Minimum Transportation Cost Research Articles

OUTSOURCING OF TRANSPORT AND FORWARDING COMPANIES FOR EXECUTION OF PLANNED VOLUMES OF TRANSPORTATION

The purpose of this work is to understand the transport and logistics scheme of cargo delivery with minimal transportation costs and the direct process of providing cargo transportation services on the most attractive terms for all participants in this process and effective use of vehicles. Transport forwarding activity is a component of transport outsourcing. The paper considers the general characteristics of PL levels of logistics and the given characteristics of each of them and the differences between them. The description of transport documentation, which is used when using different types of transport for intrastate and interstate transportation of goods, is given. Recommendations for choosing a service transport and forwarding company are provided. The practical significance of the article lies in the use of the specified requirements by the enterprise to make the optimal choice of the transport and forwarding company for joint work on the delivery of goods to customers.

МЕТОДИЧНІ АСПЕКТИ РОЗМІЩЕННЯ ЛОГІСТИЧНИХ ЦЕНТРІВ КУР’ЄРСЬКИХ ТА ПОШТОВИХ СЛУЖБ

The task of finding organizational reserves to increase the efficiency of business processes of logistics companies by optimizing the movement of material flows is considered. The focus on organizational reserves, the implementation of which requires the involvement of minimal investments compared to other types of reserves, considering the limited access to sources of financial resources in the conditions of martial law, is rational. At the level of individual logistics companies, one of the areas of implementation of organizational reserves is solving the problem of optimal placement of logistics centers, which creates prerequisites for increasing the efficiency of business processes. Methodical aspects of forming a network of transit terminals of a logistics company are considered. A feature of the proposed approach is the use of an iterative procedure, which allows considering a significant number of network construction options and selecting the optimal one based on the criterion of minimizing the total cost of cargo transportation. The minimization of transport costs is ensured by an effective combination of clustering methods, that ensure the optimal distribution of the network of cargo-sending branches into groups, and methods of determining the optimal location of the transit terminal for the serviced group of cargo-sending branches. Among the methods of clustering in the context of the considered problem, it is especially possible to highlight the methods of hard clustering, namely the K-means method, the K-median method, the method of mean shift, methods of hierarchical clustering, etc. Methods for determining the optimal location of a transit terminal for a group of cargo-shipping departments include the Euclidean distance method, the method of the center of gravity of cargo flows, and others.

A multi-objective network design for recycling healthcare waste from large-scale immunization

This paper presents a Goal programming-based optimization model for managing the recycling operations of healthcare waste generated from large-scale vaccination drives. The model proposes an efficient system by integrating the decisions of locating recycling units and the routing of generated waste to them, considering the risks to the environment and the associated population. The objectives include the minimization of setup and transportation costs, risks to the population, and the number of installed units. A set of randomly selected test instances is used to test the effectiveness of the model. The results reveal that a compromised solution offers cost advantages and population risk mitigation. The approach effectively supports the strategic choices in recycling healthcare waste generated from immunization.

A Lagrangian Relaxation Heuristic for a Bi-Objective Multimodal Transportation Planning Problem

We study a realistic Bi-objective Multimodal Transportation Planning Problem (BMTPP) faced by logistics companies when trying to obtain cost advantages and improve the customer satisfaction in a competitive market. The two objectives considered are: the minimization of total transportation cost and the maximization of service quality. Given a set of transportation orders described by an origin, a destination and a time window, solving BMTPP involves determining the delivery path for each order in a capacitated network as well as selecting the carrier with the best service quality for each edge of the path. The BMTPP is formulated as a novel bi-objective mixed integer linear programming model and an iterative <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> -constraint method is applied to solve it. As the NP-hardness of the single-objective problems derived from BMTPP, a Lagrangian Relaxation (LR) heuristic which can not only provide a near-optimal solution but also a lower bound for each of the single-objective problems is developed. 100 randomly generated instances are tested and the computational results demonstrate the effectiveness of the heuristic in obtaining a tight lower bound and a high-quality near-optimal solution for the derived single-objective problem. Various performance indicators show the high-quality of the Pareto front of the bi-objective problem obtained by the heuristic. We also provide a case study for the proposed LR heuristic in a logistics network in China.

A Mathematical Optimization Model for the Pharmaceutical Waste Location‐Routing Problem Using Genetic Algorithm and Particle Swarm Optimization

Pharmaceutical waste management is a significant concern that poses risks to human and environmental health. The ineffective management of expired and unused medications can harm individuals and communities. This study proposes a novel approach to address the issue of pharmaceutical waste management by developing a location‐routing problem (LRP) model using mixed‐integer linear programing (MILP) to optimize the collection and disposal of pharmaceutical waste. The proposed model aims to minimize transportation costs, construction of collection centers, disposal costs, and carbon dioxide emissions, making it a cost‐effective and environmentally sustainable approach to managing pharmaceutical waste. Initially, the feasibility, validity, and efficiency of the proposed model are examined by solving the problem in the GAMS software using CPLEX solver for small‐scale problems. Sensitivity analyses are conducted to ensure the accuracy, reliability, robustness, and usefulness of the mathematical model for decision‐making. In view of the inherent computational complexity of the proposed model, which is classified as nondeterministic polynomial time‐hard and poses considerable difficulties when exact solutions are sought for large‐scale problems, the present study resorts to two metaheuristic algorithms, specifically particle swarm optimization (PSO) and genetic algorithm (GA), as a means minimizing the computational burden. The results indicate that GA outperforms PSO in terms of objective function and solution time, with an average improvement of approximately 1% and 20%, respectively. The proposed model and algorithms provide a comprehensive approach to addressing the critical issue of pharmaceutical waste management, benefiting the healthcare industry, and society as a whole.

An improved genetic algorithm for multi-AGV dispatching problem with unloading setup time in a matrix manufacturing workshop

This paper investigates a novel problem concerning material delivery in a matrix manufacturing workshop, specifically the multi-automated guided vehicle (AGV) dispatching problem with unloading setup time (MAGVDUST). The objective of the problem is to minimize transportation costs, including travel costs, time penalty costs, AGV costs, and unloading setup time costs. To solve the MAGVDUST, this paper builds a mixed-integer linear programming model and proposes an improved genetic algorithm (IGA). In the IGA, an improved nearest-neighbor-based heuristic is proposed to generate a high-quality initial solution. Several advanced technologies are developed to balance local exploitation and global exploration of the algorithm, including an optimal solution preservation strategy in the selection process, two well-designed crossovers in the crossover process, and a mutation based on Partially Mapped Crossover strategy in the mutation process. In conclusion, the proposed algorithm has been thoroughly evaluated on 110 instances from an actual electronic factory and has demonstrated its superior performance compared to state-of-the-art algorithms in the existing literature.

Arrangement of equipment at production site of multi-product production using heuristic algorithm

This article considers the task finding of the optimum equipment arrangement on the production site. Naturally, even earlier, before the development of the concept of lean manufacturing, the designers of machine-building enterprises faced the task of minimizing transportation costs. However, the techniques used in the past are usually focused on mass production, not lean, and therefore cannot be correctly used in the design of modern production systems. In addition, such methods, often developed several decades ago, are based on a broad regulatory framework and accumulated experience in designing enterprises. Still, they are not designed to use computer technology to find solutions to optimization problems typical for production design. As a result, today, the task of creating methods for designing enterprises, taking into account the specifics of lean production and involving the use of computers to solve optimization problems of designing machine-building production, is relevant. Flexible and rigid settlement models of the solution of a task are described, and the shortcomings and advantages of both models are specified. The algorithm of the solution of a task when using a flexible model is offered. This algorithm is constructed on a synthesis of a heuristic algorithm of imitation of annealing with linear programming methods.

Design and Optimization of an Eco-Efficient Vehicle Routing Model for Heterogeneous Fleets and Multi Depot Systems

In the dynamic landscape of vehicle routing problems (VRP), the traditional focus on cost minimization has significantly evolved due to the pressing need to address environmental concerns. With the logistics industry experiencing exponential growth and increased vehicle utilization, sustainability has become a central consideration both globally and in strategic planning. The Vehicle Routing Problem (VRP) remains a key challenge in logistics and transportation management, aiming to optimize routes and vehicle allocation to minimize transportation costs. This study introduces an innovative VRP model that intricately balances economic efficiency with ecological responsibility, addressing the urgent call for sustainable solutions amidst the ongoing environmental crisis. The proposed model features a diverse fleet of trucks, each designated for specific product types, creating a heterogeneous fleet. Originating from multiple depots, these trucks ensure comprehensive geographic coverage and enhance logistical efficiency. The core objective of the model is twofold: minimizing the financial transportation costs associated with supplying multiple products from multiple depots to customers and significantly reducing the carbon footprint, thereby addressing environmental sustainability. Two distinct solutions are proposed: one based on mixed-integer linear programming (MILP) and the other utilizing a heuristic approach. The choice of mixed-integer linear programming instead of linear programming is due to the need to make binary route decisions. Results are compared, focusing on outcome variation and computation time. Additionally, the solution demonstrates upto 100% variation in results when comparing the heuristic model with the optimal values derived from mixed-integer linear programming, though it requires less computation time. This research underscores the importance of seamlessly integrating economic and environmental considerations in the logistics industry. The proposed model embodies this principle, representing a substantial step towards a more sustainable future. By aligning objectives with global sustainability goals set by the United Nations in 2015, this research contributes to the discourse on sustainable development. It lays the foundation for future innovations in vehicle routing and logistics.

Comparison of completion of VAM, TCOM-SUM Transportation problems with Stepping Stone to Determine optimal solutions

This research discusses to optimize the path and find out the distribution of minimum transportation costs using the VAM (Vogel’s Approximation Method) method, TOCM-SUM is then optimized through the stepping stone test. The VAM method is one of the methods of preparing a initial feasible solution by Determining the distribution table allocation to the cell that has the least cost. TOCM-SUM method to find a suitable initial solution of the transportation problem. The optimal stepping stone test aims to test the initial solution table by calculating the cost of empty cells that are passed by the stepping stone path.

Penentuan Rute Pengangkut Limbah Medis Optimal Menggunakan Vehicle Routing Problem with Time Window pada Kasus Multi Depot

The current COVID-19 pandemic has created new environmental problems. The increase of medical waste due to increased activity in public health facilities is a new threat to environmental health. Unfortunately, not all health facilities have the capacity to manage their own waste. This causes the medical waste need to be taken to a waste treatment center for proper treatment that meets environmental health standards. Reverse logistics is one of the activities that can help the process of transporting medical waste. This study uses a vehicle routing problem with a time window (VRPTW) for multi depot cases. The limitations used in this study are the operational time of each Puskesmas, vehicle capacity and operational working hours of waste transport officers. In this case, the optimization of the cost calculation for the transportation of waste from 42 health centers in the DKI Jakarta area is carried out to be taken to three central locations for waste treatment. The purpose of the model used is to minimize transportation costs, namely fixed costs and variable costs of the vehicle used. The routes generated from this research are six transportation routes with a total distance of 412,31 km, with a total cost of Rp 5.928.498,75.

Optimizing environmental and economic aspects of collaborative transportation and logistics related to infrastructure projects – A case study from Norway

The main challenge addressed in this paper is how to handle and recycle the large amount of C&D waste that is generated from infrastructure projects. The study is motivated by Bærum Ressursbank in Norway and their aim of finding logistical solutions to an expected surplus of 15 million m3 of waste from infrastructure projects in the next decade. We identify the key decisions as the design of the distribution network for both surplus waste materials and new construction materials and the investments in processing machinery at each recycling facility, and we call the problem representing this situation the Infrastructure Waste Management Problem (IWMP). The methodologies used are mathematical programming and operations research. We formulate the IWMP as a mixed integer linear program and identify two objectives; to minimize transportation costs and to minimize the environmental impact of the operations. The description of the problem, assumptions, and data are based on cases that represent the situation of Bærum Ressursbank. A special emphasis in the analysis is to quantify the gains from collaboration. Comparing individual planning of each project with an ideal situation of complete collaboration gives a cost reduction of more than 29% and a reduction in emissions of more than 14%. The study supports the conjecture by Bærum Ressursbank that large cost savings and considerable reductions in environmental impact are possible through collaboration.

Physical Internet-enabled E-grocery delivery Network:A load-dependent two-echelon vehicle routing problem with mixed vehicles

Autonomous delivery robots (ADRs) as green last-mile delivery alternatives to traditional vans have received much attention due to the rapid development of Physical Internet (PI). However, their low efficiency limits the application in the last-mile delivery because few orders per trip are delivered. Also, the cost and emission impact of new motilities on the PI-enabled last-mile delivery network is still not clear. To address these issues, this study developed an innovative two-echelon delivery system that combines traditional vans and ADRs to take their advantages for the last-mile delivery. The objective of this study was to minimize transportation costs and emissions through solving an extension of a two-echelon vehicle routing problem considering load-dependent unit transport costs and unit emissions with mixed vehicles and multiple depots. The problem was formulated as a mixed-integer programming model and then efficiently solved by a cluster-based artificial immune algorithm, in which an improved clustering method was employed to assign the customers. The experimental results showed that the proposed solution approach efficiently solved the problem with better solutions than existing approaches in the relevant literature. Several managerial implications on configuring the two-echelon delivery system were also provided for potential applications.

Using dynamic-locational material flow analysis to model the development of urban stock

ABSTRACT Construction and demolition waste (CDW) constitutes the biggest share of all waste generated. In addition to the environmental advantages, CDW recycling provides also high resource conservation benefits if used as recycled aggregates. Integrated forecasting of demolition and construction activities is crucial for quantifying and reconciling future supply and demand. While spatial characteristics are significant to minimize transportation costs, temporal aspects and material characteristic are important to regard for the development of the urban stock over time. This paper presents a Dynamic-Locational MFA as a model that integrates spatial, temporal and CDW material characteristics into material flow analysis at the demand and supply side. The relevant drivers of construction and demolition activities are defined in order to derive the required equations. Also, the empirical analysis is used to conclude the parameters and demonstrate the model on a real case study. Hence, the synergies between the recycling and construction activities are identified and the supply and demand of materials can be reconciled. The outcomes are not only important in terms of resource efficiency, but also help in anticipating and planning the restructuring of the value chain of the construction sector, such as the locations and capacities of concrete and recycling plants.

Multi-objective non-linear solid transportation problem with fixed charge, budget constraints under uncertain environments

ABSTRACT Transportation-based models are widely used to solve real-life problems such as supply chain problems, inventory level problems, business models, management problems, etc. The parameters of these problems have been conventionally measured as deterministic. However, these parameters have vagueness due to the uncertainty in the data sets. Such uncertainty of the parameters is generally measured using randomness (probability theory) or fuzziness (fuzzy theory), with certain assumptions and limitations. Closed intervals interpretation is another concept to deal with the uncertainties of real-world issues in recent decades, avoiding the limitations in probability theory and fuzzy set theory. This study formulates a fixed-charged multi-objective non-linear solid transportation with parameters such as availability of items, conveyance capacity, transportation cost, requirement, and budget for destinations in the form of closed intervals, where objectives are minimization of total transportation cost and action of time under the restriction over budget. An efficient solution procedure is developed with the help of interval analysis based on the parametric perception of intervals. Initially, the objective functions are converted into a crisp function using multiple integrations, and as a result, the problem is converted into a deterministic multi-objective non-linear programming problem. In addition, a real-world numerical problem is considered for testing the developed solution process for greater comprehension and clarity.

A New Approached Method for Solving the Four Index Transportation Problem with Fuzzy Parameters and Application Perspective in Industry

The four index transportation problem (4ITP: origin, destination, goods type, vehicle type) is an extension of the Hitchcock problem, a problem best suited for the goods allocation planning in the case for using the shuttle type. Based on the research results in 1979, in 2011, we developed an exact method for fully solving the 4ITP with determinist parameters. However, because of the limitation of human in recognizing the imprecise natural events in the decision making science, it is necessary to use fuzzy numbers for uncertain condition. By extending the problem toward this direction, in this paper, we develop a method, an extension of fuzzy programming, solving the 4ITP when all parameters such as the cost coefficients, the supply and demand quantities, the goods type and vehicle type quantities are fuzzy numbers and obey the Gaussian law in order 2. We use Gauss method in the fuzzy programming to obtain the costs under form of point cloud. From these results, we use Pham’s method, a method for the conventional 4ITP, to obtain a point cloud with the agglomerating costs. By using Kronechker’s method, we extract the obtained minimal cost under form of an interval for a given solution. It allows us to obtain a goods allocation planning with the minimal total transportation cost.

Application of Linear Programming to Minimize Transportation Cost in Nigeria Breweries Plc, Ibadan, Oyo State, Nigeria

This research paper work analyzed the transportation cost of Nigeria Breweries Plc, Ibadan in Oyo State in relation to the units of its products demanded at the various deports and the capacity of the factories. Five origins which are Ibadan, Osogbo, Ikeja, Oyo and Ondo as well as twelve destinations (warehouses): Gbagi, Dugbe, Oje, Ikorodu, Mowe, Ife, Ilesa, Iseyin, Ogbomosho, Owode, Akure and Ore were examined. The data also include the requirements at each destination and capacity at each source. The costs represent the average transportation cost from June to December 2019. Initial feasible solution was obtained for the secondary data collected by using North-West corner Method, and Vogel Approximation Method. The result of the analysis showed that the allocations of Vogel Approximation Method will give the optimal transportation cost of the company and is recommended to the company for reduction in transportation with a minimum transportation cost of ₦1,168,431.

Mitigating Sustainability Risks in a Road Hazardous Materials Transport Network

Many materials which are important for a sustainable society are hazardous materials (hazmat), and there is the risk of potential threats with undesirable impacts on the sustainability of transport systems. The goal of this study is the mitigation of sustainability risks associated with hazmat transport processes. Moreover, it is necessary to design reliable road hazmat transport networks based on risk mitigation. This research combines route planning of hazmat shipments with location for emergency response centers in a road hazmat transport network. In addition, a new subsidy policy is also proposed. Hazmat carriers are able to choose routes for shipments in accordance with minimum transport costs. As a game scenario, a bi-level programming model is established to describe the problem. A genetic algorithm is developed to solve the problem, and computational experiments are made to testify the effectiveness of the proposed method. The results show the reasonableness of the model and the effectiveness of the algorithm, calibrating the influence of the parameters and the optimization characteristics of certain practical scenarios. Overall, the purpose of this paper is to develop a complete optimization approach for the design of road hazmat transport networks with sustainable risk as the key innovation.

Path Optimization of Low-Carbon Container Multimodal Transport under Uncertain Conditions

The development of multimodal transport has had a significant impact on China’s transportation industry. Due to the variability of the market environment, in this study, based on the context of the official launch of the national carbon emission trading market, the uncertainty of the demand and the randomness of carbon trading prices were considered. Taking minimum total transportation cost as the objective function, a robust stochastic optimization model of container multimodal transport was constructed, and a hybrid fireworks algorithm with gravitational search operator (FAGSO) was designed to solve and verify the effectiveness of the algorithm. Using a 35-node multimodal transportation network as an example, the multimodal transportation costs and schemes under three different modes were compared and analyzed, and the influence of parameter uncertainty was determined. The results show that the randomness of carbon trading prices will lead to an increase or decrease in the total transport cost, while robust optimization with uncertain demand will be affected by the regret value constraint, resulting in an increase in the total transport cost. Multimodal carriers can reduce transportation costs, reduce carbon emissions, and improve the transportation efficiency of multimodal transportation by comprehensively weighing the randomness of carbon trading prices, the nondeterminism of demand, and the relationship between the selection of maximum regret values and transportation costs.

An Improved PSO Algorithm for Optimized Material Scheduling in Emergency Relief

Efficient emergency material dispatch, amid the aftermath of an emergency event, can help control the spread of the disaster and reduce disaster losses. Herewith, we propose a model with the urgency of material demand as the target coefficient, and the minimum load time and the minimum transportation cost as the total cost. For this model, an improved particle swarm optimization (PSO) algorithm is proposed as the means to optimize the initial positions of particles with good point sets and improve the convergence speed with adaptive dynamic weights to improve the optimization of the emergency material dispatch model. In order to verify the effectiveness of the proposed model and algorithm improvement strategy, the experimental results are verified by means of simulation experiments and algorithm comparison experiments, which show that the proposed emergency material dispatch model and the improved PSO algorithm cannot only solve the post-disaster relief material distribution and dispatching problem but also effectively reduce the total cost of emergency material dispatching.

Optimizing and Analyzing the Cost of Collection and Transportation of Solid Waste Using GIS

Abstract: This study aimed to analyze the cost of the waste collection and transportation system of zone 2 ward 95, Kanpur. Population growth, rapid economic development and urbanization have led to an increase in the generation of solid waste. For this purpose, the maps of collection routes and locations of waste storage were drawn using Arc &amp; QGIS software. According to the GIS maps software, the main waste collection cost was supplying human resources and fuel charges. Transfer stations are an integral part of present-day municipal solid waste management systems. The main criteria used to decide on the location of a transfer station has traditionally been the minimization of transport costs since it is cheaper to transport great amounts of waste over long distances in large loads than in small ones.Municipal solid waste (MSW) has been produced since the establishment of mankind. Solid wastes are wastes generated due to human and animal activities and are unavoidable byproducts of human activities. Routing is one of the main components of solid wastemanagement to optimize of collection route and reduce travel distance which could reduce disposal costs. Waste collection and transport can generate up to 70% of the MSW system costs. Separate collection of recyclables or organic waste raises the costs of waste collection. The proper estimation and monitoring of waste collection costs are essential to define the most cost-effective waste collection system. This has become a critical issue for every MSW company aimed at providing their services in the best possible way.