Composite particle algorithm for sustainable integrated dynamic ship routing and scheduling optimization

Ship routing and scheduling problem is considered to meet the demand for various products in multiple ports within the planning horizon. The ports have restricted operating time, so multiple time windows are taken into account. The problem addresses the operational measures such as speed optimisation and slow steaming for reducing carbon emission. A Mixed Integer Non-Linear Programming (MINLP) model is presented and it includes the issues pertaining to multiple time horizons, sustainability aspects and varying demand and supply at various ports. The formulation incorporates several real time constraints addressing the multiple time window, varying supply and demand, carbon emission, etc. that conceive a way to represent several complicating scenarios experienced in maritime transportation. Owing to the inherent complexity, such a problem is considered to be NP-Hard in nature and for solutions an effective meta-heuristics named Particle Swarm Optimization-Composite Particle (PSO-CP) is employed. Results obtained from PSO-CP are compared using PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) to prove its superiority. Addition of sustainability constraints leads to a 4–10% variation in the total cost. Results suggest that the carbon emission, fuel cost and fuel consumption constraints can be comfortably added to the mathematical model for encapsulating the sustainability dimensions

3-D Packing in Container using Teaching Learning Based Optimization Algorithm

تهدف الورقة إلى اقتراح خوارزمية التحسين القائم على التعلم (TLBO) لحل مشكلة التعبئة ثلاثية الأبعاد في الحاويات. الهدف الذي يمكن تقديمه في نموذج رياضي هو تحسين استخدام المساحة في الحاوية. ، تراقب هذه الخوارزمية أيضًا، إلى جانب تأثير التفاعل بين الطلاب والمدرس، عملية التعلم بين الطلاب في الفصل والتي لا تحتاج إلى أي معلمات تحكم. وبالتالي ، يوفر TLBO لمرحلة المعلمين ومرحلة الطلاب كعملية تحديث رئيسية لإيجاد أفضل حل. بتعبير أدق ، للتحقق من فعالية الخوارزمية ، فقد تم تنفيذها في ثلاث حالات نموذجية. كانت هناك بيانات صغيرة تحتوي على 5 أنواع من الأحجام مع 12 وحدة ، وبيانات متوسطة تحتوي على 10 أنواع من الأحجام مع 106 وحدة ، وبيانات كبيرة تحتوي على 20 نوعًا من أنواع الأحجام مع 110 وحدة. وتمت مقارنتها، علاوة على ذلك ، بخوارزمية أخرى تسمى خوارزمية البحث عن الجاذبية (GSA). وفقًا للنتائج الحسابية في تلك الحالات النموذجية ، يمكن استنتاج أن العدد الأكبر من السكان والتكرارات يمكن أن يجلب فرصًا أكبر للحصول على حل أفضل حل. ويُظهر TLBO أداءً أفضل في حل مشكلة التعبئة ثلاثية الأبعاد مقارنةً بـ GSA.The paper aims to propose Teaching Learning based Optimization (TLBO) algorithm to solve 3-D packing problem in containers. The objective which can be presented in a mathematical model is optimizing the space usage in a container. Besides the interaction effect between students and teacher, this algorithm also observes the learning process between students in the classroom which does not need any control parameters. Thus, TLBO provides the teachers phase and students phase as its main updating process to find the best solution. More precisely, to validate the algorithm effectiveness, it was implemented in three sample cases. There was small data which had 5 size-types of items with 12 units, medium data which had 10 size-types of items with 106 units, and large data which had 20 size-types of items with 110 units. Moreover, it was also compared with another algorithm called Gravitational Search Algorithm (GSA). According to the computational results in those example cases, it can be concluded that higher number of population and iterations can bring higher chances to obtain a better solution. Finally, TLBO shows better performance in solving the 3-D packing problem compared with GSA.         

A Vitual-Force Based Swarm Algorithm for Balanced Circular Bin Packing Problems

Balanced circular bin packing problems consist in positioning a given number of weighted circles in order to minimize the radius of a circular container while satisfying equilibrium constraints. These problems are NP-hard, highly constrained and dimensional. This paper describes a swarm algorithm based on a virtual-force system in order to solve balanced circular bin packing problems. In the proposed approach, a system of forces is applied to each component allowing to take into account the constraints and minimizing the objective function using the fundamental principle of dynamics. The proposed algorithm is experimented and validated on benchmarks of various balanced circular bin packing problems with up to 300 circles. The reported results allow to assess the effectiveness of the proposed approach compared to existing results from the literature.Comment: 23 pages including reference

Application of Cargo Distribution Computation in Airbus A330 Cargo Aircraft with Optimization Algorithms

Weight and balance problems are one of the main reasons for cargo aircraft accidents including around 30% of accidents that are due to Center of Gravity (CG). Because the pilots often calculate CG index using Load & Trim Sheets manually or use a set of simple formulas, in these calculations, it is only checked whether CG index is within the safe zone instead of determining the ideal value. In order for the safety and fuel economy to be maximized in an aircraft, CG index should be calculated at the ideal value given in the Aircraft Handling Manual. Due to safety and cost concerns, airline companies prefer non-commercial optimization solutions. Therefore, we proposed new heuristic approaches that have been motivated by a real-world application for a major airline company. First, we applied standard GA, WSA, PSO algorithms to obtain a solution that is as close as possible to the ideal CG index in an Airbus A330 cargo plan. Then, we modified standard WSA and PSO algorithms to decrease the error value and to better achieve the ideal CG index. These proposed heuristic solutions have the potential to help the pilots flying cargo aircraft with maximum safety and minimum fuel consumption

A study of a kanban based assembly line feeding system through integration of simulation and particle swarm optimization

With increase in differentiation and decreasing batch size of products, feeding the assembly line at regular intervals is considered to be a critical problem in today's manufacturing sector. Yet no clear solution has been developed for this problem; therefore, the main focus of this research is to discuss the different aspects of line feeding, the latest trend in literature, and to propose an innovative method to support solving the problem. A discrete event simulation model is developed and a mathematical model based on particle swarm optimization is used to support the simulation. The hybrid model is finally applied to practical situations. Results show how different settings of kanban influence the performance of the assembly line feeding system. The biggest novelty item is certainly the recognition of the trade-off between kanban size and number of kanban and the importance of investigating its behaviour during the design of the system. (C) 2019 by the authors; licensee Growing Science, Canad

Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review

Industry 4.0 has become a crucial part in the majority of processes, components, and related modelling, as well as predictive tools that allow a more efficient, automated and sustainable approach to industry. The availability of large quantities of data, and the advances in IoT, AI, and data-driven frameworks, have led to an enhanced data gathering, assessment, and extraction of actionable information, resulting in a better decision-making process. Product picking and its subsequent packing is an important area, and has drawn increasing attention for the research community. However, depending of the context, some of the related approaches tend to be either highly mathematical, or applied to a specific context. This article aims to provide a survey on the main methods, techniques, and frameworks relevant to product packing and to highlight the main properties and features that should be further investigated to ensure a more efficient and optimised approach