Supply chain network design under profit maximization and oligopolistic competition

In this paper, we model the supply chain network design problem with oligopolistic firms who are involved in the competitive production, storage, and distribution of a homogeneous product to multiple demand markets. The profit-maximizing firms select both the capacities associated with the various supply chain network activities as well as the product quantities. We formulate the governing Nash–Cournot equilibrium conditions as a variational inequality problem and identify several special cases of the model, notably, a generalization of a spatial oligopoly and a classical oligopoly problem to include design capacity variables. The proposed computational approach, which is based on projected dynamical systems, fully exploits the network structure of the problems and yields closed form solutions at each iteration. In order to illustrate the modeling framework and the algorithm, we also provide solutions to a spectrum of numerical supply chain network oligopoly design examples.This paper makes a contribution to game theoretic modeling of competitive supply chain network design problems in an oligopolistic setting.     

Towards enhancing the last-mile delivery: An effective crowd-tasking model with scalable solutions

In urban logistics, the last-mile delivery from the warehouse to the consumer’s home has become more and more challenging with the continuous growth of E-commerce. It requires elaborate planning and scheduling to minimize the global traveling cost, but often results in unattended delivery as most consumers are away from home. In this paper, we propose an effective large-scale mobile crowd-tasking model in which a large pool of citizen workers are used to perform the last-mile delivery. To efficiently solve the model, we formulate it as a network min-cost flow problem and propose various pruning techniques that can dramatically reduce the network size. Comprehensive experiments were conducted with Singapore and Beijing datasets. The results show that our solution can support real-time delivery optimization in the large-scale mobile crowd-sourcing problem.     

Stochastic resource allocation for containerized cargo transportation networks when capacities are uncertain

We consider the stochastic resource allocation problem for containerized cargo transportation with uncertain capacities and network effects, in which a freight operator needs to allocate a certain amount of capacity to each product to maximize the expected profit. We formulate the problem as a constrained stochastic programming model and provide theoretical results that completely characterize the optimal solution to the model under a special case. Under a general case, we build an approximation model of the problem and propose a sampling based algorithm to solve the approximation model. A number of numerical experiments are offered to test the algorithm.     

Traveler satisfaction in rapid rail systems: The case of Istanbul metro

Multifaceted characteristics of urban travel have an impact on the passengers' overall satisfaction with the transport system. In this study, we investigate the interrelationships among traveler satisfaction, travel and traveler characteristics, and service performance in a multimodal network that comprises of a trunk line and its feeder lines. We analyze the factors influencing the choices of access to rail transit stations and the satisfaction of transit travelers with the rapid rail transit systems. We quantitatively study these relationships and demonstrate the complexity of evaluating transit service performance. Since the interrelationships among variables affecting this system are mainly stochastic, we analyze the satisfaction with transit system problem using a Bayesian Belief Network (BBN), which helps capture the causality among variables with inherent uncertainty. Using the case of Istanbul, we employ the BBN as a decision support tool for policy-makers to analyze the rapid rail transit services and determine policies for improving the quality and the level of service to increase the satisfaction with transit system. In the case study, satisfaction with accessibility and access mode variables are found to be more effective variables than total travel time for travel time satisfaction, confirming the significant role of access in multimodal travels.     

Errors in variables in multinomial choice modeling: A simulation study applied to a multinomial logit model of travel mode choice

Modeling travel demand is a vital part of transportation planning and management. Level of service (LOS) attributes representing the performance of transportation system and characteristics of travelers including their households are major factors determining the travel demand. Information on actual choice and characteristics of travelers is obtained from a travel survey at an individual level. Since accurate measurement of LOS attributes such as travel time and cost components for different travel modes at an individual level is critical, they are normally obtained from network models. The network-based LOS attributes introduce measurement errors to individual trips thereby causing errors in variables problem in a disaggregate model of travel demand. This paper investigates the possible structure and magnitude of biases introduced to the coefficients of a multinomial logit model of travel mode choice due to random measurement errors in two variables, namely, access/egress time for public transport and walking and cycling distance to work. A model was set up that satisfies the standard assumptions of a multinomial logit model. This model was estimated on a data set from a travel survey on the assumption of correctly measured variables. Subsequently random measurement errors were introduced and the mean values of the parameters from 200 estimations were presented and compared with the original estimates. The key finding in this paper is that errors in variables result in biased parameter estimates of a multinomial logit model and consequently leading to poor policy decisions if the models having biased parameters are applied in policy and planning purposes. In addition, the paper discusses some potential remedial measures and identifies research topics that deserve a detailed investigation to overcome the problem. The paper therefore significantly contributes to bridge the gap between theory and practice in transport.     

Aircraft line maintenance scheduling and optimisation

With the advent of new technologies and more modern aircraft, many of the maintenance jobs traditionally scheduled for periodic block checks can now be performed in the ‘‘line maintenance” environment, i.e., during layovers between scheduled flights of an aircraft. This flexibility can be exploited to reduce maintenance costs and improve fleet utilisation of an airline. In this paper we introduce and study the Line Maintenance Scheduling Problem (LMSP). The LMSP assigns jobs to available maintenance opportunities, defined by aircraft routes, and sets the starting time for each job. Its objective is to minimise the deviation from this schedule with respect to given due dates for each task, without exceeding resource capacity at the airports at any moment. We formulate the LMSP as a mixed integer programming problem, and describe and compare two solution approaches for this problem: an integrated exact solution algorithm, which solves job assignment and timetabling simultaneously, and a sequential, heuristic approach. We tested our algorithms on a set of instances inspired on data provided by an industry partner. Our experiments show the applicability of both approaches on realistic settings: the exact approach was able to find the optimal solution for all instances, in less than 10 min on average. Our analysis also shows with an example that line maintenance can be more efficient when capacity is spatially spread, even if the total capacity is reduced.     

Optimizing inventory decisions in a multi-stage multi-customer supply chain: A note

In this paper, we propose an n-stage-multi-customer supply chain inventory model where there is a company that can supply products to several customers. We formulate the model for the simplest inventory coordination mechanism which is referred to as the same cycle time for all companies in the supply chain. We conclude that it is possible to use an algebraic approach to optimize the supply chain model without the use of differential calculus.     

Stochastic seat allocation models for passenger rail transportation under customer choice

We study the seat allocation problem for passenger rail revenue management, in which a rail operator attempts to determine the optimal quantity of seats to be allocated to each cabin class for each train service. We formulate the problem with single-stage and multi-stage decisions as two stochastic programming models that incorporate passengers’ choice behavior. We transform the stochastic models into equivalent deterministic mathematical programs that are easy to solve. Then, we form a variety of seat allocation polices from the optimal solutions to the seat allocation models. A number of simulation tests are offered to test the policies.     

Impact of COVID-19 on the number of days working from home and commuting travel: A cross-cultural comparison between Australia,South America and South Africa

The COVID-19 pandemic has changed the way we go about our daily lives in ways that are unlikely to return to the pre-COVID-19 levels. A key feature of the COVID-19 era is likely to be a rethink of the way we work and the implications on commuting activity. Working from home (WFH) has been the ‘new normal’ during the period of lockdown, except for essential services that require commuting. In recognition of the new normal as represented by an increasing amount of WFH, this paper develops a model to identify the incidence of WFH and what impact this could have on the number of weekly commuting trips. Using data collected in eight countries (Argentina, Australia, Brazil, Chile, Colombia, Ecuador, Peru and South Africa), we developed a Poisson regression model for the number of days individuals worked from home during the pandemic. Simulated scenarios quantify the impact of the different variables on the probability of WFH by country. The findings provide a reference point as we continue to undertake similar analysis at different points through time during the pandemic and after when restrictions are effectively removed.     

Channel coordination under consignment and vendor-managed inventory in a distribution system

This paper deals with the problem of coordinating a vertically separated distribution system under vendor-managed inventory and consignment arrangements. We formulate the profit-maximization problem and carry out equilibrium analysis under cooperative and non-cooperative settings. In addition, a revenue-sharing scheme joint with a side-payment is proposed, which leads to Pareto improvements among channel participants. Our analysis reveals that the non-cooperative decentralization tends to price higher and stock less, which leads to a lower channel-wide profit. The consistent bias can be rectified by the dominant, cooperative wholesaler or by using the proposed two-part revenue-sharing mechanism.     

Partial link flow observability in the presence of initial sensors: Solution without path enumeration

Recently, a new methodology (“synergistic sensor location”) has been introduced to efficiently determine all link flows in a road network by using only a subset of the link flow measurements. In this paper, we generalize this previous work by solving the following problem: Suppose that one is only interested in a subset of the link flows, and that certain link flows are known a priori. At a minimum, what link flows are needed to be able to uniquely determine the desired link flows? An algorithm is presented that does not require the need for path enumeration.     

A tabu search heuristic for the heterogeneous vehicle routing problem on a multigraph

We study a time-constrained heterogeneous vehicle routing problem on a multigraph where parallel arcs between pairs of vertices represent different travel options based on criteria such as time, cost, and distance. We formulate the problem as a mixed-integer linear programming model and develop a tabu search heuristic that efficiently addresses computational challenges due to parallel arcs. Numerical experiments show that the heuristic is highly effective and that freight operators can achieve advantages in cost and customer service by considering alternative paths, especially when route duration limits are restrictive and/or when vehicles of smaller capacity are dispatched to serve remote customers.     

Recovering from demand disruptions on an air cargo network

In this work we introduce the Air Cargo Schedule Recovery Problem (ACSRP). In this problem, a carrier airline has to reschedule flights and requests to adapt to last-minute demand changes. We consider three different possible crew management policies that translate into three different way to evaluate the costs of deviating from the original schedule. We formulated the ACSRP as a mixed integer linear programming problem, and tested our implementation on 24 original schedules with up to 30 orders and 8 airports, and 4 different disruption scenarios for each one. Our results show that, against a benchmark recovery policy where only cargo is re-routed, recovery can yield savings of roughly 10%.     

Selecting container port via a fuzzy ANP-based approach: A case study in the Marmara Region,Turkey

In this case study, a real world problem of a production firm in the Marmara Region, Turkey was considered. In the current situation, the firm works with different third-party logistics firms and uses different ports each time. However, they have some quality problems and decided to work with a logistics firm by using the most convenient port alternative. A number of conflicting qualitative and quantitative criteria exist for evaluating alternative ports. Qualitative criteria are often accompanied by ambiguities and vagueness. To cope with this problem, the fuzzy analytic network process (FANP) method is used in this study. First, we defined the region specific criteria consisting of the twenty sub-criteria under the six main criteria clusters that influence the selection of container port. Then seven alternative container ports located in the Marmara Sea were determined. The results showed that the most convenient district for the container port is Istanbul District, which is one of the biggest economical centers in Eastern Europe.     

An outer approximation algorithm for the robust shortest path problem

This paper describes a new algorithm for the stochastic shortest path problem where path costs are a weighted sum of expected cost and cost standard deviation. We allow correlation between link costs, subject to a regularity condition excluding unbounded solutions. The chief complication in this variant is that path costs are not an additive sum of link costs. In this paper, we reformulate this problem as a conic quadratic program, and develop an outer-approximation algorithm based on this formulation. Numerical experiments show that the outer-approximation algorithm significantly outperforms standard integer programming algorithms implemented in solvers.     

Modelling and analysing effects of complex seasonality and weather on an area's daily transit ridership rate

Adverse weather is generally perceived as deterrent for public transit uses. This has also been highlighted in previous literatures. In contrary, our previous study found no association between weather and transit ridership while investigating the underlying temporal influences behind variation in daily ridership across the sub-tropical city of Brisbane, Australia. This contraindication led to the primary focus of this research. This research acknowledged that Inclusion of weather variables in conjunction with other relatively strong independent variables might result in washout of the weather effects on ridership. Variables such as rainfall do not recur on a daily basis throughout the year. Thus, generalising their effect on ridership with other independent variables that consistently influence ridership may create a similar problem. Hence, weather variables were converted into their normalised factors and combined with other independent variables while formulated the optimised the daily ridership rate estimation model. Several models were developed concerning various combinations of weather variables and through rigorous analysis it was identified that only the rain variable has noticeable effect on daily ridership. Evidently, this study functions as an update of our former study by directing towards a new approach to the analysis of the relationship between weather and transit ridership.     

Measuring the perceived container leasing prices in liner shipping network design with empty container repositioning

This paper aims to measure the perceived container leasing prices at different ports by presenting a two-stage optimization method. In stage I, we propose a practical liner shipping network design problem with empty container repositioning. The proposed problem further considers the use of foldable containers and allows the mutual substitution between empty containers to decrease the number of empty containers to be repositioned. In stage II, the inverse optimization technique is used to determine the perceived container leasing prices at different ports, based on the solution obtained in stage I. Based on a set of candidate liner shipping service routes, a mixed-integer nonlinear programming model is built for the proposed problem in stage I. The nonlinear terms are linearized by introducing the auxiliary variables. Numerical experiments based on a realistic Asia-Europe-Oceania liner shipping network are carried out to account for the effectiveness of our two-stage optimization method.     

Pareto efficient allocation of an in-motion wireless charging infrastructure for electric vehicles in a multipath network

Electric vehicles (EV) use an eco-friendly technology that limits the greenhouse gas emissions of the transport sector, but the limited battery capacity and the density of the battery are the major barriers to the widespread adoption of EV. To mitigate this, a good method seems to be the innovative wireless charging technology called ‘On-Line EV (OLEV)’, which is a contactless electric power transfer technology. This EV technology has the potential to charge the vehicle’s battery dynamically while the vehicle is in motion. This system helps to reduce not only the size of the battery but also its cost, and it also contributes to extending the driving range before the EV has to stop. The high cost of this technology requires an optimal location of the infrastructure along the route. For this reason, the objective of this paper is to study the problem of the location of the wireless charging infrastructure in a transport network composed of multiple routes between the origin and the destination. To find a strategic solution to this problem, we first and foremost propose a nonlinear integer programming solution to reach a compromise between the cost of the battery, which is related to its capacity, and the cost of installing the power transmitters, while maintaining the quality of the vehicle’s routing. Second, we adapt the multi-objective particle swarm optimization (MPSO) approach to our problem, as the particles were robust in solving nonlinear optimization problems. Since we have a multi-objective problem with two binary variables, we combine the binary and discrete versions of the particle swarm optimization approach with the multi-objective one. The port of Le Havre is presented as a case study to illustrate the proposed methodology. The results are analyzed and discussed in order to point out the efficiency of our resolution method.     

Multi-period heterogeneous vehicle routing considering carbon emission trading

This study considers the problem of determining heterogeneous vehicle routes in each period of a given planning horizon while satisfying service combinations, customer demands and vehicle capacities. The objective is to minimize the sum of vehicle operation costs and carbon emission trading cost/benefit, where the trading cost is incurred to purchase the carbon emission right if the total emission exceeds an upper limit in each period, while the trading benefit can be obtained by selling the right in each period, otherwise. A mixed integer programming model is developed to formulate the problem mathematically. Then, a tabu search algorithm is proposed that incorporates the characteristics of the heterogeneous and the period vehicle routing problems while considering the amount of carbon emission in each period. Computational experiments were done on modified benchmark instances and additional random instances, and the results show that the multi-period approach outperforms the existing single-period one in overall average. In particular, the test results show that the multi-period approach can reduce carbon emission more significantly than the single-period one without sacrificing the total cost.     

Identifying cycling-inducing neighborhoods: A latent class approach

Understanding how spatial attributes of cities and neighborhoods induce cycling is relevant for urban planning and policy making. In this work, ordered logit and latent class models are specified and estimated to analyze how the built environment affects bicycle-commuting frequency. Data come from a survey to 1,487 people in the city of Santiago, Chile, including sociodemographic information, travel behavior patterns and place of residence and work. Using geographic information systems tools, the built environment was characterized with variables calculated for a 500-m-radius buffer around the residential and work locations of each individual. Two models are estimated, first an ordered logit model confirms that built environment variables effect on cycling is similar to what has been reported in the literature, with some new findings such as an increase in cycling when public transport accessibility is low and the role of built environment attributes at the destination. Second, a latent class ordered logit is used to identify two classes of neighborhood in term of their cycling patterns, as a function of their density, presence of cycling infrastructure and distance to the main activity center of the city. This result allows to map the class membership probabilities, potentially helping to identify neighborhoods that encourage cycling and providing relevant information for policy making and infrastructure decisions.