The Refrigerated Capacitated Vehicle Routing Problem (RCVRP) considers a homogeneous fleet with a refrigerated system to decide the selection of routes to be performed according to customers' requirements. The aim is to keep the energy consumption of the routes as low as possible. We use a thermodynamic model to understand the unloading of products from trucks and the variables' efficiency, such as the temperature during the day influencing energy consumption. By considering various neighborhoods and a shaking procedure, this paper proposes a Granular Tabu Search scheme to solve the RCVRP. Computational tests using adapted benchmark instances from the literature demonstrate that the suggested method delivers high-quality solutions within short computing times, illustrating the refrigeration system's effect on routing decisions.

In today’s world, due to rapid changes in the market, scheduling as one of the most fundamental issues of competitive production, plays a very important role in maintaining the competitive position and survival of manufacturing organizations, therefore, development of scheduling models in order to improve the timing criteria is of great importance. In this research, we put the development of a no-wait flow-shop scheduling model alongside with the effect of learning into consideration to minimize the cost of consumption of resources. Finding the correct sequence of two machines’ performance and optimized allocation of the resources for any performance on each machine, were considered as the main goal in this study. To solve the problem, metaheuristic genetic algorithms, particle swarm optimization, imperialist competitive algorithms, optimization of the whale and the League Champions algorithms, have been used. The statistical comparisons and also using of TOPSIS Multi-Criteria Decision Making method, indicate high level of efficiency of the League Champions algorithm with the utility weight of 0.9516.

One subcategory of project scheduling is the resource constrained project scheduling problem (RCPSP). The present study proposes a differential evolution algorithm for solving the RCPSP making a small change in the method to comply with the model. The RCPSP is intended to program a group of activities of minimal duration while considering precedence and resource constraints. The present study introduces a differential evolution algorithm and local search was added to improve the performance of the algorithm. The problems were then solved to evaluate the performance of the algorithm and the results are compared with genetic algorithm. Computational results confirm that the differential evolution algorithm performs better than genetic algorithm.

Planning is one of the most important components of gas industry production. Most big gas companies usually look for effective planning approaches to accomplish the organizational objectives including cost and time reduction as well as enhancing quality and efficiency. For planning gas refinery production, important parameters including production time, production volume, production cost and storage need to be considered. Planning different units ought to be integrated and coordinated with other departments. This article presents an intensive arithmetic model to determine the production of gas derivatives. The proposed model of this paper is formulated as a mixed integer programming and the resulted problem is solved using NSGA-II algorithm and a hybrid method called BBO/NSGA-II. The problem is also applied for a real-world case study and the results are discussed.