Tactical crew planning in railways

Tactical crew capacity planning problem in railways involves finding the minimum number of crews in a region required to operate a predetermined set of train duties satisfying the strict day-off requirement for crew. For the single-region problem, we develop two solution approaches based on a space–time network representation: the sequential approach and the integrated approach. We also study the multi-regional capacity planning problem where we minimize total system-wide capacity by simultaneously considering multiple regions within a neighborhood search algorithm based on our solution methods for the single-region problem. We present the computational study on problem instances from Turkish State Railways.     

A decomposition scheme for large-scale Service Network Design with asset management

In this paper, we address a large-scale freight transportation problem for maximizing the profit of a carrier. We propose two solving algorithms using a decomposition of the problem into three main steps: construction of the network, filling vehicles with commodities and construction of the vehicle plannings. The resolution of these steps involves heuristic schemes, Mixed Integer Programming and Constraint Programming techniques. To evaluate the model and the solution algorithms, we produce instances based on a study of real-life data. The results show that the methods without transhipment provide solutions with a good computation time/quality trade-off.     

Liner shipping cargo allocation with service levels and speed optimization

The cargo allocation problem is a key strategic problem that determines the profitability of a liner shipping network. We present a novel mixed-integer programming model for this problem that introduces service levels for transit time requirements and optimizes the vessel speed on each leg of a service. These extensions to the cargo allocation problem greatly increase its realism and value for carriers. We evaluate our model on realistic data from the LINER-LIB and perform a sensitivity analysis of transit times versus bunker costs. Furthermore, we show how carriers can use our model to make data driven decisions in their operations.     

The time constrained multi-commodity network flow problem and its application to liner shipping network design

The multi-commodity network flow problem is an important sub-problem in several heuristics and exact methods for designing route networks for container ships. The sub-problem decides how cargoes should be transported through the network provided by shipping routes. This paper studies the multi-commodity network flow problem with transit time constraints which puts limits on the duration of the transit of the commodities through the network. It is shown that for the particular application it does not increase the solution time to include the transit time constraints and that including the transit time is essential to offer customers a competitive product.     

Hierarchical multi-objective evacuation routing in stadium using ant colony optimization approach

Evacuation planning is a fundamental requirement to ensure that most people can be evacuated to a safe area when a natural accident or an intentional act happens in a stadium environment. The central challenge in evacuation planning is to determine the optimum evacuation routing to safe areas. We describe the evacuation network within a stadium as a hierarchical directed network. We propose a multi-objective optimization approach to solve the evacuation routing problem on the basis of this hierarchical directed network. This problem involves three objectives that need to be achieved simultaneously, such as minimization of total evacuation time, minimization of total evacuation distance and minimal cumulative congestion degrees in an evacuation process. To solve this problem, we designed a modified ant colony optimization (ACO) algorithm, implemented it in the MATLAB software environment, and tested it using a stadium at the Wuhan Sports Center in China. We demonstrate that the algorithm can solve the problem, and has a better evacuation performance in terms of organizing evacuees’ space-time paths than the ACO algorithm, the kth shortest path algorithm and the second generation of non-dominated sorting genetic algorithm were used to improve the results from the kth shortest path algorithm.     

Concurrent design of product family and supply chain network considering quality and price

We study the problem of concurrent design of a product family and its supply chain (SC) network. Inspired by a real-life case in computer industry, the impact of quality and price in SC demand is investigated. Two different models are proposed, the former maximizes the company’s profit with respect to customers’ priorities on quality and price; the latter is a bi-objective programming, which consider two extreme customer groups: for one group quality has the highest priority and for the other price; the intermediate groups falls between these two. The performance of the models is analyzed through a case problem.     

The multi-class, multi-criterion traffic equilibrium and the efficiency of congestion pricing

We consider the effect of the so-called second-best tolls on the price of anarchy of the traffic equilibrium problem where there are multiple classes of users with a discrete set of values of time. We derive several bounds of the price of anarchy for this problem when the tolls are considered and not considered as part of the system cost, with the time-based criterion and the cost-based criterion, respectively. All the bounds give us useful information on the effect of the tolls, which can be used to design network toll schemes.     

Model and algorithm for an unpaired pickup and delivery vehicle routing problem with split loads

This paper addresses the routing problem with unpaired pickup and delivery with split loads. An interesting factor of our problem is that the quantity and place for pickup and delivery are decision variables in the network. We develop an easy-to-implement heuristic in order to gain an efficient and feasible solution quickly. Then, a local search algorithm based on the variable neighborhood search (VNS) method is developed to improve the performance of the heuristic. Computational results show that the proposed VNS method is able to obtain an optimal or near optimal solution in reasonable time for the formulated problem.     

Hierarchical multimodal hub location problem with time-definite deliveries

Hierarchical multimodal hub location problem is a cost-minimizing hub covering problem where two types of hubs and hub links, accounting for ground and air transportation, are to be established, while ensuring time-definite deliveries. We propose a mixed-integer programming formulation and perform a comprehensive sensitivity analysis on the Turkish network. We show that the locations of airport hubs are less sensitive to the cost parameters compared to the locations of ground hubs and it is possible to improve the service quality at not much additional cost in the resulting multimodal networks. Our methodology provides the means for a detailed trade-off analysis.     

Hierarchical multimodal hub location problem with time-definite deliveries

Hierarchical multimodal hub location problem is a cost-minimizing hub covering problem where two types of hubs and hub links, accounting for ground and air transportation, are to be established, while ensuring time-definite deliveries. We propose a mixed-integer programming formulation and perform a comprehensive sensitivity analysis on the Turkish network. We show that the locations of airport hubs are less sensitive to the cost parameters compared to the locations of ground hubs and it is possible to improve the service quality at not much additional cost in the resulting multimodal networks. Our methodology provides the means for a detailed trade-off analysis.     

A dynamic evacuation network optimization problem with lane reversal and crossing elimination strategies

This paper discusses a dynamic evacuation network optimization problem that incorporates lane reversal and crossing elimination strategies. These two lane-based planning strategies complement one another by increasing capacity in specific directions through the evacuation network. A bi-level network optimization model is formulated, in which the upper level aims at optimizing the network evacuation performance subject to the lane-reversal and crossing-elimination constraints and the lower level conveys a cell transmission-based dynamic traffic assignment problem. An integrated Lagrangian relaxation and tabu search method is devised for approximating optimal problem solutions through an iterative optimization-evaluation process. The numerical results of implementing the developed modeling and solution approach to a synthetic network and a real-world example application justify its theoretical and practical value.     

Incorporating spatial equity into interurban road network design

Methods for the road network design problem, typically, are based on optimization of the network efficiency measures (e.g. network-wide travel time) under a predefined budget. In these approaches, equity issues are not taken into account and, consequently, most of the road improvements are planned next to large cities. Thus, disparities between large and small cities increase, which does not conform to sustainable development objectives. In this paper, to overcome concerns associated with traditional methods, equity is incorporated into the interurban road network design problem. To this end, accessibility concepts are employed. However, unlike previous studies, instead of maximizing the total accessibility, a new definition is proposed for inaccessibility, and total inaccessibility is minimized throughout the network. Using this new definition not only is more compatible with the equity issue, but also helps to eliminate the nonlinearity of the problem. Average travel time to neighboring opportunities is utilized to propose this definition for inaccessibility, which captures the reality more effectively. With the aim of this definition, equity is incorporated into the road network design problem implicitly. This is another improvement over previous methods, where a new term in the objective function or a new constraint is added to include the equity. The proposed model is formulated as a mixed integer linear programming (MILP) problem, where the objective is to minimize the aggregate inaccessibility over all the population centers in the network. To illustrate the application of the model, the Northwest region of the United States is used as the case study. The respective exact solution of the example is found using a commercial solver (CPLEX). This new solution is also compared with the solutions from the traditional methods.     

Routing and refueling plans to minimize travel time in alternative-fuel vehicles

A driver who drives an alternative-fuel vehicle (AFV) from an origin point to a destination point needs to consider how to get there (i.e., the routing problem), when to stop, and how and when to refuel (i.e., the refueling plan). In this study, models and algorithms are proposed that optimize a one-way-trip path such that the total travel time from the origin to the destination is minimized. The travel time consists of the setup time, the refueling time and the driving time. The setup time includes waiting for the AFV to be served at a refueling station and the preparation time of charging the machine. We categorized the problems into two types: (1) the refueling plan problem when the routing decision is given and (2) an integrated problem of routing and refueling. Another axis of categorization is when (1) setup time and refueling times are site-independent and (2) parameters are site-dependent. We propose optimal algorithms for site-independent problems and the integrated problem of routing and refueling planning with site-independent parameters. We also conduct experiments and sensitivity analyses for the site-dependent integrated problems of routing and refueling.     

Integrated production and intermodal transportation planning in large scale production–distribution-networks

We present a model and solution approach for combining production and intermodal transportation planning in a supply network. A close and detailed integration of both decision fields is missing in the literature so far. The model includes relevant decisions regarding production setups and output volumes of plants, cargo consolidation at intermodal terminals, and capacity bookings for road and rail transports. A Branch-and-Cut method and heuristics are designed for solving the problem. A comprehensive case study for a chemical company identified a 6%-cost saving from the integrated planning. At the same time, companies are successfully supported in establishing eco-friendly distribution processes.     

Optimal traffic calming: A mixed-integer bi-level programming model for locating sidewalks and crosswalks in a multimodal transportation network to maximize pedestrians’ safety and network usability

We study the effect that installing sidewalks and crosswalks, as traffic calming facilities, has on the safety and usability of a transportation network with automobile, public transit and walking as modes of transportation. A mathematical programming model is proposed for this problem whose objective is to minimize the safety hazard for pedestrians and the total transportation cost of the network. We utilize a customized greedy heuristic and a simulated annealing algorithm for solving the problem. The computational results indicate that installing sidewalks and crosswalks at proper locations can reduce the overall transportation cost and improve pedestrians’ safety.     

Fleet deployment, network design and hub location of liner shipping companies

A mixed integer linear programming formulation is proposed for the simultaneous design of network and fleet deployment of a deep-sea liner service provider. The underlying network design problem is based on a 4-index (5-index by considering capacity type) formulation of the hub location problem which are known for their tightness. The demand is elastic in the sense that the service provider can accept any fraction of the origin–destination demand. We then propose a primal decomposition method to solve instances of the problem to optimality. Numerical results confirm superiority of our approach in comparison with a general-purpose mixed integer programming solver.     

Reducing truck emissions at container terminals in a low carbon economy: Proposal of a queueing-based bi-objective model for optimizing truck arrival pattern

This study proposes a methodology to optimize truck arrival patterns to reduce emissions from idling truck engines at marine container terminals. A bi-objective model is developed minimizing both truck waiting times and truck arrival pattern change. The truck waiting time is estimated via a queueing network. Based on the waiting time, truck idling emissions are estimated. The proposed methodology is evaluated with a case study, where truck arrival rates vary over time. We propose a Genetic Algorithm based heuristic to solve the resulting problem. Result shows that, a small shift of truck arrivals can significantly reduce truck emissions, especially at the gate.     

Ant colony optimization for disaster relief operations

This paper presents a meta-heuristic of ant colony optimization (ACO) for solving the logistics problem arising in disaster relief activities. The logistics planning involves dispatching commodities to distribution centers in the affected areas and evacuating the wounded people to medical centers. The proposed method decomposes the original emergency logistics problem into two phases of decision making, i.e., the vehicle route construction, and the multi-commodity dispatch. The sub-problems are solved in an iterative manner. The first phase builds stochastic vehicle paths under the guidance of pheromone trails while a network flow based solver is developed in the second phase for the assignment between different types of vehicle flows and commodities. The performance of the algorithm is tested on a number of randomly generated networks and the results indicate that this algorithm performs well in terms of solution quality and run time.     

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.     

An integrated modeling framework for design of logistics networks with expedited shipment services

This paper studied an integrated logistics network problem that determines optimal supplier locations, assignments of these suppliers to terminal facilities, expedited shipment configurations, and inventory management strategies in an uncertain environment. We studied the problem structure and proposed mathematical models to determine the optimal network design that minimizes the expected total system cost. We developed a customized solution approach based on Lagrangian relaxation that can solve these models efficiently and accurately. Numerical examples are conducted to draw managerial insights into how problem settings and key parameter values affect the optimal design results.