The two-stage batch ordering strategy of logistics service capacity with demand update

This paper discusses a logistics service integrator (LSI) orders logistics service capacity from a functional logistics service provider (FLSP) before the selling season with two opportunities. The optimal two-stage batch ordering strategy of LSI with demand update is studied. Standard batch size is introduced into LSI’s two-stage capacity ordering strategy model, which is built upon the decision-making sequence and actually observed demand signals between the two ordering instants. The model solution method is designed based on scenario analysis and enumerative algorithm. Sensitivity analysis of the optimal adjustment ordering time point is then carried out and a numerical analysis is presented.     

Selecting between batch and zone order picking strategies in a distribution center

An order picking strategy in a distribution center (DC) defines the manner in which pickers navigate the picking area to pick items from storage locations. We focus on the problem of selecting between a batch picking and a zone picking strategy. For this problem, we propose a cost model to estimate the cost of each type of picking strategy. In our cost model we consider the effects of pick-rate, picker blocking, workload-imbalance, and the sorting system requirement. Through an example problem, we show how system throughput, order sizes, item distribution in orders, and wavelength affect the picking strategy selection decision.     

Effect of block width and storage yard layout on marine container terminal performance

Block widths ranging from two to fifteen rows in a marine container terminal are evaluated by a fully-integrated, discrete event simulation model. Experiments consider dozens of yard configurations and four container terminal settings that are designed to reproduce the microscopic, stochastic, real-time environment at a multiple-berth facility. Results show that the quay crane rate is concave with respect to block width when the yard storage capacity and amount of yard equipment is constant. The optimal block width ranges from 6 to 12 rows depending on the amount of equipment deployed and the size, shape, and throughput of the terminal.     

Modeling public transport corridors with aggregate and disaggregate demand

Microeconomic public transport models aimed at maximizing social benefits usually consider demand in an aggregate manner. In this paper we examine the effect of this approach on the optimal values of frequency and vehicle size by comparison with models where demand is described in detail as a matrix of flows between every station in a single line service. The theoretical analysis and the numerical examples suggest that the spatially aggregated model underestimates optimal frequency and overestimates vehicle size.     

Optimizing the block size in container yards

A block is the basic unit of storage space in container terminals. This study proposes two methods for optimizing the block size, by considering the throughput requirements of yard cranes (YCs) and the block storage requirements. To estimate the YC performance, cycle-time models of various handling operations of YCs are analytically derived. Two types of container yards are examined: those with blocks that are laid out parallel to the quay and those laid out vertical to the quay.     

Processing passengers efficiently: An analysis of airport processing times for international passengers

With immense and growing pressure on stakeholders in international airport terminals to process passengers faster than previously, there is a great benefit to understanding which factors affect passenger processing times and in which situations. In addition, storing and analysing the collected data in batch is itself a difficult and time consuming task that could be made much simpler with sequential analysis. We aim to present a method for airport managers to discover which variables are important to understanding passenger processing times and identifying problematic passenger profiles without the need for high computational capacity and full historical datasets.In this paper we introduce Bayesian hierarchical models as a method of sequentially processing data, reducing computation time and obviating storage of large amounts of raw data. We use a range of exploratory models to identify which variables are important to predicting passenger processing time using a dataset from a day of operations at an international airport terminal, then compare a range of regression models. A Bayesian hierarchical regression model based on the model of best fit discovered through exploration is then applied to two subsets of data. We demonstrate that sequential updating based on daily data achieves similar results to batch processing based on full historical datasets and can therefore be used as an alternative in appropriate circumstances. Using the presented models, we find that the airline operating a flight is the most important variable to determining passenger processing time, followed by each passenger's age, sex and nationality. We demonstrate that in our dataset, the passenger profiles correlated with higher mean processing times overall were not the same as those passengers most problematic for meeting processing time targets.     

Optimizing the service area and trip selection of an electric automated taxi system used for the last mile of train trips

We propose two integer programming models for optimizing an automated taxi (AT) system for last mile of train trips. Model S1: trip reservations are accepted or rejected by the operator according to the profit maximization; model S2: any reservation on a selected zone by the model must be satisfied. Models were applied to a case-study. Results indicate that fleet size influences the profitability of the taxi system: a fleet of 40 ATs is optimal in S1 and 60 ATs in S2. Having electric ATs constrains the system for small fleets because ATs will not have time for charging.     

An utility-based decision support sustainability model in slow steaming maritime operations

This paper analyses slow steaming sustainability initiatives and generalizes the traditional discrete cost-based decision support model into novel continuous utility-based models. Two models based on logarithmic and linear utility functions are developed for risk-averse and risk-neutral decision makers respectively. The models, considering fuel consumption, carbon emission, and on time delivery, are applied to a Trans-pacific trade service route. A sensitivity analysis is conducted on parameters of sailing distance, expected transit time, quantity, and emission policies. The model contributes to ship liners on the optimal speed decisions in continuous utility-based slow steaming operations.     

Designing minimum-cost recycling collection networks with required throughput

The problem of designing minimum-cost recycling networks with required throughput is a subset of the broader class of facility location problems in which the recycler wishes to determine the optimal number and location of receiving centers as well as the correct financial incentive to be offered in order to stimulate collection of used or unrecoverable products to a degree required for regulatory reasons or otherwise. This paper presents a conceptual framework, an analytical model, and a three-stage algorithmic solution for this problem. An illustrative case study in the recycling of unrecoverable tires in southern states of Brazil is presented.     

Efficient aircraft spare parts inventory management under demand uncertainty

In airline industries, the aircraft maintenance cost takes up about 13% of the total operating cost. It can be reduced by a good planning. Spare parts inventories exist to serve the maintenance planning. Compared with commonly used reorder point system (ROP) and forecasting methods which only consider historical data, this paper presents two non-linear programming models which predict impending demands based on installed parts failure distribution. The optimal order time and order quantity can be found by minimizing total cost. The first basic mathematical model assumes shortage period starts from mean time to failure (MTTF). An iteration method and GAMS are used to solve this model. The second improved mathematical model takes into account accurate shortage time. Due to its complexity, only GAMS is applied in solution methodology. Both models can be proved effective in cost reduction through revised numerical examples and their results. Comparisons of the two models are also discussed.     

Mergers and acquisitions in aviation – Management and economic perspectives on the size of airlines

This paper reviews literature and management perspectives on airline mergers and acquisitions. We find that M&A/consolidation is seen as a “game-changer” and mandatory to survive in aviation markets. We, therefore, apply DEA models to 66 airlines to evaluate whether big is indeed always beautiful. Our results suggest that the optimal airline size is between 34 and 52 bn available seat kilometre capacity and that airlines with more than 200 bn ASK are definitely too large to operate efficiently. This also applies when revenues are included in the DEA models, which is central as yield management and ancillary revenues are increasingly important.     

Internet book retailing and supply chain management: an analytical study of inventory location speculation and postponement

Internet retailing models support supply chains where consumer order locations are decoupled from inventory locations. In this setting, retailers dynamically consider inventory location speculation and postponement to fulfill their orders. Particularly, retailers can manage inventory to fulfill orders through two opposing strategies: in-stock inventory and drop-shipping. This paper extends the supply chain management literature by modeling Internet retailers' decisions to balance their offerings between these two strategies. The results show how retailers depend more on both of these strategies as their market share and product popularity increase. Thus, both inventory management strategies may be considered simultaneously to better manage Internet retailers' inventory.     

Optimal distance tolls under congestion pricing and continuously distributed value of time

This paper addresses the optimal distance-based toll design problem for cordon-based congestion pricing schemes. The optimal distance tolls are determined by a positive and non-decreasing toll-charge function with respect to the travel distance. Each feasible toll-charge function is evaluated by a probit-based SUE (Stochastic User Equilibrium) problem with elastic demand, asymmetric link travel time functions, and continuously distributed VOT, solved by a convergent Cost Averaging (CA) method. The toll design problem is formulated as a mixed-integer mathematical programming with equilibrium constraints (MPEC) model, which is solved by a Hybrid GA (Genetic Algorithm)–CA method. Finally, the proposed models and algorithms are assessed by two numerical examples.     

Multi-objective design of an organ transplant network under uncertainty

We propose a novel multi-period location–allocation model for the design of an organ transplant transportation network under uncertainty. The model consists of a bi-objective mathematical programming model that minimizes total cost and time, including waiting time in the queue for the transplant operation, while considering organs’ priorities. A fuzzy multi-objective programming based approach is presented to solve the small and medium size problems to optimality. For larger problems, we propose two meta-heuristics based algorithms. Lower bounds, and several numerical examples with managerial insights are discussed. A real case-study is provided, and the existing and the proposed optimal solutions are compared.     

Modelling new pricing strategies for the Santiago Metro

After twenty years of operation the Santiago Metro decided to start a new strategy of price differences by period in order to decrease congestion in the morning peak and to postpone the acquisition of new equipment. In this paper we report the results of a model specifically designed to test the impact of different price levels on patronage by period. Stated preference (SP) data was collected, focusing on three aspects: change in time of travel, price differences and comfort improvements. A goods-leisure microeconomics framework was adopted to decide on users segmentation and model specification. Two methodological aspects were explored, the non-linear marginal valuation of time displacements and the different known ways to analyse SP rating experiments. In this quest, models were calibrated both with a standard and an optimal probabilistic interpretation of the semantic scale and with the ordinal probit and binary logit models. Comparisons were made and conclusions reached about the more appropriate approach.Finally, the current time-of-day pricing strategy of the Santiago Metro, which commenced operation in February 1994, was simulated using the best models reported in the paper. This was done in order to test their validity against an observed reality.     

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.     

An integrated production-inventory system in a multi-stage multi-firm supply chain

We first generalize a number of integrated models with/without lot streaming and with/without complete backorders under the integer–multiplier coordination mechanism, and then individually derive the optimal solution to the three- and four-stage model, using algebraic methods of complete squares and perfect squares. We subsequently deduce optimal expressions for some well-known models. For our model, we check that the optimal solution, which is algebraically derived, is a global one. We present three numerical examples for illustrative purposes. We finally suggest some future research work involving extension or modification of the generalized model.     

选编调车与装卸作业进度配合的智能化控制

在分析调车作业与装卸作业相互联系的基础上,论述寻求两者之间进度配合的必要性。研究确定各阶段选编作业时分与划分车流批次的内在联系,建立估算作业量和作业时分的数学模型。提出在不同时间约束条件下合理划分作业阶段和选编批次的原则及方法。通过实例计算描述智能化划分选编批次和作业阶段的具体过程。     

Modeling aggregate air-travel itinerary shares: logit model development at a major US airline

This study reports the results of aggregate air-travel itinerary share models estimated at the city-pair level for all city-pairs in the US. These models determine the factors that influence airline ridership at the itinerary level and support carrier decision-making. The models are estimated using aggregate multinomial logit methodology and use comprehensive data. Independent variables for the models measure various itinerary service characteristics: level-of-service, connection quality, carrier, carrier market presence, fares, aircraft size and type, and time of day. The results are intuitive, and validation tests indicate that the models outperform existing methods. Finally, the impacts of changing various itinerary service attributes on carrier market share are discussed.     

A generalized algebraic model for optimizing inventory decisions in a centralized or decentralized multi-stage multi-firm supply chain

First of all, a number of integrated models with/without lot streaming under the integer multiplier coordination mechanism is generalized by allowing lot streaming and three types of inspection for some/all upstream firms. Secondly, the optimal solutions to the three- and four-stage models are individually derived, both using the perfect squares method, which is a simple algebraic approach so that ordinary readers unfamiliar with differential calculus can easily understand how to obtain the optimal solution procedures. Thirdly, optimal expressions for some well-known models are deduced. Fourthly, expressions for sharing the coordination benefits based on Goyal’s (1976) scheme are derived, and a further sharing scheme is introduced. Fifthly, two numerical examples for illustrative purposes are presented. Finally, some future research works involving extension or modification of the generalized model are suggested.