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Simulation-Optimization in Logistics, Transportation, and SCM

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A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: closed (15 November 2020) | Viewed by 50091

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Prof. Dr. Angel A. Juan

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Department of Applied Statistics and Operations Research, Universitat Politècnica de València, 03801 Alcoy, Spain
Interests: metaheuristics; simulation; machine learning; smart cities
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Prof. Dr. Markus Rabe

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Department of IT in Production and Logistics, TU Dortmund University, 44227 Dortmund, Germany
Interests: computer simulation; logistics assistance systems; industrial IT systems; business processes; intralogistics; supply chains; urban last-mile delivery; data farming and data analysis
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Prof. Dr. David Goldsman

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Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, GA 30332, USA
Interests: simulation output analysis, statistical ranking and selection methods, and medical and humanitarian applications of operations research
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Prof. Dr. Javier Faulin

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Operations Research and Statistics at Institute of Smart Cities, Public University of Navarre, Pamplona, Spain
Interests: smart cities; transportation; vehicle routing problems; logistics; simheuristics
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Special Issue Information

Dear Colleagues,

Transportation, logistics, and supply chain systems and networks constitute one of the pillars of modern economies and societies. From sustainable traffic management in smart cities or air transportation to green and socially responsible logistics practices, many enterprises and governments around the world have to make decisions that affect the efficiency of these complex systems. Typically, optimization algorithms are employed to deal with these challenges, and simulation approaches are utilized when considering scenarios under uncertainty. However, better results might be achieved by hybridizing both optimization algorithms with simulation techniques to deal with real-life transportation, logistics, and SCM challenges, which often are large-scale and NP-hard problems under uncertainty conditions. Hence, simheuristic algorithms (combining metaheuristics with simulation) as well as other simulation-optimization approaches constitute an effective way to support decision makers in such complex scenarios.

This Special Issue aims at presenting a collection of high-quality papers on simulation-optimization in transportation, logistics, and supply chain management. Simulation-optimization algorithms, including simheuristics and simulation-based optimization, and their practical applications in the solving of rich and realistic scenarios under uncertainty are welcome. The Special Issue is open to well-known researchers in these topics. In particular, this Special Issue is strongly connected to the topics covered in the Winter Simulation Conference (WSC) track on logistics, transportation, and SCM, which includes a stream in simheuristic algorithms as well. Extended versions of the best papers presented at the WSC’19 and WSC’20 (as well as at other conferences of similar quality) are also invited.

Prof. Dr. Angel A. Juan
Prof. Dr. Markus Rabe
Prof. Dr. David Goldsman
Prof. Dr. Javier Faulin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Simulation
Heuristics and metaheuristics
Simheuristics
Transportation
Logistics
Supply chain management
Smart cities
Intelligent transportation systems
Sustainable transportation and logistics
Simulation-based optimization
Machine learning
Learnheuristics
Biased-randomized algorithms

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Published Papers (12 papers)

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20 pages, 2027 KiB

Open AccessArticle

Modeling and Optimization in Resource Sharing Systems: Application to Bike-Sharing with Unequal Demands

by Xiaoting Mo, Xinglu Liu and Wai Kin (Victor) Chan

Algorithms 2021, 14(2), 47; https://doi.org/10.3390/a14020047 - 30 Jan 2021

Cited by 5 | Viewed by 3203

Abstract

The imbalanced distribution of shared bikes in the dockless bike-sharing system (a typical example of the resource-sharing system), which may lead to potential customer churn and lost profit, gradually becomes a vital problem for bike-sharing firms and their users. To resolve the problem, we first formulate the bike-sharing system as a Markovian queueing network with higher-demand nodes and lower-demand nodes, which can provide steady-state probabilities of having a certain number of bikes at one node. A model reduction method is then designed to reduce the complexity of the proposed model. Subsequently, we adopt an operator-based relocation strategy to optimize the reduced network. The objective of the optimization model is to maximize the total profit and act as a decision-making tool for operators to determine the optimal relocation frequency. The results reveal that it is possible for most of the shared bikes to gather at one low-demand node eventually in the long run under the influence of the various arrival rates at different nodes. However, the decrease of the number of bikes at the high-demand nodes is more sensitive to the unequal demands, especially when the size of the network and the number of bikes in the system are large. It may cause a significant loss for operators, to which they should pay attention. Meanwhile, different estimated values of parameters related with revenue and cost affect the optimization results differently. Full article

(This article belongs to the Special Issue Simulation-Optimization in Logistics, Transportation, and SCM)

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23 pages, 500 KiB

Open AccessArticle

Combining Heuristics with Simulation and Fuzzy Logic to Solve a Flexible-Size Location Routing Problem under Uncertainty

by Rafael D. Tordecilla, Pedro J. Copado-Méndez, Javier Panadero, Carlos L. Quintero-Araujo, Jairo R. Montoya-Torres and Angel A. Juan

Algorithms 2021, 14(2), 45; https://doi.org/10.3390/a14020045 - 30 Jan 2021

Cited by 9 | Viewed by 4465

Abstract

The location routing problem integrates both a facility location and a vehicle routing problem. Each of these problems are NP-hard in nature, which justifies the use of heuristic-based algorithms when dealing with large-scale instances that need to be solved in reasonable computing times. This paper discusses a realistic variant of the problem that considers facilities of different sizes and two types of uncertainty conditions. In particular, we assume that some customers’ demands are stochastic, while others follow a fuzzy pattern. An iterated local search metaheuristic is integrated with simulation and fuzzy logic to solve the aforementioned problem, and a series of computational experiments are run to illustrate the potential of the proposed algorithm. Full article

(This article belongs to the Special Issue Simulation-Optimization in Logistics, Transportation, and SCM)

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21 pages, 1631 KiB

Open AccessArticle

Integrated Simulation-Based Optimization of Operational Decisions at Container Terminals

by Marvin Kastner, Nicole Nellen, Anne Schwientek and Carlos Jahn

Algorithms 2021, 14(2), 42; https://doi.org/10.3390/a14020042 - 28 Jan 2021

Cited by 11 | Viewed by 3933

Abstract

At container terminals, many cargo handling processes are interconnected and occur in parallel. Within short time windows, many operational decisions need to be made and should consider both time efficiency and equipment utilization. During operation, many sources of disturbance and, thus, uncertainty exist. For these reasons, perfectly coordinated processes can potentially unravel. This study analyzes simulation-based optimization, an approach that considers uncertainty by means of simulation while optimizing a given objective. The developed procedure simultaneously scales the amount of utilized equipment and adjusts the selection and tuning of operational policies. Thus, the benefits of a simulation study and an integrated optimization framework are combined in a new way. Four meta-heuristics—Tree-structured Parzen Estimator, Bayesian Optimization, Simulated Annealing, and Random Search—guide the simulation-based optimization process. Thus, this study aims to determine a favorable configuration of equipment quantity and operational policies for container terminals using a small number of experiments and, simultaneously, to empirically compare the chosen meta-heuristics including the reproducibility of the optimization runs. The results show that simulation-based optimization is suitable for identifying the amount of required equipment and well-performing policies. Among the presented scenarios, no clear ranking between meta-heuristics regarding the solution quality exists. The approximated optima suggest that pooling yard trucks and a yard block assignment that is close to the quay crane are preferable. Full article

(This article belongs to the Special Issue Simulation-Optimization in Logistics, Transportation, and SCM)

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18 pages, 913 KiB

Open AccessArticle

Simulation-Optimization Approach for Multi-Period Facility Location Problems with Forecasted and Random Demands in a Last-Mile Logistics Application

by Markus Rabe, Jesus Gonzalez-Feliu, Jorge Chicaiza-Vaca and Rafael D. Tordecilla

Algorithms 2021, 14(2), 41; https://doi.org/10.3390/a14020041 - 28 Jan 2021

Cited by 34 | Viewed by 5183

Abstract

The introduction of automated parcel locker (APL) systems is one possible approach to improve urban logistics (UL) activities. Based on the city of Dortmund as case study, we propose a simulation-optimization approach integrating a system dynamics simulation model (SDSM) with a multi-period capacitated facility location problem (CFLP). We propose this integrated model as a decision support tool for future APL implementations as a last-mile distribution scheme. First, we built a causal-loop and stock-flow diagram to show main components and interdependencies of the APL systems. Then, we formulated a multi-period CFLP model to determine the optimal number of APLs for each period. Finally, we used a Monte Carlo simulation to estimate the costs and reliability level with random demands. We evaluate three e-shopper rate scenarios with the SDSM, and then analyze ten detailed demand configurations based on the results for the middle-size scenario with our CFLP model. After 36 months, the number of APLs increases from 99 to 165 with the growing demand, and stabilizes in all configurations from month 24. A middle-demand configuration, which has total costs of about

750, 000

€, already locates a suitable number of APLs. If the budget is lower, our approach offers alternatives for decision-makers. Full article

(This article belongs to the Special Issue Simulation-Optimization in Logistics, Transportation, and SCM)

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23 pages, 1088 KiB

Open AccessArticle

Simheuristics Approaches for Efficient Decision-Making Support in Materials Trading Networks

by Markus Rabe, Majsa Ammouriova, Dominik Schmitt and Felix Dross

Algorithms 2021, 14(1), 23; https://doi.org/10.3390/a14010023 - 14 Jan 2021

Cited by 4 | Viewed by 3075

Abstract

The distribution process in business-to-business materials trading is among the most complex and in transparent ones within logistics. The highly volatile environment requires continuous adaptations by the responsible decision-makers, who face a substantial number of potential improvement actions with conflicting goals, such as simultaneously maintaining a high service level and low costs. Simulation-optimisation approaches have been proposed in this context, for example based on evolutionary algorithms. But, on real-world system dimensions, they face impractically long computation times. This paper addresses this challenge in two principal streams. On the one hand, reinforcement learning is investigated to reduce the response time of the system in a concrete decision situation. On the other hand, domain-specific information and defining equivalent solutions are exploited to support a metaheuristic algorithm. For these approaches, we have developed suitable implementations and evaluated them with subsets of real-world data. The results demonstrate that reinforcement learning exploits the idle time between decision situations to learn which decisions might be most promising, thus adding computation time but significantly reducing the response time. Using domain-specific information reduces the number of required simulation runs and guides the search for promising actions. In our experimentation, defining equivalent solutions decreased the number of required simulation runs up to 15%. Full article

(This article belongs to the Special Issue Simulation-Optimization in Logistics, Transportation, and SCM)

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22 pages, 2298 KiB

Open AccessArticle

Urban e-Grocery Distribution Design in Pamplona (Spain) Applying an Agent-Based Simulation Model with Horizontal Cooperation Scenarios

by Adrian Serrano-Hernandez, Rocio de la Torre, Luis Cadarso and Javier Faulin

Algorithms 2021, 14(1), 20; https://doi.org/10.3390/a14010020 - 12 Jan 2021

Cited by 7 | Viewed by 4201

Abstract

E-commerce has boosted in the last decades because of the achievements of the information and telecommunications technology along with the changes in the society life-style. More recently, the groceries online purchase (or e-grocery), has also prevailed as a way of making the weekly shopping, particularly, the one including fresh vegetables and fruit. Furthermore, this type of virtual shopping in supermarkets is gaining importance as the most efficient delivery system in cost and time. Thus, we have evaluated in this study the influence of the cooperation-based policies on costs and service quality among different supermarkets in Pamplona, Spain. Concerning methodology, first of all, we carried out a survey in Pamplona having the purpose of modelling the demand patterns about e-grocery. Second, we have developed an agent-based simulation model for generating scenarios in non-cooperative, limited cooperation, and full cooperation settings, considering the real data obtained from the survey analysis. At this manner, Vehicle Routing Problems (VRP) and Multi Depot VRPs (MDVRP) are dynamically generated and solved within the simulation framework using a biased-randomization algorithm. Finally, the results show significant reductions in distance driven and lead times when employing horizontal cooperation in e-grocery distribution. Full article

(This article belongs to the Special Issue Simulation-Optimization in Logistics, Transportation, and SCM)

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20 pages, 1098 KiB

Open AccessArticle

An Algorithm for Efficient Generation of Customized Priority Rules for Production Control in Project Manufacturing with Stochastic Job Processing Times

by Mathias Kühn, Michael Völker and Thorsten Schmidt

Algorithms 2020, 13(12), 337; https://doi.org/10.3390/a13120337 - 13 Dec 2020

Cited by 7 | Viewed by 3156

Abstract

Project Planning and Control (PPC) problems with stochastic job processing times belong to the problem class of Stochastic Resource-Constrained Multi-Project Scheduling Problems (SRCMPSP). A practical example of this problem class is the industrial domain of customer-specific assembly of complex products. PPC approaches have to compensate stochastic influences and achieve high objective fulfillment. This paper presents an efficient simulation-based optimization approach to generate Combined Priority Rules (CPRs) for determining the next job in short-term production control. The objective is to minimize project-specific objectives such as average and standard deviation of project delay or makespan. For this, we generate project-specific CPRs and evaluate the results with the Pareto dominance concept. However, generating CPRs considering stochastic influences is computationally intensive. To tackle this problem, we developed a 2-phase algorithm by first learning the algorithm with deterministic data and by generating promising starting solutions for the more computationally intensive stochastic phase. Since a good deterministic solution does not always lead to a good stochastic solution, we introduced the parameter Initial Copy Rate (ICR) to generate an initial population of copied and randomized individuals. Evaluating this approach, we conducted various computer-based experiments. Compared to Standard Priority Rules (SPRs) used in practice, the approach shows a higher objective fulfilment. The 2-phase algorithm can reduce the computation effort and increases the efficiency of generating CPRs. Full article

(This article belongs to the Special Issue Simulation-Optimization in Logistics, Transportation, and SCM)

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20 pages, 2960 KiB

Open AccessArticle

Applying Neural Networks in Aerial Vehicle Guidance to Simplify Navigation Systems

by Raúl de Celis, Pablo Solano and Luis Cadarso

Algorithms 2020, 13(12), 333; https://doi.org/10.3390/a13120333 - 11 Dec 2020

Cited by 2 | Viewed by 2702

Abstract

The Guidance, Navigation and Control (GNC) of air and space vehicles has been one of the spearheads of research in the aerospace field in recent times. Using Global Navigation Satellite Systems (GNSS) and inertial navigation systems, accuracy may be detached from range. However, these sensor-based GNC systems may cause significant errors in determining attitude and position. These effects can be ameliorated using additional sensors, independent of cumulative errors. The quadrant photodetector semiactive laser is a good candidate for such a purpose. However, GNC systems’ development and construction costs are high. Reducing costs, while maintaining safety and accuracy standards, is key for development in aerospace engineering. Advanced algorithms for getting such standards while eliminating sensors are cornerstone. The development and application of machine learning techniques to GNC poses an innovative path for reducing complexity and costs. Here, a new nonlinear hybridization algorithm, which is based on neural networks, to estimate the gravity vector is presented. Using a neural network means that once it is trained, the physical-mathematical foundations of flight are not relevant; it is the network that returns dynamics to be fed to the GNC algorithm. The gravity vector, which can be accurately predicted, is used to determine vehicle attitude without calling for gyroscopes. Nonlinear simulations based on real flight dynamics are used to train the neural networks. Then, the approach is tested and simulated together with a GNC system. Monte Carlo analysis is conducted to determine performance when uncertainty arises. Simulation results prove that the performance of the presented approach is robust and precise in a six-degree-of-freedom simulation environment. Full article

(This article belongs to the Special Issue Simulation-Optimization in Logistics, Transportation, and SCM)

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16 pages, 1038 KiB

Open AccessArticle

A Simulation-Based Optimization Method for Warehouse Worker Assignment

by Odkhishig Ganbold, Kaustav Kundu, Haobin Li and Wei Zhang

Algorithms 2020, 13(12), 326; https://doi.org/10.3390/a13120326 - 4 Dec 2020

Cited by 16 | Viewed by 5747

Abstract

The general assignment problem is a classical NP-hard (non-deterministic polynomial-time) problem. In a warehouse, the constraints on the equipment and the characteristics of consecutive processes make it even more complicated. To overcome the difficulty in calculating the benefit of an assignment and in finding the optimal assignment plan, a simulation-based optimization method is introduced. We first built a simulation model of the warehouse with the object-oriented discrete-event simulation (O2DES) framework, and then implemented a random neighborhood search method utilizing the simulation output. With this method, the throughput and service level of the warehouse can be improved, while keeping the number of workers constant. Numerical results with real data demonstrate the reduction of discrepancy between inbound and outbound service level performance. With a less than

10 %

reduction in inbound service level, we can achieve an over

30 %

increase in outbound service level. The proposed decision support tool assists the warehouse manager in dealing with warehouse worker allocation problem under conditions of random daily workload. Full article

(This article belongs to the Special Issue Simulation-Optimization in Logistics, Transportation, and SCM)

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22 pages, 2180 KiB

Open AccessArticle

Combining Optimization and Simulation for Designing a Robust Short-Sea Feeder Network

by Carl Axel Benjamin Medbøen, Magnus Bolstad Holm, Mohamed Kais Msakni, Kjetil Fagerholt and Peter Schütz

Algorithms 2020, 13(11), 304; https://doi.org/10.3390/a13110304 - 20 Nov 2020

Cited by 9 | Viewed by 2975

Abstract

Here we study a short-sea feeder network design problem based on mother and daughter vessels. The main feature of the studied system is performing transshipment of cargo between mother and daughter vessels at appropriate locations at sea. This operation requires synchronization between both types of vessels as they have to meet at the same location at the same time. This paper studies the problem of designing a synchronized feeder network, explicitly accounting for the effect of uncertain travel times caused by harsh weather conditions. We propose an optimization-simulation framework to find robust solutions for the transportation system. The optimization model finds optimal routes that are then evaluated by a discrete-even simulation model to measure their robustness under uncertain weather conditions. This process of optimization simulation is repeated until a satisfactory condition is reached. To find even better solutions, we include different performance-improving strategies by adding robustness during route generation or exploiting flexibility in sailing speed to recover from delays. We apply the solution method to a case based on realistic data from a Norwegian shipping company. The results show that the method finds near-optimal solutions that offer robustness against schedule perturbations due to harsh weather. They also highlight the importance of considering uncertainty when designing a short-sea feeder network with transshipment at sea. Full article

(This article belongs to the Special Issue Simulation-Optimization in Logistics, Transportation, and SCM)

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26 pages, 1742 KiB

Open AccessArticle

Scheduling Algorithms for a Hybrid Flow Shop under Uncertainty

by Christin Schumacher and Peter Buchholz

Algorithms 2020, 13(11), 277; https://doi.org/10.3390/a13110277 - 31 Oct 2020

Cited by 5 | Viewed by 4594

Abstract

In modern production systems, scheduling problems have to be solved in consideration of frequently changing demands and varying production parameters. This paper presents a approach combining forecasting and classification techniques to predict uncertainty from demands, and production data with heuristics, metaheuristics, and discrete event simulation for obtaining machine schedules. The problem is a hybrid flow shop with two stages, machine qualifications, skipping stages, and uncertainty in demands. The objective is to minimize the makespan. First, based on the available data of past orders, jobs that are prone to fluctuations just before or during the production phase are identified by clustering algorithms, and production volumes are adjusted accordingly. Furthermore, the distribution of scrap rates is estimated, and the quantiles of the resulting distribution are used to increase corresponding production volumes to prevent costly rescheduling resulting from unfulfilled demands. Second, Shortest Processing Time (SPT), tabu search, and local search algorithms are developed and applied. Third, the best performing schedules are evaluated and selected using a detailed simulation model. The proposed approach is validated on a real-world production case. The results show that the price for a very robust schedule that avoids underproduction with a high probability can significantly increase the makespan. Full article

(This article belongs to the Special Issue Simulation-Optimization in Logistics, Transportation, and SCM)

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22 pages, 750 KiB

Open AccessArticle

A Simheuristic Algorithm for Solving the Stochastic Omnichannel Vehicle Routing Problem with Pick-up and Delivery

by Leandro do C. Martins, Christopher Bayliss, Pedro J. Copado-Méndez, Javier Panadero and Angel A. Juan

Algorithms 2020, 13(9), 237; https://doi.org/10.3390/a13090237 - 19 Sep 2020

Cited by 5 | Viewed by 4581

Abstract

Advances in information and communication technologies have made possible the emergence of new shopping channels. The so-called ‘omnichannel’ retailing mode allows customers to shop for products online and receive them at home. This paper focuses on the omnichannel delivery concept for the retailing industry, which addresses the replenishment of a set of retail stores and the direct shipment of the products to customers within an integrated vehicle routing formulation. Due to its NP-Hardness, a constructive heuristic, which is extended into a biased-randomized heuristic and which is embedded into a multi-start procedure, is introduced for solving the large-sized instances of the problem. Next, the problem is enriched by considering a more realistic scenario in which travel times are modeled as random variables. For dealing with the stochastic version of the problem, a simheuristic algorithm is proposed. A series of computational experiments contribute to illustrate how our simheuristic can provide reliable and low-cost solutions under uncertain conditions. Full article

(This article belongs to the Special Issue Simulation-Optimization in Logistics, Transportation, and SCM)

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