A novel mathematical model for a multi-period,multi-product optimal ordering problem considering expiry dates in a FEFO system

One of the main challenges of retail units is to determine the order quantities of different types of products, each with a specific expiry date, so that the system cost including shortage cost is minimized. We study a new multi-product multi-period replenishment problem for a First Expired-First Out (FEFO) based warehouse management system. The proposed nonlinear model is first converted to a linear one and then solved by applying two evolutionary algorithms: the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), in which design parameters are set using Taguchi method. Computational results demonstrate the applicability of the proposed model for perishable items and comparing the results shows the efficiency of the proposed metaheuristics as well.     

The joint replenishment and delivery scheduling of the one-warehouse, n-retailer system

We deal with the joint replenishment and delivery scheduling of the one-warehouse, n-retailer system in this paper. We suggest a more flexible policy for the joint replenishment and delivery scheduling of a warehouse compared with the existing researches. We introduce the mathematical model and two efficient algorithms for the joint replenishment and delivery scheduling of the warehouse. Subsequently, we develop the hybrid genetic algorithm (GA) and compare it with two efficient heuristic algorithms for extensive computational experiments. Further, we show the advantages of our GA in dealing easily with resource restrictions.     

The time dependent vehicle routing problem with time windows: Benchmark problems, an efficient solution algorithm, and solution characteristics

An algorithm that can tackle time dependent vehicle routing problems with hard or soft time windows without any alteration in its structure is presented. Analytical and experimental results indicate that average computational time increases proportionally to the number of customers squared. New replicable test problems that capture the typical speed variations of congested urban settings are proposed. Solution quality, time window perturbations, and computational time results are discussed as well as a method to study the impact of perturbations by problem type. The algorithm efficiency and simplicity is well suited for urban areas where fast running times may be required.     

Transit accessibility for commuters considering the demand elasticities of distance and transfer

In this paper, a “by transit accessibility (BTA)” measure to evaluate the impacts of travel distance and transfer tolerance on the convenience of commuting by transit on a regional scale is proposed. Considering the spatiotemporal factors for commuting efficiency evaluation, the timetable-dependent passenger carrying capacity of the transit station and the time-varying passenger demand at originating sites have been formulated into the BTA model. Moreover, the proposed BTA measure could reflect the commuting trip demand elasticity, which is caused by travel distance and transfer tolerance. In the meantime, this BTA measure can provide an important basis for transit timetable adjustment in the study area during different time periods. The proposed measure is tested on a small transit network to display its function, and finally, it is applied to an empirical case to draw practical findings.     

Estimation of interregional input–output table using hybrid algorithm of the RAS method and real-coded genetic algorithm

In this paper, we propose a method for improving the accuracy of the estimation of interregional input–output tables, by combining the RAS method and the real-coded Genetic Algorithm (GA); these are simple representative methods for the estimation of an interregional input–output table. By comparing the performance evaluation results obtained using the proposed method, the RAS method, and Simulated Annealing, we verified that the combination of the genetic algorithm and the RAS method can enhance the estimation accuracy of an interregional input–output table. In addition, performance is further enhanced by adjusting GA parameters.     

Accounting for population exposure to vehicle-generated pollutants and environmental equity in the toll design problem

The emissions generated by motor vehicles remain a major source of air pollutants that affect public health and contribute to anthropogenic climate change. These negative externalities can be reduced, in part, with the implementation of environmentally oriented road pricing schemes, which can be designed using optimization-based approaches. In this paper, a toll design problem is proposed for determining toll locations and levels that minimize the expected human exposure to air pollutants and the related environmental inequalities, subject to constraints on pollutant concentration levels and implementation costs. The practical use of the proposed problem is hindered in most real-world applications by the computational costs associated with the evaluation of candidate solutions, as is common for network design problems. Furthermore, the problem cannot be expressed analytically given the multiple types of models (e.g., traffic assignment, emissions, air dispersion models) that would be required to evaluate a single design alternative. For these reasons, a derivative-free surrogate-based solution algorithm is proposed for mixed integer problems like the ones considered here. Numerical examples are used to illustrate possible applications of the proposed model and to test the performance of the surrogate-based algorithm. Relative to a joint simulated annealing-genetic algorithm heuristic and a genetic algorithm-based approach, the proposed algorithm found better solutions in fewer function evaluations.     

Using time-varying tolls to optimize truck arrivals at ports

An analytical point-wise stationary approximation model is proposed to analyze time-dependent truck queuing processes with stochastic service time distributions at gates and yards of a port terminal. A convex nonlinear programming model is developed which minimizes the total truck turn time and discomfort due to shifted arrival times. A two-phase optimization approach is used to first compute a system-optimal truck arrival pattern, and then find a desirable pattern of time-varying tolls that leads to the optimal arrival pattern. Numerical experiments are conducted to test the computational efficiency and accuracy of the proposed optimization models.     

A polynomial-time heuristic for the quay crane double-cycling problem with internal-reshuffling operations

One of great challenges in seaport management is how to handle containers under reshuffling, called reshuffles. Repositioning reshuffles in a bay (internal reshuffling) can improve the efficiency of quay cranes and help ports to reduce ship turn-around time. This paper studies the quay crane double-cycling problem with internal-reshuffling operations, and presents a fast solution algorithm. To reduce the number of operations necessary to turn around a bay of a vessel, the problem is first formulated as a new integer program. A polynomial-time heuristic is then developed. The analysis is made on the worst-case error bound of the proposed algorithm. Results are presented for a suite of combinations of problem instances with different bay sizes and workload scenarios. Comparisons are made between our algorithm and the start-of-the-art heuristic. The computational results demonstrate that our model can be solved more efficiently with CPLEX than the model proposed by Meisel and Wichmann (2010), and the proposed algorithm can well solve real-world problem instances within several seconds.     

An accelerated Benders decomposition algorithm for sustainable supply chain network design under uncertainty: A case study of medical needle and syringe supply chain

This paper proposes a multi-objective possibilistic programming model to design a sustainable medical supply chain network under uncertainty considering conflicting economic, environmental and social objectives. Effective social and environmental life cycle assessment-based methods are incorporated in the model to estimate the relevant environmental and social impacts. An accelerated Benders decomposition algorithm utilizing three efficient acceleration mechanisms is devised to cope with computational complexity of solving the proposed model. Computational analysis is also provided by using a medical industrial case study to present the significance of the proposed model as well as the efficiency of the accelerated Benders decomposition algorithm.     

Liner shipping hub network design in a competitive environment

A mixed integer programming formulation is proposed for hub-and-spoke network design in a competitive environment. It addresses the competition between a newcomer liner service provider and an existing dominating operator, both operating on hub-and-spoke networks. The newcomer company maximizes its market share—which depends on the service time and transportation cost—by locating a predefined number of hubs at candidate ports and designing its network. While general-purpose solvers do not solve instances of even small size, an accelerated Lagrangian method combined with a primal heuristic obtains promising bounds. Our computational experiments on real instances of practical size indicate superiority of our approach.     

A Linear Programming approach for robust network revenue management in the airline industry

The classical revenue management problem consists of allocating a fixed network capacity to different customer classes, so as to maximize revenue. This area has been widely applied in service industries that are characterized by a fixed perishable capacity, such as airlines, cruises, hotels, etc.It is traditionally assumed that demand is uncertain, but can be characterized as a stochastic process (See Talluri and van Ryzin (2005) for a review of the revenue management models). In practice, however, airlines have limited demand information and are unable to fully characterize demand stochastic processes. Robust optimization methods have been proposed to overcome this modeling challenge. Under robust optimization framework, demand is only assumed to lie within a polyhedral uncertainty set (Lan et al. (2008); Perakis and Roels (2010)).In this paper, we consider the multi-fare, network revenue management problem for the case demand information is limited (i.e. the only information available is lower/upper bounds on demand). Under this interval uncertainty, we characterize the robust optimal booking limit policy by use of minimax regret criterion. We present an LP (Linear Programming) solvable mathematical program for the maximum regret so our model is able to solve large-scale problems for practical use. A genetic algorithm is proposed to find the booking limit control to minimize the maximum regret. We provide computational experiments and compare our methods to existing ones. The results demonstrate the effectiveness of our robust approach.     

Variable neighborhood search heuristic for storage location assignment and storage/retrieval scheduling under shared storage in multi-shuttle automated storage/retrieval systems

This paper examines the joint optimization of storage location assignment and storage/retrieval scheduling in multi-shuttle automated storage/retrieval systems (AS/RSs) under shared storage, in which the reuse of empty location yielded by retrieval operation is allowed. From the view of analytical model, the advantage of operational mode under shared storage is verified. A variable neighborhood search (VNS) algorithm is developed to solve the large-sized problems. Various numerical experiments are conducted to evaluate the performance of the proposed algorithm and investigate the impact of different parameters on computational efficiency.     

A model to forecast airport-level General Aviation demand

General Aviation (GA) demand forecast plays an important role in aviation management, planning and policy making. The objective of this paper is to develop an airport-level GA demand forecast model. The GA demand at an airport is modeled as a function of social-economic and demographic factors, the availability of supply factors, the competition from the commercial aviation, the number of based aircraft, and the presence of a flight school. Our models suggest that the relative fuel price – fuel price compared with personal income – is a significant determinant of airport level GA demand. The elasticity of itinerant and local GA demand with respect to the relative fuel price is −0.43 and −0.52, respectively. Our results are compared with those reported in other studies. Furthermore, we made projections of GA demand for the airports in the Terminal Area Forecast (TAF) using three fuel price scenarios from the Energy Information Administration. Our projections under the “business-as-usual” fuel price scenario are close to those in the TAF. Our models could prove useful, for example, for the Federal Aviation Administration and airport planners to prepare airport-level GA demand forecast.     

Control-based optimization approach for aircraft scheduling in a terminal area with alternative arrival routes

This paper presents an optimization approach for dynamically scheduling aircraft operations and supporting air traffic controllers in both determining and implementing operationally feasible landing and departure times at an airport. The mixed integer linear programming model proposed incorporates air traffic control infrastructure in terms of route network, introduces the concept of alternative approach routes and is designed to generate an output that can be converted into effective advisories for executable flight commands. It shows reasonable computational times for obtaining the optimal solution and delay reductions of up to 35% with practical size instances from Sao Paulo/Guarulhos International Airport.     

Capacity-oriented passenger flow control under uncertain demand: Algorithm development and real-world case study

This paper proposes a problem of passenger flow organization in subway stations under uncertain demand. The existing concepts of station service capacity are extended and further classified into three in different demand scenarios. Mathematical models are put forward to measure the three capacities and a unified simulation-based algorithm is developed to solve them. To increase computing speed, data envelopment analysis (DEA) and genetic algorithms (GA) are embedded in this algorithm. A case study will demonstrate the performance of the proposed algorithm and give a detailed procedure of passenger flow control based on station service capacity in various demand scenarios.     

Truthful multi-unit multi-attribute double auctions for perishable supply chain trading

This paper aims to propose multi-attribute double auctions for perishable supply chain trading (PSCT). We first construct a multi-unit/single-unit multi-attribute double auction (MS-MDA) for PSCT where suppliers can submit bids on a single unit of one item (i.e., single output restriction). We then relax the single output restriction and propose a multi-unit multi-attribute double auction (M-MDA) for PSCT in which each supplier offers multiple units of one item. Both the MS-MDA and M-MDA mechanisms are incentive compatible, individually rational, budget balanced and computationally efficient. The computational study shows that all proposed mechanisms are of high allocation efficiency and practically implementable.     

Criteria selection and multi-objective optimization of aircraft landing problem

Inspired by the similarities of the aircraft landing problem (ALP) and the single machine scheduling problem, we propose a criteria selection method that has been used successfully in the single machine scheduling problem to determine appropriate objective functions of ALP. First, for four different types of criteria—min-max, min-sum, completion time related, and due-dates related criteria—their corresponding physical meanings in ALP are elaborated. Then, a criteria selection method is proposed to determine several appropriate criteria, which are taken as the multi-objective while modeling ALP. Different solution algorithms, including Imperialist Competitive Algorithm (ICA), are adopted to solve the multi-objective ALP. Finally, the performance of the proposed model and method are evaluated using a set of benchmark instances. The computational results demonstrate the efficiency of our approach for solving ALP, which can simultaneously improve punctual performance, enhance runway utilization, reduce air traffic controller workload, and maintain equity among airlines.     

A combinatorial benders’ cuts algorithm for the quayside operation problem at container terminals

The quayside operation problem is one of the key components in the management system for a container terminal. In this paper, the integrated models proposed in the previous studies to address the quayside operation problem are examined and one of the potential frameworks is identified. A new method called combinatorial benders’ cuts algorithm is developed to solve the berth-level model in the framework. The computational experiment conducted in this research shows that the proposed approach is more efficient than the branch and cut algorithm embedded in CPLEX.     

A memetic algorithm for the open capacitated arc routing problem

In this paper, an open capacitated arc routing problem (OCARP) is defined and considered. The OCARP seeks to find a set of minimum-cost open routes that can serve the tasks (i.e., required arcs) of a given graph, subject to the vehicle capacity and travel distance. A mathematical programming formulation and a lower bound are established. An effective memetic algorithm is developed for solving the OCARP. Computational experiments demonstrate that the proposed algorithm can produce high quality solutions within a reasonable computational time span, and the proposed memetic algorithm is superior to the classical genetic algorithm in solution quality.     

Multi-period hub location problems in transportation

Many transport service providers operate on hub-and-spoke network structures. Major operators may have several dedicated hub facilities that are leased for a time horizon rather than being owned or constructed. For a given discrete planning horizon, service providers must decide on the location of the hub ports (i.e. terminals), the period when the lease contract starts, the period when the existing contracts must be terminated and the flow routing over the entire planning horizon so as to minimize the total operational cost. Thus, we propose a mathematical model for a Multi-period Uncapacitated Multiple Allocation Hub Location Problem with Budget Constraint. The proposed model incorporates several features of practice, particularly from maritime and land transport practices. We also propose a meta-heuristic solution algorithm that produces high-quality solutions in a reasonable amount of time. By exploiting the decomposable structure of the model, we extended a Benders decomposition approach by proposing several improvements. Extensive computational experiments confirm the efficiency of the proposed methods and also show its limitations.