The multi-period service territory design problem – An introduction,a model and a heuristic approach

In service territory design applications, a field service workforce is responsible for providing recurring services at their customers’ sites. We introduce the associated planning problem, which consists of two subproblems: In the partitioning subproblem, customers must be grouped into service territories. In the scheduling subproblem, customer visits must be scheduled throughout the multi-period planning horizon. The emphasis of this paper is put on the scheduling subproblem. We propose a mixed integer programming model for this subproblem and present a location-allocation heuristic. The results of extensive experiments on real-world instances show that the proposed heuristic produces high-quality solutions.     

The evaluation of demand responsive transport services in Europe

Over the last decade Demand Responsive Transport (DRT) services have grown in popularity for several reasons including: the shortcomings of conventional regular bus and taxi services; shortcomings of special transport services; and new developments in community transport. Traditional dial-a-ride services have often been criticised because of their relatively high cost of provision, their lack of flexibility in route planning and their inability to manage high demand. The potential for overcoming these limitations may be realised through the introduction of telematics-based DRT and this has been widely demonstrated, for example, in recent research funded by the European Commission (e.g. the DGXIII-funded SAMPO and SAMPLUS projects). This paper provides an introduction to the concept of telematics-based DRT services and presents the results of the evaluation of a set of DRT technologies and operations at urban and rural sites across Europe. The paper offers discussion in terms of the key issues that influence the introduction of DRT services.     

The attitude of potentially less mobile people towards demand responsive transport in a rural area in central Germany

Demand Responsive Transport (DRT) systems are often discussed to expand public transport in rural areas, where conventional buses are often used below capacity. DRT systems can particularly improve the mobility situation for less mobile people or other people who depend on public transport and ultimately prevent them from social exclusion. This paper examines whether people who show characteristics that indicate a mobility deficit are more willing to access the DRT system EcoBus in a rural area of Germany. Using survey data from 156 respondents from households in the EcoBus service area and utilizing linear regression analysis, this study confirms that car availability harms the intended use of DRT and that physical impairment has a positive effect, as identified several times in the literature. Furthermore, the influence of age on the intended EcoBus use depends on the residence place's city size and hence on its offer of daily public services. While residents of a sub-centre of the region are significantly less willing to use the EcoBus with increasing age, there is an opposite tendency for small villages. Thus, in small inaccessible localities, the likelihood of using EcoBus increases with age, suggesting that DRT systems benefit the doubly constrained population, namely the elderly population in localities with few public services.     

Demand responsive transport: towards the emergence of a new market segment

This paper focuses on recent British experience with telematics-based Demand Responsive Transport (DRT) services in rural areas. In recent years, the ability of DRT concepts to provide efficient, viable transport services has been greatly enhanced by the use of transport telematics as demonstrated in a variety of environments across Europe. The success of British local authorities in winning substantial funding under the Rural and Urban Bus Challenge programmes for the implementation of DRT has resulted in widespread interest in flexible forms of transport. It is thus timely to evaluate the impact of this substantial investment. Drawing on the experience of a number of UK schemes, the paper assesses the reasons for the new-found success of what is becoming a relatively well-accepted mode by concentrating on a variety of factors including: service characteristics (particularly route flexibility, flexibility of booking method and pre-booking regime), emerging markets and the overall contribution of DRT to increased social inclusion and intermodality. Impediments to the development of DRT services are highlighted. The paper also discusses current research into the next generation of DRT services and concludes by identifying some key issues for policy-makers concerned with the future implementation of DRT services.     

Scheduling aircraft take-offs and landings on interdependent and heterogeneous runways

This paper presents an optimization method for the aircraft scheduling problem with general runway configurations. Take-offs and landings have to be assigned to a runway and a time while meeting the sequence-dependent separation requirements and minimizing the costs incurred by delays. Some runways can be used only for take-offs, landings, or certain types of aircraft while schedules for interdependent runways have to consider additional diagonal separation constraints.Our dynamic programming approach solves realistic problem instances to optimality within short computation times. In addition, we propose a rolling planning horizon heuristic for large instances that returns close-to-optimal results.     

Robust planning and disruption management in roll-on roll-off liner shipping

This paper presents different strategies for handling disruptions in fleet deployment in roll-on roll-off liner shipping, which basically consists of assigning a fleet of vessels to predefined voyages at minimum cost. A new mathematical model of the problem is presented, including a set of robust planning strategies, such as adding slack and rewarding early arrivals. To solve real-life instances a rolling horizon heuristic is proposed. A computational study, where we also propose some recovery planning strategies, is conducted, and simulation results show that adding robustness significantly reduces the actual cost of the plan and the total delays of the voyages.     

The Electric Traveling Salesman Problem with Time Windows

To minimize greenhouse gas emissions, the logistic field has seen an increasing usage of electric vehicles. The resulting distribution planning problems present new computational challenges.We address a problem, called Electric Traveling Salesman Problem with Time Windows. We propose a mixed integer linear formulation that can solve 20-customer instances in short computing times and a Three-Phase Heuristic algorithm based on General Variable Neighborhood Search and Dynamic Programming.Computational results show that the heuristic algorithm can find the optimal solution in most small-size instances within a tenth of a second and achieves goods solutions in instances with up to 200 customers.     

Hybrid metaheuristic solutions to inventory location routing problem

This paper considers a supply chain network with multiple depots and geographically dispersed customers, each of which faces non-constant demand over a discrete planning horizon. The goal is to determine a set of depots to open, the delivery quantities to customers per period and the sequence in which they are replenished by a vehicle fleet such that the total system-wide cost is minimized. To solve it, first we construct a mixed integer program, and then propose a hybrid metaheuristic consisting of initialization, intensification and post-optimization. Results show that the proposed heuristic is considerably efficient and effective for many classical instances.     

A matheuristic approach to the air-cargo recovery problem under demand disruption

Air cargo transport is subject to unpredictable changes in expected demand, necessitating adjustments to itinerary planning to recover from such disruptions. We study a flight rescheduling problem to react to cargo demand disruptions in the short run. To increase flexibility, we consider two different cargo assignment policies. We propose a matheuristic approach to solve the problem that provides high-quality solutions in a short computational time, based on column generation in which each subproblem is solved using an ad-hoc heuristic. The approach is tested on demand disruption instances containing up to 75 air cargo orders with different penalty levels. The results show that the proposed method improves profit by 54% over the solution generated by a commercial MIP solver within a 1-h time limit, and by 15% over the solution with the routes fixed as in the original flight planning that only allows cargo to be re-routed. We also show that there exist incremental benefits in the range of 3–5% by allowing cargo for a given order to be transported by various aircraft.     

A rolling planning horizon heuristic for scheduling agents with different qualifications

This paper presents an optimization model for the tour scheduling problem for agents with multiple skills and flexible contracts in check-in counters at airports. The objective is to minimize the total assignment costs subject to demand fulfillment and labor regulations. In order to solve this problem we develop a rolling planning horizon-based heuristic. Our heuristic is robust and provides near-optimal schedules within reasonable computation time for real-world cases, although the parameter selection is important to its performance. In addition, we discuss the impact of the skill distribution on the scheduling costs for several instances.     

Joint location-inventory problem with differentiated service levels using critical level policy

This paper analyzes the design of a distribution network for fast-moving items able to provide differentiated service levels in terms of product availability for two demand classes (high and low priority) using a critical level policy. The model is formulated as a MINLP with chance constraints for which we propose a heuristic to solve it. Although the heuristic does not guarantee an optimal solution, our computational experiments have shown that it provides good-quality solutions that are on average 0.8% and at worst 2.7% from the optimal solution.     

Three-stage airline fleet planning model

One of the main factors affecting airline success is bringing supply and demand as closely together as possible. In order to achieve this goal, an airline needs to adopt an appropriate methodological approach for the fleet planning process. Selection of an aircraft for operating a defined route network is a key element which has a direct impact on the increase of an airline's profitability and on the reduction of an airline's costs. The objective of this paper is to develop a robust model for fleet planning that deals with both fleet size and fleet composition problems for airlines operating on short haul and medium haul routes. The three-stage model for fleet planning involves approximate fleet composition, fleet sizing and aircraft type selection based on fuzzy logic, heuristic and analytic approaches, and multi-criteria decision making, respectively. This model is exemplified with a hypothetical airline based at Belgrade Airport.     

A location-routing model for prepositioning and distributing emergency supplies

We propose a two-stage location-routing model with recourse for integrated preparedness and response planning under uncertainty. The model is used for risk management in disaster situations where there are uncertainties in demand and the state of the infrastructure. We solve the two-stage model by converting it into a single-stage counterpart. The latter is then implemented in an illustrative example. Comparative analyses are run to investigate the (1) value of planning location and routing in a single model, (2) value of transshipment, (3) differences when an expected-value objective is used, and (4) value of transshipment in the expected-value model.     

The planning and real-time adjustment of courier routing and scheduling under stochastic travel times and demands

This study develops a planning and a real-time adjustment model to plan courier routes and schedules in an urban area and to adjust the planned routes in actual operations. The stochastic travel time aspect of the problem has been addressed at both the planning stage and the operation stage. A heuristic is developed to efficiently solve the stochastic real-time adjustment model and a simulation-based evaluation method is also developed to compare the performance of the proposed models. The test results, related to an international express company’s operations, show the good performance of the proposed models.     

A location-inventory model for large three-level supply chains

We study the location-inventory problem in three-level supply networks. Our model integrates three decisions: the distribution centers location, flows allocation, and shipment sizes. We propose a nonlinear continuous formulation, including transportation, fixed, handling and holding costs, which decomposes into a closed-form equation and a linear program when the DC flows are fixed. We thus develop an iterative heuristic that estimates the DC flows a priori, solves the linear program, and then improves the DC flow estimations. Extensive numerical experiments show that the approach can design large supply networks both effectively and efficiently, and a case study is discussed.     

Joint planning of berth and yard allocation in transshipment terminals using multi-cluster stacking strategy

In this work, a joint planning problem for berth and yard allocation in transshipment terminals is addressed. Multi-cluster stacking strategy is proposed to split each transshipment flow into a number of container clusters and then stack each cluster in different yard blocks. A mixed integer quadratic programming model is formulated to minimize the total distance of exchanging containers between mother vessels and feeders, and the workload imbalance among yard blocks. A novel three-stage heuristic solution approach is developed and extensive numerical experiments are conducted to show the effectiveness of the proposed approach and the benefit of the multi-cluster strategy.     

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.     

Using GIS to visualise and evaluate student travel behaviour

Using GIS to evaluate travel behaviour is an important technique to increase our understanding of the relationship between accessibility and transport demand. In this paper, the activity space concept was used to identify the nature of participation in activities (or lack of it) amongst a group of students using a 2 day travel-activity diary. Three different indicators such as the number of unique locations visited, average daily distance travelled, and average daily activity duration were used to measure the size of activity spaces. These indicators reflect levels of accessibility, personal mobility, and the extent of participation, respectively. Multiple regression analyses were used to assess the impacts of students socio-economic status and the spatial characteristics of home location. Although no differences were found in the levels of accessibility and the extent of participation measures, home location with respect to a demand responsive transport (DRT) service was found to be the most important determinant of their mobility patterns. Despite being able to travel longer distances, students who live outside of the DRT service area were found to be temporally excluded from some opportunities. Student activity spaces were also visualised within a GIS environment and a spatial analysis was conducted to underpin the evaluation of the performance of the DRT. This approach was also used to identify the activity spaces of individuals that are geographically excluded from the service. Evaluation of these results indicated that although the service currently covers areas of high demand, 90% of the activity spaces remained un-served by the DRT service. Using this data six new routes were designed to meet the coverage goal of public transport based on a measure of network impedance based on inverse activity density. Following assessment of public transport service coverage, the study was extended using a spatial multi criteria evaluation (SMCE) technique to assess the effect of service provision on patronage.     

Exact and heuristic methods to solve the berth allocation problem in bulk ports

In this paper, we study the dynamic hybrid berth allocation problem in bulk ports with the objective to minimize the total service times of the vessels. We propose two exact methods based on mixed integer programming and generalized set partitioning, and a heuristic method based on squeaky wheel optimization, explicitly considering the cargo type on the vessel. The formulations are compared through extensive numerical experiments based on instances inspired from real bulk port data. The results indicate that the set partitioning method and the heuristic method can be used to obtain near-optimal solutions for even larger problem size.     

A GRASP algorithm for the container stowage slot planning problem

This work presents a generalization of the Slot Planning Problem which raises when the liner shipping industry needs to plan the placement of containers within a vessel (stowage planning). State-of-the-art stowage planning relies on a heuristic decomposition where containers are first distributed in clusters along the vessel. For each of those clusters a specific position for each container must be found. Compared to previous studies, we have introduced two new features: the explicit handling of rolled out containers and the inclusion of separations rules for dangerous cargo. We present a novel integer programming formulation and a Greedy Randomized Adaptive Search Procedure (GRASP) to solve the problem. The approach is able to find high-quality solution within 1 s. We also provide comparison with the state-of-the-art on an existing and a new set of benchmark instances.