Multimodal Transportation Problem Research Articles

Maximizing the Supply Chain Profit in Multimodal Transportation Problem with Transfer Part using Two-Echelon Genetic Algorithm

Multimodal transportation is a highly effective method for optimizing deadlines and reducing inventory costs, both of which are crucial in a supply chain environment. This study employs a mathematical programming model to optimize the supply chain profit for multimodal transportation distribution within a specified time window. The model considers five factors, such as production cost, transportation cost, transport time, penalty cost, and sales price. Additionally, a Two-Echelon Genetic Algorithm (TEGA) is proposed to solve the optimization problem, and a numerical example is provided to validate the model and algorithm. The study compares the performance of the proposed algorithm with the exact solution from a previous study, presents implementation details and numerical experiment results, and analyses the findings. The results demonstrate the efficiency and robustness of the algorithm, making it a significant contribution to transportation planning for freight transportation and supply chain management.

A multiobjective mathematical model for a humanitarian logistics multimodal transportation problem

Purpose The purpose of this paper is to introduce a three-echelon multimodal transportation problem applied to a humanitarian logistic case study that occurred in Mexico. Design/methodology/approach This study develops a methodology combining a transshipment problem and an adaptation of the multidepot heterogeneous fleet vehicle routing problem to construct a mathematical model that incorporates the use of land-based vehicles and drones. The model was applied to the case study of the Earthquake on September 19, 2017, in Mexico, using the Gurobi optimization solver. Findings The results ratified the relevance of the study, showing an inverse relationship between transportation costs and delivery time; on the flip side, the model performed in a shorter CPU time with medium and small instances than with large instances. Research limitations/implications While the size of the instances limits the use of the model for big-scale problems, this approach manages to provide a good representation of a transportation network during a natural disaster using drones in the last-mile deliveries. Originality/value The present study contributes to a model that combines a vehicle routing problem with transshipment, multiple depots and a heterogeneous fleet including land-based vehicles and drones. There are multiple models present in the literature for these types of problems that incorporate the use of these transportation modes; however, to the best of the authors’ knowledge, there are still no proposals similar to this study.

Solving the multi-modal transportation problem via the rough interval approach

This research studies a transportation problem to minimize total transportation cost under the rough interval approximation by considering the multi-modal transport framework, referred to here as the rough Multi-Modal Transportation Problem (MMTP). The parameters of MMTP are rough intervals, because the problem is chosen from a real-life scenario. To solve MMTP under a rough environment, we employ rough chance-constrained programming and the expected value operator for the rough interval and then outline the main advantages of our suggested method over those existing methods. Next, we propose an algorithm to optimally solve the problem and present a numerical example to examine the proposed technique. Finally, the solution is analyzed by the proposed method with rough-chance constrained programming and expected value operator.

Optimum Intervention in Transportation Networks Using Multimodal System under Fuzzy Stochastic Environment

Multimodal transport refers to the transportation of goods under a single contract but performed with at least two different modes of transport. This research designs a new method for solving the Transportation Problem (TP) by introducing multimodal transport systems under fuzzy-stochastic environment, which we refer to as Fuzzy-Stochastic Multimodal Transportation Problem (FSMMTP). An algorithm is developed to reduce FSMMTP to a deterministic TP, which is mainly based on α -cut of fuzzy numbers, and uses a signed distance function based on the mean expectation of the fuzzy-stochastic cost parameters. We derive the optimal solution as well as optimal selection of mode for transporting the goods in our proposed model. A numerical example justifies the effectiveness of our proposed study. The paper ends with a conclusion and an outlook to future studies.

Factors Affecting Multimodal Transport during COVID-19: A Thai Service Provider Perspective

Multimodal transport is a critical component in developing the international trade economy, and logistics service providers are a critical component in multimodal transport. However, the recent COVID-19 pandemic has seriously affected the transport system, especially in light of the ongoing rise in transportation costs which has increased firm operating costs. Furthermore, the COVID-19 pandemic has severely influenced the economic sector, resulting in decreased growth. This research aims to determine the priorities of the most important factors for developing and improving multimodal transport compared to pre-COVID-19 and during the COVID-19 outbreak. This research consisted of two stages. The first stage used bibliometric analysis to define multimodal transport dimensions and criteria based on the previous and current literature studies on multimodal transport and used confirmatory factor analysis (CFA) to verify the relationship between factors and multimodal transport. The second stage used the fuzzy best-worst method (FBW) combined benefit, opportunity, cost and risk (BOCR) to prioritize the improvement and development of multimodal transport during the COVID-19 crisis, which considers the perspective of logistics service providers in Thailand. These factors, when identified, would help policymakers design more efficient policies to improve and solve multimodal transport problems mainly caused by strict public health measures during COVID-19.

Constructing Reference Plans of Two-Criteria Multimodal Transport Problem

Abstract The object of this study is a multicriteria transport problem, being stated for availability of several means of cargo delivery, meaning a multimodal transport problem. The optimization criteria of the multimodal transport problem described above are two objective functions of minimizing total transportation costs and level of transport risks. Three types of transport were selected for research: automobile, rail and river (inland waterway). The results of the study lay the foundation for development of a new valid algorithm for solving multimodal transport problems like multi-criteria optimization ones. The main advantage of such an algorithm lies in its higher potential convergence rate compared to classical numerical optimization methods, which now are predominantly used to solve the problems of this type. This advantage may not be decisive, but it appears to be at least quite an important argument when choosing the method of realization for two-criteria multimodal transport problems earlier considered, especially, in case of a large dimension. Moreover, the algorithm described in the work can be applied to similar problems with any number of types of transport and optimization criteria.

Multi-modal and multi-route transportation problem for hazardous materials under uncertainty

ABSTRACT In this article, the multi-modal and multi-route transportation problem for hazardous materials under uncertainty is studied, and an interval 0–1 integer programming model is proposed to optimize the transportation risks and transportation costs during the transportation of hazardous materials. Factors such as freight rates and loading and unloading costs are different when the transportation modes, transportation time and transportation areas are different. Therefore, some parameters in the model are assumed as interval numbers. The proposed model is transformed to a deterministic model by the interval ranking method. In addition, a genetic algorithm is proposed to solve the model. Finally, a real-world case of the multi-modal and multi-route transportation problem for hazardous materials under uncertainty is studied to demonstrate the rationality and effectiveness of proposed model and algorithm.

РЕАЛІЗАЦІЯ МУЛЬТИМОДАЛЬНИХ ТРАНСПОРТНИХ ЗАДАЧ В РІЗНИХ ПРОГРАМНИХ СЕРЕДОВИЩАХ

Multimodal problem is the classic transport problem that assumes simultaneous (parallel) application of several means of cargo delivery. It means, the criterion for optimizing a multimodal transportation problem is, as compared with the classic one, the minimal ultimate transportation cost, whereas there are more than one accessible means of cargo delivery. Today’s scientific description, both in the Ukrainian and foreign investigations, considers the problems of this type insufficiently, therefore the issue of testing their models and methodologies is quite significant. Apparently, the calculations of these problems appear to be as well complicated, so it is rather correct to employ for it special programming methods. The article regards the multimodal transport problem presented and realized through different programming means, among them MS Excel, Mathcad and Matlab. The transportation means are presented as equal to three, since we regard the automobile, the railroad and the river deliveries. The problem requires, for various programming performances, the standard (built-in) functions and methods like Solver for MS Excel, Minimize for Mathcad (as well as Given block) and Linprog for Matlab (using a multimodal transport problem modified into a linear programming problem). The research has resulted in the analysis of peculiar algorithms for multimodal and classic transport problems, the above mentioned programming methods. Also, the model data has demonstrated identical results obtained through the application of various programming means. The obtained algorithms for solving the multimodal transport problem can be expanded for a larger, than three, number of means for transport delivery. In perspective, the research holds to solve the multicriteria, at least, two-criteria, multimodal transport problem that would assume more than one criterion of optimization. One of these would be minimizing the level of risks in transportation logistics.

Evaluation of Multimodal Transport in China Based on Hesitation Fuzzy Multiattribute Decision-Making

Intermodal transportation, as an efficient form of transport organization, is a key technology and means for the logistics industry to balance transport costs and efficiency. How to deeply analyze the key factors constraining the level of development of multimodal transportation in China, building multimodal transportation development evaluation index system and evaluation model, evaluating comprehensively the multimodal transportation development level in China, and putting forward targeted countermeasures is of great practical significance. Based on the hesitant fuzzy multiattribute decision-making method, this paper analyzes the key factors influencing the level of multimodal transportation development from the perspective of sea-rail intermodal transport and further uses the interval number discretization and likelihood deviation maximization multiattribute decision-making method to construct a mixed-data index evaluation model, which is combined with data from 11 provinces (cities) in China. The study shows that there is a big gap among different provinces (cities) in China concerning the level of multimodal transportation development. Besides, the volume of intermodal container transportation and railway mileage are the core factors affecting the level of multimodal transportation development, and the results of the evaluation model can objectively reflect the level of multimodal transportation development and problems in each province (city). The research results further enrich the theoretical system of intermodal transportation development and provide a reference for the relevant management departments in formulating intermodal transportation plans and policies by putting forward pertinent suggestions in the field of management. In the future, the research direction should include the ecological environment, economic benefits, and other indicators into the evaluation system, expand the research scope of the modes of combined transportation such as public rail transport and public air transport, and improve the applicable scope of the evaluation model and the evaluation capability of specific scenarios.

A New Multi-Objective Firework Algorithm to Solve the Multimodal Planning Network Problem

This article introduces a new approach called multi-objective firework algorithm (MFWA). The proposed approach allows for solving the multimodal transportation network problem (MTNP). The main goal is to develop a decision system that optimizes and determines the planning network of the multimodal transportation (PNMT) problem. The optimization involves reaching the efficient transport mode and multimodal path, in order to move from one country to another while satisfying the set of objectives. Moreover, the firework algorithm has distinct advantages in solving complex optimization problems and in obtaining a solution by a distributed and oriented research system. This approach presents a search way, which is different from the swarm intelligence-based stochastic search technique. For each firework, the process starts by exploding a firework in the sky. The search space is filled with a shower of sparks to get diversity solutions. This new approach proves their efficacy in solving the multi-objective problem, which is shown by the experimental results.

Soft Set Based Intelligent Assistive Model for Multiobjective and Multimodal Transportation Problem

The real world conditions of transportation pose new challenges for development of intelligent assistive systems for effective decision making. The ignorance of relationships among various attributes and parameters is one of the major causes of uncertainty in existing models of transportation problems. Fuzzy sets have been explored by various investigators to address the uncertainty issues due to relationships among various attributes. However fuzzy sets are not fully capable of dealing all kinds of uncertainty to provide intelligent solutions of transportation problems. A soft set is explored in this paper to model the uncertainty arising from the relationships of attributes with the parameters in a multimodal and multi objective transportation problem. The three modes of transportation incorporated in this model are road, rail and air. The real data set is prepared using the tariff, distance and time duration of transport available on websites of transport service agencies. The objectives are to minimize the cost and time duration of transportation. The proposed model evaluates multiple criteria using various combinations of different modes of transport to optimize the objectives of the problem. The model has been illustrated using the real data set using existing methods of solution. The soft set approach is found to be quite effective in dealing with the relationships of attributes with the parameters arising due to multi criteria decision making and leads to intelligent optimal solutions. The proposed model can be used as intelligent assistive system for decision making of multi objective and multimodal transportation problems.

Multi-Objective Optimization Methods for Transportation Network Problems

This article recapitulates literature research solving transportation problems and these variants, notably the multimodal transportation problems variants. Moreover, the existing optimization methods critiqued and synthesized their efficiency to solve the transportation problem. This problem can be identified by various criteria and objectives functions that distinguished according to the case study. Based on the existing literature research, a taxonomy is proposed to distinguish different factors and criteria that perform and influence the multi-objective optimization on the transportation network planning problems. The transportation problems are cited according to these objective functions, and the variant of the problem by referring to the previous studies. In this article, the authors have focused their attention on a recent multi-objective mathematical model to solve the planning network of the multimodal transportation problem.

A Risk Analysis Based on a Two-Stage Model of Fuzzy AHP-DEA for Multimodal Freight Transportation Systems

Multimodal transportation has become a main focus of logistics systems due to environmental concerns, road safety issues, and traffic congestion. Consequently, research and policy interests in multimodal freight transportation problems are increasing. However, there are major challenges in the development of multimodal transportation associated with inherent risks and numerous uncertainties. Since risks are potential threats that directly impact logistics and transportation systems, comprehensive risk analysis should be carried out. Risk analysis is a critical process of identifying and analyzing significant issues to help industry mitigate those risks. However, identifying and prioritizing risks is more complex because of the ambiguity of the relevant data. This study proposes the integration of the fuzzy analytic hierarchy process (FAHP) and data envelopment analysis (DEA) for identifying and assessing quantitative risks. The proposed FAHP-DEA methodology uses the FAHP method to determine the weights of each risk criterion. The DEA method is employed to evaluate the linguistic variables and generate the risk scores. The simple additive weighting (SAW) method is used to aggregate risk scores under different risk criteria into an overall risk score. A case study of the coal industry demonstrates that the proposed risk analysis model is practical and allows users to more accurately prioritize risks while selecting an optimal multimodal transportation route. The process raises user's attention to the high-priority risks and is useful for industries in optimizing a multimodal transportation route under risk decision criteria.

Analyzing multimodal transportation problem and its application to artificial intelligence

In recent decades, there has been increased interest among both transportation researchers and practitioners in exploring the application of artificial intelligence (AI) paradigms to address the real-life problems in order to improve the efficiency, safety and environmental compatibility of transportation systems. In this paper, our main interest is to solve transportation problem by considering the multimodal transport systems and then utilize it to solve neural network (NN) problem in AI. The multimodal transportation problem (MMTP) is nothing but a linear programming problem, and so it is easy to solve by any simplex algorithm. To analyze the proposed method, a numerical example is included and solving it we reveal a better impact for analyzing the real-life decision-making problems. Thereafter, we revoke our approach for solving NN problems, which enhances a connection between MMTP and NN problems. Finally, conclusion and future research directions are presented regarding our study.

Optimization of the method of constructing reference plans of multimodal transport problem

The classical transport problem is in determination of the optimal plan for the transportation of goods from the points of departure to the points of delivery, taking into account the criterion of the minimum cost of such transportation. Such a problem takes into account only one type of transport, which does not fully correspond to the practical needs of modern logistics enterprises. That is why the object of this research is the classical transport problem, the formulation of which takes into account the presence of several means of cargo delivery, namely: automobile, railway and water. This type of transport problem is defined as multimodal. The implementation of the multimodal transport problem involves the use of various numerical methods and is carried out using software. In fact, the conceptual approach to its solution is a simple selection of possible results. Given the large dimension of the problem, such an approach can be extremely cumbersome, and therefore requires some improvement. During the study, the method for constructing a reference plan for such a problem was optimized based on the criterion of minimizing the number of numerical iterations, and the advantages of the proposed approach compared to those already known were substantiated. The basis of the new approach is the previously known minimal element method, which is to be used to solve the transportation problem, and an analogy with the Steiner problem was drawn. The latter, in turn, made it possible to define a new approach as the Steiner method. The research result is development of a general algorithm for the implementation of the proposed Steiner method. As an approbation of this algorithm, a model example is provided. It demonstrated the identity of the results of solving a multimodal transport problem using all the methods discussed in the article. The development of new methods for the implementation of the multimodal transport problem will make it possible to construct efficient algorithms for solving more complex problems of transport logistics. The criterion for reducing the number of numerical iterations, used at all stages of the implementation of such problems, significantly reduces the time to search for their solutions.

Multimodal Path Optimization and Logistics Distribution of Military Transportation

The problem of route optimization of large volume and long distance material distribution is carried out from the rear strategic logistics center to the front line distribution center, researched by turning into multimodal transport problems, and a single-path multimodal transport path optimization model is established. Decision variables about stage safety probability are introduced; the nonlinear model is simplified into a second-order programming model, by changing the probability of task safety and simplified the model to a linear programming model by using the monotonicity of logarithmic functions. An example is presented to solve the simulation with LINGO software, made a reference plan under different constraints, the correctness of the model is verified.

Firework Algorithm For Multi-Objective Optimization Of A Multimodal Transportation Network Problem

This study suggests a multi-objective firework algorithm based on the Pareto-dominance method in order to select the optimistic solution from the multimodal transportation problem. The aim is to determine a shortest and efficient itinerary of satisfying a certain set of demands and operational constraints. Several publications have appeared in recent years documenting the transportation network analysis. This fact reflects the importance and complexity of this problem. In order to reach a sustainable planning in a rather complicated transport system, it is of high interest to use an approach that belongs to the field of artificial intelligence method. This paper introduces a new multi-objective approach based on firework algorithm that generates n solutions which are then selected according to the Pareto-dominance which is the best compromise between goals. Finally, we present our obtained experimental results, then we compare them with the obtained results by Cplex in order to prove the efficiency of this algorithm.

Bulk wheat transportation and storage problem of public distribution system

This research investigates the multi-period multi-modal bulk wheat transportation and storage problem in a two-stage supply chain network of Public Distribution System (PDS). The bulk transportation and storage can significantly curtail the transit and storage losses of food grains, which leads to substantial cost savings. A mixed integer non-linear programming model (MINLP) is developed after studying the Indian wheat supply chain scenario, where the objective is to minimize the transportation, storage and operational cost of the food grain incurred for efficient transfer of wheat from producing states to consuming states. The cost minimization of Indian food grain supply chain is a very complex and challenging problem because of the involvement of the many entities and their constraints such as seasonal procurement, limited scientific storages, varying demand, mode of transportation and vehicle capacity constraints. To address this complex and challenging problem of food grain supply chain, we have proposed the novel variant of Chemical Reaction Optimization (CRO) algorithm which combines the features of CRO and Tabu search (TS) and named it as a hybrid CROTS algorithm (Chemical reaction optimization combined with Tabu Search). The numerous problems with different sizes are solved using the proposed algorithm and obtained results have been compared with CRO. The comparative study reveals that the proposed CROTS algorithm offers a better solution in less computational time than CRO algorithm and the dominance of CROTS algorithm over the CRO algorithm is demonstrated through statistical analysis.

A generalized interval fuzzy mixed integer programming model for a multimodal transportation problem under uncertainty

ABSTRACTA generalized interval fuzzy mixed integer programming model is proposed for the multimodal freight transportation problem under uncertainty, in which the optimal mode of transport and the optimal amount of each type of freight transported through each path need to be decided. For practical purposes, three mathematical methods, i.e. the interval ranking method, fuzzy linear programming method and linear weighted summation method, are applied to obtain equivalents of constraints and parameters, and then a fuzzy expected value model is presented. A heuristic algorithm based on a greedy criterion and the linear relaxation algorithm are designed to solve the model.

Optimization of River Transport to Strengthen Multimodal Passenger Transport System in Inland Region

Kapuas Hulu regency which is located in the easternmost of West Kalimantan province is still highly dependent on river transportation mode. Considering that multimodal transportation problems are lying at the level of respective services and ease of intermodal transfer, the performance of each mode should be evaluated for improvement. Based on the survey conducted to users of river transport modes, the following data have been obtained: 58% of users are private sector workers and 42% are civil servants, 85% of them make more than 2 million rupiah while another 15% earns between 1 to 2 million rupiah. With regard to travelers’ age, it is dominated by people who are at the age of 30 to 40 years old, 33% of 40-50 years old and 7% of those between 20 to 30 years old. With regard to the purpose of the trip, as many as 61% use it to reach work place, 17% to visit their relatives and 6% with the aim of getting medical treatment. All respondents (100%) explain the reason for using river transportation mode is due to a very low level of accessibility (narrow and damaged roads) to reach their destination should they choose land transportation. The evaluation on the level of connectivity of transportation infrastructures provide us with three districts namely Semitau, Badau and South Putussibau having low level of connectivity between wharfs and terminals for passengers to change modes and continue their journeys. There are two districts whose high level of inter-connectivity infrastructure is high, i.e. Suhaid and North Putussibau districts where they have high levels of connectivity between wharf and terminal. And one district with a very high level of connectivity, namely Jongkong district, where the wharf and terminal are situated at one location thus making it easier for passengers to intermodal transfer.