Gurkirat Wadhwa, Tushar Shankar Walunj, V. Kavitha
{"title":"Partition-Form Cooperative Games in Two-Echelon Supply Chains","authors":"Gurkirat Wadhwa, Tushar Shankar Walunj, V. Kavitha","doi":"10.5220/0012432600003639","DOIUrl":"https://doi.org/10.5220/0012432600003639","url":null,"abstract":"Competition and cooperation are inherent features of any multi-echelon supply chain. The interactions among the agents across the same echelon and that across various echelons influence the percolation of market demand across echelons. The agents may want to collaborate with others in pursuit of attracting higher demand and thereby improving their own revenue. We consider one supplier (at a higher echelon) and two manufacturers (at a lower echelon and facing the customers) and study the collaborations that are `stable'; the main differentiator from the existing studies in supply chain literature is the consideration of the following crucial aspect -- the revenue of any collaborative unit also depends upon the way the opponents collaborate. Such competitive scenarios can be modeled using what is known as partition form games. Our study reveals that the grand coalition is not stable when the product is essential and the customers buy it from any of the manufacturers without a preference. The supplier prefers to collaborate with only one manufacturer, the one stronger in terms of market power; further, such collaboration is stable only when the stronger manufacturer is significantly stronger. Interestingly, no stable collaborative arrangements exist when the two manufacturers are nearly equal in market power.","PeriodicalId":235376,"journal":{"name":"International Conference on Operations Research and Enterprise Systems","volume":"48 3-4","pages":"158-170"},"PeriodicalIF":0.0,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140510793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Simulation Study for the Comparison of Power Flow Models for a Line Distribution Network with Stochastic Load Demands","authors":"Mark Christianen, M. Vlasiou, B. Zwart","doi":"10.5220/0011670600003396","DOIUrl":"https://doi.org/10.5220/0011670600003396","url":null,"abstract":"We use simulation to compare different power flow models in the process of charging electric vehicles (EVs) by considering their random arrivals, their stochastic demand for energy at charging stations, and the characteristics of the electricity distribution network. We assume the distribution network is a line with charging stations located on it. We consider the Distflow and the Linearized Distflow power flow models and we assume that EVs arrive at the network with an exponential rate, have an exponential charging requirement, and that voltage drops on the distribution network stay under control. We provide extensive numerical results investigating the effect of using different power flow models on the performance of the network.","PeriodicalId":235376,"journal":{"name":"International Conference on Operations Research and Enterprise Systems","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114964459","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Robust Optimization","authors":"Jean-Philippe Vial","doi":"10.1515/9781400831050","DOIUrl":"https://doi.org/10.1515/9781400831050","url":null,"abstract":"Course aims What this course is This is a PhD course on robust optimization. The decision-making problem requires the parameters of underlying models. While applying data to estimate parameters, the uncertainty is unavoidable. Robust optimization is an emerging area to incorporate uncertainty into mathematical programming models. This course will cover various aspects of robust optimization and satisficing frameworks, including nonstochastic and stochastic models.","PeriodicalId":235376,"journal":{"name":"International Conference on Operations Research and Enterprise Systems","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125592779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Duleabom An, Sophie N. Parragh, Markus Sinnl, Fabien Tricoire
{"title":"A LP Relaxation based Matheuristic for Multi-objective Integer Programming","authors":"Duleabom An, Sophie N. Parragh, Markus Sinnl, Fabien Tricoire","doi":"10.5220/0010347000880098","DOIUrl":"https://doi.org/10.5220/0010347000880098","url":null,"abstract":"Motivated by their success in the single-objective domain, we propose a very simple linear programming-based matheuristic for tri-objective binary integer programming. To tackle the problem, we obtain lower bound sets by means of the vector linear programming solver Bensolve. Then, simple heuristic approaches, such as rounding and path relinking, are applied to this lower bound set to obtain high-quality approximations of the optimal set of trade-off solutions. The proposed algorithm is compared to a recently suggested algorithm which is, to the best of our knowledge, the only existing matheuristic method for tri-objective integer programming. Computational experiments show that our method produces a better approximation of the true Pareto front using significantly less time than the benchmark method on standard benchmark instances for the three-objective knapsack problem.","PeriodicalId":235376,"journal":{"name":"International Conference on Operations Research and Enterprise Systems","volume":"152 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129671844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Chaeibakhsh, Roya Sabbagh Novin, Tucker Hermans, A. Merryweather, A. Kuntz
{"title":"Optimizing Hospital Room Layout to Reduce the Risk of Patient Falls","authors":"S. Chaeibakhsh, Roya Sabbagh Novin, Tucker Hermans, A. Merryweather, A. Kuntz","doi":"10.5220/0010226300360048","DOIUrl":"https://doi.org/10.5220/0010226300360048","url":null,"abstract":"Despite years of research into patient falls in hospital rooms, falls and related injuries remain a serious concern to patient safety. In this work, we formulate a gradient-free constrained optimization problem to generate and reconfigure the hospital room interior layout to minimize the risk of falls. We define a cost function built on a hospital room fall model that takes into account the supportive or hazardous effect of the patient's surrounding objects, as well as simulated patient trajectories inside the room. We define a constraint set that ensures the functionality of the generated room layouts in addition to conforming to architectural guidelines. We solve this problem efficiently using a variant of simulated annealing. We present results for two real-world hospital room types and demonstrate a significant improvement of 18% on average in patient fall risk when compared with a traditional hospital room layout and 41% when compared with randomly generated layouts.","PeriodicalId":235376,"journal":{"name":"International Conference on Operations Research and Enterprise Systems","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124843438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Methodology for Deriving Evaluation Criteria for Software Solutions","authors":"Harald M Papp, Marc Hanussek","doi":"10.5220/0010203001450152","DOIUrl":"https://doi.org/10.5220/0010203001450152","url":null,"abstract":"Finding a suited software solution for a company poses a resource-intensive task in an ever-widening market. Software should solve the technical task at hand as perfectly as possible and, at the same time, match the company strategy. Based on these two dimensions, domain knowledge and industry context, we propose a methodology for deriving individually tailored evaluation criteria for software solutions to make them assessable. The approach is formalized as a three-layer model, that ensures the encoding of said dimensions, where each layer holds a more refined and individualized criteria list, starting from a general softwareagnostic catalogue we composed. Finally, we exemplarily demonstrate our method for Machine-Learning-as-a-Service platforms (MaaS) for small and medium-sized enterprises (SME).","PeriodicalId":235376,"journal":{"name":"International Conference on Operations Research and Enterprise Systems","volume":"72 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120863915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Michal Bouška, A. Novák, P. Šůcha, I. Módos, Z. Hanzálek
{"title":"Data-driven Algorithm for Scheduling with Total Tardiness","authors":"Michal Bouška, A. Novák, P. Šůcha, I. Módos, Z. Hanzálek","doi":"10.5220/0008915300590068","DOIUrl":"https://doi.org/10.5220/0008915300590068","url":null,"abstract":"In this paper, we investigate the use of deep learning for solving a classical NP-Hard single machine scheduling problem where the criterion is to minimize the total tardiness. Instead of designing an end-to-end machine learning model, we utilize well known decomposition of the problem and we enhance it with a data-driven approach. We have designed a regressor containing a deep neural network that learns and predicts the criterion of a given set of jobs. The network acts as a polynomial-time estimator of the criterion that is used in a single-pass scheduling algorithm based on Lawler's decomposition theorem. Essentially, the regressor guides the algorithm to select the best position for each job. The experimental results show that our data-driven approach can efficiently generalize information from the training phase to significantly larger instances (up to 350 jobs) where it achieves an optimality gap of about 0.5%, which is four times less than the gap of the state-of-the-art NBR heuristic.","PeriodicalId":235376,"journal":{"name":"International Conference on Operations Research and Enterprise Systems","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125294778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"On Idle Energy Consumption Minimization in Production: Industrial Example and Mathematical Model","authors":"Ondřej Benedikt, P. Šůcha, Z. Hanzálek","doi":"10.5220/0008877400350046","DOIUrl":"https://doi.org/10.5220/0008877400350046","url":null,"abstract":"This paper, inspired by a real production process of steel hardening, investigates a scheduling problem to minimize the idle energy consumption of machines. The energy minimization is achieved by switching a machine to some power-saving mode when it is idle. For the steel hardening process, the mode of the machine (i.e., furnace) can be associated with its inner temperature. Contrary to the recent methods, which consider only a small number of machine modes, the temperature in the furnace can be changed continuously, and so an infinite number of the power-saving modes must be considered to achieve the highest possible savings. To model the machine modes efficiently, we use the concept of the energy function, which was originally introduced in the domain of embedded systems but has yet to take roots in the domain of production research. The energy function is illustrated with several application examples from the literature. Afterward, it is integrated into a mathematical model of a scheduling problem with parallel identical machines and jobs characterized by release times, deadlines, and processing times. Numerical experiments show that the proposed model outperforms a reference model adapted from the literature.","PeriodicalId":235376,"journal":{"name":"International Conference on Operations Research and Enterprise Systems","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122615794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rabin Sahu, Clarisse Dhaenens, Nadarajen Veerapen, M. Davy
{"title":"An Approximate Method for Integrated Stochastic Replenishment Planning with Supplier Selection","authors":"Rabin Sahu, Clarisse Dhaenens, Nadarajen Veerapen, M. Davy","doi":"10.5220/0008970500800088","DOIUrl":"https://doi.org/10.5220/0008970500800088","url":null,"abstract":"A practical methodology for integrated stochastic replenishment planning with supplier selection is proposed for the single item inventory system. A rolling horizon strategy is adopted to implement the ordering decisions. Our method works in two stages. The first stage is a general black box stage that gives the minimum expected \"coverage period\" cost. The second stage uses a dynamic programming approach to compute the minimum expected cost for the rolling horizon. The proposed method is applicable for both stationary and non-stationary demand distributions and even for problems with minimum order quantity constraints. We also propose to examine the benefits of a dynamic supplier selection approach in comparison to selecting a common supplier. We conduct extensive numerical analyses on synthetic data sets for validation.","PeriodicalId":235376,"journal":{"name":"International Conference on Operations Research and Enterprise Systems","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126030090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fatima Ezzahra Achamrah, F. Riane, A. Bouras, E. Sahin
{"title":"Collaboration Mechanism for Shared Returnable Transport Items in Closed Loop Supply Chains","authors":"Fatima Ezzahra Achamrah, F. Riane, A. Bouras, E. Sahin","doi":"10.5220/0009162402470254","DOIUrl":"https://doi.org/10.5220/0009162402470254","url":null,"abstract":"This paper addresses a relevant practical approach of collaboration in supply chains including reverse flows of materials. The objective is to simulate a two-stage closed loop supply chains in which two producers use reusable pallets to distribute their finished products to the same retailers. The producers supply raw materials and new pallets they need from suppliers. For each producer, the flows of raw material, loaded/empty pallets and finished products are triggered by information flows. Two simulation models are considered. In the first model, supply chains are non-collaborative. Each producer manages his own pool of pallets. After receiving replenishment orders, trucks deliver loaded pallets and simultaneously pick-up empty ones from retailers to be returned to the producer. In the second model, the two producers share their pool of empty pallets. The results show that collaboration can lead to economies of scale and costs reduction. They also highlight the need for a third part y to manage the entire system to promise mutual benefits for the concerned parties.","PeriodicalId":235376,"journal":{"name":"International Conference on Operations Research and Enterprise Systems","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114532945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}