Aljoscha Gruler

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

Waste collection is one of the most critical logistics activities in modern cities with considerable impact on the quality of life, urban environment, city attractiveness, traffic flows and municipal budgets. Despite the problem’s relevance, most existing work addresses simplified versions where container loads are considered to be known in advance and served by a single vehicle depot. Waste levels, however, cannot be estimated with complete certainty as they are only revealed at collection. Furthermore, in large cities and clustered urban areas, multiple depots from which collection routes originate are common, although cooperation among vehicles from different depots is rarely considered. This paper analyses a rich version of the waste collection problem with multiple depots and stochastic demands by proposing a hybrid algorithm combining metaheuristics with simulation. Our ‘simheuristic’ approach allows for studying the effects of cooperation among different depots, thus quantifying the potential savings this cooperation could provide to city governments and waste collection companies.

Waste collection under uncertainty: a simheuristic based on variable neighbourhood search

Ongoing population growth in cities and increasing waste production has made the optimisation of urban waste management a critical task for local governments. Route planning in waste collection can be formulated as an extended version of the well-known vehicle routing problem, for which a wide range of solution methods already exist. Despite the fact that real-life applications are characterised by high uncertainty levels, most works on waste collection assume deterministic inputs. In order to partially close this literature gap, this paper first proposes a competitive metaheuristic algorithm based on a variable neighbourhood search framework for the deterministic waste collection problem. Then, this metaheuristic is extended to a simheuristic algorithm in order to deal with the stochastic problem version. This extension is achieved by integrating simulation into the metaheuristic framework, which also allows a closer risk analysis of the best-found stochastic solutions. Different computational experiments illustrate the potential of our methodology. [Received: 13 January 2016; Revised: 25 April 2016; Revised: 19 September 2016; Revised: 18 October 2016; Accepted: 25 October 2016]

Quantifying Potential Benefits of Horizontal Cooperation in Urban Transportation Under Uncertainty: A Simheuristic Approach

Horizontal Cooperation (HC) in transportation activities has the potential to decrease supply chain costs and the environmental impact of delivery vehicles related to greenhouse gas emissions and noise. Especially in urban areas the sharing of information and facilities among members of the same supply chain level promises to be an innovative transportation concept. This paper discusses the potential benefits of HC in supply chains with stochastic demands by applying a simheuristic approach. For this, we integrate Monte Carlo Simulation into a metaheuristic process based on Iterated Local Search and Biased Randomization. A non-cooperative scenario is compared to its cooperative counterpart which is formulated as multi-depot Vehicle Routing Problem with stochastic demands (MDVRPSD).

A biased-randomized heuristic for the waste collection problem in smart cities

This paper describes an efficient heuristic to solve the Waste Collection Problem (WCP), which is formulated as a special instance of the well-known Vehicle Routing Problem (VRP). Our approach makes use of a biased-randomized version of a savings-based heuristic. The proposed procedure is tested against a set of benchmark instances, obtaining competitive results.

Combining variable neighborhood search with simulation for the inventory routing problem with stochastic demands and stock-outs

Abstract Vendor managed inventory aims at reducing supply chain costs by centralizing inventory management and vehicle routing decisions. This integrated supply chain approach results in a complex combinatorial optimization problem known as the inventory routing problem (IRP). This paper presents a variable neighborhood search metaheuristic hybridized with simulation to solve the IRP under demand uncertainty. Our simheuristic approach is able to solve large sized instances for the single period IRP with stochastic demands and stock-outs in very short computing times. A range of experiments underline the algorithm’s competitiveness compared to previously used heuristic approaches. The results are analyzed in order to provide closer managerial insights.

Using simheuristics to promote horizontal collaboration in stochastic city logistics

This paper analyzes the role of horizontal collaboration (HC) concepts in urban freight transportation under uncertainty scenarios. The paper employs different stochastic variants of the well-known vehicle routing problem (VRP) in order to contrast a non-collaborative scenario with a collaborative one. This comparison allows us to illustrate the benefits of using HC strategies in realistic urban environments characterized by uncertainty in factors such as customers’ demands or traveling times. In order to deal with these stochastic variants of the VRP, a simheuristic algorithm is proposed. Our approach integrates Monte Carlo simulation inside a metaheuristic framework. Some computational experiments contribute to quantify the potential gains that can be obtained by the use of HC practices in modern city logistics.

Using horizontal cooperation concepts in integrated routing and facility-location decisions

In a global and competitive economy, efficient supply networks are essential for modern enterprises. Horizontal cooperation (HC) concepts represent a promising strategy to increase the performance of supply chains. HC is based on sharing resources and making joint decisions among different agents at the same level of the supply chain. This paper analyzes different cooperation scenarios concerning integrated routing and facility-location decisions in road transportation: (a) a noncooperative scenario in which all decisions are individually taken (each enterprise addresses its own vehicle routing problem [VRP]); (b) a semicooperative scenario in which route-planning decisions are jointly taken (facilities and fleets are shared and enterprises face a joint multidepot VRP); and (c) a fully cooperative scenario in which route-planning and facility-location decisions are jointly taken (also customers are shared, and thus enterprises face a general location routing problem). Our analysis explores how this increasing level of HC leads to a higher flexibility and, therefore, to a lower total distribution cost. A hybrid metaheuristic algorithm, combining biased randomization with a variable neighborhood search framework, is proposed to solve each scenario. This allows us to quantify the differences among these scenarios, both in terms of monetary and environmental costs. Our solving approach is tested on a range of benchmark instances, outperforming previously reported results.

Combining simulation with a GRASP metaheuristic for solving the permutation flow-shop problem with stochastic processing times

Greedy Randomized Adaptive Search Procedures (GRASP) are among the most popular metaheuristics for the solution of combinatorial optimization problems. While GRASP is a relatively simple and efficient framework to deal with deterministic problem settings, many real-life applications experience a high level of uncertainty concerning their input variables or even their optimization constraints. When properly combined with the right metaheuristic, simulation (in any of its variants) can be an effective way to cope with this uncertainty. In this paper, we present a simheuristic algorithm that integrates Monte Carlo simulation into a GRASP framework to solve the permutation flow shop problem (PFSP) with random processing times. The PFSP is a well-known problem in the supply chain management literature, but most of the existing work considers that processing times of tasks in machines are deterministic and known in advance, which in some real-life applications (e.g., project management) is an unrealistic assumption.

Optimizing Airline Crew Scheduling Using Biased Randomization: A Case Study

Various complex decision making problems are related to airline planning. In the competitive airline industry, efficient crew scheduling is hereby of major practical importance. This paper presents a metaheuristic approach based on biased randomization to tackle the challenging Crew Pairing Problem (CPP). The objective of the CPP is the establishment of flight pairings allowing for cost minimizing crew-flight assignments. Experiments are done using a real-life case with different constraints. The results show that our easy-to-use and fast algorithm reduces overall crew flying times and the necessary number of accompanying crews compared to the pairings currently applied by the company.

Enhancing and extending the classical GRASP framework with biased randomisation and simulation

Abstract Greedy Randomised Adaptive Search Procedure (GRASP) is one of the best-known metaheuristics to solve complex combinatorial optimisation problems (COPs). This paper proposes two extensions of the typical GRASP framework. On the one hand, applying biased randomisation techniques during the solution construction phase enhances the efficiency of the GRASP solving approach compared to the traditional use of a restricted candidate list. On the other hand, the inclusion of simulation at certain points of the GRASP framework constitutes an efficient simulation–optimisation approach that allows to solve stochastic versions of COPs. To show the effectiveness of these GRASP improvements and extensions, tests are run with both deterministic and stochastic problem settings related to flow shop scheduling, vehicle routing, and facility location.