmental impacts, using of the economic indicator 99 method. Methodology: In this paper, two methods of Epsilon constraint and NSGA II algorithm are used to solve the two-objective model with the objective functions of minimizing network costs and minimizing emissions. Findings: The results show that the introduced NSGA II algorithm has a high efficiency in forming efficient solutions in a short time. Originality/Value: In this paper, a two-objective model for green supply chain network is modeled and solved with the aim of reducing network costs and reducing greenhouse gas emissions