Supporting multi-depot and stochastic waste collection management in clustered urban areas via simulation–optimization

Gruler, Aljoscha; Fikar, Christian; Hirsch, Patrick; Contreras‐Bolton, Carlos

doi:10.1057/s41273-016-0002-4

Cited by 58 publications

(36 citation statements)

References 28 publications

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“…() combine discrete‐event simulation with a heuristic to enhance the allocation of computing resources in distributed networks over the Internet. Smart Cities : Gruler et al. (, ) analyze the waste collection problem in modern urban areas and propose a simheuristic algorithm to solve its stochastic variant. Finance : Panadero et al. () consider a project portfolio optimization problem under uncertainty conditions, and employ a simheuristic to support decision making in this context. Methodology : Grasas et al.…”

Section: Literature Reviewmentioning

confidence: 99%

A reactive simheuristic using online data for a real‐life inventory routing problem with stochastic demands

Raba

Estrada‐Moreno

Panadero

et al. 2020

Int Trans Operational Res

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In the context of a supply chain for the animal‐feed industry, this paper focuses on optimizing replenishment strategies for silos in multiple farms. Assuming that a supply chain is essentially a value chain, our work aims at narrowing this chasm and putting analytics into practice by identifying and quantifying improvements on specific stages of an animal‐feed supply chain. Motivated by a real‐life case, the paper analyses a rich multi‐period inventory routing problem with homogeneous fleet, stochastic demands, and maximum route length. After describing the problem and reviewing the related literature, we introduce a reactive heuristic, which is then extended into a biased‐randomized simheuristic. Our reactive approach is validated and tested using a series of adapted instances to explore the gap between the solutions it provides and the ones generated by existing nonreactive approaches.

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Section: Literature Reviewmentioning

confidence: 99%

A reactive simheuristic using online data for a real‐life inventory routing problem with stochastic demands

Raba

Estrada‐Moreno

Panadero

et al. 2020

Int Trans Operational Res

View full text Add to dashboard Cite

show abstract

“…At the same time, our approach had to be 'agile', in the sense that effective and efficient results were expected in a few minutes of computation, including aspects, such as the generation of the travel times for the daily set of customers. Based on our previous experience on related vehicle-routing optimization problems [3], we decided to develop a metaheuristic algorithm to solve the problem.…”

Section: Introductionmentioning

confidence: 99%

The Non-Smooth and Bi-Objective Team Orienteering Problem with Soft Constraints

et al. 2020

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In the classical team orienteering problem (TOP), a fixed fleet of vehicles is employed, each of them with a limited driving range. The manager has to decide about the subset of customers to visit, as well as the visiting order (routes). Each customer offers a different reward, which is gathered the first time that it is visited. The goal is then to maximize the total reward collected without exceeding the driving range constraint. This paper analyzes a more realistic version of the TOP in which the driving range limitation is considered as a soft constraint: every time that this range is exceeded, a penalty cost is triggered. This cost is modeled as a piece-wise function, which depends on factors such as the distance of the vehicle to the destination depot. As a result, the traditional reward-maximization objective becomes a non-smooth function. In addition, a second objective, regarding the design of balanced routing plans, is considered as well. A mathematical model for this non-smooth and bi-objective TOP is provided, and a biased-randomized algorithm is proposed as a solving approach.

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“…For instance, they have been used to solve different rich and realistic variants of the well-known vehicle routing problem (VRP), including the two-dimensional VRP [13], VRP variants with horizontal cooperation [14], multi-agent versions of the VRP [15], the location routing problem [16], the fleet mixed VRP with backhauls [17,18], the multi-period VRP [19], and even other versions of the multi-depot VRP [20]. BRAs have also been employed in solving other OPs, such as the single-round divisible load scheduling [21], the stochastic flow-shop scheduling [22], scheduling heterogeneous multi-round systems [23], the minimization of open stacks problem [24], the dynamic home service routing [25], waste collection management [26], or the maximum quasi-clique problem [27].…”

Section: Introductionmentioning

confidence: 99%

On the Use of Biased-Randomized Algorithms for Solving Non-Smooth Optimization Problems

et al. 2019

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Soft constraints are quite common in real-life applications. For example, in freight transportation, the fleet size can be enlarged by outsourcing part of the distribution service and some deliveries to customers can be postponed as well; in inventory management, it is possible to consider stock-outs generated by unexpected demands; and in manufacturing processes and project management, it is frequent that some deadlines cannot be met due to delays in critical steps of the supply chain. However, capacity-, size-, and time-related limitations are included in many optimization problems as hard constraints, while it would be usually more realistic to consider them as soft ones, i.e., they can be violated to some extent by incurring a penalty cost. Most of the times, this penalty cost will be nonlinear and even noncontinuous, which might transform the objective function into a non-smooth one. Despite its many practical applications, non-smooth optimization problems are quite challenging, especially when the underlying optimization problem is NP-hard in nature. In this paper, we propose the use of biased-randomized algorithms as an effective methodology to cope with NP-hard and non-smooth optimization problems in many practical applications. Biased-randomized algorithms extend constructive heuristics by introducing a nonuniform randomization pattern into them. Hence, they can be used to explore promising areas of the solution space without the limitations of gradient-based approaches, which assume the existence of smooth objective functions. Moreover, biased-randomized algorithms can be easily parallelized, thus employing short computing times while exploring a large number of promising regions. This paper discusses these concepts in detail, reviews existing work in different application areas, and highlights current trends and open research lines.

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Supporting multi-depot and stochastic waste collection management in clustered urban areas via simulation–optimization

Cited by 58 publications

References 28 publications

A reactive simheuristic using online data for a real‐life inventory routing problem with stochastic demands

A reactive simheuristic using online data for a real‐life inventory routing problem with stochastic demands

The Non-Smooth and Bi-Objective Team Orienteering Problem with Soft Constraints

On the Use of Biased-Randomized Algorithms for Solving Non-Smooth Optimization Problems

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