The multi-objective Steiner pollution-routing problem on congested urban road networks

This paper introduces the Steiner Pollution-Routing Problem (SPRP) as a realistic variant of the PRP that can take into account the real operating conditions of urban freight distribution. The SPRP is a multi-objective, time and load dependent, fleet size and mix PRP, with time windows, flexible departure times, and multi-trips on congested urban road networks, that aims at minimising three objective functions pertaining to (i) vehicle hiring cost, (ii) total amount of fuel consumed, and (iii) total makespan (duration) of the routes. The paper focuses on a key complication arising from emissions minimisation in a time and load dependent setting, corresponding to the identification of the full set of the eligible road-paths between consecutive truck visits a priori, and to tackle the issue proposes new combinatorial results leading to the development of an exact Path Elimination Procedure (PEP). A PEP-based Mixed Integer Programming model is further developed for the SPRP and embedded within an efficient mathematical programming technique to generate the full set of the non-dominated points on the Pareto frontier of the SPRP. The proposed model considers truck instantaneous Acceleration/Deceleration (A/D) rates in the fuel consumption estimation, and to address the possible lack of such data at the planning stage, a new model for the construction of reliable synthetic spatiotemporal driving cycles from available macroscopic traffic speed data is introduced. Several analyses are conducted to: (i) demonstrate the added value of the proposed approach, (ii) exhibit the trade-off between the business and environmental objectives on the Pareto front of the SPRP, (iii) show the benefits of using multiple trips, and (iv) verify the reliability of the proposed model for the generation of driving cycles. A real road network based on the Chicago's arterial streets is also used for further experimentation with the proposed PEP algorithm. © 2019 Elsevier Lt

An integrated modelling approach for the bicriterion vehicle routing and scheduling problem with environmental considerations

The consideration of pollution in routing decisions gives rise to a new routing framework where measures of the environmental implications are traded off with business performance measures. To address this type of routing decisions, we formulate and solve a bi-objective time, load and path-dependent vehicle routing problem with time windows (BTL-VRPTW). The proposed formulation incorporates a travel time model representing realistically time varying traffic conditions. A key feature of the problem under consideration is the need to address simultaneously routing and path finding decisions. To cope with the computational burden arising from this property of the problem we propose a network reduction approach. Computational tests on the effect of the network reduction approach on determining non-dominated solutions are reported. A generic solution framework is proposed to address the BTL-VRPTW. The proposed framework combines any technique that creates capacity-feasible routes with a routing and scheduling method that aims to convert the identified routes to problem solutions. We show that transforming a set of routes to BTL-VRPTW solutions is equivalent to solving a bi-objective time dependent shortest path problem on a specially structured graph. We propose a backward label setting technique to solve the emerging problem that takes advantage of the special structure of the graph. The proposed generic solution framework is implemented by integrating the routing and scheduling method into an Ant Colony System algorithm. The accuracy of the proposed algorithm was assessed on the basis of its capability to determine minimum travel time and fuel consumption solutions. Although the computational results are encouraging, there is ample room for future research in algorithmic advances on addressing the proposed problem

Considering stakeholders’ preferences for scheduling slots in capacity constrained airports

Airport slot scheduling has attracted the attention of researchers as a capacity management tool at congested airports. Recent research work has employed multi-objective approaches for scheduling slots at coordinated airports. However, the central question on how to select a commonly accepted airport schedule remains. The various participating stakeholders may have multiple and sometimes conflicting objectives stemming from their decision-making needs. This complex decision environment renders the identification of a commonly accepted solution rather difficult. In this presentation, we propose a multi-criteria decision-making technique that incorporates the priorities and preferences of the stakeholders in order to determine the best compromise solution

The effects of behavioural supply chain relationship antecedents on integration and performance

Structured Abstract: Purpose: To examine the effects of behavioural antecedents of collaboration in supply chain relationships on supply chain integration and performance by developing and empirically validating a model linking these constructs. Design/methodology/approach: A conceptual model was developed based on Relational Exchange Theory, Social Exchange Theory and Resource-Based View. An international survey with supply chain/logistics managers from manufacturing focal firms based in Europe, US and Asia was conducted; they provided input on upstream and downstream relationships based on their actual interaction and experience with supply chain partners. The collected data, which reflect supply chain managers’ perceptions on the above described phenomena, were analysed using the Partial Least Squares (PLS) method. Findings: Mutuality, reciprocity, trust and commitment are instrumental for the formation of supply chain relationships characterised by higher information integration. In turn, information integration has much stronger impact on the coordination of operational decisions related to production and demand planning than on decisions related to actual production processes but, interestingly, the latter affects supply chain performance much more than the former. Research limitations/implications: The research could benefit from a) a longitudinal rather than cross-sectional approach, b) incorporating multiple respondents such as representatives of supply chain partners and senior management of the focal firm, to capture potentially varying opinions on the supply chain phenomena under examination. Practical implications: The results can assist supply chain decision-makers in understanding the importance of behavioural closeness between supply chain partners for the development of collaborative supply chain relationships that lead to higher integration and superior performance. Insight is provided on linkages between examined dimensions of supply chain integration. A process view of intermediate steps needed to translate collaborative relationships into higher supply chain integration and performance across the supply chain is offered. Originality/value: The development and testing of an integrated model examining linkages between supply chain relationship antecedents, integration and performance is an original contribution. By proposing and confirming a sequential order in the influence of behavioural antecedents, integration dimensions, and their impact on supply chain performance, the paper sets foundations of a roadmap for achieving higher supply chain performance from collaborative supply chain relationships. Finally, the paper contributes to the limited theoretical justification on the development of knowledge for assisting decision-making in SCM/logistics and its integration into models, processes and tasks

An integrated optimization-simulation framework for vehicle and personnel relocations of electric carsharing systems with reservations

One-way electric vehicle carsharing systems are receiving increasing attention due to their mobility, environmental, and societal benefits. One of the major issues faced by the operators of these systems is the optimization of the relocation operations of personnel and vehicles. These relocation operations are essential in order to ensure that vehicles are available for use at the right place at the right time. Vehicle availability is a key indicator expressing the level of service offered to customers. However, the relocation operations, that ensure this availability, constitute a major cost component for the provision of these services. Therefore, clearly there is a trade-off between the cost of vehicle and personnel relocation and the level of service offered. In this paper we are developing, solving, and applying, in a real world context, an integrated multi-objective mixed integer linear programming (MMILP) optimization and discrete event simulation framework to optimize operational decisions for vehicle and personnel relocation in a carsharing system with reservations. We are using a clustering procedure to cope with the dimensionality of the operational problem without compromising on the quality of the obtained results. The optimization framework involves three mathematical models: (i) station clustering, (ii) operations optimization and (iii) personnel flow. The output of the optimization is used by the simulation in order to test the feasibility of the optimization outcome in terms of vehicle recharging requirements. The optimization model is solved iteratively considering the new constraints restricting the vehicles that require further charging to stay in the station until the results of the simulation are feasible in terms of electric vehicles’ battery charging levels. The application of the proposed framework using data from a real world system operating in Nice, France sheds light to trade-offs existing between the level of service offered, resource utilization, and certainty of fulfilling a trip reservation

Resource allocation in congested queueing systems with time-varying demand:An application to airport operations

Motivated by the need to develop time-efficient methods for minimizing operational delays at severely congested airports, we consider a problem involving the distribution of a common resource between two sources of time-varying demand. We formulate this as a dynamic program in which the objective is based on second moments of stochastic queue lengths and show that, for sufficiently high volumes of demand, optimal values can be well-approximated by quadratic functions of the system state. We identify conditions which enable the strong performance of myopic policies and develop approaches to the design of heuristic policies by means of approximate dynamic programming (ADP) methods. Numerical experiments suggest that our ADP-based heuristics, which require very little computational effort, are able to improve substantially upon the performances of more naive decision-making policies, particularly if exogenous system parameters vary considerably as functions of time

Incorporating Stakeholders’ priorities and preferences in 4D trajectory optimization

A key feature of trajectory based operations (TBO) -- a new concept developed to modernize the air traffic system -- is the inclusion of preferences and priorities of the air traffic management (ATM) stakeholders. In this paper, we present a new mathematical model to optimize flights' 4D-trajectories. This is a multi-objective binary integer programming (IP) model, which assigns a 4D-trajectory to each flight, while explicitly modeling priorities and highlighting the trade off involved with the Airspace Users (AUs) preferences. The scope of the model (to be used at pre-tactical level) is the computation of optimal 4D pre-departure trajectory for each flight to be shared or negotiated with other stakeholders and subsequently managed throughout the flight. These trajectories are obtained by minimising the deviation (delay and re-routing) from the original preferred 4D-trajectories as well as minimizing the air navigation service (ANS) charges subject to the constraints of the system. Computational results for the model are presented, which show that the proposed model has the ability to identify trade-offs between the objectives of the stakeholders of the ATM system under the TBO concept. This can therefore provide the ATM stakeholders with useful decision tools to choose a trajectory for each flight