Operational Level Decisions Research Articles

Integrated multi-manned disassembly line balancing problem with reverse supply chain design strategies by considering lot sizing

ABSTRACT Due to strict governmental regulations, manufacturers have increasingly focused on recovering end-of-life (EoL) products through disassembly lines, aiming to achieve economic benefits while addressing the environmental aspects of supply chain sustainability. Disassembly line balancing (DLB) has become a key challenge for enterprises seeking to perform environmentally conscious manufacturing as part of a closed-loop supply chain policy. Therefore, designing a sustainable closed-loop supply chain requires analyzing interrelated problems by focusing on strategic-, tactical-, and operational-level decisions simultaneously. This study introduces an integrated problem that addresses multi-manned DLB, lot sizing, and reverse supply chain (RSC) design problems. To mathematically represent the problem, a generic optimization model is developed to minimize overall costs, including station opening, inventory holding, shortage, transportation, and collection center/facility opening costs. This study addresses several research questions to explore the impact of distribution strategies (centralized and decentralized) on multi-manned DLB and lot size decisions within the context of reverse supply chain design.

Integrated People and Freight Transportation: A Literature Review

Increasing environmental and economic pressures have led to numerous innovations in the logistics sector, including integrated people and freight transport (IPFT). Despite growing attention from practitioners and researchers, IPFT lacks extensive research coverage. This study aims to bridge this gap by presenting a general framework and making several key contributions. It identifies, researches, and explains relevant terminologies, such as cargo hitching, freight on transit (FoT), urban co-modality, crowd-shipping (CS), occasional drivers (OD), crowdsourced delivery among friends, and share-a-ride, illustrating the interaction of IPFT with different systems like the sharing economy and co-modality. Furthermore, it classifies IPFT-related studies at strategic, tactical, and operational decision levels, detailing those that address uncertainty. The study also analyzes the opportunities and challenges associated with IPFT, highlighting social, economic, and environmental benefits and examining challenges from a PESTEL (political, economic, social, technological, environmental, and legal) perspective. Additionally, it discusses practical applications of IPFT and offers recommendations for future research and development, aiming to guide practitioners and researchers in addressing existing challenges and leveraging opportunities. This comprehensive framework aims to significantly advance the understanding and implementation of IPFT in the logistics sector.

Adoption of Multi-Modal Transportation for Configuring Sustainable Agri-Food Supply Chains in Constrained Environments

Agri-food supply chains have the potential to make a significant contribution to achieving sustainable development goals through ongoing improvements in their configurations. A range of strategic, tactical, and operational level decisions pertaining to the design and operation of sustainable supply chains have been studied in the extant literature. However, investigations into the adoption of multi-modal transportation as a strategic decision in the context of agri-food supply chains operating in constrained environments are limited. As such, in this study, the adoption of bi-modal transportation for the domestic vegetable supply chain in a developing country context under certain constraints was examined. A mixed-integer linear programming model was developed to determine the volume and direction of the product flow to achieve the minimum total food-miles and smallest emissions footprint. As a case study, a Sri Lankan mainstream vegetable supply chain was used to investigate the applicability of a combination of truck and railway modes to transport vegetables from farms to retailer locations via economic (consolidation) centers. The adoption of a bi-modal transportation structure demonstrated the potential to reduce food miles by 32%, transportation costs by 36%, contributions to global warming potential by 35%, and empty truck hauls by 38%, compared to a structure with truck-based, uni-modal transportation.

Considering economic-environmental dimension in the integrated strategic and tactical optimization of Ethiopia's bioethanol supply chain coupled with operational planning

The global supply chain has garnered significant attention in recent years due to its intricate nature and operational challenges. Companies face the crucial task of integrating various components to maintain competitiveness and enhance overall performance across the supply chain. Achieving this goal necessitates vertical integration to ensure coherence and coordination among decision-making processes at different temporal and spatial scales, bridging the gap between strategic, tactical, and operational decisions. This article proposes a mathematical model that integrates the three previous decisions within the supply chain context, aiming to maintain coherence while considering economic and environmental objectives (few papers cover this challenge). By addressing the complexities of multi-scale modeling and optimization, the model seeks to provide a holistic view of supply chain operations. The model focuses on the transport decisions at the operational level, resulting in the coupling between a strategic and tactical model and a vehicle routing problem. Nonetheless, achieving this integration effectively from a modeling and resolution point of view leads to computational and methodological challenges due to the multi-scale, multi-site, and multi-objective nature of the problem, requiring an innovative approach for effective model resolution. The contributions of this article pave the way towards more sustainable and efficient supply chain operations. The model capabilities are exemplified through the real case study of the bioethanol supply chain in Ethiopia. For the location of the different firms of the supply chain, the country is divided into 58 zones (second level administrative subdivision in Ethiopia) to have a precise mapping of the area. The result of the case study shows that in adding the operational level decisions to the strategic-tactical ones has not only changed the topology of the network configurations but has greatly affected the economic and environmental criteria due to more precise estimation of all activities. The results put also in highlight that there is a compromise to find between both previous objectives by identifying Pareto alternative scenarios.

A Systematic Review of Sustainable Supply Chain Network Design: Optimization Approaches and Research Trends

In response to the ever-increasing pursuit of competitiveness among organizations in today’s global business landscape, the subject of supply chain management has become a vital domain encompassing a wide range of sectors and industries across the economy. The growing concern about sustainable development has prompted public and private supply chain players to incorporate the three pillars of sustainability, namely, economic, environmental, and social, into the design of their supply chain networks. This study reviews and examines the content of 102 relevant papers to discuss the mathematical models, modeling approaches, and solutions that have been explored in the existing literature on forward sustainable supply chain network design. This paper also investigates the sustainability elements and supply chain network peculiarities including design factors and decision levels. In this review, several limitations in the current literature on sustainable supply chain network design optimization models are highlighted. According to the analysis, it was found that a better understanding of the industry and its sustainability requirements and priorities is essential for designing sustainable supply chain networks that are tailored to the needs of a specific industry rather than achieving general sustainability objectives. In addition, integrating strategic, tactical, and operational decision levels in the design of supply chain networks is critical for evaluating their impact on each other in terms of sustainability. More sophisticated mathematical solution methods for dealing with real-life scenarios including nonlinearity and uncertainty sources are required. The paper concludes with new prospects of research to promote a better integration of sustainability into supply chain networks.

Developing a bi-objective maintenance optimization model for process industries by prioritizing resilience and robustness using dynamic Bayesian networks

In this paper, we developed a new model that combines resilience concept with robust maintenance resource allocation, and team assignment strategies to evaluate system performance after disruption. This model offers insights for proactive preparation before disruptive events and facilitates optimal decision-making in the aftermath of such disruptions. Our approach involves a non-linear programming model that operates at both strategic and operational decision levels. It aims to simultaneously minimize total maintenance costs while maximizing resilience by determining the allocation of backup resources to system components before a disruption occurs and suggesting emergency maintenance team assignments post-disruption, all while adhering to resilience constraints.We quantify resilience using a dynamic Bayesian Network method and Markov process, utilizing metrics based on absorption, adaptation, and restoration performance (reliability) curves. We propose a bi-objective optimization model and leverage the genetic algorithm NSGA-III to obtain Pareto-optimal solutions. To address the inherent uncertainty of various disruption scenarios, we generate a range of possible scenarios and employ robust optimization techniques to derive solutions that can withstand these uncertainties. To demonstrate the effectiveness of our proposed model, we conduct a case study involving a City Gate Station in agas industry.

Integrating Production Scheduling and Vehicle Routing Decisions at the Operational Decision Level: A Review and Discussion Extension

As a method of reducing costs and enhancing service quality, companies integrate production and distribution scheduling to improve performance. Thirty-two articles published between 2017 and 2022 are classified based on a review methodology as a continuation of a literature review of the integrated production and distribution scheduling problems at the operational level that was published in 2016. The characteristics of mathematical models and the solution algorithms used are discussed in order to identify gaps and determine future research directions. Through the review, it was found that some real-life characteristics still need further study. Future research may need to consider uncertainty, stochastic elements and evaluating different heuristics and metaheuristics in different production environments. Finally, additional research is necessary to determine under what conditions integration is most effective.

Queueing network models for the analysis and optimisation of material handling systems: a systematic literature review

Material handling systems (MHSs) are an integral part of logistics functions in manufacturing and service organizations. Material handling equipment (MHE) is considered the pivotal actor of any given MHS. Decisions ranging from the strategic level, such as selecting the proper MHE, capacity, and ownership (in-house or outsourcing) to operational level decisions such as resource allocation, scheduling, and routing of MHEs, are critical to the efficiency of an MHS. Industry practitioners use various methods and tools to evaluate these MHSs to find the best policies for their operations. This study identifies past works related to the performance evaluation and optimisation of MHSs using queueing network models. Moreover, this study provides a comprehensive analysis of identified research questions. The study methodology adopts a systematic literature review, bibliometric, and content analysis techniques proposed in similar research studies. This study provides material logistics scholars and practitioners with a thorough understanding of queueing networks as a modelling tool for analysing MHS applications in various domains.

Switching fuel or scrubbing up? A mixed compliance strategy with the 2020 global sulphur limit

This article examines how a shipping fleet, mixed with scrubber-fitted and fuel-switching types of ships, copes with the “IMO 2020” global sulphur limit by taking the effects of potential decarbonizing market-based measures into account. We first develop an “IMO 2020” compliance model that combines a strategic-level decision on “what is the optimal structure of either ship type” and an operational-level decision on “what are their optimal sailing strategies”. We prove that the two questions can be solved simultaneously, and that their solutions are unique. Then, we extend the compliance model to incorporate the effects of potential market-based measures that might be used to control carbon emissions. We find that, because of decarbonization, it is better to maintain a percentage of fewer than 50% of scrubber-fitted ships within a mixed fleet.

Hub Network Design Problem with Capacity, Congestion, and Stochastic Demand Considerations

Our study introduces the hub network design problem with congestion, capacity, and stochastic demand considerations (HNDC), which generalizes the classical hub location problem in several directions. In particular, we extend state-of-the-art by integrating capacity acquisition decisions and congestion cost effect into the problem and allowing dynamic routing for origin-destination (OD) pairs. Connecting strategic and operational level decisions, HNDC jointly decides hub locations and capacity acquisitions by considering the expected routing and congestion costs. A path-based mixed-integer second-order cone programming (SOCP) formulation of the HNDC is proposed. We exploit SOCP duality results and propose an exact algorithm based on Benders decomposition and column generation to solve this challenging problem. We use a specific characterization of the capacity-feasible solutions to speed up the solution procedure and develop an efficient branch-and-cut algorithm to solve the master problem. We conduct extensive computational experiments to test the proposed approach’s performance and derive managerial insights based on realistic problem instances adapted from the literature. In particular, we found that including hub congestion costs, accounting for the uncertainty in demand, and whether the underlying network is complete or incomplete have a significant impact on hub network design and the resulting performance of the system. Funding: This work was supported by Türkiye Bilimsel ve Teknolojik Araştırma Kurumu [Grant 218M520]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2022.0112 .

Refuse-derived fuel-3 production simulation using network flow modeling: Predicting the uncertainty in quality standards

Municipal solid waste management requires intelligent and integrated decision-making to achieve sustainable waste treatment processes. In particular, a waste treatment system that transforms municipal solid wastes into a commodity called refuse-derived fuel (RDF) is being researched as a promising waste to energy solution. This waste processing faces limitations in maintaining consistent production and quality control standards of RDF. In this context, a network flow modeling technique is used to design a stochastic discrete-event simulation model for the production in a general material recovery facility (MRF) to evaluate its performance. The developed model supports revisions in the strategic, tactical, and operational decision levels and is integrated with varied uncertainties like probability distributions of in-feed waste compositions, moisture content, and calorific value of individual waste components, affecting the energy performance of a MRF. The model provides improvements to operating conditions and enables prediction for quality standards of RDF, enabling the waste management authority to meet their outlined quality specification for the final product. The validation of the model is conducted in a way, where the quality measures of the final product collected from an MRF are compared with the estimated values those from the simulation. The comparison reveals that precision in results from the developed model, in all performed tests is consistent with actual observed results, inferring the developed simulation model as a viable tool for estimating quality measures for RDF. The foundations of the model are based of assumptions like emphasis on general representation but not physical properties of MRF, only selected sets of uncertainties are studied, and variations in the operating conditions can affect estimated quality of RDF.

Hybrid dynamic modeling and receding horizon speed optimization for liner shipping operations from schedule reliability and energy efficiency perspectives

Uncertainties in port handling efficiency can cause port delays in the liner shipping system. Furthermore, policies on carbon emission reduction, such as EEXI standards, restrict the potential for speed optimization in liner shipping operations. Traditional tactical planning speed optimization is unsuitable for operational-level decision making, leading to unreliable schedules. From a schedule-reliability and energy-efficiency perspective, we propose a real-time speed optimization method based on discrete hybrid automaton (DHA) and decentered model predictive control (DMPC). We use a dynamic adjustment of sailing speed to offset the disturbance caused by port handling efficiency uncertainties. First, we establish a DHA model that describes each ship’s hybrid dynamics of state switching between sailing and berthing; then, we develop a prediction model for the DMPC controller, which is analogous to the DHA model. The schedule is transferred into time–position coordinates as controller reference trajectories in the receding horizon speed optimization framework. We consider determining tracking errors, carbon emissions, and fuel consumption as our objectives, and we carry out engine power limitation (EPL) analysis for the sample ship, which turns the EEXI standards into constraints. We attain the recommended speed by solving a mixed-integer optimization. We carry out a case study, and our results indicate the effectiveness of our proposed DHA-DMPC scheme in lowering port delays and achieving the best trade-off between schedule reliability and energy efficiency. Additionally, we conduct further experiments to analyze the impacts of various carbon reduction policies on the performance levels of liner shipping operations.

An optimization-based methodology equipped with lean tools to strengthen medical supply chain resilience during a pandemic: A case study from Turkey

Drawing upon economic and environmental sustainability, this study explores how developing the operational resilience of the medical supply chain (MSC) contributes to maintaining healthcare in the face of disruption risks, such as the COVID-19 pandemic. To this end, an optimization-based roadmap is proposed by employing lean tools to achieve and realize MSC resilience. A novel two-stage stochastic optimization model and robust counterpart are developed with the objective of overall cost minimization to cope with the unknowable demand uncertainty represented by scenarios. The reason behind proposing a scenario-based stochastic model is to implement preparedness strategies during the (re)design phase by making strategic and operational level decisions. That being the case, seven cases are generated based on the demand uncertainty intervals along with seven different reliability levels for sensitivity analysis. Computational experiments are conducted through a real case study to compare the centralized and decentralized distribution models in terms of efficiency and responsiveness. The results obtained by the stochastic model and robust counterpart are compared to demonstrate how strong the proposed model is. On top of that, lean tools are used to visualize and analyze the improvement opportunities to contribute to the methodology. By doing so, this paper presents novel theoretical and empirical insights regarding MSC resilience. The computational results emphasize the importance of employing a pre-disruption strategy via the proposed methodology to design a resilient MSC to be prepared for pandemic-related risk. The findings from the sensitivity analysis also verify that regardless of the disruption degree, the developed roadmap with the centralized distribution model leads to up to 40% improvements in terms of the overall cost, order lead time, emission amount, and inventory shortage metrics.

An algorithmic application with flexible airspace approach

PurposeThe purpose of this study is to develop an operational level decision support system model for air traffic controllers (ATCos) within the framework of the Flexible Use of Airspace (FUA) concept to enable more efficient use of airspace capacity. This study produces a systematic solution to the route selection process so that the ATCo can determine the most efficient route with an operational decision support system model using Dijkstra’s Shortest Path Algorithm.Design/methodology/approachIn this study, a new decision support system model for ATCos in decision-making positions was recommended and used. ATCos use this model as a main model for determining the shortest and safest route for aircraft as an operational-level decision support system. Dijkstra Algorithm, used in the model, is defined step by step and then explained with the pseudocode.FindingsIt has been determined that when the FUA concept and DSS are used while the ATCo chooses a route, significant fuel, time and capacity savings are achieved in flight operations. Emissions resulting from the negative environmental effects of air transportation are reduced, and significant capacity increase can be achieved. The operational level decision support system developed in the study was tested with 55 scenarios on the Ankara–Izmir flight route compared to the existing fixed route. The results for the proposed most efficient route were achieved at 11.22% distance (nm), 9.36%-time (min) savings and 837.71 kg CO2 emission savings.Originality/valueAs far as the literature is reviewed, most studies aimed at increasing airspace efficiency produce solutions that try to improve rather than replace the normal process. Considering the literature positioning of this study compared to other studies, the proposed model provides a new systematic solution to the problems that cause human-induced route inefficiency within the framework of the FUA concept.

Planning of a distribution network utilizing a heterogeneous fixed fleet with a balanced workload

PurposeThis study aims to minimize the transportation-related cost in distribution while utilizing a heterogeneous fixed fleet to deliver distinct demand at different geographical locations with a proper workload balancing approach. An increased cost in distribution is a major problem for many companies due to the absence of efficient planning methods to overcome operational challenges in distinct distribution networks. The problem addressed in this study is to minimize the transportation-related cost in distribution while using a heterogeneous fixed fleet to deliver distinct demand at different geographical locations with a proper workload balancing approach which has not gained the adequate attention in the literature.Design/methodology/approachThis study formulated the transportation problem as a vehicle routing problem with a heterogeneous fixed fleet and workload balancing, which is a combinatorial optimization problem of the NP-hard category. The model was solved using both the simulated annealing and a genetic algorithm (GA) adopting distinct local search operators. A greedy approach has been used in generating an initial solution for both algorithms. The paired t-test has been used in selecting the best algorithm. Through a number of scenarios, the baseline conditions of the problem were further tested investigating the alternative fleet compositions of the heterogeneous fleet. Results were analyzed using analysis of variance (ANOVA) and Hsu’s MCB methods to identify the best scenario.FindingsThe solutions generated by both algorithms were subjected to the t-test, and the results revealed that the GA outperformed in solution quality in planning a heterogeneous fleet for distribution with load balancing. Through a number of scenarios, the baseline conditions of the problem were further tested investigating the alternative fleet utilization with different compositions of the heterogeneous fleet. Results were analyzed using ANOVA and Hsu’s MCB method and found that removing the lowest capacities trucks enhances the average vehicle utilization with reduced travel distance.Research limitations/implicationsThe developed model has considered both planning of heterogeneous fleet and the requirement of work load balancing which are very common industry needs, however, have not been addressed adequately either individually or collectively in the literature. The adopted solution methodologies to solve the NP-hard distribution problem consist of metaheuristics, statistical analysis and scenario analysis are another significant contribution. The planning of distribution operations not only addresses operational-level decision, through a scenario analysis, but also strategic-level decision has also been considered.Originality/valueThe planning of distribution operations not only addresses operational-level decisions, but also strategic-level decisions conducting a scenario analysis.

Energy minimizing order picker forklift routing problem

The material handling systems used in warehouses involve important operations such as the usage of order picker forklifts. These order picker forklifts provide efficient utilization of the storage space by their ability in moving in narrow aisles and picking items from high level racks. Routing the order picker forklifts to pick ordered items belongs to the operational decision level and is done in high frequency. Therefore, finding an energy-efficient route for an order picker forklift can yield significant savings in the energy consumption in warehouses and the resulting CO2 emission. In this paper, we introduce and study the energy minimizing order picker forklift routing problem (EMFRP) which aims to find an energy-efficient route for an order picker forklift to pick a given list of items. To our knowledge, this is the first study that considers the order picker forklifts in the context of the order picking problem. We calculate the forklift’s energy consumption in both horizontal and vertical moves considering the effects of friction forces, the acceleration and deceleration of the forklift, and its load. A mixed integer programming formulation and a dynamic programming approach are developed to solve small size instances of the EMFRP exactly. To solve larger instances, we provide tour construction and tour improvement heuristics and integrate them into a single solution approach. Computational results show that the proposed heuristic approach finds high quality solutions. Moreover, it is observed that significant energy savings can be achieved by solving the EMFRP instead of the classical distance minimization problem.

Who should hold the baton of aviation sustainability?

Purpose In the past four decades, substantial air traffic growth has triggered enthusiasm in the aviation sector. At the same time, this growth has posed challenges to its financial and environmental sustainability commitments. A buzz has been centered on introducing and supporting aviation sustainability initiatives. These challenges have led to acknowledging the need to reduce aviation fuel consumption, a function of multiple factors. The different stakeholders having a diverse type of interplay govern the effective implementation of the factors at different decision levels (strategic, tactical and operational). Thus, the present study aims to critically examine various decision levels involved to understand opportunities and requirements related to aviation sustainability. Design/methodology/approach In this study, the best–worst method is used to quantify different decision levels’ role on various factors affecting aviation fuel consumption. Findings The results of this study signify that tactical-level decisions are most influential in reducing aviation fuel consumption with the highest impact (0.41) followed by operational-level decisions (0.30) and strategic-level decisions (0.29), respectively. Research limitations/implications The results point toward the critical role of middle-level hierarchy, i.e. aircraft manufacturers, airlines and others in the aviation industry’s sustainable growth. Thus, middle-level stakeholders must be inspired and empowered to act, being at the center they link the other two levels. Originality/value This study has added to the body of knowledge by exploring the decision-making competencies needed by different aviation sector stakeholders. It also presents the possible options available in the sector and the role of stakeholders at different levels in exploiting and implementing the sustainable aviation sector changes.

Optimal storage and shipment management of perishable agri-products with a hybrid priority policy: a case study

ABSTRACT Management of agricultural supply chains is a very complex task due to some critical factors like perishability, demand- and price-variability, yield uncertainty. Motivated by a large agricultural firm that deals with planting, growing, harvesting and distributing cauliflowers, an optimization model is introduced for the simultaneous management of storage and shipment of agricultural products, aiming for profit maximization, while accounting for product perishability. In view of a contractual agreement, the firm has to ensure, along the entire season, the delivery of at least a given share of harvested products to the main contract-customer. Nonetheless, the firm can catch more profitable opportunities on the daily spot-market. The model embeds a hybrid fresh/old-first policy to account for the firm priority policy that aims to balance quality of products delivered to the main customer. The model has been adopted at both the tactical and the operational decision level. At the tactical level, the model has been used to select the fleet size and the maximum in-stock time of products, while at the operational one, the model has been used for the day-by-day planning of storage and shipment. Computational experiments show the possibility for the company to profitably upgrade its current operation practices.

Efficient matheuristics to solve a rich production-routing problem

We present a rich production-routing problem having limited production and storage capacities at the plant, limited storage capacity at the clients, a heterogeneous fleet subjected to a maximum riding time, and allowing for back-orders to meet unfulfilled demands at penalty cost. As the problem scales quickly with the number of customers, periods, products, and vehicles, three hybrid two-level decomposition approaches using a top-down strategy were devised. The top tier determines the production and inventory levels, and the distribution of goods via CPLEX, that is, it makes tactical decisions, while the bottom tier heuristically routes a heterogeneous fleet in each period, that is, it makes operational decisions. The proposed methods rely on an iterated local search framework that combines tailored perturbation schemes prioritizing either tactical or operational decisions, or both. The main new feature of the algorithms is the adoption of an implicit cost that estimates the delivery routing costs when making production, holding, and transportation decisions. This implicit cost serves as an important guide to obtain improved solutions. The algorithms were tested over an extensive set of instances, and the results demonstrated that all methods overcome CPLEX by obtaining more, better, and faster solutions with much less computational effort. The devised heuristic, which prioritizes operational-level decisions during the perturbation phase, attained the best overall results.

Do road users play Nash Equilibrium? A comparison between Nash and Logistic stochastic Equilibriums for multiagent modeling of road user interactions in shared spaces

Analyzing and modeling multiagent transportation systems such as cyclist-pedestrian interactions and their evasive-action mechanisms in shared spaces are important for operation and safety evaluations. However, there are limited studies that (1) modeled the multiagent nature of road user interactions and their concurrent sequential decision processes, and (2) investigated the ability of different equilibrium behavioral theories in predicting road user operational-level decisions and evasive-action mechanisms. This study proposes two novel multiagent approaches based on different equilibrium theories for modeling road user interactions: (1) the Multiagent Generative Adversarial Imitation Learning (MAGAIL), which utilizes Nash-Equilibrium (NE) theory that assumes fully rational and optimal road user behavior, and (2) the Multiagent Adversarial Inverse Reinforcement Learning (MAAIRL), which utilizes Logistic-Stochastic-Best-Response-Equilibrium (LSBRE) theory that handles bounded rationality (sub-optimal) behavior. Unlike many of the traditional game-theoretic modeling approaches, which consider single time-step payoff modeling, the proposed approaches depend on Markov-Game that accounts for the stochastic nature of road user interactions and their sequential decision processes. The models recover road user multiagent reward functions and estimate their strategies using Multiagent Deep Reinforcement Learning. Using trajectories from three shared spaces in the USA and Canada, the study compared the proposed approaches’ results and determined a behavior-based consistent paradigm to model equilibrium in multiagent transportation systems. The results show that LSBRE-based model predicted road user trajectories and their evasive action mechanisms with higher accuracy compared to the NE-based model.