In this paper, a new variant of the pickup and delivery problem with time windows (PDPTW) named the Fleet Size and Mix Pickup and Delivery Problem with Time Windows (FSMPDPTW) is addressed. This work is motivated by f...
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In this paper, a new variant of the pickup and delivery problem with time windows (PDPTW) named the Fleet Size and Mix Pickup and Delivery Problem with Time Windows (FSMPDPTW) is addressed. This work is motivated by fleet sizing for a daily route planning arising at a Hospital center. In fact, a fleet of heterogeneous rented vehicles is used every day to pick up goods to locations and to deliver it to other locations. The heterogeneous aspect of the fleet is in term of capacity, fixed cost and fuel mileage. The objective function is the minimization of the total fixed cost of vehicles used and the minimization of the total routing cost. A set partitioning model is proposed to model the problem, and an efficient columngenerationalgorithm is used to solve it. In order to test our method, we propose a new set of benchmarks based on Li and Lim's benchmark (altered Solomon's benchmark) for demands and from Lui and Shen's benchmark for types of vehicles. In the propounded column-generation algorithm, the pricing problem is divided in sub-problems such that each vehicle type have its own pricing problem. A mixed integer linear program is proposed to model and solve the pricing sub-problems. Regret heuristics are proposed to speed-up the resolution of pricing sub-problems. Computational experiments are done on 56 (with up to 100 customers) new proposed instances. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
The health and economic devastation caused by the COVID-19 pandemic has created a significant global humanitarian disaster. Pandemic response policies guided by geospatial approaches are appropriate additions to tradi...
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The health and economic devastation caused by the COVID-19 pandemic has created a significant global humanitarian disaster. Pandemic response policies guided by geospatial approaches are appropriate additions to traditional epidemiological responses when addressing this disaster. However, little is known about finding the optimal set of locations or jurisdictions to create policy coordination zones. In this study, we propose optimization models and algorithms to identify coordination communities based on the natural movement of people. To do so, we develop a mixed-integer quadratic-programming model to maximize the modularity of detected communities while ensuring that the jurisdictions within each community are contiguous. To solve the problem, we present a heuristic and a column-generation algorithm. Our computational experiments highlight the effectiveness of the models and algorithms in various instances. We also apply the proposed optimization-based solutions to identify coordination zones within North Carolina and South Carolina, two highly interconnected states in the U.S. Results of our case study show that the proposed model detects communities that are significantly better for coordinating pandemic related policies than the existing geopolitical boundaries. (c) 2022 Elsevier B.V. All rights reserved.
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