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PROMOTION OPTIMIZATION IN COMPETITIVE ENVIRONMENTS BY CONSIDERING THE CANNIBALIZATION EFFECT

INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICE 2023年第1期30卷 166-187页

作者： Mohammadipour, Faezeh Amiri, Maghsoud Vanani, Iman Raeesi Soofi, Jahanyar Bamdad Allameh Tabatabai Univ Fac Management & Accounting Dept Ind Management Tehran Iran

This study proposes a new model to optimize sales promotion in competitive markets and examines the impact of competition on sales promotion planning and business performance in retail chains. The model can be used to determine the best promotional discount for different products with a cannibalization effect when competitors are present in the retail market and offer the same products with different discounts. An integer nonlinear programming problem is proposed to model the above issue. To solve the model, it is reformulated as a mixed-integer linear programming problem. Consequently, a MIP solver can be used to solve the model in a reasonable CPU time. Several examples are solved and a sensitivity analysis of the model parameters is performed. The results of our numerical study show interesting findings that considering different competitors is very important in promotion planning and optimization. Failure to take them into account can lead to loss of profits.

关键词： Competitive environment Promotion Cannibalization effect mixed integer linear programming

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A mathematical programming approach for resource allocation of data analysis workflows on heterogeneous clusters

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JOURNAL OF SUPERCOMPUTING 2023年第17期79卷 19019-19048页

作者： Mohammadi, Somayeh PourKarimi, Latif Droop, Felix De Mecquenem, Ninon Leser, Ulf Reinert, Knut Freie Univ Dept Math & Comp Sci Berlin Germany Razi Univ Dept Math & Comp Sci Kermanshah Iran Humboldt Univ Dept Math & Comp Sci Berlin Germany

Scientific communities are motivated to schedule their large-scale data analysis workflows in heterogeneous cluster environments because of privacy and financial issues. In such environments containing considerably diverse resources, efficient resource allocation approaches are essential for reaching high performance. Accordingly, this research addresses the scheduling problem of workflows with bag-of-task form to minimize total runtime (makespan). To this aim, we develop a mixed-integer linear programming model (MILP). The proposed model contains binary decision variables determining which tasks should be assigned to which nodes. Also, it contains linear constraints to fulfill the tasks requirements such as memory and scheduling policy. Comparative results show that our approach outperforms related approaches in most cases. As part of the post-optimality analysis, some secondary preferences are imposed on the proposed model to obtain the most preferred optimal solution. We analyze the relaxation of the makespan in the hope of significantly reducing the number of consumed nodes.

关键词： Heterogeneous cluster environments Scheduling Data analysis workflow mixed integer linear programming Makespan minimization Post-optimality analysis

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Near-face stockpile open pit mining: a method to enhance NPV and quality of the plant throughput

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INTERNATIONAL JOURNAL OF MINING RECLAMATION AND ENVIRONMENT 2023年第3期37卷 200-215页

作者： Gong, Hongshuo Tabesh, M. Afrapoli, A. Moradi Askari-Nasab, H. Univ Alberta Min Optimizat Lab MOL 6-237 Donadeo Innovat Ctr Engn ICE Bldg9211-116 Edmonton AB T6G 1H9 Canada Laval Univ IntelMine Lab Quebec City AB Canada

Stockpiles are of great importance in open pit mine production scheduling and are widely used for different reasons while being placed in different locations. Near face stockpile (NFS) is a new mining concept which combines the IPCC (in-pit crushing and conveying) method with an in-pit near-face (and near crusher) stockpile. The stockpile decouples the whole mining flow into two weakly related subsystems, which are the mining subsystem and processing subsystem. There are many theoretical advantages in comparison to the traditional open-pit mining method, such as higher tolerance on uncertainties without compromising production, higher equipment utilisation, less operating cost, better blending results, etc. The introduction of NFS, however, requires reconsideration of production planning in open pit mines. In this paper, we developed a mixed integer linear programming model to solve long-term production scheduling problem in open pit mines. To quantitatively measure the performance of the NFS mining method, we implemented the model in a real mining case study and compared the results with the traditional open pit mining method with an out-of-pit crusher. The results reveal that implementing the NFS method can improve the net present value by 9.3% and decrease the plant MWT head grade deviation by 20%.

关键词： Open pit mines production planning near face stockpiling mixed integer linear programming optimisation

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Sequencing two classes of jobs on a machine with an external no-idle constraint

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2023年第7期61卷 2178-2189页

作者： Agnetis, Alessandro Pranzo, Marco Univ Siena Dipartimento Ingn Informaz Via Roma 56 I-53100 Siena Italy

In this paper, we deal with a special type of scheduling problem. There are two classes of jobs to be processed on a single machine. Jobs of class A are directly delivered to the customers and we want to minimise their total flow time. Jobs of class B do not contribute to the objective function but must respect a no-idle constraint (i.e. they are required to keep an external downstream machine busy). This problem arises in some real-world production environments where the downstream process must not be interrupted because of technological constraints, economic viability or because the firm is bound to keep the external process continuously active (e.g. a contract with a downstream firm imposing penalties if the supply is interrupted). We prove that the general problem is NP-Hard. We introduce two mathematical programming-based approaches and some constructive heuristics. The various approaches are compared on the basis of a large computational campaign.

关键词： Scheduling complexity analysis heuristics mixed integer linear programming

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Blocking OMARS designs and definitive screening designs

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JOURNAL OF QUALITY TECHNOLOGY 2023年第4期55卷 489-509页

作者： Ares, Jose Nunez Goos, Peter Katholieke Univ Leuven Dept Biosyst Leuven Belgium Univ Antwerp Dept Engn Management Antwerp Belgium

The family of orthogonal minimally aliased response surface or OMARS designs comprises traditional response surface designs, such as central composite designs and Box-Behnken designs, as well as definitive screening designs. Key features of OMARS designs are the facts that they are orthogonal for the main effects and that the main effects are not at all aliased with any two-factor interaction effect or with any quadratic effect. In this article, we present a method to arrange the runs of an OMARS design in blocks of equal size, so that the main effects can be estimated independently from the blocks, and the interaction effects and the quadratic effects are confounded as little as possible with the blocks. We show that our new method for blocking OMARS designs offers much flexibility when it comes to choosing the number of runs, the number of blocks and the block sizes, and that it often outperforms the blocking arrangements of definitive screening designs available in the literature and in commercial software.

关键词： foldover design mixed integer linear programming mixed-level OMARS design orthogonal blocking response surface design

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mixed-integer linear programming for the optimal design and management of air compressor rooms

Mixed-integer linear programming for the optimal design and ...

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Conference Record of the IEEE Industry Applications Society Annual Meeting (IAS)

作者： Giusi Quartarone Norma Anglani University of Pavia Pavia

In this work the application of mixed integer linear programming (MILP) techniques is proposed for choosing the optimal topology of an air compressor room, while (i) focusing on energy efficiency and (ii) keeping the input power to the lowest value. Usually the proposal of the best configuration (type of machines, size and number) is driven by the experience of the personnel performing the assessment, according to his own knowledge of the available products/machines on the market. The paper proposes the formulation of two optimization problems which enable (i) to perform the best configuration of a compressor room, according with an available machines database; (ii) to ensure that the best choice implies the minimization of the input power for the compressor room, thus, the minimum consumption for the end-user. The presented formulations are tested using both simulated and real case studies, showing how a higher level of knowledge of the flow profile can help in the reduction of the energy consumption. It will be also demonstrated that important energy savings improvements can be obtained if we compare the choices derived from our methodology with real compressor rooms, whose choice was done without applying such method, as it may happen on the market.

关键词： Optimization linear programming Databases Minimization mixed integer linear programming

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Sound mixed Fixed-Point Quantization of Neural Networks

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ACM TRANSACTIONS ON EMBEDDED COMPUTING SYSTEMS 2023年第5期22卷 1-26页

作者： Lohar, Debasmita Jeangoudoux, Clothilde Volkova, Anastasia Darulova, Eva MPI SWS Saarland Informat Campus Campus E1 5 D-66123 Saarbrucken Germany MPI SWS G 26Paul Ehrlich Str 26 Saarbrucken Germany Nantes Univ 2 Chemin Houssiniere F-44322 Nantes France Uppsala Univ Lagerhyddsvagen 1 S-75105 Uppsala Sweden

Neural networks are increasingly being used as components in safety-critical applications, for instance, as controllers in embedded systems. Their formal safety verification has made significant progress but typically considers only idealized real-valued networks. For practical applications, such neural networks have to be quantized, i.e., implemented in finite-precision arithmetic, which inevitably introduces roundoff errors. Choosing a suitable precision that is both guaranteed to satisfy a roundoff error bound to ensure safety and that is as small as possible to not waste resources is highly nontrivial to do manually. This task is especially challenging when quantizing a neural network in fixed-point arithmetic, where one can choose among a large number of precisions and has to ensure overflow-freedom explicitly. This paper presents the first sound and fully automatedmixed-precision quantization approach that specifically targets deep feed-forward neural networks. Our quantization is based on mixed-integer linear programming (MILP) and leverages the unique structure of neural networks and effective over-approximations to make MILP optimization feasible. Our approach efficiently optimizes the number of bits needed to implement a network while guaranteeing a provided error bound. Our evaluation on existing embedded neural controller benchmarks shows that our optimization translates into precision assignments that mostly use fewer machine cycles when compiled to an FPGA with a commercial HLS compiler than code generated by (sound) state-of-the-art. Furthermore, our approach handles significantly more benchmarks substantially faster, especially for larger networks.

关键词： Fixed-point arithmetic mixed precision mixed integer linear programming

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MILP modeling and optimization of multi-objective flexible job shop scheduling problem with controllable processing times

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SWARM AND EVOLUTIONARY COMPUTATION 2023年 82卷

作者： Meng, Leilei Zhang, Chaoyong Zhang, Biao Gao, Kaizhou Ren, Yaping Sang, Hongyan Liaocheng Univ Sch Comp Sci Liaocheng 252000 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Taipa 999078 Macao Peoples R China Macau Univ Sci & Technol Collaborat Lab Intelligent Sci & Syst Taipa 999078 Macao Peoples R China Jinan Univ Sch Intelligent Syst Sci & Engn Dept Ind Engn Zhuhai 519070 Peoples R China

This paper addresses the flexible job shop scheduling problem with controllable processing times (FJSP-CPT). The objective is to simultaneously minimize makespan and total energy consumption. To solve the problem, a mixed integer linear programming (MILP) model is developed, and then the epsilon method is used to obtain the optimal Pareto front for small-scale instances. In order to obtain approximate Pareto fronts for medium-and large-sized problems, we propose an efficient multi-objective hybrid shuffled frog-leaping algorithm (MO-HS-FLA). In the proposed MO-HSFLA, the encoding method, the decoding method, the initiation method of the population and the evolution processes are designed. Specifically, an energy-efficient decoding with three energy-saving strategies, namely decelerating, Turning Off/On and postponing, is designed. In addition, a multi -objective variable local search (MO-VNS) algorithm is designed and embedded in the algorithm to enhance its local exploitation capability. Finally, numerical experiments are conducted to evaluate the performances of the proposed MILP model and MO-HFSLA.

关键词： Flexible job shop scheduling problem Controllable processing times Energy consumption mixed integer linear programming Shuffled frog -leaping algorithm Turning Off/On strategy Postponing strategy

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Green smart manufacturing: energy-efficient robotic job shop scheduling models

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2023年第17期61卷 5791-5805页

作者： Wen, Xin Sun, Yige Ma, Hoi-Lam Chung, Sai-Ho Hong Kong Polytech Univ Dept Ind & Syst Engn Hung Hom Hong Kong Peoples R China Hang Seng Univ Hong Kong Dept Supply Chain & Informat Management Shatin Hong Kong Peoples R China

Smart manufacturing has boosted the wide application of mobile robots in robotic cells for automated material delivery. However, the mismatching between machine production process and robot movement process causes extensive energy waste. Nevertheless, most existing robotic job-shop scheduling (RJSP) studies mainly focus on minimising makespan but overlook the low energy efficiency problem faced by robotic cells. Motivated by the importance of green smart manufacturing, in this study, we innovatively propose to achieve robotic cell energy saving through coordinating the machine production process and robot movement process. Specifically, both machines and the mobile robot can flexibly adjust operating speeds with a V-scale speed framework. Two novel energy-efficient RJSP approaches (i.e. the RJSP-E and the RJSP-EM) are thus proposed. The RJSP-E focuses on minimising energy consumption, while the RJSP-EM simultaneously considers makespan (i.e. productivity) and energy consumption. Through computational experiments, the RJSP-E demonstrates superior performances in reducing energy consumption (15% on average), at a loss of productivity (20% on average). On the other hand, the RJSP-EM can select the most suitable energy-saving operating speeds without much sacrifice in productivity. Notably, the RJSP-EM can reduce energy consumption by a mean of 10% even without increasing makespan. The RJSP-EM also demonstrates higher solution efficiency.

关键词： Green production smart manufacturing robotic job-shop scheduling energy saving mixed integer linear programming

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AccMILP: An Approach for Accelerating Neural Network Verification Based on Neuron Importance 28th

AccMILP: An Approach for Accelerating Neural Network Verific...

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28th International Conference on Engineering of Complex Computer Systems (ICECCS)

作者： Zheng, Fei Xu, Qingguo Lei, Zhou Miao, Huaikou Shanghai Univ Sch Comp Engn & Sci Shanghai Peoples R China Shanghai Key Lab Comp Software Evaluating & Testi Shanghai Peoples R China

ISBN: (纸本)9783031664557;9783031664564

Deep Neural Networks (DNNs) have found successful applications in various non-safety-critical domains. However, given the inherent lack of interpretability in DNNs, ensuring their prediction accuracy through robustness verification becomes imperative before deploying them in safety-critical applications. Neural Network Verification (NNV) approaches can broadly be categorized into exact and approximate solutions. Exact solutions are complete but time-consuming, making them unsuitable for large network architectures. In contrast, approximate solutions, aided by abstraction techniques, can handle larger networks, although they may be incomplete. This paper introduces AccMILP, an approach that leverages abstraction to transform NNV problems into mixed integer linear programming (MILP) problems. AccMILP considers the impact of individual neurons on target labels in DNNs and combines various relaxation methods to reduce the size of NNV models while ensuring verification accuracy. The experimental results indicate that AccMILP can reduce the size of the verification model by approximately 30% and decrease the solution time by at least 80% while maintaining performance equal to or greater than 60% of MIPVerify. In other words, AccMILP is well-suited for the verification of large-scale DNNs.

关键词： Neural network Formal verification mixed integer linear programming Neuron importance

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