A fast heuristic for routing in post-disaster humanitarian relief logistics

In the last decades, natural disasters have been affecting the human life of millions of people. The impressive scale of these disasters has pointed out the need for an effective management of the relief supply operations. One of the crucial issues in this context is the routing of vehicles carrying critical supplies and help to disaster victims. This problem poses unique logistics challenges, including damaged transportation infrastructure and limited knowledge on the road travel times. In such circumstances, selecting more reliable paths could help the rescue team to provide fast services to those in needs. The classic cost-minimizing routing problems do not properly reflect the relevant issue of the arrival time, which clearly has a serious impact on the survival rate of the affected community. In this paper, we focus specifically on the arrival time objective function in a multi-vehicle routing problem where stochastic travel times are taken into account. The considered problem should be solved promptly in the aftermath of a disaster, hence we propose a fast heuristic that could be applied to solve the problem

a fast heuristic for routing in post disaster humanitarian relief logistics

Abstract In the last decades, natural disasters have been affecting the human life of millions of people. The impressive scale of these disasters has pointed out the need for an effective management of the relief supply operations. One of the crucial issues in this context is the routing of vehicles carrying critical supplies and help to disaster victims. This problem poses unique logistics challenges, including damaged transportation infrastructure and limited knowledge on the road travel times. In such circumstances, selecting more reliable paths could help the rescue team to provide fast services to those in needs. The classic cost-minimizing routing problems do not properly reflect the relevant issue of the arrival time, which clearly has a serious impact on the survival rate of the affected community. In this paper, we focus specifically on the arrival time objective function in a multi-vehicle routing problem where stochastic travel times are taken into account. The considered problem should be solved promptly in the aftermath of a disaster, hence we propose a fast heuristic that could be applied to solve the problem

The multi-vehicle profitable pick up and delivery routing problem with uncertain travel times

Abstract This paper addresses a variant of the known selective pickup and delivery problem with time windows. In this problem, a fleet composed of several vehicles with a given capacity should satisfy a set of customers requests consisting in transporting goods from a supplier (pickup location) to a customer (delivery location). The selective aspect consists in choosing the customers to be served on the basis of the profit collected for the service. Motivated by urban settings, wherein road congestion is an important issue, in this paper, we address the profitable pickup and delivery problem with time windows with uncertain travel times. The problem under this assumption, becomes much more involved. The goal is to find the solution that maximizes the net profit, expressed as the difference between the collected revenue, the route cost and the cost associated to the violation the time windows. This study introduces the problem and develops a solution approach to solve it. Very preliminary tests are performed in order to show the efficiency of developed method to cope with the problem at hand

The multi-depot k-traveling repairman problem

In this paper, we study the multi-depot k-traveling repairman problem. This problem extends the traditional traveling repairman problem to the multi-depot case. Its objective, similar to the single depot variant, is the minimization of the sum of the arrival times to customers. We propose two distinct formulations to model the problem, obtained on layered graphs. In order to find feasible solutions for the largest instances, we propose a hybrid genetic algorithm where initial solutions are built using a splitting heuristic and a local search is embedded into the genetic algorithm. The efficiency of the mathematical formulations and of the solution approach are investigated through computational experiments. The proposed models are scalable enough to solve instances up to 240 customers

Variable Neighborhood Descent Matheuristic for the Drone Routing Problem with Beehives Sharing

In contemporary urban logistics, drones will become a preferred transportation mode for last-mile deliveries, as they have shown commercial potential and triple-bottom-line performance. Drones, in fact, address many challenges related to congestion and emissions and can streamline the last leg of the supply chain, while maintaining economic performance. Despite the common conviction that drones will reshape the future of deliveries, numerous hurdles prevent practical implementation of this futuristic vision. The sharing economy, referred to as a collaborative business model that foster sharing, exchanging and renting resources, could lead to operational improvements and enhance the cost control ability and the flexibility of companies using drones. For instance, the Amazon patent for drone beehives, which are fulfilment centers where drones can be restocked before flying out again for another delivery, could be established as a shared delivery systems where different freight carriers jointly deliver goods to customers. Only a few studies have addressed the problem of operating such facilities providing services to retail companies. In this paper, we formulate the problem as a deterministic location-routing model and derive its robust counterpart under the travel time uncertainty. To tackle the computational complexity of the model caused by the non-linear energy consumption rates in drone battery, we propose a tailored matheuristic combining variable neighborhood descent with a cut generation approach. The computational experiments show the efficiency of the solution approach especially compared to the Gurobi solver

Supply chain coordination contracts with free replacement warranty

This thesis investigates a coordination mechanism for a supply chain with one manufacturer and one retailer in a single period, single product newsvendor model. It looks beyond the conventional supply chain coordination problem by incorporating a specific form of warranties. The manufacturer provides a free replacement warranty in case of product failure within a specified after-sale interval. We assume that the expected value of stochastic market demand is an increasing function of this warranty period length. The supply chain is coordinated if its optimal actions (production quantity and warranty length) are realized while each party maximizes its own respective profit. Any deviation by either party from the terms of a coordinated contract cannot improve its performance.We consider different types of contracts between the two parties: a wholesale price only or a revenue sharing contract with shared warranty costs or such costs borne by the manufacturer alone. The manufacturer decides the warranty period, K, and other contract parameters, such as the wholesale price, shares of revenue, and warranty cost sharing arrangements. The retailer accepts the contract and determines the order amount, as long as it is able to make positive profit. The manufacturer then produces and delivers the order quantity for the selling season. Each party makes its decisions to maximize its own profit, hence the realized decisions may differ from the supply chain’s optimal solutions, if the contract is not coordinated. Thus, we examine whether the supply chain can be coordinated under each type of contract outlined above. For coordinated contracts we focus on the issue of profit allocation. If a contract type is non-coordinating, we attempt to highlight the factors that affect its efficiency, where the efficiency of a non-coordinating contract is defined as the ratio of realized supply chain profit over its optimal profit.The results obtained from this research leads to some interesting managerial insights. Under the wholesale price only contract types, we find that even if the retailer is willing to share the warranty fulfillment costs with the manufacturer, the resulting supply chain profit is less than the optimal value, leading to suboptimal performance. Under a revenue sharing contract, however, the production/ order quantity and the warranty length are coordinated, if the warranty costs are shared by the two parties in the same proportion as the profits. The profit allocation of each party under coordination is flexible from 0 to 100% of chain profit. This concept is illustrated by a numerical example of additive demand case followed by an extensive sensitivity analysis, which leads to some important insight.The major contribution of this thesis is its novel aspect of considering warranty period optimization towards supply chain coordination. We provide the guidelines for designing a contract between a manufacturer and a retailer so that the supply chain’s performance is optimized in terms of the production/ order quantity and the warranty period, while each party in the chain achieves its maximal profit. Without the presence of a third party, the contract coordinates the supply chain with less cost. A non-coordinating contract may still be desirable if it entails relatively low administrative cost while achieving a high degree of efficiency, as defined before. The concepts developed here are easy to implement in real world supply chain, and can provide valuable insights into more complex types of supply chain contracts.Ph.D., Decision Sciences -- Drexel University, 200

Applied (Meta)-Heuristic in Intelligent Systems

Engineering and business problems are becoming increasingly difficult to solve due to the new economics triggered by big data, artificial intelligence, and the internet of things. Exact algorithms and heuristics are insufficient for solving such large and unstructured problems; instead, metaheuristic algorithms have emerged as the prevailing methods. A generic metaheuristic framework guides the course of search trajectories beyond local optimality, thus overcoming the limitations of traditional computation methods. The application of modern metaheuristics ranges from unmanned aerial and ground surface vehicles, unmanned factories, resource-constrained production, and humanoids to green logistics, renewable energy, circular economy, agricultural technology, environmental protection, finance technology, and the entertainment industry. This Special Issue presents high-quality papers proposing modern metaheuristics in intelligent systems

Merchant mothers and fishermen fathers : parental investment and subsistence work among the boat-dwelling Shodagor of rural Bangladesh

This dissertation addresses three general research questions. First, what are the socioecological conditions that lead Shodagor families to employ particular strategies in order to balance subsistence work and childcare? Second, why do Shodagor men and women pursue particular occupational strategies that are cross-culturally unusual while others divide labor in ways that are more in line with other societies? And third, how do Shodagor subsistence and childcare strategies, all of which are concentrated within the nuclear family and influenced by the constraints of the Shodagor socioecology, influence nutritional outcomes for children and parents? I find that the concentration of resource sharing and childcare duties within the nuclear family is associated with husbands and wives cooperating in order to fulfill a family's subsistence and childcare needs. I also find that specific aspects of the ecology -- how far an individual lives from a major market, and how far he or she lives from the Meghna River -- as well as a family's childcare needs play key roles in determining the specific strategies families will employ in order to meet those needs. Finally, I show that while some factors concentrated within the nuclear family influence Shodagor health outcomes within the family in accordance with theoretical and cross-cultural predictions, others do not. These findings have implications for human behavioral ecological theory, which are discussed