1,720,973 research outputs found
Exact and heuristic solution approaches for the multi-objective AGV scheduling problem with battery constraints
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Exact and heuristic approaches for the Modal Shift Incentive Problem
Get PDFIt is widely acknowledged that freight transportation in general, and road freight transportation in particular, contributes significantly to greenhouse gas and pollutant emissions. In response, many countries have implemented incentive schemes to divert freight traffic from roads towards more environmentally friendly transportation modes (e.g., rail, maritime). However, incentive-based policies to promote modal shift are often sporadic and uncoordinated across non-road modes, thus preventing full leverage of dedicated budgets. In this study, we present a strategic problem encountered by national and supranational entities aiming to reduce emissions from the freight transportation sector, referred to as the Modal Shift Incentive Problem (MSIP). The problem involves designing incentive policies in the form of subsidies aimed at reducing the cost of eco-friendly transportation modes, with the aim of minimizing the volume of freight transported via less environmentally friendly modes. These incentive policies must satisfy various constraints, including a limited budget for incentives, equity requirements, and compliance with regulations pertaining to competitiveness and free-market dynamics. To address the MSIP, we propose an original mathematical formulation on the basis of a deterministic threshold modal choice model used to simulate the shipper behavior in selecting the transportation modes. Then, we develop a two-phase solution method, able to determine good solutions with a small computational burden based on the generation of a subset of promising incentive schemes. Finally, we solve real-world instances generated utilizing data derived from a comprehensive case study in Italy. The results on different instance types, in terms of number of shipments, budget, and costs, show the relationship between these factors, the complexity of the MSIP, and the modal shift
The Minimum Routing Cost Tree Problem State of the art and a core-node based heuristic algorithm
No full textThe minimum routing cost tree problem arises when we need to find the tree minimizing the minimum travel/communication cost, i.e., the tree which presents the minimal difference with the same cost computed on the whole network. This paper provides the state of the art of the problem and proposes a new heuristic based on the identification of a core of the network around which the solution can be built. The algorithm has been tested on literature instances of up to one thousand nodes. The results, compared with those of other heuristic algorithms, prove the competitiveness of the proposed one both in terms of the quality of the solution and computation time
A graph clustering based decomposition approach for large scale p-median problems
No full textThe p-median problem (PMP) is the well known network optimization problem of discrete location theory. In many real applications PMPs is defined on very large scale networks, for which ad-hoc exact and/or heuristic methods have to be developed. To this aim, in this work we propose a heuristic decomposition approach which exploits the decomposition of the network into disconnected components obtained by a graph clustering algorithm. Then, in each component several PMPs are solved for suitable ranges of p by a Lagrangian dual and simulated annealing based algorithm. The solution of the whole initial problem is obtained combining all the PMPs solutions through a multi-choice knapsack model. The proposed approach is tested using several graph clustering algorithms and compared with the results of the state-of-the-art heuristic methods
Multi-echelon facility location models for the reorganization of the Blood Supply Chain at regional scale
No full textBlood and blood products are crucial resources requiring effective management strategies and policies due to the potential severe consequences that could arise from their lack. Over the past two decades, the global healthcare community has recognized the significance of managing the Blood Supply Chain (BSC) efficiently and effectively. This includes policy-making, system design and organization. In this context, the Italian Healthcare Ministry issued a decree aimed at improving the BSC efficiency at regional level while reducing costs by providing several indications and restrictions to be accounted for. To address the need for improved BSC system management and design, we propose a mathematical modeling framework that builds upon and extends multi-echelon facility location and scenario-based mathematical models coming from literature, integrating soft constraints to achieve system aims with a multi-objective viewpoint. The proposed modeling framework has been implemented in two different perspectives: case-based and scenario-based. These two perspectives approaches are conceived to provide a comprehensive solution to the issue at hand, performing sensitivity analysis, and enabling the design of an efficient and effective BSC at the regional level, capable of handling inherent system uncertainty. To this aim, the proposed modeling framework comprises several objectives, including minimizing transportation costs, rationalizing the number and type of facilities, ensuring self-sufficiency, guaranteeing an average accessibility threshold, satisfying imposed restrictions and system constraints, and designing a system robust to varying exogenous and endogenous conditions. Real-world data sets were utilized to test and validate the proposed formulations. The obtained results demonstrate that they can be a valuable decision support tool for decision-makers, providing managerial insights and enabling the simulation of different system configurations
The parallel AGV scheduling problem with battery constraints: A new formulation and a matheuristic approach
No full textNowadays, automated guided vehicles (AGVs) are frequently used in larger systems, known as AGV-based transportation systems, for the movement of goods and materials from one location to another. The design of an efficient and effective AGV-based transportation system requires to address many tactical and operational issues. Among the others, the scheduling of transfer jobs on the AGVs represents one of the main operational issues that has to be solved to overcome delays in production and material handling processes. In this context, a significant research activity on AGV systems and on related scheduling problems has been conducted in the last twenty years. However, most of the contributions neglected the issues related to the AGV battery depletion and recharge. Thus, in this work, we study the AGV scheduling problem with battery constraints (ASP-BC). It consists in determining the scheduling of transfer jobs and charging operations of a fleet of homogeneous AGVs such that the makespan of the handling process is minimized. The methodological contribution of our work is twofold. On one side, we propose an original mixed integer linear programming formulation based on the bottleneck generalized assignment problem. On the other side, we propose a three step matheuristic based on the sequential solution of the two subproblems arising from the natural decomposition of the ASP-BC and a local search heuristic. The proposed approaches have been tested and validated on simulated and real instances provided by a manufacturing company. The results show the effectiveness and the scalability of the proposed solution methods
Integrated operating room planning and scheduling: an ILP-Based off-line approach for emergency responsiveness at a local hospital in Naples
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