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In the realm of supply chain logistics, the Multi-Depot Multi-Supplier Vehicle Routing Problem (MDMSVRP) poses a significant challenge in optimizing the transportation process to minimize costs and enhance operational efficiency. This problem involves determining the most cost-effective routes for a fleet of vehicles to deliver goods from multiple suppliers to multiple depots, considering various constraints and non-linear relationships. The routing problem (RP) is a critical element of many logistics systems that involve the routing and scheduling of vehicles from a depot to a set of customer nodes. One of the most studied versions of the RP is the Vehicle Routing Problem with Time Windows (VRPTW), in which each customer must be visited at certain time intervals, called time windows. In this paper, it is considered that there are multiple depots (supply centers) and multiple suppliers, along with a fleet of vehicles. The goal is to efficiently plan routes for these vehicles to deliver goods from the suppliers to various customers while considering relaxed time windows. This research is intended to establish a new relaxation scheme that relaxes the time window constraints in order to lead to feasible and good solutions. In addition, this study develops a discrete optimization model as an alternative model for the time-dependent VRPTW involving multi-suppliers. This research also develops a metaheuristic algorithm with an initial solution that is determined through time window relaxation.

Disaster logistics management is vital in planning and organizing humanitarian assistance distribution. The planning problem faces challenges, such as coordinating the allocation and distribution of essential resources while considering the severity of the disaster, population density, and accessibility. This study proposes an optimized disaster relief management model, including distribution center placement, demand point prediction, prohibited route mapping, and efficient relief goods distribution. A dynamic model predicts the location of post-disaster distribution centers using the K-Means method based on impacted demand points’ positions. Artificial Neural Networks (ANN) aid in predicting assistance requests around formed distribution centers. The forbidden route model maps permitted and prohibited routes while considering constraints to enhance relief supply distribution efficacy. The objective function aims to minimize both cost and time in post-disaster aid distribution. The model deep location routing problem (DLRP) effectively handles mixed nonlinear multi-objective programming, choosing the best forbidden routes. The combination of these models provides a comprehensive framework for optimizing disaster relief management, resulting in more effective and responsive disaster handling. Numerical examples show the model’s effectiveness in solving complex humanitarian logistics problems with lower computation time, which is crucial for quick decision making during disasters.

This study examines a multi-product fish production and distribution system in which multi-fish products are produced simultaneously from a wide range of raw resource classes. The objective of environmentally sustainable production planning is to meet market demand in accordance with environmental constraints. This paper sets out a management model that converts fisheries into multiple marine objects and moves them to various dispensing centers. It also incorporates a model to improve production and distribution planning at the same time. The problem is formulated as a mixed integer programming model. Then, we addressed a strategy of releasing non-basic variables from their bounds to force basic non-integer variables to take integer value. As an implementation, we solved a fish production planning problem faced by an industry located in Kisaran city, North Sumatra province, Indonesia.

This study aims to determine: (1) Whether the increased ability of the mathematical modeling of students who are taught with Realistic Mathematics learning is higher than students taught by expository (2) Whether there is interaction between learning motivation of student learning on the ability of the mathematical modeling students (3) How the process of resolving questions related to the ability of the mathematical modeling of the students on the Realistic Mathematics learning and expository. This research is a quasi experiment with research population is all students of class VIII MTs Al-Majidiyah Chart Stone. The sample in this research is class VIII A and VIII class B. The instrument used consisted of: (1) test the ability of mathematical modeling with the material of the cube and beam (2) pieces of the Question. The Data in this research were analyzed by using descriptive statistical analysis, analysis and parametric statistics. Statistical analysis of data was done with the t test analysis and anova 2 lines. The results showed that: (1) Increase the ability of the mathematical modeling of students who are taught with learning PMR higher than students taught by expository (2) there is No interaction between the learning that is used with the learning motivation of the students to the ability of the mathematical modeling student (3) the Process of the completion of the answers of students who are taught by using learning model PMR is better compared to the expository

The type of this study is a quasi-experiment study with its purpose to know any difference in students’ achievement in mathematics which using the model of guided discovery learning with cooperative learning model JIGSAW type. The population of this study is all students in SMA N 3 P. Siantar. The sampling technique applied was cluster random sampling. The experimental class I that chosen is X-1 consisted of 36 students, meanwhile, the experimental class II that chosen is X-6 consisted of 36 students. The instrument used to measure the students’ mathematics achievement was an essay test. The normality test used was Lilliefor’s test, get that data is normal and the homogeneity test by using Fisher test, get that data is homogeny. The data analysis technique was t-test at the level of significance α = 5%.The study result showed that there is the difference of students’ achievement in mathematics which using the guided discovery learning model with cooperative learning model JIGSAW type in grade X SMA N 3 P. Siantar where obtained tcalculation = 2.504 at a = 0.05 and ttable = t(0.975,70)= 1.995, then tcalculation = ttable

Implementation of Uncertainty model has not given maximum result in forming rule on an inference of a case. For testing to determine whether water quality is high, medium and low. The input variables used are temperature, pH, salinity and Disolved Oxygen. Testing is done by looking at the water turbidity change in the shrimp pond, to determine the water quality. Its water quality determines in the control module of the waterwheel rotation.Rolling the waterwheel moves quickly if pond water quality is low, moving slowly if water quality is medium and immobile if water quality is good. And the establishment of the rule with the approach of knowledge of Ontology to determine the relation between several variables (temperature, Ph, Disolved Oxygen and salinity). Each variable is set to its certainty value in the form of fuzzy value. Next is determined the relation of the four variables for the formation of rule.