Total Transportation Time Research Articles

Multi-objective intermodal transportation planning with real-life application

ABSTRACT This paper explores the optimal route selection problem within an intermodal transportation network that comprises road, rail, sea, and inland waterways. Initially, we propose a multi-objective integer linear programming model that aims to minimize total cost, transport time, and carbon emissions. Subsequently, we apply two multi-objective programming approaches: Fuzzy Goal Programming and Compromise Programming. These approaches are implemented in a real-life case study, aiming to determine the optimal intermodal transportation route from Türkiye (Denizli) to Germany (Duisburg). Four out of the six methods suggest the same road and maritime pathway. Furthermore, this model has the potential to aid decision-makers in selecting routes based on customer requirements and serve as a valuable tool for policymakers in establishing an efficient intermodal transport network.

Research on Optimizing Human Resource Expenditure in the Allocation of Materials in Universities

This paper establishes a multivariate function model for natural human load-carrying walking in some typical scenarios such as college equipment and material relocation by students and a large amount of identical freight relocation in commercial activities. For classified material relocation needs and constraints, we obtain the relationship between walking speed and load weight for a single person, as well as the time cost for different round trips. By establishing an integer programming model with the minimum total transportation time cost and shelf life as the objective function and the requirements of negative weight and speed as the constraint conditions, we reach the optimal item allocation methods considering time cost and shelf life. We discover that there is an approximate linear relationship between the change in natural walking speed and travel time when the load is small, thus obtaining the time cost of student transportation under different round-trip situations. The Monte Carlo simulation algorithm, which is more efficient compared with other methods such as the integer programming method, is used to obtain the optimal allocation scheme that meets the efficiency and quality requirements. The analysis methods and results can be used as guidance for task scheduling optimization for material relocation in educational organizations as well as commercial agencies.

Optimizing perishable food products across states: A multi objective evolutionary algorithm for surplus to deficit transportation

Ensuring the seamless flow of goods from farmer to stakeholder is paramount especially considering the facts such as demand fluctuations, quantity availability, vehicle capacity, inventory limits and truck preferences. In this paper, we proposed a mixed integer linear programming (MILP) model for distributing perishable food goods from surplus states to surplus and deficit state wholesalers in the Indian subcontinent, with the goal of minimizing total cost (fixed, variable, and operational) and transportation time. To ensure product delivery on time, we looked at various scenarios including a case with real-life constraints. Solving this computationally complex task with conflicting objectives in a reasonable time is difficult. To handle the aforementioned multi-objective problem, we proposed an evolutionary algorithm based modified non-dominated sorting genetic algorithm II (MNSGA-II). Furthermore, the suggested algorithm’s performance is tested against the most extensively used multi-objective algorithm, Non-dominated Sorting Genetic method-II (NSGA-II). According to the findings, the proposed MNSGA-II outperforms practically all instances.

Improved of Multiobjective Fuzzy Probabilistic Solid Transportation Models in Transportation Systems

Transportation activities are stages in the product distribution system. The purpose of the transport system is to minimize the total cost. If there is more than one objective function and consider more than one type of vehicle, it is called a multiobjective solid transportation problem. In some cases, the parameter transportation model is under uncertainty. A probabilistic and fuzzy approach can be used. This research introduces a probabilistic fuzzy multiobjective solid transportation model where the source, destination and vehicle parameters follow the Pareto distribution. A triangular fuzzy number expresses the coefficient of the objective function. The obtained model is applied to the problem of the metal crate transportation system. There are two objective functions; the first is the objective function to minimize the total cost. The second is the objective function to minimize the total transportation time. Three types of vehicles are considered: HDL, Engkel and Wingbox. The result is that the total cost is Rp. 3836595, and the total time is 757.245 minutes or 13 hours.

Research on emergency logistics information traceability model and resource optimization allocation strategies based on consortium blockchain.

In response to increasingly complex social emergencies, this study realizes the optimization of logistics information flow and resource allocation by constructing the Emergency logistics information Traceability model (ELITM-CBT) based on alliance blockchain technology. Using the decentralized, data immutable and transparent characteristics of alliance blockchain technology, this research breaks through the limitations of traditional emergency logistics models and improves the accuracy and efficiency of information management. Combined with the hybrid genetic simulated Annealing algorithm (HGASA), the improved model shows significant advantages in emergency logistics scenarios, especially in terms of total transportation time, total cost, and fairness of resource allocation. The simulation results verify the high efficiency of the model in terms of timeliness of emergency response and accuracy of resource allocation, and provide innovative theoretical support and practical scheme for the field of emergency logistics. Future research will explore more efficient consensus mechanisms, and combine big data and artificial intelligence technology to further improve the performance and adaptability of emergency logistics systems.

Fourth Party Logistics Routing Problem Under Uncertain Time and Random Demand

With the rapid development of the economy, the fourth party logistics (4PL) has become an indispensable part of economic development, and the path optimization is an important topic in the research of 4PL routing problem. This paper studies the fourth party logistics routing problem with random demand and uncertain transportation time and transfer time by considering the randomness and uncertainty of the environment. In order to solve the path optimization strategy that minimizes the total transportation cost, this paper establishes an uncertain random programming model under the constraints of the total transportation time, the carrying capacity of the logistics provider, the transfer capacity of the transfer station, and the demand of the distributor. Subsequently, this paper transforms the uncertain random programming model into an equivalent deterministic programming model based on the distribution functions of random demand and uncertain time, and verifies the effectiveness of the model through two examples. The results show that the uncertain random programming model can give a reasonable path optimization strategy.

Efficiency Improvement Mechanism of Smart Logistics Technology in Rural E-commerce Logistics and Distribution

Abstract This paper addresses the problems existing in the current two-way logistics and distribution model of rural e-commerce and proposes the study of rural e-commerce distribution efficiency with the technical support of intelligent logistics. Based on the knowledge of multi-objective optimization theory, the maximization of distribution efficiency of logistics enterprises and the maximization of demand coverage as the objective function, while the constraints are vehicle load, service time, path constraints, and finally, complete the construction of the multi-objective optimization model of rural e-commerce logistics and distribution efficiency. According to the NSGA-II algorithm with low computational complexity, fast operation speed, and good convergence, the NSGA-II algorithm is used to solve the constructed model, and then the optimal solution of rural e-commerce logistics distribution efficiency is derived. With regional logistics as the research subject, a multi-objective optimization model for rural e-commerce logistics distribution efficiency supported by intelligent logistics technology is simulated and analyzed. The data show that compared with other algorithms, the NSGAII algorithm has a smoother learning curve oscillation, while the final result of convergence (-1.473), the objective function is smaller (20.42), and the optimal total transportation time is smaller (609.22s). Finally, in order to solve the current rural e-commerce logistics and distribution problems in part, from the improvement of rural e-commerce logistics and distribution system, strengthen the rural logistics infrastructure construction of the two aspects of rural e-commerce logistics and distribution efficiency improvement strategy.

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.

Hypertension during transfer is associated with poor outcomes in unstable patients with ruptured abdominal aortic aneurysm

ObjectiveLimited data exist for optimal blood pressure (BP) management during transfer of patients with ruptured abdominal aortic aneurysm (rAAA). This study evaluates the effects of hypertension and severe hypotension during interhospital transfers in a cohort of patients with rAAA in hemorrhagic shock. MethodsWe performed a retrospective, single-institution review of patients with rAAA transferred via air ambulance to a quaternary referral center for repair (2003-2019). Vitals were recorded every 5 minutes in transit. Hypertension was defined as a systolic BP of ≥140 mm Hg. The primary cohort included patients with rAAA with hemorrhagic shock (≥1 episode of a systolic BP of <90 mm Hg) during transfer. The primary analysis compared those who experienced any hypertensive episode to those who did not. A secondary analysis evaluated those with either hypertension or severe hypotension <70 mm Hg. The primary outcome was 30-day mortality. ResultsDetailed BP data were available for 271 patients, of which 125 (46.1%) had evidence of hemorrhagic shock. The mean age was 74.2 ± 9.1 years, 93 (74.4%) were male, and the median total transport time from helicopter dispatch to arrival at the treatment facility was 65 minutes (interquartile range, 46-79 minutes). Among the cohort with shock, 26.4% (n = 33) had at least one episode of hypertension. There were no significant differences in age, sex, comorbidities, AAA repair type, AAA anatomic location, fluid resuscitation volume, blood transfusion volume, or vasopressor administration between the hypertensive and nonhypertensive groups. Patients with hypertension more frequently received prehospital antihypertensives (15% vs 2%; P = .01) and pain medication (64% vs 24%; P < .001), and had longer transit times (36.3 minutes vs 26.0 minutes; P = .006). Episodes of hypertension were associated with significantly increased 30-day mortality on multivariable logistic regression (adjusted odds ratio [aOR], 4.71; 95% confidence interval [CI], 1.54-14.39; P = .007; 59.4% [n = 19] vs 40.2% [n = 37]; P = .01). Severe hypotension (46%; n = 57) was also associated with higher 30-day mortality (aOR, 2.82; 95% CI, 1.27-6.28; P = .01; 60% [n = 34] vs 32% [n = 22]; P = .01). Those with either hypertension or severe hypotension (54%; n = 66) also had an increased odds of mortality (aOR, 2.95; 95% CI, 1.08-8.11; P = .04; 58% [n = 38] vs 31% [n = 18]; P < .01). Level of hypertension, BP fluctuation, and timing of hypertension were not significantly associated with mortality. ConclusionsHypertensive and severely hypotensive episodes during interhospital transfer were independently associated with increased 30-day mortality in patients with rAAA with shock. Hypertension should be avoided in these patients, but permissive hypotension approaches should also maintain systolic BPs above 70 mm Hg whenever possible.

The impact of new western land and sea corridor development on port deep hinterland transport service and route selection

New transportation corridor development aims to provide more convenient transportation conditions for port deep hinterland areas and to enhance the two-way opening of land and sea through the utilization of various transport modes, such as railroad, highway, and water transportation. In this paper, the microcontainer transport service and route selection problem is introduced within a new transportation corridor context, and a decision-making tool is provided for shippers to develop door-to-door service plans. A multiobjective 0–1 planning model is constructed with the objective of minimizing the total transportation cost, transportation time, and carbon emissions. The loading and unloading time windows of railroad and waterway transportation, as well as the time window of consignee receipt, are considered in model construction to obtain the nondominated Pareto optimal front. The validity of both the model and algorithm is demonstrated in this paper by using the Chongqing-Hamburg and Chengdu-Hamburg routes, following the development of the New Western Land and Sea Corridor (NWLSC), as examples. The example results show that the NWLSC is appealing for time-sensitive cargoes, and its economic benefits can be enhanced if relevant policies such as railroad freight reductions are supported. The findings in this paper can provide decision support for western provinces in the implementation of targeted policies to regulate and optimize the regional transportation structure, which can further promote the opening up of western Chinese provinces as well as the integrated and coordinated development of coastal and inland regions, providing practical value for reducing carbon emissions.

Feasibility of establishing an integrated railway-based containerised cargo transport network between the Port of Colombo and free trade zones in Sri Lanka

A large percentage of Sri Lanka's exports and imports are generated by the manufacturing sector and originates from facilities dispersed throughout Export Processing Zones (EPZs) around the country. An efficient hinterland transport network is required between the Port of Colombo and these EPZs. However, road trucks are used as the dominant transport mode for moving containerised cargo to/from Colombo Port. This study proposes a dry port-based containerised import/export cargo transportation method using a railway network to connect the Port of Colombo and the EPZs under several alternative network configurations. This study conducts a questionnaire survey with 40 experts to identify issues with the current road-based container transport network and ascertain the benefits of proposed railway-based networks in Sri Lanka. A majority of respondents highlight the benefits of the proposed network from multiple perspectives along with several drawbacks with the current road-based network. Initially, the feasibility of two alternative dry port locations in Orugodawatta and Kerawalapitiya are analysed. A mathematical model is developed to analyse the feasibility of the proposed alternative networks. Finally, the optimum dry port location is selected as Aluthwaththa using a simulation-based Greenfield analysis approach. Results highlight the advantages of proposed networks under several scenarios. The study demonstrates a systematic approach to decision-making by optimising the local cargo handling process, where, Sri Lankan inland logistic operations will become more efficient, with total transport costs, environmental pollution, and transportation time decreasing significantly.

Algorithmic framework for enhancement of information control in integrated transportation systems

The challenge of information control of complex systems has been studied especially précised during the last years due to the increased level of digital transformation. The integrated nature of modern economic systems requires advanced solutions based on combination of state-of-the-art digital techniques with revised classical managerial, economical and mathematical approaches. The research gap is emphasized with the less of literature sources devoted to practical aspects of such combined tools applications. On this issue the authors suggest the algorithmic framework with analytical model on information control enhancement to transport-related operations within complex transportation systems. The article proposes a model of total transportation time (TTT) estimation considering various restrictions, elasticity coefficient of customer satisfaction, sustainable development and social aspects. As a result of better information control, transportation system becomes more flexible for alternative solutions. It explains the high applied value of the conducted research aimed also on planning improvement through the precise control procedures in transportation systems. Holistic view on the problem of TTT definition through the prism of information integration brings the discussion forward on how many factors should be considered in planning and controlling of integrated transportation systems. The given numerical example for multimodal international conveyance highlights the role of the proposed algorithmic foundations towards information management and control in multi-object systems. Probabilistic estimation of process reliability corresponds to the need of planning agile transportation systems.The purpose of the study is to contribute to the research field of stochastic on-time arrival (SOTA) problem with consideration of many factors influencing on performance in integrated transportation systems. The algorithmic framework proposes a conceptual basis for management, planning and information control. Application of the proposed methodology helps obtaining higher probability of on time arrival and increase reliability of transportation systems.

Multi-Objective Optimization of the Multimodal Routing Problem Using the Adaptive ε-Constraint Method and Modified TOPSIS with the D-CRITIC Method

This paper proposes a multi-criteria decision-making approach for the multimodal routing problem (MRP) of bulk transportation in Thailand to minimize the total cost, transportation time, and total carbon dioxide-equivalent (CO2e) emissions simultaneously. The proposed approach has three phases: The first phase is generating all nondominated solutions using Kirlik and Sayin’s adaptive ε-constraint method. In the second phase, the Distance Correlation-based Criteria Importance Through Inter-criteria Correlation (D-CRITIC) method is used to determine the weight of each objective function and assign it to the modified technique for order of preference by similarity to ideal solution (modified TOPSIS) model in next phase. The third phase consists of ranking Pareto solutions obtained from the first phase using the modified TOPSIS. This proposed approach is applied to a real-world problem to enable the selection of the best route for transporting goods from the anchorage area in the Gulf of Thailand to the destination factory throughout a multimodal transportation network in Thailand. The computational results indicate that the proposed approach is superior to the current approach utilizing the ε-constraint method (ECM) regarding the number of Pareto solutions obtained and the proportion of computational time to the number of Pareto solutions obtained. Finally, the proposed method can solve the MRP with three or more objective functions and provide a multimodal route selection approach that is suitable for decision makers to offer a multimodal route to customers in the negotiation process for outsourcing transportation.

A Literature Review of Distribution Solving Problems by Various Transportation Methods

The aim of this study to review the limited survey of a systematic various existing transportation problems and the application developed by different researchers and applied in some case studies. The methodology applied in this paper through various research papers that available at Google Scholar, Science direct, Elsevier, IOP and another publisher from 2014-2022 (twenty-four international researches). The main purpose of this review paper is to discuss, analyze and the existing forms of various types of transportation problems and their systematic development, Findings show that in addition to the MODI and VAM methods, other methods have also emerged to solve the transportation problem. One of them is Improve VAM (IVAM) that built by Nahar et.al (2018). IVDM on transportation or delivery strategy. Further, this paper concludes that mathematical models and algorithm method of transportation derivatives/techniques of MODI and VAM are most used to optimize single or multipurpose parameters such as distribution cost, profit earned, total transportation time etc.

Tick-tock: Prehospital intubation is associated with longer field time without any survival benefit

BackgroundPrehospital endotracheal intubation is a debated topic, and few studies have found it beneficial after trauma. A growing body of evidence suggests that prehospital endotracheal intubation is associated with increased morbidity and mortality. Our study was designed to compare patients with attempted prehospital endotracheal intubation to those intubated promptly upon emergency department arrival. MethodsA retrospective review of a single-center trauma research data repository was utilized. Inclusion criteria included age ≥15 years, transport from the scene by ground ambulance, and undergoing prehospital endotracheal intubation attempts or intubation within 10 minutes of emergency department arrival without prior prehospital endotracheal intubation attempt. Propensity score matching was used to minimize differences in baseline characteristics between groups. Standard mean differences are also presented for pre- and post-matching datasets to evaluate for covariate balance. ResultsIn total, 208 patients met the inclusion criteria. Of these, 95 patients (46%) underwent prehospital endotracheal intubation, which was successful in 47% of cases. A control group of 113 patients (54%) were intubated within 10 minutes of emergency department arrival. We performed propensity score matching between cohorts based on observed differences after univariate analysis and used standard mean differences to estimate covariate balance. After propensity score matching, patients who underwent prehospital endotracheal intubation experienced a longer time on scene as compared with those intubated in the emergency department (9 minutes [interquartile range 6–12] vs 6 minutes [interquartile range 5–9], P < .01) without difference in overall mortality (67% vs 65%, P = 1.00). Rapid sequence intubation was not used in the field; however, it was used for 58% of patients intubated within 10 minutes of emergency department arrival. After matched analysis, patients with a failed prehospital intubation attempt were equally likely to receive rapid sequence intubation during re-intubation in the emergency department as compared with those undergoing a first attempt (n = 13/28, 46% vs n = 28/63, 44%, P = 1.00, standard mean differences 0.04). Among patients with prehospital arrest (n = 98), prehospital endotracheal intubation was associated with shorter time to death (8 minutes [interquartile range 3–17] vs 14 minutes [interquartile range 8–45], P = .008) and longer total transport time (23 minutes [interquartile range 19–31] vs 19 minutes [interquartile range 16–24], P = .006), but there was no difference in observed mortality (n = 29/31, 94% vs n = 30/31, 97%, P = 1.00, standard mean differences = 0.15) after propensity score matching. ConclusionPrehospital providers should prioritize expeditious transport over attempting prehospital endotracheal intubation, as prehospital endotracheal intubation is inconsistently successful, may delay definitive care, and appears to have no survival benefit.

Prehospital physiological parameters related illness severity scores can accurately discriminate the severe/critical state in adult patients with COVID-19

Background Whether the National Early Warning Score 2 (NEWS2) can effectively discriminate the severe/critical state of patients with coronavirus disease 2019 (COVID-19) at the prehospital stage remains unknown. We aimed to assess the performance of NEWS2 in rapidly discriminating severe/critical COVID-19 and its relationship with prehospital medical services. Methods Six illness severity scores of 414 patients were calculated at the prehospital stage. Receiver operating characteristic curves were generated to explore the ability of these scores to discriminate severe/critical patients from mild/moderate patients. A logistic regression analysis was conducted to evaluate independent predictors associated with severe/critical state. Results The age, numbers of comorbidities, prehospital care workload, consumption of medical human resources, and illness severity scores of severe/critical patients were higher than those of mild/moderate patients (p < 0.05). When NEWS2 scores >2, the sensitivity, specificity, positive predictive value, and negative predictive value were 93.5%, 90.7%, 74.1%, and 98.0%, respectively. The C-statistic of NEWS2 (0.963) was higher than that of quick Sequential Organ Failure Assessment (0.680, p < 0.001), CRB-65 (0.879, p < 0.001), Rapid Acute Physiology Score (0.692, p < 0.001), and Rapid Emergency Medicine Score (0.879, p < 0.001). NEWS2 was positively correlated with the numbers of prehospital treatment measures (r = 0.732, p < 0.001), numbers of medical staff (r = 0.615, p < 0.001), and total transport time (r = 0.595, p < 0.001). Age ≥65 years (OR = 5.43, p = 0.016), hypertension (OR = 5.39, p < 0.001), active malignancy (OR = 5.94, p = 0.005), and NEWS2 scores >2 (OR = 124.88, p < 0.001) were independent predictors to discriminate severe/critical patients. Oxygen saturation (SpO2) (OR =1.87, p < 0.001) was the unique independent predictor to discriminate false positive patients from true positive patients. Conclusions Prehospital NEWS2 can accurately and rapidly discriminate severe/critical COVID-19 during the Omicron variant wave. High levels of NEWS2 indicate an increase in prehospital care workload and consumption of medical human resources.

Solving a multi-objective solid transportation problem: a comparative study of alternative methods for decision-making

The transportation problem in operations research aims to minimize costs by optimizing the allocation of goods from multiple sources to destinations, considering supply, demand, and transportation constraints. This paper applies the multi-dimensional solid transportation problem approach to a private sector company in Egypt, aiming to determine the ideal allocation of their truck fleet.In order to provide decision-makers with a comprehensive set of options to reduce fuel consumption costs during transportation or minimize total transportation time, a multi-objective approach is employed. The study explores the best compromise solution by leveraging three multi-objective approaches: the Zimmermann Programming Technique, Global Criteria Method, and Minimum Distance Method. Optimal solutions are derived for time and fuel consumption objectives, offering decision-makers a broad range to make informed decisions for the company and the flexibility to adapt them as needed.Lingo codes are developed to facilitate the identification of the best compromise solution using different methods. Furthermore, non-dominated extreme points are established based on the weights assigned to the different objectives. This approach expands the potential ranges for enhancing the transfer problem, yielding more comprehensive solutions.This research contributes to the field by addressing the transportation problem practically and applying a multi-objective approach to support decision-making. The findings provide valuable insights for optimizing the distribution of the truck fleet, reducing fuel consumption costs, and improving overall transportation efficiency.

Multi-objective optimization for multimodal transportation routing problem with stochastic transportation time based on data-driven approaches

We study a multi-objective optimization model of a stochastic multimodal transportation network considering key impact factors such as transit cost, time, and transport mode schedule while minimizing total transportation cost and transportation time. In this study, we apply the Monte Carlo simulation to deal with the stochastic transportation time in the network and propose a data-driven approach that combines historical data and the dataset generated by the data mining algorithm to accelerate the search for the nondominated solution in the simulation. To validate the effectiveness of the proposed Data-Driven Multi-Objective Simulation Ant Colony (DD-MSAC) algorithm, we compare the optimum-seeking performance and the running time consumption of the Nondominated Sorting Genetic Algorithm-II (NSGA-II) and the Multi-Objective Simulation Ant Colony (MSAC) algorithm. Then, the MSAC algorithm is adopted as the benchmark for the comparison study on the solving performance of the proposed DD-MSAC algorithm. We conducted 30 times simulation run under different network scales in our numerical examples to show that the DD-MSAC algorithm can be equally effective as the non-data-driven MSAC algorithm in finding a nondominated solution as the average error does not exceed 5%. Meanwhile, we analyze the impact of different data-driven approaches, including data pool and support vector machine, on the solution quality and the running time. Finally, we use an example of China’s Belt Road Initiative to verify the effectiveness of the proposed algorithm.

A data-driven optimization model to response to COVID-19 pandemic: a case study.

COVID-19 is a highly prevalent disease that has led to numerous predicaments for healthcare systems worldwide. Owing to the significant influx of patients and limited resources of health services, there have been several limitations associated with patients' hospitalization. These limitations can cause an increment in the COVID-19-related mortality due to the lack of appropriate medical services. They can also elevate the risk of infection in the rest of the population. The present study aims to investigate a two-phase approach to designing a supply chain network for hospitalizing patients in the existing and temporary hospitals, efficiently distributing medications and medical items needed by patients, and managing the waste created in hospitals. Since the number of future patients is uncertain, in the first phase, trained Artificial Neural Networks with historical data forecast the number of patients in future periods and generate scenarios. Through the use of the K-Means method, these scenarios are reduced. In the second phase, a multi-objective, multi-period, data-driven two-stage stochastic programming is developed using the acquired scenarios in the previous phase concerning the uncertainty and disruption in facilities. The objectives of the proposed model include maximizing the minimum allocation-to-demand ratio, minimizing the total risk of disease spread, and minimizing the total transportation time. Furthermore, a real case study is investigated in Tehran, the capital of Iran. The results showed that the areas with the highest population density and no facilities near them have been selected for the location of temporary facilities. Among temporary facilities, temporary hospitals can allocate up to 2.6% of the total demand, which puts pressure on the existing hospitals to be removed. Furthermore, the results indicated that the allocation-to-demand ratio can remain at an ideal level when disruptions occur by considering temporary facilities. Our analyses focus on: (1) Examining demand forecasting error and generated scenarios in the first phase, (2) exploring the impact of demand parameters on the allocation-to-demand ratio, total time and total risk, (3) investigating the strategy of utilizing temporary hospitals to address sudden changes in demand, (4) evaluating the effect of disruption to facilities on the supply chain network.

Multi-objective Optimal Scheduling of Stacker–reclaimers Using the RPCNSGA II Algorithm

Background: The stacker-reclaimer is a device for transporting bulk materials in ironmaking raw material yards. An excellent scheduling plan can provide a good raw material supply basis for steel enterprises. It is of great significance to improve the efficiency of steel production, reduce unnecessary operating waste and management costs, and realize scientific management of steel production. Objective: This patent aims to optimize the total material transportation time and equipment utilization balance within a single operation plan of the stacker-reclaimer involved in the raw material yard. Methods: A multi-objective optimization model for the stacker reclaimer is established, and the Reverse learning and Population Competitive-NSGA II (RPC-NSGA II) algorithm is introduced for solving. This algorithm uses reverse learning and population competition mechanism to improve the convergence and diversity of the algorithm. Results: The proposed method was experimentally verified in a raw material yard with a 360m2 sintering machine and a bulk material port. The method converges well and obtains a Pareto front with a uniform distribution. Compared with the actual scheduling plan, the scheduling plan under the optimal compromise solution reduces the maximum completion time by 11.23 minutes and increases the equipment utilization balance rate by 11.70%. Conclusion: The proposed method can consider the material transportation time and equipment utilization balance, which is of great significance for the optimized use of the stacker reclaimer in steel enterprises and the quality assurance of raw material supply