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Recent applications in low-power (1-20 mW) near-sensor computing require the adoption of floating-point arithmetic to reconcile high precision results with a wide dynamic range. In this article, we propose a low-power multi-core computing cluster that leverages the fined-grained tunable principles of transprecision computing to provide support to near-sensor applications at a minimum power budget. Our solution - based on the open-source RISC-V architecture - combines parallelization and sub-word vectorization with a dedicated interconnect design capable of sharing floating-point units (FPUs) among the cores. On top of this architecture, we provide a full-fledged software stack support, including a parallel low-level runtime, a compilation toolchain, and a high-level programming model, with the aim to support the development of end-to-end applications. We performed an exhaustive exploration of the design space of the transprecision cluster on a cycle-accurate FPGA emulator, varying the number of cores and FPUs to maximize performance. Orthogonally, we performed a vertical exploration to identify the most efficient solutions in terms of non-functional requirements (operating frequency, power, and area). We conducted an experimental assessment on a set of benchmarks representative of the near-sensor processing domain, complementing the timing results with a post place-&-route analysis of the power consumption. A comparison with the state-of-the-art shows that our solution outperforms the competitors in energy efficiency, reaching a peak of 97 Gflop/s/W on single-precision scalars and 162 Gflop/s/W on half-precision vectors. Finally, a real-life use case demonstrates the effectiveness of our approach in fulfilling accuracy constraints. ISSN:1045-9219 ISSN:1558-2183 ISSN:2161-9883

Modern industrial robots can work alongside human workers and coordinate with other robots. This means they can perform complex tasks, but doing so requires complex programming. Therefore, robots are typically programmed by experts, but there are not enough to meet the growing demand for robots. To reduce the need for experts, researchers have tried to make robot programming accessible to factory workers without programming experience. However, none of that previous work supports coordinating multiple robot arms that work on the same task. In this paper we present four block-based programming language designs that enable end-users to program two-armed robots. We analyze the benefits and trade-offs of each design on expressiveness and user cognition, and evaluate the designs based on a survey of 273 professional participants of whom 110 had no previous programming experience. We further present an interactive experiment based on a prototype implementation of the design we deem best. This experiment confirmed that novices can successfully use our prototype to complete realistic robotics tasks. This work contributes to making coordinated programming of robots accessible to end-users. It further explores how visual programming elements can make traditionally challenging programming tasks more beginner-friendly.

In the era of big data analytics, data owner is more concern about the data privacy. Data anonymization approaches such as k-anonymity, l-diversity, and t-closeness are used for a long time to preserve privacy in published data. However, these approaches cannot be directly applicable to a large amount of data. Distributed programming framework such as MapReduce and Spark are used for big data analytics which add more challenges to privacy preserving data publishing. Recently, we identified few scalable approaches for Privacy Preserving Big Data Publishing in literature and majority of them are based on k-anonymity and l-diversity. However, these approaches require a significant improvement to reach the level of existing privacy preserving data publishing approaches, therefore, we propose Improved Scalable l-Diversity (ImSLD) approach which is the extension of Improved Scalable k-Anonymity (ImSKA) for scalable anonymization in this paper. Our approaches are based on scalable k-anonymization that uses MapReduce as a programming paradigm. We use poker dataset and synthesize big data versions of poker dataset to test our approaches. The result analysis shows significant improvement in terms of running time due to the lesser number of MapReduce iterations and also exhibits lower information loss as compared to existing approaches while providing the same level of privacy due to tight arrangement of the records in the initial equivalence class.

Frequency competition is critical for a full-service airline in gaining market share, and adopting a proper strategy can improve an airline’s profits. This study proposes a new equilibrium programming model with flow balance to address frequency competition on airports network with time slot constraints. We first show that a pure-strategy Nash equilibrium may not always exist, and thus forming a pure strategy profile in frequency competition among airlines may naturally lead to deviation from current frequency. Therefore, we formulate the problem as a programming model with a mixed-strategy Nash equilibrium. To avoid shocks from dramatic frequency changes across the network, airlines tend to fine-tune frequencies on select segments during each adjustment. We propose a procedure to generate a computationally tractable amount of representative strategies from a finite set of feasible strategies to demonstrate mixed-strategy Nash equilibrium. We conduct an empirical analysis using an example in which industry profitability increased by as much as 7.89%. We then extend the model to formulate frequency competition among metal-neutral alliances. The results show that forming metal-neutral alliances can improve total industry profits by 10.59%. In particular, a sensitivity analysis with real data on the tolerance of flow imbalance demonstrates that deducting the potential costs due to the relaxation of flow balance between congested airports may earn additional total industry profits in a frequency competition.

Disassembly is an essential step in a remanufacturing process via which valuable parts and material of end-of-life (EOL) products can be well reused and resource waste is reduced. Disassembly sequence planning focuses on finding the best disassembly sequence for a given EOL product by considering economic and environmental performance. In a practical disassembly process, one may face a disassembly operation failure risk due to the difficulty of knowing EOL products' exact information in advance. Despite its importance in impacting disassembly outcomes, the existing work fails to consider it comprehensively. This work proposes a stochastic biobjective DSP problem with the objectives of maximizing disassembly profit and minimizing energy consumption by doing so. A chance-constrained programming model is established, where a chance constraint ensures a fixed confidence level of disassembly failure. To solve it efficiently, a multiobjective multiverse optimization algorithm with stochastic simulation is proposed. Experiments are carried out on four products. Results demonstrate that it outperforms some state-of-the-art algorithms in terms of solution performance.

Supercomputing applications are increasingly adopting the MPI+threads programming model over the traditional “MPI everywhere” approach to better handle the disproportionate increase in the number of cores compared with other on-node resources. In practice, however, most applications observe a slower performance with MPI+threads primarily because of poor communication performance. Recent research efforts on MPI libraries address this bottleneck by mapping logically parallel communication, that is, operations that are not subject to MPI’s ordering constraints to the underlying network parallelism. Domain scientists, however, typically do not expose such communication independence information because the existing MPI-3.1 standard’s semantics can be limiting. Researchers had initially proposed user-visible endpoints to combat this issue, but such a solution requires intrusive changes to the standard (new APIs). The upcoming MPI-4.0 standard, on the other hand, allows applications to relax unneeded semantics and provides them with many opportunities to express logical communication parallelism. In this article, we show how MPI+threads applications can achieve high performance with logically parallel communication. Through application case studies, we compare the capabilities of the new MPI-4.0 standard with those of the existing one and user-visible endpoints (upper bound). Logical communication parallelism can boost the overall performance of an application by over 2×.

While the open-source software development model has led to successful large-scale collaborations in building software systems, data science projects are frequently developed by individuals or small teams. We describe challenges to scaling data science collaborations and present a conceptual framework and ML programming model to address them. We instantiate these ideas in Ballet, the first lightweight framework for collaborative, open-source data science through a focus on feature engineering, and an accompanying cloud-based development environment. Using our framework, collaborators incrementally propose feature definitions to a repository which are each subjected to software and ML performance validation and can be automatically merged into an executable feature engineering pipeline. We leverage Ballet to conduct a case study analysis of an income prediction problem with 27 collaborators, and discuss implications for future designers of collaborative projects.

This paper aims to develop a new axiomatic design-based mathematical programming method for heterogeneous multi-criteria group decision making (HMCGDM) problems with linguistic fuzzy truth degrees (LFTDs). The main contributions of this paper are summarized in five aspects: (1) The information content definitions for six types of fuzzy numbers are initially provided according to axiomatic design . (2) Considering the authority of experts on different criteria and group consensus, a bi-objective programming model is constructed to derive experts’ weights by maximizing individual deviation and minimizing group discordance. (3) Each alternative is assessed on the basis of its information content to a fuzzy positive ideal solution. Information content is firstly used to define the linguistic fuzzy consistency and inconsistency indices. (4) A bi-objective linguistic fuzzy mathematic programming model is built to determine the criteria weights, which considers consistency and inconsistency indices simultaneously. This model can be dexterously transformed into a crisp linear programming model for resolution by the linguistic scale function. (5) The group information content of each alternative to fuzzy positive ideal solution is calculated to determine the ranking order of alternatives. Finally, an example of blockchain service provider selection is given to validate the proposed method.

Cloud computing can be considered as a disruptive technology that is making life easier for Cloud users. Determining a focus of research in a specific subject area is sometimes challenging. A systematic map enables a synthesis of a scheme for categorizing data in a field of interest. The goal of this research paper is to carry out a systematic mapping study of policy language and programming models on the cloud. The mapping involved contribution category such as method, research category such as evaluation and major topics extracted from the abstracts of primary studies. The result indicated there are more publications on evaluation research in term of security with 8.9%. There were more papers published on validation research, solution proposal and experience research on the topic of paradigms with 7.53%, 6.85% and 4.11% respectively. Also, there were more publications on philosophical research in terms of privacy with 4.11%. In addition, there were more articles published on opinion research in terms of the survey with 4.11%. On the other hand, there were no articles on metric in terms of framework, paradigms and accountability, and reliability to the best of the researchers’ knowledge. The outcome of this systematic study will be of benefit to cloud users, researchers, practitioners and providers.

Adoption of multi- and many-core processors in real-time systems has so far been slowed down, if not totally barred, due do the difficulty in providing analytical real-time guarantees on worst-case execution times. The Predictable Execution Model (PREM) has been proposed to solve this problem, but its practical support requires significant code refactoring, a task better suited for a compilation tool chain than human progranuners. Implementing a PREM compiler presents significant challenges to conform to PREM requirements, such as guaranteed upper bounds on memory footprint and the generation of efficient schedulable non-preemptive regions. This article presents a comprehensive description on how a PREM compiler can be implemented, based on several years of experience from the community. We provide accumulated insights on how to best balance conformance to real-time requirements and performance and present novel techniques that extend the applicability from simple benchmark suites to real-world applications. We show that code transformed by the PREM compiler enables timing predictable execution on modern commercial off-the-shelf hardware, providing novel insights on how PREM can protect 99.4% of memory accesses on random replacement policy caches at only 16%. performance loss on benchmarks from the PolyBench benchmark suite. Finally, we show that the requirements imposed on the programming model are well-aligned with current coding guidelines for timing critical software, promoting easy adoption. ISSN:1539-9087 ISSN:1558-3465

In order to improve customer satisfaction and reduce the cost of vehicle logistics transportation, this paper adopts the mixed-integer programming model to analyze the delivery routes of vehicle logistics and make simulation and analysis based on the real delivery case. The results show that, compared with a single transportation scheme, the vehicle logistics optimization scheme based on the mixed-integer programming model is able to produce the optimal multimodal transportation plan, which can reduce the transportation costs, improve the service of transportation enterprises, and enhance their core competitiveness.

Hesitant fuzzy preference relations (HFPRs) have been widely applied in multicriteria decision-making (MCDM) for their ability to efficiently express hesitant information. To address the situation where HFPRs are necessary, this paper develops several decision-making models integrating HFPRs with the best worst method (BWM). First, consistency measures from the perspectives of additive/multiplicative consistent hesitant fuzzy best worst preference relations (HFBWPRs) are introduced. Second, several decision-making models are developed in view of the proposed additive/multiplicatively consistent HFBWPRs. The main characteristic of the constructed models is that they consider all the values included in the HFBWPRs and consider the same and different compromise limit constraints. Third, an absolute programming model is developed to obtain the decision-makers’ objective weights utilizing the information of optimal priority weight vectors and provides the calculation of decision-makers’ comprehensive weights. Finally, a framework of the MCDM procedure based on hesitant fuzzy BWM is introduced, and an illustrative example in conjunction with comparative analysis is provided to demonstrate the feasibility and efficiency of the proposed models.

Ethereum, the largest blockchain for running smart contracts, charges the people who send transactions to deploy or invoke smart contracts for thwarting resource abuse. The amount of transaction fee depends on the size of that contract and the operations executed by that contract. Consequently, smart contracts with inefficient code will waste money. In this article, we propose and develop the first tool, named GasChecker , for automatically identifying gas-inefficient code in smart contracts, and conduct the first empirical study on the prevalence of gas-inefficient code in the deployed smart contracts. More precisely, we first summarize ten gas-inefficient programming patterns and propose a new approach based on symbolic execution (SE) to detect them in the bytecode of smart contracts. To make our approach scalable to analyze millions of smart contracts, we parallelize SE by tailoring it to the MapReduce programming model, and propose a new feedback-based load balancing strategy to effectively utilize cloud resources. Extensive experiments show that GasChecker scales well with the increase of workers. The empirical study demonstrates that lots of real smart contracts contain various inefficient code. Manual investigation demonstrates that only 2.5 percent of discovered gas-inefficient instances are false positives.

Automated guided vehicles are widely used in various applications, especially in manufacturing. In this paper, we present a novel parallel algorithm for multi-AGV systems. The overall structure is composed of three parts: task assignment, path planning, and vehicle navigation. According to the priorities of AGVs, a greedy method is introduced to assign jobs. For path planning, a mixed-integer programming model of a multi-AGV system is formulated, which can be transformed into a series of sub-problems under certain conditions. Then, an improved routing method with a penalty item is adopted to generate the optimal paths of AGVs. To avoid collisions between vehicles, a simple and effective control method based on resource locking is proposed under the premise of parallelization. Furthermore, we design the experiments according to the warehousing systems. Various practical simulations are performed to illustrate the efficiency and robustness of the new algorithm.

Food banks operate with an objective to serve as many of food-insecure people as possible with the limited supply available to them. This paper presents a mixed-integer programming model to identify the efficient assignment of demand zones (counties) to distribution centers (branches) and equitable allocation of donated food from the food bank branches to the demand zones. The model objective function minimizes the total cost of branch operation, the cost of receiving and distributing food, the cost of undistributed food while maintaining the maximum allowed deviation from perfect equity. Data from the Food Bank of Central and Eastern North Carolina (FBCENC) are used to characterize the major attributes controlling the food distribution system of a food bank. Results from the optimization model using FBCENC data show that the optimal allocation under perfect equity follows a particular structure depending on the shipping cost and the cost of undistributed supply. Sensitivity analyses exploring the trade-offs between efficiency and effectiveness as a function of the cost of shipping, truck capacity, and a user-specified maximum inequity cap show that marginal sacrifice in equity can significantly improve effectiveness. The corresponding improvement in effectiveness is greater when comparatively larger trucks are used and the cost of shipping is relatively higher. The analyses also suggest that while efficiency is less sensitive to the allowable limit on the deviation from perfect equity, it is sensitive to truck size. A comparison of direct shipping to branches to operating a local hub suggests the former option to be more cost efficient.

The area of declarative data analytics explores the application of the declarative paradigm on data science and machine learning. It proposes declarative languages for expressing data analysis tasks and develops systems which optimize programs written in those languages. The execution engine can be either centralized or distributed, as the declarative paradigm advocates independence from particular physical implementations. The survey explores a wide range of declarative data analysis frameworks by examining both the programming model and the optimization techniques used, in order to provide conclusions on the current state of the art in the area and identify open challenges., 36 pages, 2 figures

Byte-addressable, non-volatile memory (NVM) presents an opportunity to rethink the entire system stack. We present Twizzler, an operating system redesign for this near-future. Twizzler removes the kernel from the I/O path, provides programs with memory-style access to persistent data using small (64 bit), object-relative cross-object pointers, and enables simple and efficient long-term sharing of data both between applications and between runs of an application. Twizzler provides a clean-slate programming model for persistent data, realizing the vision of Unix in a world of persistent RAM. We show that Twizzler is simpler, more extensible, and more secure than existing I/O models and implementations by building software for Twizzler and evaluating it on NVM DIMMs. Most persistent pointer operations in Twizzler impose less than 0.5 ns added latency. Twizzler operations are up to faster than Unix , and SQLite queries are up to faster than on PMDK. YCSB workloads ran 1.1– faster on Twizzler than on native and NVM-optimized SQLite backends.

Game theory has found successful applications in different areas to handle competitive situations among different persons or organizations. Several extensions of ordinary game theory have been studied by the researchers to accommodate the uncertainty and vagueness in terms of payoffs and goals. Matrix games with payoffs represented by interval numbers, fuzzy numbers, and intuitionistic fuzzy numbers have considered only the quantitative aspects of the problems. But in many situations, qualitative information plays a crucial role in representing the payoffs of a game problem. This work presents a valuable study on matrix games with payoff represented by linguistic intuitionistic fuzzy numbers (LIFNs). First, the paper defines some new operational-laws for LIFNs based on linguistic scale function (LSF) and studies their properties in detail. Next, we define a new aggregation operator called ‘generalized linguistic intuitionistic fuzzy weighted average (GLIFWA)’operator for aggregating LIFNs. Several properties and special cases of GLIFWA operator are also discussed. The LSF provides an ability to consider the different semantic situations in a single formulation during the aggregation process. Further, the paper introduces some basic results of matrix games with payoffs represented by LIFNs. We develop solution methods using a pair of auxiliary linear/nonlinear-programming models derived from a pair of nonlinear bi-objective programming models. Finally, a real-life numerical example is considered to demonstrate the validity and applicability of the developed methods.

In this paper, a deterministic bounding procedure for the global optimization of a mixed-integer bi-level programming problem is proposed. The aim has been to develop an efficient algorithm to deal with a case study in the electricity retail market. In this problem, an electricity retailer wants to define a time-of-use tariff structure to maximize profits, but he has to take into account the consumers’ reaction by means of re-scheduling appliance operation to minimize costs. The problem has been formulated as a bi-level mixed-integer programming model. The algorithm we propose uses optimal-value-function reformulations based on similar principles as the ones that have been used by other authors, which are adapted to the characteristics of this type of (pricing optimization) problems where no upper (lower) level variables appear in the lower (upper) level constraints. The overall strategy consists of generating a series of convergent upper bounds and lower bounds for the upper-level objective function until the difference between these bounds is below a given threshold. Computational results are presented as well as a comparison with a hybrid approach combining a particle swarm optimization algorithm to deal with the upper-level problem and an exact solver to tackle the lower-level problem, which we have previously developed to address a similar case study. When the lower-level model is difficult, a significant relative MIP gap is unavoidable when solving the algorithm's subproblems. Novel reformulations of those subproblems using “elastic” variables are proposed trying to obtain meaningful lower/upper bounds within an acceptable computational time.

This article considers a biobjective location-routing problem to deliver relief resources to the victims affected by a disaster under uncertainty in demand, transportation infrastructure, and travel time. Since transportation networks are exposed to a considerable level of uncertainty, choosing the reliable path for relief goods to be transmitted to the affected areas ensures the arrival of these supplies. For the first time, route reliability is calculated based on the multistate theory, and the universal generating function technique is used for network reliability assessment. The problem is formulated as a multiperiod robust biobjective mixed-integer programming model. Two objective functions are considered: (a) decreasing the sum of arrival times of relief vehicles at the demand nodes for delivering aids to the affected areas, and (b) increasing the minimum route reliability for all the serving vehicles. A novel multiobjective grouping algorithm is proposed to obtain the Pareto-optimal solutions of the problem. Then, its performance is compared with two other multiobjective grouping algorithms. To evaluate the solution method, the algorithms are implemented on various test problems and compared statistically. A case study is presented to illustrate the potential applicability of our model. Additionally, to determine the effect of the changes in the main parameters of the problem on the value of objective functions, the sensitivity analyses are performed and the managerial insights are given.

As the occurrence of disasters has increased frequently and has resulted in growing concern about their adverse effects on the environment, Sustainable Humanitarian Logistics (SHL) has received great attention recently. SHL aims to reduce disaster damages in an environmentally-friendly manner in the shortest possible time. The terms including ‘environmentally-friendly’ and ‘shortest possible time’ refer to the environmental and social aspects of sustainability. This research proposes a stochastic multi-objective mixed-integer programming model to configure an SHL network during the response phase. Having compared to the research literature, this is the first study that considers economic, social, and environmental aspects of sustainability by incorporating relief cost, deprivation cost, and carbon emissions, respectively. Then, the improved multi-choice goal programming approach is applied to solve the proposed multi-objective model. To indicate the validity of the proposed model, an earthquake that occurred in a region of Kermanshah, Iran, in 2017 is investigated as a real case study. Finally, sensitivity analysis is performed and several managerial and theoretical insights are provided. The results show that exerting environmental issues in humanitarian logistics does not necessarily increase the relief costs, but can be in contrast with the social aspect. Furthermore, a minor increase in the budget of the preparedness phase drastically decreases the response costs.

In recent decades, municipal solid waste (MSW) management has become one of the major concerns of human societies. Therefore, an efficient MSW system of collecting, recycling, and disposing is required to be designed, maintained and improved continuously. However, designing a proper waste collection management system by controlling the spread of diseases and environmental pollutants is very costly. To provide an efficient MSW management system, this study proposes an integrated location-allocation problem to plan the operations such as collection, recycling, disposal, and transportation under uncertainty. To deal with the problem uncertainty, a triangular fuzzy number approach is employed and finally, a fuzzy chance-constrained programming model is developed. Moreover, the proposed bi-objective mathematical programming model aims to minimize the total costs and the total volume of pollutant emissions simultaneously. To solve and validate the model, a weighted goal programming (WGP) method is employed and solved by CPLEX solver of GAMS software. Finally, a real case study problem was implemented in Qazvin, Iran. The results showed that, in most cases, increasing the confidence level of the case study problem leads to the improvement of objective functions. Managers can take into account the output of this research provided along with a sensitivity analysis under different conditions.

Liner container shipping company satisfies container shipping demand from both long-term contracts and spot market. Different from the shipping demand from long-term contracts whose freight rate is usually negotiated between the shipping company and the customer, the freight rate of the spot demand is usually determined by the shipping company itself. This paper thus focuses on how to determine the optimal freight rate of spot containers for each origin and destination (OD) pairs of a shipping network to maximize the total profit. To further reflect industry practice, two types of uncertainties are considered, i.e., the uncertain spot demand volume due to stochastic slot booking arrivals and cancelations, and the uncertain remaining ship capacity for the spot demand after satisfying other demand types with higher priority (e.g. delayed containers). This problem is first formulated as a two-stage stochastic nonlinear and nonconvex programming model. A tailored spatial branch-and-bound-and-Benders algorithm is then developed to obtain the global optimal solution of the model. Numerical experiments also demonstrate the efficiency of the solution algorithm and the applicability of the proposed model in improving the profit from the spot shipping demand.

Triangular multiplicative preference relation (TMPR) is an efficient technology which can make experts to comfortably express their paired comparisons. Existing consistency definitions of TMPR overlook experts' trust levels on their judgments. Considering experts' trust levels, this paper proposes a left and right (L-R) geometric consistency definition of TMPR and develops a new group decision-making (GDM) method. The L-R geometric means of triangular fuzzy number (TFN) are defined. Then, L-R geometric consistency of TMPR is given involving experts' trust levels about their judgments. To improve the consistency, a programming model is constructed to derive an acceptably consistent TMPR from an unacceptably consistent one. Using the difference degree between two TMPRs, a new approach is presented to extract experts' weights. Two algorithms are established for the methods of individual decision-making (IDM) with a TMPR and GDM with TMPRs. A simulation algorithm is devised to verify the superiority of the proposed IDM with a TMPR. Simulation results reveal that the proposed IDM method outperforms existing methods in logarithmic Hamming distance and difference degree. L-R geometric consistency makes contribution to TMPR since it considers experts' trust levels and has reciprocity, invariance and robustness. Several real-life examples are furnished to explain the validity of two proposed algorithms.

Rust is a relatively new programming language that has gained significant traction since its v1.0 release in 2015. Rust aims to be a systems language that competes with C/C++. A claimed advantage of Rust is a strong focus on memory safety without garbage collection. This is primarily achieved through two concepts, namely, reference lifetimes and borrowing . Both of these are well-known ideas stemming from the literature on region-based memory management and linearity / uniqueness . Rust brings both of these ideas together to form a coherent programming model. Furthermore, Rust has a strong focus on stack-allocated data and, like C/C++ but unlike Java, permits references to local variables. Type checking in Rust can be viewed as a two-phase process: First, a traditional type checker operates in a flow-insensitive fashion; second, a borrow checker enforces an ownership invariant using a flow-sensitive analysis. In this article, we present a lightweight formalism that captures these two phases using a flow-sensitive type system that enforces “ type and borrow safety .” In particular, programs that are type and borrow safe will not attempt to dereference dangling pointers. Our calculus core captures many aspects of Rust, including copy- and move-semantics, mutable borrowing, reborrowing, partial moves, and lifetimes. In particular, it remains sufficiently lightweight to be easily digested and understood and, we argue, still captures the salient aspects of reference lifetimes and borrowing. Furthermore, extensions to the core can easily add more complex features (e.g., control-flow, tuples, method invocation). We provide a soundness proof to verify our key claims of the calculus. We also provide a reference implementation in Java with which we have model checked our calculus using over 500B input programs. We have also fuzz tested the Rust compiler using our calculus against 2B programs and, to date, found one confirmed compiler bug and several other possible issues.

Master production schedules (MPS) and Material requirements planning (MRP) are two important planning modules in the production planning system. In this paper, a new fuzzy credibility-based double-sided chance-constrained programming (FCDCP) model is proposed for the capacitated MPS/MRP integrated programming problem under complex uncertainty. The proposed model supports simultaneous lot sizing and scheduling decisions and can deal with uncertainty presented as fuzziness in both right-hand and left-hand sides of constraints. To tackle the FCDCP model, the corresponding defuzzification methods based on credibility measure are proposed for transforming the fuzzy MPS/MRP integrated programming model into a crisp equivalent mixed-integer programming model. When decision-makers decide on MPS/MRP integrated programming problem, the optimal lot sizing and scheduling solutions from the FCDCP model can not only provide an effective method for production planners to deal with the uncertainty in the production system, making the production plan more reasonable and accurate, but it can also help decision-makers obtain the optimal production plans and determine the best allocation of production resources, effectively reducing the total cost of the production system. Finally, an industrial example from a transmission manufacturer is provided to show the applicability and flexibility of the model.

Satisfying the clients' uncompromising priorities is a challenge for decision makers of organizations that face multiple projects. This paper considers an organization with a multi-skilled workforce working on several predetermined projects under time-of-use energy tariffs where distributed load energy usage is the primary concern of energy suppliers. This paper also considers different time-of-use energy tariffs, which are among the most common strategies to reach a more balanced energy utilization. The problem is stated as a bi-objective mixed-integer programming model containing two conflicting objectives; to minimize the total cost of the multi-skilled workforce and obtain a sustainable schedule with minimum deviation from the projects' priorities. The problem is formulated mathematically, and the GAMS solver is applied for justifying the conflict between the objectives and validating the proposed formulation. In order to tackle real-life instances of the problem, intelligent algorithms based on cuckoo search, particle swarm, and genetic algorithms are developed. In the proposed algorithms, the application of a well-designed encoding and decoding structure efficiently ensures the generated solutions' feasibility. The Taguchi method is used for calibrating the parameters of the proposed meta-heuristics. Performance of the solving methods is evaluated based on some experiments, where ELECTRE method is utilized as a decision-making technique to prioritize the developed algorithms. To this aim, some well-known multi-objective measures are applied for comparative analysis of the results, where the supremacy of FSCS in terms of all metrics is declared.

An effective method for disassembling large-sized waste products is to utilise two-sided disassembly lines. A mixed-integer programming model for two-sided disassembly lines is constructed in this ...

This study proposes a scenario-based mixed-integer programming model to investigate the responsive-resilient inventory-location problem under uncertainty. The proposed model minimizes the total costs and makes decisions about the location, allocation, and inventory problems. The literature showed that simultaneous consideration of responsiveness and resilience measures has been ignored by the researchers. Hence, to fill this gap, this study considers responsiveness and resilience measures in the proposed model. Also, since the uncertainty exists in the nature of the research problem due to changes in the business environment, this paper applies the queuing theory and robust fuzzy stochastic optimization to cope with uncertainty. At first, the existed uncertainty in lead time and demand is tackled by employing the queuing theory, and some performance measures of the system are calculated. Then, the achieved results are incorporated into the fuzzy robust stochastic model. As the problem is an NP-Hard, this study develops several metaheuristic algorithms to solve the proposed model in a reasonable time. Then, the applicability of the proposed model and efficiency of the developed algorithms are shown by several numerical examples. Eventually, several sensitivity analyses are conducted on some important parameters of the model, and useful managerial insights are provided.

Hierarchical density-based clustering is a powerful tool for exploratory data analysis, which can play an important role in the understanding and organization of datasets. However, its applicability to large datasets is limited because the computational complexity of hierarchical clustering methods has a quadratic lower bound in the number of objects to be clustered. MapReduce is a popular programming model to speed up data mining and machine learning algorithms operating on large, possibly distributed datasets. In the literature, there have been attempts to parallelize algorithms such as Single-Linkage, which in principle can also be extended to the broader scope of hierarchical density-based clustering, but hierarchical clustering algorithms are inherently difficult to parallelize with MapReduce. In this paper, we discuss why adapting previous approaches to parallelize Single-Linkage clustering using MapReduce leads to very inefficient solutions when one wants to compute density-based clustering hierarchies. Preliminarily, we discuss one such solution, which is based on an exact, yet very computationally demanding, random blocks parallelization scheme. To be able to efficiently apply hierarchical density-based clustering to large datasets using MapReduce, we then propose a different parallelization scheme that computes an approximate clustering hierarchy based on a much faster, recursive sampling approach. This approach is based on HDBSCAN*, the state-of-the-art hierarchical density-based clustering algorithm, combined with a data summarization technique called data bubbles. The proposed method is evaluated in terms of both runtime and quality of the approximation on a number of datasets, showing its effectiveness and scalability.

NVIDIA A100 Tensor Core GPU is NVIDIA's latest flagship GPU. It has been designed with many new innovative features to provide performance and capabilities for HPC, AI, and data analytics workloads. Feature enhancements include a Third-Generation Tensor Core, new asynchronous data movement and programming model, enhanced L2 cache, HBM2 DRAM, and third-generation NVIDIA NVLink I/O.

In this paper, a novel chance-constrained programming model has been proposed for handling uncertainties in green closed loop supply chain network design. In addition to locating the facilities and establishing a flow between them, the model also determines the transportation mode between facilities. The objective functions are applied to minimize the expected value and variance of the total cost CO2 released is also controlled by providing a novel chance-constraint including a stochastic upper bound of emission capacity. To solve the mathematical model using the General Algebraic Modeling System (GAMS) software, four multi-objective decision-making (MODM) methods were applied. The proposed methodology was subjected to various numerical experiments. The solutions provided by different methods were compared in terms of the expected value of cost, variance of cost, and CPU time using Pareto-based analysis and optimality-based analysis. In Pareto-based analysis, a set of preferable solutions were presented using the Pareto front; then optimality-based optimization was chosen as the best method by using a Simple Additive Weighting (SAW) method. Experimental experiments and sensitivity analysis demonstrated that the performance of the goal attainment method was 13% and 24% better that of global criteria and goal programming methods, respectively.

Purpose This paper aims to provide an integrated production-routing model in a three-echelon supply chain containing a two-layer transportation system to minimize the total costs of production, transportation, inventory holding and expired drugs treatment. In the proposed problem, some specifications such as multisite manufacturing, simultaneous pickup and delivery and uncertainty in parameters are considered. Design/methodology/approach At first, a mathematical model has been proposed for the problem. Then, one possibilistic model and one robust possibilistic model equivalent to the initial model are provided regarding the uncertain nature of the model parameters and the inaccessibility of their probability function. Finally, the performance of the proposed model is evaluated using the real data collected from a pharmaceutical production center in Iran. The results reveal the proper performance of the proposed models. Findings The results obtained from applying the proposed model to a real-life production center indicated that the number of expired drugs has decreased because of using this model, also the costs of the system were reduced owing to integrating simultaneous drug pickup and delivery operations. Moreover, regarding the results of simulations, the robust possibilistic model had the best performance among the proposed models. Originality/value This research considers a two-layer vehicle routing in a production-routing problem with inventory planning. Moreover, multisite manufacturing, simultaneous pickup of the expired drugs and delivery of the drugs to the distribution centers are considered. Providing a robust possibilistic model for tackling the uncertainty in demand, costs, production capacity and drug expiration costs is considered as another remarkable feature of the proposed model.

Dynamic path flows, referring to the number of vehicles that choose each path in a network over time, are generally estimated with the partial observations as the input. The automatic vehicle identification (AVI) system and probe vehicle trajectories are now popular and can provide rich and complementary trip information, but the data fusion was rarely explored. Therefore, in this paper, the dynamic path flow estimation is based on these two data sources and transformed into a feature learning problem. To fuse the two data sources belonging to different detection ways at the data level, the virtual AVI points, analogous to the real AVI points (turning movements at nodes with AVI detectors), are defined and selected to statically observe the dynamic movement of the probe vehicles. The corresponding selection principles and a programming model considering the distribution of real AVI points are first established. The selected virtual AVI points are used to construct the input tensor, and the turning movement-based observations from both the data sources can be extracted and fused. Then, a three-dimensional (3D) convolutional neural network (CNN) model is designed to exploit the hidden patterns from the tensor and establish the high-dimensional correlations with path flows. As the path flow labels commonly with noises, the bootstrapping method is adopted for model training and the corresponding relabeling principle is defined to purify the noisy labels. The entire model is extensively tested based on a realistic road network, and the results show that the designed CNN model with the presented data fusion method can perform well in training time and estimation accuracy. The robustness of a model to noisy labels is also improved through the bootstrapping method. The dynamic path flows estimated by the trained model can be applied to travel information provision, proactive route guidance, and signal control with high real-time requirements.

In the era of sharing economy and the mobility-as-service, the last mile delivery system is undergoing revolutionary changes with emerging a lot of new operation modes and policies. The crowdsourced delivery mode has become a novel and popular practice in the last mile delivery industry. This mode brings a novel way for facilitating the efficiency of last mile delivery on the basis of the shared mobility. This paper studies an open vehicle routing problem (OVRP) for the shared mobility based last mile delivery mode, in which parcel delivery orders are disseminated within a circle to attract occasional vehicles (or couriers) to fulfill these orders. Different from the traditional OVRP, this study further considers the decision of the above mentioned circle’s radius, which should be balanced between attracting enough vehicles and reducing additional cost (reward) for covering vehicles’ empty trips to the pickup location. This paper proposes a nonlinear mixed-integer programming model for the problem. A column generation based solution method is implemented to solve the model. Some numerical experiments are also conducted to validate the efficiency of the solution method and draw out some managerial implications for the practitioners. The proposed methodology in this paper may be potentially benefit not only for reducing the last mile delivery cost but also for promoting the modern transportation paradigm revolution to ridesharing and mobility-as-service.

This paper studies the housing and town building policies. It analyzes reasons that new towns failed to achieve their strategic goals. This paper addresses the question of why the new towns are not functional. The research methods are classic linear programming, a benchmarking technique, and a case study strategy. The benchmarking procedure compares the existing urban development pattern to optimal building models. This study explores fundamental theories concerning housing and urban development, proportional to the inhabitants’ requirements, lifestyles, and livelihoods. The purpose of this study is to emphasize the natural and social characteristics of each region, as well as the livelihood needs of indigenous peoples in housing and urban policies. The case study of this research uses a market analysis procedure to discover rational and optimal housing policies. Finally, this paper suggests a feasible programming model that changes the present unsuccessful ways toward improved methods of housing policies. The model predicts required urban spaces in the new towns. It recommends design procedures based on the real needs of local people. The presented model of this research has a local application, but it is also helpful for other new urban development projects everywhere. Key words: Housing, new towns, urban planning and design, market analysis, linear programming.

This paper combines a risk-based two-stage stochastic robust programming (RTSRP) model into an agricultural-ecological system dynamics (AESD) to conduct agricultural-ecological water resources management. It integrates the chance- constrained programming, conditional risk-at-value, two-stage stochastic robust programming with the AESD model. The RTSRP model improves upon the TSRP model by introducing the risk control measurements. The agricultural-ecological dynamic system (AEDS) model can build water connections amid wheat, field corn, seed corn, economic crops, woodland, meadow, and city greening and farmland shelterbelts. Besides, the ecological service value is transformed into the irrigation benefit of ecological sector to guide water allocation amid sub-ecological sectors at a more applicable detailed scale. It can tradeoff relationships between economic benefit and ecological recovery by formulating two enlarging ecological areas scenarios. The combinations of the RTSRP model and the AEDS model can get optimal water allocation schemes, and simulate future sustainable degree (SD) of optimal water allocation schemes and explore relationships amid water allocation, economic benefit, and SD and risk and robustness of model. It is used to the middle reaches of the Heihe River Basin in China to verify its application. The results show that water allocation schemes have different responses on various risk scenarios. Higher water allocation corresponds to bigger water shortage risk and higher robustness of the model. The economic benefit positive with water allocation is contradictory with SD. Enlarging ecological areas contributes to improvements of economic benefit and ecological recovery, but not the SD under the condition of enough water resources.

The paper proposed a method to study and obtain a set of Pareto optimal solutions or a set of representative solutions to a quadratically constrained multi-level multiobjective quadratic fractional programming problem. This problem involves several objectives to be fulfilled at multi levels under a common set of quadratic constraints. Initially, we used parametric approach to convert the fractional programming model to an equivalent non-fractional programming model by allocating a parametric vector to each fractional objective. Then, $$\varepsilon$$ -constraint method is used to convert this multiobjective programming model into an equivalent model with single objective. The solution of every previous level is followed by the next level in succession to find a solution which is suitable to each level decision maker. An algorithm and numerical example are also presented at the end of the paper to validate the proposed methodology for the Model.

Industries are increasingly looking for opportunities at utilizing collaborative robots in assembly lines to perform the tasks independently or assist the human workers due to the advancement of industry 4.0 technologies. Purchasing cost is one of the important factors to be considered by production managers, while designing or redesigning assembly line when collaborative robots are being utilized. Several objectives are to be optimized in an assembly line balancing problem and optimizing line efficiency along with purchasing cost sometimes results in conflicting situation. This paper presents the first study to tackle the cost-oriented assembly line balancing problem with collaborative robots, where several different types of collaborative robots with different purchasing costs are available and selected. A multi-objective mixed-integer programming model is developed to minimize the cycle time and the total collaborative robot purchasing cost. The multi-objective migrating bird optimization algorithm is developed to obtain a set of high-quality Pareto solutions. This algorithm utilizes the fast non-dominated sorting approach to update the population and develops a restart mechanism to select one solution in the permanent Pareto archive to replace the abandoned solution which remains unchanged for several iterations. The computational study validates that the utilization of the multi-objective model is reasonable and developed algorithm produces competing performance in comparison with multi-objective non-dominated sorting genetic algorithm II, multi-objective simulated annealing algorithm and two multi-objective artificial bee colony algorithms.

This study proposes an integrated approach to plan tank-truck shipments of hazardous materials in intercity road networks. Considering the conventional damage and the diffusion of volatile toxicants separately, we define the risk indicators of tank-truck hazardous materials transportation. Moreover, we study the network consisting of a local distribution sub-network and a long-distance transportation sub-network. They can be implemented by a capacitated p-hub location model and a bi-level programming model. Furthermore, an exact algorithm and an improved imperialist competitive algorithm are developed to solve the bi-level programming model, respectively. Besides, there is a case study completed in a local region of East China. The numerical results demonstrate the research advantages in method innovation and engineering operation.

In Data analysis process clustering is one of the standard methods, which is used in many area such as pattern recognition and image segmentation and statistics, bioinformatics etc. Clustering have several algorithms in that most well know simplest algorithm is K-means, because of its simplicity, empirical success and efficiency. This present reality applications produce tremendous volumes of data, subsequently, how to productively deal with these data in a significant mining task has been a difficult and huge issue. Likewise Message passing Interface (MPI) as a Programming model for increases the scalability, performance and execution speed. Enthused by this, MPI with strong reinforcement parallel K- means clustering algorithm is implemented in this paper. The efficiency of the strong reinforcement K means clustering algorithm is improved in parallel environment by implementing in MPI methodology. In this paper performance of the clustering the data using K- means is compared between sequentially run of K- means algorithm and parallel run of K-means algorithm in Message passing interface architecture in terms of overhead cost and execution.

In an effort towards the democratization of Robotics, this article presents a novel End-User Development framework called Robot Interfaces From Zero Experience (RIZE). The framework provides a set of useful software tools for the creation of robot-oriented software architectures and programming interfaces, as well as the modeling and execution of robot behaviors, with a specific emphasis on social behaviors. Programming interfaces built on top of RIZE enable professionals with different backgrounds and interests to design, adapt, and scale-up robotics applications. As an example of a programming interface, we present Open RIZE, which exploits an End-User Programming paradigm combining blocks, tables, and forms-filling interfaces. Unlike previous approaches, robot behavioral code generated by Open RIZE is intrinsically modular, re-usable, scalable, neutral to the employed programming language, and platform-agnostic. In the article, we present the main design guidelines and features of Open RIZE. Additionally, we perform an initial usability evaluation of the Open RIZE interface in an online workshop. Preliminary results using the System Usability Scale with 10 novice end-users indicate that Open RIZE is easy-to-use and learn.

Uniqueness and Pareto-optimality of cross-efficiency scores are two main issues in the cross-efficiency evaluation. We address the Pareto-optimality issue in the cross-efficiency evaluation by presenting a natural notion of Pareto-optimality in the cross-efficiency evaluation which is based on a new self-prioritizing principle and aligned with the concept of dominance. This Pareto-optimality notion is devised using less stringent postulates, compared to those used to devise the existing Pareto-optimality notions in the current literature. We then propose a multi-objective model whose Pareto-optimal solutions generate all the Pareto-optimal cross-efficiency scores sets that fulfill the self-prioritizing principle. It is shown that there is a Pareto-optimal solution to the proposed multi-objective programming model which assigns a common set of weights to all decision making units. We then propose a linear model to obtain such an optimal solution using the weighted sum technique, thereby we can determine an optimal weights profile to generate a set of Pareto-optimal cross-efficiency scores by solving only one linear model. Having started with less stringent postulates, we have more flexibility in enhancing cross-efficiency scores of decision making units and can hence provide a non-dominated set of cross-efficiency scores as illustrated by a real data example. We also demonstrate the possibility of better, compared to other cross-efficiency evaluation methods, resource allocation using another real data example.

The Internet of Things (IoT) is becoming an increasingly common paradigm. As IoT usage scenarios have increased, many challenges in IoT operating systems’ safety and adaptability have remained. According to the programming model, IoT operating systems can be categorized into three types: multithreading, event-driven, and hybrid. Different operating system models are applied in different scenarios depending on the real-time requirements or resource richness. The safety of IoT operating systems is critical; hence, formal verification is an important method of detecting potential vulnerabilities and providing safety guarantees. This paper proposes a hybrid model for an IoT operating system and employs the Event-B method for modeling and verification. We rewrite the requirements and divide the Event-Bus hybrid operating system model into eight levels for refinement. The safety and liveness properties of Event-Bus are guaranteed by generating and proving the proof obligations at each model level. A large proportion of the proof obligations (91%) are automatically proven on the Rodin platform to simplify the development process.

Seru production is one of the latest manufacturing modes arising from Japanese production practice. Seru can achieve efficiency, flexibility, and responsiveness simultaneously. To accommodate the current business environment with volatile demands and fierce competitions, seru has attracted more and more attention both from researchers and practitioners. A new planning management system, just-in-time organization system (JIT-OS), is used to manage and control a seru production system. The JIT-OS contains two decisions: seru formation and seru loading. By seru formation, a seru system with one or multiple appropriate serus is configured; by seru loading, customer ordered products are allocated to serus to implement production plans. In the process of seru formation, workers have to be assigned to serus. In this paper, a seru loading problem with worker assignment is constructed as a bi-level programming model, and the worker assignment on the upper level is to minimize total idle time while the lower level is to minimize the makespan by finding out optimal product allocation. A product lot can be splitted and allocated to different serus. The problem of this paper is shown to be NP-hard. Therefore, a simulated annealing and genetic algorithm (SA-GA) is developed. The SA is for the upper level programming and the GA is for the lower level programming. The practicality and effectiveness of the model and algorithm are verified by two numerical examples, and the results show that the SA-GA algorithm has good scalability.

High Performance Computing has been a driving force behind important tasks such as scientific discovery and deep learning. It tends to achieve performance through greater concurrency and heterogeneity, where the underlying complexity of richer topologies is managed through software abstraction. In this paper, we present our assessment of NVSHMEM, an experimental programming library that supports the Partitioned Global Address Space programming model for NVIDIA GPU clusters. NVSHMEM offers several concrete advantages. One is that it reduces overheads and software complexity by allowing communication and computation to be interleaved vs. separating them into different phases. Another is that it implements the OpenSHMEM specification to provide efficient fine-grained one-sided communication, streamlining away overheads due to tag matching, wildcards, and unexpected messages which have compounding effect with increasing concurrency. It also offers ease of use by abstracting away low-level configuration operations that are required to enable low-overhead communication and direct loads and stores across processes. We evaluated NVSHMEM in terms of usability, functionality, and scalability by running two math kernels, matrix multiplication and Jacobi solver, and one full application, Horovod, on the 27,648-GPU Summit supercomputer. Our exercise of NVSHMEM at scale contributed to making NVSHMEM more robust and preparing it for production release.

Home health care structures provide care for the elderly, people with disabilities as well as patients with chronic conditions. Since there has been an increase in demand, organizations providing home health care are eager to optimize their activities. In addition, the increase in patient numbers has led organizations to expand their geographical reach. As a result, home health care structures tend to be located in different offices to limit their travel time and, consequently, caregivers employed by these various structures must be assigned to one of the offices so they start and end their workday at their associated office. Unlike the existing literature where an upstream assignment of caregivers is performed to become a parameter of the model, the assignment of caregivers to offices is solved during the resolution of the problem in order to obtain the best possible combinations. Thus, we suggest a mixed-integer programming model of the multi-depot home health care assignment, routing, and scheduling problem without prior assignment of caregivers to the home health care offices. In addition, we propose an original matheuristic-based approach with different assignment strategies to assign visits and caregivers to the home health care offices in order to solve the problem. The experiments are conducted on a set of 56 heterogeneous instances of various sizes. Results are compared with best solutions obtained by a commercial solver, and with a lower bound obtained by Lagrangian relaxation. The results highlight the efficiency of the matheuristic-based approach since it provides a low deviation ratio with a faster computational time.

This study approaches a multi-stage stochastic mixed-integer programming model for the high-level complexity of large-scale pavement maintenance scheduling problems. The substance of some parameters in the mentioned problems is uncertain. Ignoring the uncertainty of these parameters in the pavement maintenance scheduling problems may lead to suboptimal solutions and unstable pavement conditions. In this study, annual budget and pavement deterioration rate are considered uncertain parameters. On the other hand, pavement agencies generally face large-scale pavement networks. The complexity of the proposed stochastic model increases exponentially with the number of network sections and scenarios. The problem is solved using the Progressive Hedging Algorithm (PHA), which is suitable for large-scale stochastic programming problems, by achieving an effective decomposition over scenarios. A modified adaptive strategy for choosing the penalty parameter value is applied that aims to improve the solution process. A pavement network including 251 sections is considered the case study for this investigation, and the current study seeks optimal maintenance scheduling over a finite analysis period. The performance of the stochastic model is compared with that of the deterministic model. The results indicate that the introduced approach is competent to address uncertainty in maintenance and rehabilitation problems.

Building and programming tiny battery-free energy harvesting embedded computer systems is hard for the average maker because of the lack of tools, hard to comprehend programming models, and frequent power failures. With the high ecologic cost of equipping the next trillion embedded devices with batteries, it is critical to equip the makers, hobbyists, and novice embedded systems programmers with easy-to-use tools supporting battery-free energy harvesting application development. This way, makers can create untethered embedded systems that are not plugged into the wall, the desktop, or even a battery, providing numerous new applications and allowing for a more sustainable vision of ubiquitous computing. In this paper, we present BFree, a system that makes it possible for makers, hobbyists, and novice embedded programmers to develop battery-free applications using Python programming language and widely available hobbyist maker platforms. BFree provides energy harvesting hardware and a power failure resilient version of Python, with durable libraries that enable common coding practice and off the shelf sensors. We develop demonstration applications, benchmark BFree against battery-powered approaches, and evaluate our system in a user study. This work enables makers to engage with a future of ubiquitous computing that is useful, long-term, and environmentally responsible.

Software product line (SPL) is one of the most recent and effective reuse approaches. SPL derives several products from the core artefacts. SPL engineering includes two processes: domain engineering, which identifies the common and variable features to develop the core artefacts, and application engineering, which reuses the core artefacts to derive products. Once the artefacts are reused across multiple products, quality assessment is necessary to prevent inconsistencies from spreading across all SPL products. There are several frameworks and standards, as ISO/IEC 25010:2011, to evaluate quality characteristics. In this study, the authors provide an overview of the SPL quality assessment. Therefore, they perform a systematic mapping to compile and synthesise data regarding the quality characteristics assessed in studies from 2000 to 2019. The results include the identification of 346 metrics applied in 16 software properties to evaluate three quality characteristics of the ISO/IEC 25010:2011. Additionally, they find the domain engineering evaluation frequently occurs regarding the maintainability characteristic. Moreover, they provide analyses of the: (i) metrics used by programming paradigm, (ii) metrics used by software properties, (iii) software properties evaluated for each quality characteristic, (iv) tools used to extract metrics, (v) systems used as benchmarks, and (vi) datasets used for extracting metrics.