Transportation Capacity Constraints Research Articles

Short-Term Interval Prediction of Inbound Passenger Flow of Subway Station under Failure Events

Accurate forecasting of subway passenger flows is considered essential for the development of efficient train schedules. However, transport capacity constraints as well as station congestion can be caused by unexpected concerns with trains or power supply, which endanger passenger safety. Predicting passenger flows at the time of a fault is particularly challenging due to the low probability of failure and the complexity of the factors involved. In addition, deviation from the observed value may be resulted by the point-in-time prediction of passenger flow, thus affecting the efficiency of passenger flow control measures. To address this concern, a three-stage A-LSTM prediction model utilizing an attention mechanism and a double-layer LSTM (Long Short-Term Memory) neural network has been proposed. The model is used to map the impact of fault events on subway transport capacity with respect to delays onto the inbound passenger flow. By analyzing the data from the subway system in a metropolitan city of China, the range of passenger flow fluctuations in 10-minute intervals will be precisely predicted and applied to different subway stations.

Analytical Approximation-Based Approach for Passenger Flow Control Strategy in Oversaturated Urban Rail Transit Systems

Focusing on a heavily congested urban rail corridor, this study investigates the passenger flow control strategy optimization problem from a mesoscopic perspective to reduce platform congestion and enhance service quality. Based on a quadratic functional approximation for passenger arrival rates, an analytical formula for calculating passenger waiting time is derived based on the classic deterministic queueing theory. We formulate the problem as a continuous nonlinear programming model to minimize the total passenger waiting time within transportation capacity constraints. A Lagrangian relaxation approach effectively transforms the original complex problem into an unconstrained minimization program. The analytical solution relating to optimal flow control strategy is derived by directly solving the unconstrained program. To further provide an integrated optimization framework from both the supply and demand sides, we extend the abovementioned passenger flow control optimization model into an integrated mixed-integer nonlinear programming model to jointly optimize the passenger-flow control strategy and train frequency setting. Numerical examples are presented to demonstrate the applicability and effectiveness of the proposed models. The computational results show that the produced high-quality passenger flow control strategy significantly reduces total passenger delay.

Optimization Multi-Item Lot Sizing Model involve Transportation and Capacity Constraint under Stochastic Demand using Aquila Optimizer

Inventory issues are often a major concern as they significantly impact operating costs. Lot sizing is one of the key decisions in managing inventory. However, in a real context, the demand for each item is often uncertain. It is supplied from the same supplier, requiring product orders to be placed in the same period. In addition, limited vehicle capacity and transportation costs are important factors to consider in making multi-item lot sizing decisions. The purpose of this study is to propose a new inventory model multi-item lot sizing model involving transportation cost and capacity constraints under stochastic demand. The decision variables involved in the model are each item's ordering cycle and safety factor with the objective function of minimizing the total inventory cost. To optimize the inventory model, this study also offers the advanced procedure of the Aquila Algorithm. This study also presents sensitivity analysis to the appropriate policy for optimizing the multi-item lot-sizing inventory problem involving transportation cost and capacity constraint under stochastic demand.

A Network Flow Model for Operational Planning in an Underground Gold Mine

In underground mines, the problem of efficiently scheduling and allocating weekly operations has a major impact on the long-term productivity of the mine. The problem of selecting the optimal locations for operations in an underground gold mine is a complex task. It is not solved by simply selecting the levels with the richest grade because the transportation network for ore in an underground mine has a diverse set of capacity constraints that can frustrate immediate mining of all the richest levels. To solve this scheduling difficulty, we formulated a new mixed-integer network flow model of the problem of weekly allocating mining operations in an underground goldmine such that the total gold mined (in ounces) was maximized subject transportation capacity constraints. The model was applied an underground gold mine in Red Lake, Ontario, Canada. The results were compared to those of a two greedy heuristic models that were designed to represent the decision-making heuristics that are currently used at the mine. It was found that the new model yielded solutions that improved upon the two greedy heuristics by 14.7% and 6.0%, respectively. The results of this research illustrate that the development of this optimization model can support decisions to improve a gold mine’s productivity.

An Efficient Algorithm for the Joint Replenishment Problem with Quantity Discounts, Minimum Order Quantity and Transport Capacity Constraints

The joint replenishment problem has been extensively studied and the joint replenishment strategy has been adopted by a large variety of retailers in recent years. However, the joint replenishment problem under minimum order quantity and other constraints does not receive sufficient attention. This paper analyzes a retailing supply chain involving a supplier that provides quantity discount schedules and limits the order quantity. The order quantity constraints include minimum order requirements for each item and as to the total quantity; additionally, the latter cannot exceed the transport capacity constraint. These are common constraints in the retail industry today and create greater complexity and difficulty in the retailer’s decision-making. To analyze the problem, an integer nonlinear programming model is set up to maximize retailers’ profit with all practical constraints. A two-layer efficient algorithm named the Marginal and Cumulative Profit-Based Algorithm (MCPB) is then proposed to find whether to order and the optimal order quantity for each item. The results of computational experiments show that the proposed algorithm can find near-optimal solutions to the problem efficiently and is a reference for retailers to solve practical joint replenishment problems.

A Network Flow Model for Operational Planning in an Underground Gold Mine

The difficulty of effectively planning and assigning weekly activities has a significant influence on the long-term productivity of an underground mine. It is an especially difficult task to choose the best places for operations inside an underground gold mine. It cannot be resolved by only selecting the levels with the highest grade of ore because the underground mine’s ore transport network has a range of capacity limitations that may prohibit the immediate mining of all the levels with the highest grade. To solve this scheduling difficulty, we formulated a new mixed-integer network flow model of the problem of weekly allocating mining operations in an underground gold mine such that the total gold mined (in ounces) was maximized subject to the transportation capacity constraints. The model was applied to an underground gold mine in Red Lake, Ontario, Canada. The results were compared to those of two greedy heuristic models that were designed to represent the decision-making heuristics that are currently used at the mine. It was found that the new model yielded solutions that improved upon the two greedy heuristics by 14.7% and 6.0%, respectively. The results of this research illustrate that the development of this optimization model can support decisions to improve a gold mine’s productivity.

Evaluation Model for a Port Hinterland Intermodal Freight Network Considering Environmental Impacts and Capacity Constraints

The huge demand for containerized cargo and the low market share of rail and inland waterway transport in the hinterland freight system in China are causing severe traffic congestion and pollution. Therefore, this paper focuses on innovative modeling to evaluate the effects of transport policies on the reduction of carbon emissions and the mode shift from road to low-carbon modes in a three-mode port hinterland freight network. The proposed model can capture the main characteristics of the freight system such as mode transfer at inland terminals, transport capacity and processing capacity constraints, flow-dependent link performance functions, and shippers’ perceptual error of generalized transport costs. The model assumes that shippers choose transport route, mode, and inland terminal at the same time in a user equilibrium manner, and that bundles of container flow passing competing ports follow the logit formulation. Computational results based on the freight network in the Yangtze River Economic Belt in China indicate that carbon tax, intermodal transport subsidy, and capacity expansion policies can reduce total carbon emissions and promote mode shift from road transport to rail and inland waterway transport, and policy packages show better network performance compared with a single policy type. It is noted that transport policies sometimes lead to the paradoxical phenomenon. Finally, sensitivity analyses are carried out on parameters of cost, time, carbon emissions, and error to test the robustness of the model.

Collaborative passenger flow control of urban rail transit network considering balanced distribution of passengers

The increase of urban traffic demands has directly affected some large cities that are now dealing with more serious urban rail transit congestion. In order to ensure the travel efficiency of passengers and improve the service level of urban rail transit, we proposed a multi-line collaborative passenger flow control model for urban rail transit networks. The model constructed here is based on passenger flow characteristics and congestion propagation rules. Considering the passenger demand constraints, as well as section transport and station capacity constraints, a linear programming model is established with the aim of minimizing total delayed time of passengers and minimizing control intensities at each station. The network constructed by Line 2, Line 6 and Line 8 of the Beijing metro is the study case used in this research to analyze control stations, control durations and control intensities. The results show that the number of delayed passengers is significantly reduced and the average flow control ratio is relatively balanced at each station, which indicates that the model can effectively relieve congestion and provide quantitative references for urban rail transit operators to come up with new and more effective passenger flow control measures.

Competition, Collusion, and Spatial Sales Patterns: Theory and Evidence*

This article studies competition in markets with transport costs and capacity constraints. Using a rich micro‐level data set of the cement industry in Germany, we study a cartel breakdown to identify the effect of competition on transport distances. We find that when firms compete, they more often serve more distant customers. Moreover, the transport distance also varies in the ratio of capacity relative to demand, but only if firms compete and not when they coordinate their sales. We provide a theoretical model of spatial competition with capacity constraints that rationalizes the empirical results.

Maritime Cargo Prioritisation during a Prolonged Pandemic Lockdown Using an Integrated TOPSIS-Knapsack Technique: A Case Study on Small Island Developing States—The Rodrigues Island

Many remote areas, such as island states, are highly dependent on the transportation of cargo, and any disruptions similar to the 2020 pandemic lockdowns can negatively affect their respective supply chains. These disruptions could lead to a severe humanitarian crisis. It is therefore imperative to develop a cargo prioritisation process to ensure that essential commodities are transported. We propose a decision-aid tool that integrates two methods: (a) the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and (b) the knapsack problem. Containers are prioritised based on attributes such as their importance and their economic value. TOPSIS is used to calculate a score for each container and the knapsack problem determines the containers to be imported respecting the transportation capacity constraints. The practical applicability of the model is demonstrated by a case study on a Small Island Developing State. The proposed decision-aid tool could also be extended to be used in disaster relief situations.

A multi-objective decision-making model for supplier selection considering transport discounts and supplier capacity constraints

<p style='text-indent:20px;'>The present study considers the transport discounts and capacity constraints for the suppliers and manufacturers simultaneously to provide a multi-objective decision-making model for supplier selection on a three-level supply chain. For this purpose, it begins with presenting a nonlinear mixed-integer model of the problem, where the objectives include the minimization of the logistics costs and lead time. Subsequently, the NSGA-Ⅱ algorithm is developed to solve the large-scale model of the problem and simultaneously optimize the two objectives to achieve Pareto-optimal solutions. To test the efficiency of the proposed algorithm, several synthetic examples of various sizes are then generated and solved. Finally, the paper compares the performance of the proposed metaheuristic algorithm with the augmented epsilon-constraint method. In summary, the findings of this study provided researchers and industries to easily access to a cohesive model of supplier selection considering transportation that are essential to the solution of many real-world challenging logistics issues.</p>

Joint replenishment decision considering shortages, partial demand substitution, and defective items

Abstract A shortage may occur because of the insufficient production capacity or possible damages of items in transit, and the shortage can be partially fulfilled with substitutable items. In this study, a joint replenishment problem (JRP) with the shortage and partial demand substitution is investigated by developing a mixed integer nonlinear programming model. Several real-world constraints, such as budget, transportation capacity, and shipment requirement constraints are incorporated in the proposed model. Three heuristic algorithms, namely two-dimensional genetic algorithm I, two-dimensional genetic algorithm II, and differential evolution, are proposed, and numerical examples are provided to demonstrate the applicability of the proposed model in a real-world setting. The performance of the heuristic algorithms is investigated with the help of extensive computational experiments. The results show that differential evolution performs best in term of the minimum total cost among three heuristics. Sensitivity analyses are conducted to provide managerial insights. The results indicate that partial demand substitution policy can effectively decrease the total expected cost, but defective items will exponentially increase the total expect cost. The major ordering cost, budget, and truck capacity also affect the system.

Auction-based cooperation mechanism for cell part scheduling with transportation capacity constraint

This paper addresses cell part scheduling (CPS) problem with transportation capacity constraint. In the problem, parts may need to visit different cells, they have to be transferred by automated guided vehicle (AGV). The objective is to minimise the over-all process make-span. An integer nonlinear programming (INLP) model is formulated to allocate the machines, AGVs and schedule all parts. A reasonable transportation mode is presented, and an auction-based heuristic approach is proposed to solve the problem, which focuses on dealing with cooperation between machines and AGVs during processing and transferring parts. The auction consists of two aspects: auction for AGV and auction for machine. In both auctions, AGVs and machines act as auctioneers respectively, and parts act as bidders. A new improved disjunctive graph model is developed to optimise the feasible solutions obtained by auction-based approach. Numerical experiments were conducted to test the auction-based approach and improved disjunctive graph model. The results demonstrate the effectiveness of proposed auction-based approach and improved disjunctive graph model, also indicate influence of the capacity of AGV on scheduling parts.

A biased random-key genetic algorithm for the two-stage capacitated facility location problem

This paper presents a new metaheuristic approach for the two-stage capacitated facility location problem (TSCFLP), which the objective is to minimize the operation costs of the underlying two-stage transportation system, satisfying demand and capacity constraints. In this problem, a single product must be transported from a set of plants to meet customers demands passing out by intermediate depots. Since this problem is known to be NP-hard, approximated methods become an efficient alternative to solve real-industry problems. As far as we know, the TSCFLP is being solved in most cases by hybrid approaches supported by an exact method, and sometimes a commercial solver is used for this purpose. Bearing this in mind, a BRKGA metaheuristic and a new local search for TSCFLP are proposed. It is the first time that BRKGA had been applied to this problem and the computational results show the competitiveness of the approach developed in terms of quality of the solutions and required computational time when compared with those obtained by state-of-the-art heuristics. The approach proposed can be easily coupled in intelligent systems to help organizations enhance competitiveness by optimally placing facilities in order to minimize operational costs.

Competition, Collusion and Spatial Sales Patterns - Theory and Evidence

We study competition in markets with significant transport costs and capacity constraints. We compare the cases of price competition and coordination in a theoretical model and find that when firms compete, they more often serve more distant customers that are closer to plants of competitors. By means of a rich micro-level data set of the cement industry in Germany, we provide empirical evidence in support of this result. Controlling for other potentially confounding factors, such as the number of production plants and demand, we find that the transport distances between suppliers and customers were on average significantly lower in cartel years than in non-cartel years.

Modelling the public transport capacity constraints’ impact on passenger path choices in transit assignment models

The objective of this paper is to discuss the replication of passenger congestion (overcrowding) effects on output path choices in public transport assignment models. Based on a comprehensive literature review, the impact of passenger overcrowding effects was summarised in 3 main categories: the inclusion of physical capacity constraints (limits); the feedback effect between transport demand and supply performance; and the feedback effect on travel cost (discomfort penalty). Further on, sample case studies are presented, which prove that the inclusion of capacity constraints might significantly influence the assignment output and overall results in public transport projects’ assessment – yet most state-of-the-practice assignment models would either miss or neglect these overcrowding-induced phenomena. In a classical 4-step demand model, their impact on passengers’ travelling strategies is often limited to path (route) choice stage, while in reality they also have far-reaching implications for modal choices, temporal choices and long-term demand adaptation processes. This notion has been investigated in numerous research works, leading to different assignment approaches to account for impact of public transport capacity constraints – a simplified, implicit approach (implemented in macroscopic-based models, e.g. PTV VISUM), and a more complex, explicit approach (incorporated in mesoscopic-based models, e.g. BusMezzo). In the simulation part of this paper, sample tests performed on a small-scale network aim to provide a general comparison between these two approaches and arising differences in the assignment output. The implicit approach reveals some differences in assignment output once network capacity constraints are accounted for – though in a simplified manner, and producing somewhat ambiguous output (e.g. in higher congestion scenarios). The explicit approach provides a more accurate representation of overcrowding-induced phenomena - especially the evolving demand-supply interactions in the event of arising congestion in the public transport network. Further studies should involve tests on a city-scale, multimodal transport model, as well as empirical model validation, in order to fully assess the effectiveness of these distinct assignment approaches. - The paper discusses the inclusion of overcrowding effects on path choices in public transport assignment models - These can be grouped into 3 main categories: physical constraints, demand-supply feedback and path discomfort cost - Sample case studies show that their inclusion may substantially affect the assignment output - Two general methods of modelling capacity constraints are: the implicit and explicit approach - An illustrative example shows that both approaches produce different output with the explicit one being more specific and adequate

Competition, Collusion and Spatial Sales Patterns - Theory and Evidence

We study competition in markets with transport costs and capacity constraints. We compare the outcomes of price competition and coordination in a theoretical model and find that when firms compete, they more often serve more distant customers who are closer to the competitor’s plant. If firms compete, the transport distance varies in the degree of overcapacity, but not if they coordinate their sales. Using a rich micro-level data set of the cement industry in Germany, we study a cartel breakdown to identify the effect of competition on transport distances. Our econometric analyses confirm the theoretical predictions.

Bulk wheat transportation and storage problem of public distribution system

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

Dynamic activity-travel assignment in multi-state supernetworks under transport and location capacity constraints

ABSTRACTA unified framework coupling activity-based modelling and dynamic traffic assignment has recently been proposed. It formulates dynamic activity-travel assignment (DATA) in multi-state supernetworks as a dynamic user equilibrium. Choices of departure time, route, mode, activity sequence, and activity/parking location are determined endogenously, reflected in time-dependent activity-travel patterns (ATPs). However, capacity constraints associated with activities, parking, and public transit vehicles have not taken into account. This paper extends this approach by formulating these constraints and incorporating them into the DATA framework. Three numerical examples are presented to illustrate the effectiveness of the approach. It is shown that capacity constraints have significant effects on the choice facets.

A joint replenishment problem considering multiple trucks with shipment and resource constraints

A joint replenishment problem (JRP) is presented to determine the optimal reordering policy for multi-items with a percentage of defective items. This JRP also has several constraints, such as shipment constraint, budget constraint, and transportation capacity constraint. At the meantime, multiple trucks, each with a fixed transportation cost, are considered and also order quantities of restricted items are not shared among the trucks during the shipment. The objective is to minimize the total expected cost per unit time. A two-dimensional genetic algorithm (GA) is provided to determine an optimal family cycle length and the reorder frequencies. A numerical example is presented and the results are discussed. Extensive computational experiments are performed to test the performance of the GA. The JRP is also solved by using an evolutionary algorithm (EA) and the results obtained from GA and EA are compared.