Multi-objective Transportation Research Articles

Synthetic Participatory Planning of Shared Automated Electric Mobility Systems

Unleashing the synergies among rapidly evolving mobility technologies in a multi-stakeholder setting presents unique challenges and opportunities for addressing urban transportation problems. This paper introduces a novel synthetic participatory method that critically leverages large language models (LLMs) to create digital avatars representing diverse stakeholders to plan shared automated electric mobility systems (SAEMS). These calibratable agents collaboratively identify objectives, envision, and evaluate SAEMS alter-natives, and strategize implementation under risks and constraints. The results of a Montreal case study indicate that a structured and parameterized workflow provides outputs with higher controllability and comprehensiveness on an SAEMS plan than that generated using a single LLM-enabled expert agent. Consequently, this approach provides a promising avenue for cost-efficiently improving the inclusivity and interpretability of multi-objective transportation planning, suggesting a paradigm shift in how we envision and strategize for sustainable transportation systems.

A study of uncertain 4D transportation problems with rough interval parameters and additional real-life factors

In developing countries like India and Nepal, a vehicle document verification system is implicated at the checkpoints and border crossings that allows users to authenticate and verify the validity of vehicle related documents such as registration certificates, driving licenses, pollution under control certificates, and insurance policies. This results in the formation of long queues of the vehicles and hence a significant time wastage. These vehicles waiting in the long queues, continue to emit carbon dioxide and other greenhouse gases into the atmosphere. To address these issues, the government has introduced the Automated Number Plate Recognition (ANPR) technology which plays a significant role in the efficient and accurate document verification by identifying and validating vehicle registration details. It also automates toll collection by capturing the vehicle’s number plate, reducing waiting times and congestion at toll barriers. To analyze all these benefits, in this study, for the first time, ANPR technology has been introduced in the multi-objective model of transportation problem and its impact on various objectives has been observed. Also, real-life factors: fixed-charge cost, carbon emission, multiple routes have been considered in the proposed model and the problem is named as multi-objective fixed-charge four dimensional green transportation problem. Rough set theory has been employed as a tool for effectively addressing the indeterminacy and facilitating the modeling of real-life transportation problems. Two methods: the expected value method and the rough chance constrained programming method, are proposed to transform the suggested model of multi-objective transportation problem into a deterministic form. To solve these transformed models, a new multi-objective optimization approach based on the neutrosophic theory, named as neutrosophic goal programming has been introduced. To further demonstrate the practicality of the proposed study, an application for the apple fruit transportation has been solved. Detailed comparisons between obtained results are done on the basis of different modeling perspectives, different solution techniques and also with/and without ANPR technology. Based on the outcomes, the suggested neutrosophic goal programming technique proved to be superior to the existing techniques in the literature.

Improved fuzzy multi-objective transportation problem with Triangular fuzzy numbers

The present study investigates a Multi-Objective Transportation Problem within a fuzzy environment. The cost of transportation, supply, and demand data are assumed to be inaccurate due to market variations. As a result, the parameters are imprecise or fuzzy data. We offer a multi-objective, balanced transportation problem during this work, where all the parameters are fuzzy numbers. Following a mathematical formulation, fuzzy arithmetic will be used to divide the Fuzzy MOTP into three levels MOTP (lower, medium, upper). After reducing the problem to a crisp MOTP and applying a harmonic mean to each objective function, a suggested solution procedure is presented. Determining the optimal solutions for the FMOTP under unknown situations is, thus, the most important objective of this research.

Dual hesitant fuzzy set in multi-objective transportation problems in time sequence frame work

Decision makers may have different information in different situations when making decisions about the same attributes, depending on the circumstances. A time-sequential framework is necessary for navigating these kinds of circumstances. We established the notion of a Time-sequential dual hesitant fuzzy set (TS-DHFS) to illustrate the significance of time sequence in decision-making with the aid of a dual hesitant fuzzy set (DHFS), one of the important tools to handle the uncertainty embedded in the real world. The proposed set describes the hesitant situations more accurately by inducing a time sequence framework. We defined the fundamental operations, two score function for ranking and a series distance measures for the proposed set. For the pragmatic application of our proposed set, we devised the mathematical model in three different types of Time-sequential dual hesitant fuzzy multi-objective transportation problems (TS-DHF-MOTPs). The proposed three models are classified based on the nature of the parameters such as transportation cost (TC), transportation time (TT), availability, and demand. The goal of the proposed transportation systems is TC and TT simultaneously in a single framework. We established an approach utilizing one of the ranking functions that have been proposed, followed by the fuzzy programming (FP) and weighted sum technique (WST), to optimize the constructed models. Additionally, numerical computations are also carried out to emphasize the efficiency and benefits of MOTP under the TS-DHF environment.

Multi-objective optimization model for railway heavy-haul traffic: Addressing carbon emissions reduction and transport efficiency improvement

This paper establishes a multi-objective optimization model for railway heavy-haul trains, focusing on reducing carbon emissions and improving transport efficiency. The model integrates optimization of the route and the vehicle load rate, significantly reducing carbon emissions and enhancing transport efficiency. It addresses the challenges and characteristics of heavy-haul trains, introducing multi-objective optimization problems related to transport carbon emissions and efficiency. Using a pigeon-inspired optimization algorithm, the model considers joint constraints between carbon emissions and transport efficiency objectives. To overcome challenges in multi-objective transportation problems, the paper proposes a forward-learning pigeon-inspired optimization algorithm based on a surrogate-assisted model. This approach calculates the quality of the candidate solution using a surrogate model, reducing time costs. The algorithm employs a forward-learning strategy to enhance learning from non-dominant solutions. Experimental validation with benchmark functions confirms the effectiveness of the model and offers optimized solutions. The proposed method reduces carbon emissions while maintaining transport efficiency, contributing innovative ideas for the development of sustainable heavy-duty trains.

A cost-efficient relocation model minimizing economic and social disruption of the disaster-affected population of the Kedarnath considering uncertainty

In 2013, Kedarnath, India, faced a devastating landslide-induced flood, causing substantial damage, loss of life, and displacement. This event highlights a gap in post-disaster relocation models, hindering efficient transportation to preferred locations. The challenge intensifies as coordinating relocation with individual preferences, coupled with post-disaster data uncertainty, complicates efforts and triggers a series of effects on community acceptance, psychological well-being, and overall resilience. The study addresses post-disaster relocation gaps by introducing a multi-objective transportation model (MOTM) with psychological support, optimizing resettlement to minimize budget impact, distance, and economic and social disruption (ESD). The proposed interval-valued pentagonal neutrosophic number (IVPN) and defuzzification technique advances disaster management theory and offers a practical solution for uncertainty in data, bridging theory, and practical application in the disaster response phase. The MOTM efficiently minimizes transportation, psychological intervention, and rental costs through fuzzy goal programming (FGP), global criterion method (GCM), and neutrosophic compromise approach (NCA), implemented with LINGO optimization software. The values of parameters considered in the MOTM are captured in IVPN format and converted to crisp values, availing the proposed defuzzification technique. The performance evaluation of the defuzzification technique is carried out through a comparative analysis of past literature. The proposed MOTM and IVPN were implemented in the 2013 Kedarnath flood case study to generate diverse relocation options. GCM provides relocation options at a higher cost and distance but with minimal ESD by providing a preferred location to a family count as high as 3324 out of 3491 families. The FGP technique provides results similar to NCP that reduce the costs and distance of the relocation process by 6.95% and 0.88%, respectively; however, it increases ESD by allocating preferred locations to only 2623 out of 3491 families. The study provides Pareto optimal solutions for the smooth application of MOTM for diverse post-disaster instances.

Solving a multi-choice solid fractional multi objective transportation problem: involving the Newton divided difference interpolation approach

<abstract> <p>Multi-objective transportation problems (MOTPs) are mathematical optimization problems that involve simultaneously considering multiple, often conflicting objectives in transportation planning. Unlike traditional transportation problems, which typically focus on minimizing a single objective such as cost or distance, MOTPs aim to balance multiple objectives to find the optimal solution. These problems appear in various real-world applications such as logistics, supply chain management, and transportation, where decision-makers need to consider multiple criteria when designing transportation networks, routing vehicles, or scheduling deliveries. The primary challenge lies in the uncertainty in real-world transportation scenarios, where logistics involve factors beyond cost and distance. We investigated a multi-choice solid fractional multi-objective transportation problem (MCSF-MOTP) based on supply, demand, and conveyance capacity, where the coefficients of the objective functions were of the multi-choice type due to uncertainty. To address this uncertainty, the proposed model employed the Newton divided difference interpolation polynomial method, and the suitability of this model was validated through a numerical illustration employing a ranking approach.</p> </abstract>

Multi-Objective Transportation System Optimization Using Agent-Based Simulation—A Study of Cordon-and Mileage-Based Congestion Pricing

Multi-Objective Transportation System Optimization Using Agent-Based Simulation—A Study of Cordon-and Mileage-Based Congestion Pricing

Multi-Segments Stochastic Fuzzy Goal Programming: A Novel Paradigm for Multi-Objective Transport Optimization

Multi-Segments Stochastic Fuzzy Goal Programming: A Novel Paradigm for Multi-Objective Transport Optimization

An Effective Approach for Solving Multi-objective Transportation Problem

In this present study, a transportation problem is considered such that the total cost and time of transportation are minimized without taking into account their priorities. In literature, there are less techniques available for finding the efficient solutions of multi-objective transportation problem. So, we developed a heuristic algorithm to find most efficient solution of multi-objective transportation problem, which gives efficient solution with minimum difference from ideal solution. Firstly, we aim at formulating a multi-objective transportation problems along with a novel algorithm to find efficient solutions. The proposed algorithm gives optimal solution faster in comparison to other available techniques in literature for the given multi-objective transportation problem. Moreover, it avoids the degeneracy as well as requires low computational effort. Furthermore, an illustrative example is provided to show the feasibility and applicability of the proposed approach and compare the results with the existing approaches to show the effectiveness of it.

A green 4-dimensional multi objective transportation system for disaster relief operations under time-sequential complex fermatean framework with safety measure

In a post-disaster response operation of a severe cyclone, like Biparjoy, the transportation of humanitarian aid (HA) is the foremost activity in the aftermath of the disaster. Here, due to the ubiquity of uncertainty in such operations, we have first devised a time-sequential complex Fermatean hesitant fuzzy set (TS-CFHFS) with all-dimensional aspects such as its structure, fundamental operations, score and accuracy function. For the transportation of HA, we have developed a multi objective multi-item green 4-dimensional transportation system under the TS-CFHF configuration. In our proposed framework, the HA from both private and public sectors is transported to distribution centers (DCs). The aid was then distributed to the disaster relief shelters (DRS) established in the affected areas. We have also incorporated several significant factors that enhance its realistic portrayal, including vehicle speed, disturbance rate, and safety while transporting HAs. The first and foremost objective is to minimize transportation time (TT), for which we have incorporated the objective vehicle speed, while other objectives are transportation cost (TC), carbon emission (CE) and job opportunities (JO) in our proposed model. To obtain the optimal solution, three approaches namely fuzzy programming (FP), global criterion method (GCM) and weighted sum technique (WST) have been utilized. The proposed model has been complemented with two specific case studies based on the nature of the safety constraints by using a numerical computation along with a comparative analysis of the results obtained by three distinct approaches that may be useful to the decision-maker to make an empirical decision that would affect emergency response actions.

Neutrosophic hyperbolic programming strategy for uncertain multi-objective transportation problem

This paper suggests a neutrosophic hyperbolic programming approach based on the hyperbolic membership function to get an efficient solution for an uncertain multi-objective transportation problem where the uncertain normal distribution presents uncertainty. This technique provides flexibility to the decision-maker to select various confidence levels according to his/her own choice and analyze the satisfaction percentage of the obtained optimal compromise solution along with its truth, indeterminacy, and falsity values. In this approach, the parameters are changed from uncertain to crisp by using the inverse measure theorem, and the neutrosophic non-linear hyperbolic membership functions are used to convert the whole model into a non-linear programming problem in a neutrosophic environment. The advantages of our recommended method in real-world applications are effectively illustrated by a numerical illustration. This illustration also makes it easier to compare the results attained using our suggested strategy to those obtained using the methodologies put forward by Uddin et al. (2021) and Miah et al. (2022). Also, MATLAB software (version R2019a,64bit(win64)) is used to develop the code for the solution of this non-linear uncertain multi-objective transportation problem.

A generalized parametric approach for solving different fuzzy parameter based multi-objective transportation problem

A generalized parametric approach for solving different fuzzy parameter based multi-objective transportation problem

Intuitionistic fuzzy multi-objective transportation model during pandemic COVID-19

The given paper discusses an intuitionistic fuzzy discrete time-space optimization model while taking into account the COVID-19 pandemic. The novelty of this study is characterized by the proposal of a distinctive multi-objective intuitionistic fuzzy transportation model in which the demand function varies with time as the number of infected, exposed and susceptible persons increase. Forecasting of demand is executed using the epidemic diffusion model. Also, a new approach to defuzzify triangular intuitionistic fuzzy numbers, depending upon the concept of the golden ratio, is presented. Intuitionistic fuzzy programming approach is used to obtain the pareto-optimal solution. A real-life numerical illustration is explained using the proposed methodology to examine its practical suitability. Outperforming the existing traditional method of solving a transportation problem verifies its proficiency.

A fully Fermatean fuzzy multi-objective transportation model using an extended DEA technique

A fully Fermatean fuzzy multi-objective transportation model using an extended DEA technique

An overview of weighted goal programming: a multi-objective transportation problem with some fresh viewpoints

An overview of weighted goal programming: a multi-objective transportation problem with some fresh viewpoints

Multi-choice fractional stochastic multi-objective transportation problem

Multi-choice fractional stochastic multi-objective transportation problem

China-Europe Container Multimodal Transport Path Selection Based on Multi-objective Optimization

With the advancement of the "Belt and Road" initiative, trade between China and Europe has been steadily growing, and China-Europe container transportation has received increasing attention. This study analyzes the influencing factors of China-Europe container transport path selection and, based on the physical network of China-Europe container transport, constructs virtual nodes according to the transport modes that can be transited at different nodes and their own transshipment operations. By reflecting cost, time, and carbon emission factors in the virtual network, we construct a service network for China-Europe container multimodal transport, which in turn forms a multi-objective transport scheme selection model considering transportation cost, time, and carbon emissions. Subsequently, the economic and practical aspects of this transport path selection model are verified through five case studies of container transport from Dalian to Hamburg, Germany. Lastly, the sensitivity of factors, such as cost and time, to the China-Europe container multimodal transport path selection is assessed based on scenario analysis. This analysis offers valuable references for various decision-makers involved in the selection of the China-Europe container transport path.

A multi-objective transportation problem under quantity dependent credit period and cost structure policies in triangular intuitionistic fuzzy environment

One of the major economic activities of a business concern is transportation of its products without any hassle. Therefore, the research on a transportation problem is very important now-a-days. In a classical transportation problem, the amount incurred for shipping the goods from the source to a destination is determined on the basis of unit transportation cost. But now, in many practical cases, it is seen that the unit transportation cost is settled on the basis of transported amount. In such a direction, this article develops a transportation problem to transport the produced items from a production house to some retailers through some distributors. The system has two cost parameters, one is actual unit transportation cost and other is demanded unit transportation cost. The actual unit transportation cost is the cost incurred during the transportation while the demanded unit transportation cost is the cost demanded by the distributors. Here, the demanded unit transportation cost has been decided depending on the amount of transported item. Since in the system, there are more than one distributor, distributors offer a credit period policy to attract the retailers. On the basis of the deterioration of the loaded amount, the transported items are classified into two categories. For the higher deteriorated items, the transportation cost is relatively larger and for the lower deteriorated items, the transportation cost is relatively lower with the increase in the loading amount. Considering the above two categories, two crisp models are developed. Due to the uncertain information of the actual unit transportation cost, the models are illustrated considering the actual unit transportation cost as a triangular intuitionistic fuzzy number. The main objective of this research is to minimize the total cost of the retailers and to maximize the total profit of the distributors simultaneously under the above circumstances. For crispification, two different methods have been used here — one is an existing method namely ordered weighted average operator and other is a developed average value approach. Also, the elitist non-dominated sorting genetic algorithm (NSGA-II) and the fuzzy programming technique have been used to get the optimal solution of this problem. To justify the impact and significance of the proposed models on this decision making system, some numerical studies have been done and it is observed that distributors and retailers both benefit more under the credit period policy than the policy without credit period. This study discusses the situation under which the uncertain parameters are to be expressed in terms of triangular intuitionistic fuzzy numbers. Also, it is identified that more accurate crispification has been done by the average value approach than the existing approach.

An extended multi-objective transportation model based on Fermatean fuzzy sets

An extended multi-objective transportation model based on Fermatean fuzzy sets