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The self-diffusion of silicon in single crystal MoSi 2 was studied by means of a radiotracer technique using the short-lived radioisotope 31 Si (half-life ), which was produced and implanted into the samples at the ion-guide isotope separator on-line device at the University of Jyväskylä in Finland. Diffusion annealing and subsequent serial sectioning of the specimens were performed immediately after the radiotracer implantation. In the entire temperature region investigated (835-1124 K), the 31 Si diffusivities in both principal directions of the tetragonal MoSi 2 crystals obey Arrhenius laws, where the diffusion perpendicular to the tetragonal axis is faster than parallel to it. In previous studies the same features were observed for the 71 Ge diffusivities in MoSi 2 , except that these are somewhat higher than those of 31 Si. Furthermore, it is noteworthy that in MoSi 2 the diffusivities of 31 Si and 71 Ge are orders of magnitude faster than the diffusivity of 99 Mo. This large difference suggests that silicon diffusion and molybdenum diffusion are completely decoupled and that silicon diffusion takes place exclusively on the silicon sublattice. Literature data on the phase growth of MoSi 2 are in accordance with the present results on the 31 Si diffusivities; Monte Carlo simulations of the correlation effects of vacancy-mediated diffusion on the silicon sublattice of MoSi 2 lead to their rationalization. [ABSTRACT FROM AUTHOR]

Post-sale customer care (PSCC) outsourcing is an effective supply chain strategy to reduce costs, but traditional outsourcing methods cannot ensure consumers' belief in PSCC quality, harming the firms' competitive strategy. To ensure consumer satisfaction through transparency, this article examines evolving supply chain strategies of leveraging blockchain to outsource PSCC operations of competing manufacturers to third-party agents. Based on consumers' beliefs and blockchain adoption cost, manufacturers have two choices; to outsource the PSCC operations with or without blockchain, resulting in multiple outsourcing strategies. Results show that the consumers' belief in one manufacturer's outsourcing strategy has detrimental effects on the stance of another manufacturer. Moreover, the blockchain cost and consumers' beliefs dictate the manufacturers' strategies for PSCC outsourcing, resulting in a 'quadruple zone of strategic fit', where both manufacturers adopt blockchain or neither adopt blockchain, or one adopts while the other does not. Interestingly, despite the low or zero blockchain cost, both manufacturers will outsource without blockchain if their traditional business models satisfy consumers. The extended analysis reveals contradictory results that manufacturers should outsource more often with blockchain to facilitate consumers when a long-term warranty is offered, or the product has a higher failure rate, even though, overall these factors increase costs. [ABSTRACT FROM AUTHOR]

The Industry 4.0 revolution is changing the manufacturing landscape. A broad set of new technologies emerged (including software and connected equipment) that digitise manufacturing systems. These technologies bring new vitality and opportunities to the manufacturing industry, but they also bring new challenges. This paper focuses on the impact of Industry 4.0 on production planning approaches and software. We first propose a digital twin framework that integrates production planning systems and frontier technologies. The frontier technologies that may impact production planning software are the internet of things, cloud manufacturing, blockchain, and big data analytics. Second, we provide a state-of-the-art on the application of each technology in the production planning, as well as a detailed analysis of the benefit and application status. Finally, this paper discusses the future research and application directions in the production planning. We conclude that Industry 4.0 will lead to the construction of data-driven models for production planning software. These tools will include models built accurately from data, account for uncertainty, and partially actuate the decision autonomously. [ABSTRACT FROM AUTHOR]

This article studies the problem of scheduling independent jobs on parallel machines with a common server, the objective of which is to minimize the makespan. In this case, the common server is responsible for performing the setup operations and, therefore, there must be no conflicts while conducting them. Four alternative time-indexed formulations for the problem are considered and evaluated computationally. Moreover, two algorithms are presented that can significantly improve the performance of the best time-indexed formulation. The results obtained on two benchmark datasets involving up to 100 jobs suggest that the proposed improved algorithms are substantially better than existing approaches. [ABSTRACT FROM AUTHOR]

Because of the Covid-19 pandemic, urgent surging demand for healthcare products such as personal protective equipment (PPE) has caused significant challenges for multi-tier supply chain management. Although a given firm may predominantly focus on an arms-length solution by targeting the first-tier supplier, the firm can still struggle with extended multi-tier suppliers it cannot choose which use unsustainable practices. One key goal is compliance across various dimensions with production, environmental and labour standards across the multi-tier supply chain, a goal that blockchain technology can be applied to manage. Based on a comprehensive literature review, this research develops a system architecture of blockchain-based multi-tier sustainable supply chain management in the PPE industry designed to identify and coordinate standards in production and social and environmental sustainability in multi-tier PPE supply chains. The architecture was validated by theoretical basis, expert opinions and technical solutions. We conclude with managerial implications for implementing blockchain technology to advance sustainable multi-tier supply chain practices. [ABSTRACT FROM AUTHOR]

As an information technology that could significantly improve supply chain visibility and process automation, blockchain has been extensively applied in the field of supply chain finance (SCF). However, tradeoffs among the security, the operation cost, and the efficiency of the blockchain system may cause the SCF system dominated by a financial institution to inevitably fall into the dilemmas of risky or un-economic if the blockchain technology is applied inappropriately. Therefore, the objective of this paper is to optimise the blockchain application in the financial institution-based SCF system. We first analyse the application of blockchain security in SCF, and then the performance tradeoffs of blockchain and its impact on the performance of the supported SCF system. Based on the analysis above, an optimisation approach has been proposed and a corresponding non-linear integer programming (NIP) model has been constructed to select the best blockchain design schemes for the SCF system to achieve overall optimal in terms of security, cost, and efficiency. A designed ant colony algorithm is used to solve the optimisation problem. An application case analysis is used to verify the feasibility and effectiveness of the optimisation model. [ABSTRACT FROM AUTHOR]

To quickly manufacture multi-variety and low-volume products, manufacturing factories are increasingly sharing resources on collaborative production networks. However, the reliability of communication between factories cannot be fully guaranteed using traditional centralised approaches. Emerging blockchain technology can solve this problem due to its characteristics such as decentralisation and security. In this context, an integrated optimisation problem of multi-factory production and blockchain-secured collaborative maintenance is studied in this paper. Two scenarios are introduced with respective Q learning-based solution frameworks to solve the integrated problem. In the simulation scenario, preventive maintenance (PM) with flexible time windows is integrated with multi-factory production scheduling for reducing the probability of machine failures, and an initial integrated optimisation scheme is obtained. To make it more realistic, inevitable failures are considered in the actual production scenario, and the proposed collaborative maintenance strategy is triggered. Specifically, a corrective maintenance (CM) strategy is carried out immediately on the failed machine in case of a failure, followed by the PM on machines of the same type as the failed machine in other factories and the rescheduling of unprocessed jobs. Through a series of numerical studies, the effectiveness of the proposed optimisation approach and maintenance strategy is validated, and some interesting managerial implications also rise. [ABSTRACT FROM AUTHOR]

In this study, we focus on ripple effect mitigation capability of the Indian pharmaceutical distribution network during disruptions like COVID-19 pandemic. To study the mitigation capabilities, we conduct a multi-layer analysis (network, process, and control levels) using Bayesian network, mathematical optimisation, and discrete event simulation methodologies. This analysis revealed an associative relationship between ripple effect mitigation capabilities and network design characteristics of upstream supply chain entities. Using stochastic optimisation and Lagrangian relaxation, we then find ideal candidates for regional distribution centres at the downstream level. We then integrate these downstream locations with other supply chain entities for building the network optimisation and simulation model to analyse overall performance of the system. We demonstrate utility of our proposed methodology using a case study involving distribution of N95 masks to ‘Jan Aushadhi’ (peoples’ medicines) stores in India during COVID-19 pandemic. We find that supply chain reconfiguration improves service level to 95.7% and reduces order backlogs by 10.7%. We also find that regional distribution centres and backup supply sources provide overall flexibility and improve occupational health and safety. We further investigate alternate mitigation capabilities through fortification of suppliers’ workforce by vaccination. We offer recommendations for policymakers and managers and implications for academic research. [ABSTRACT FROM AUTHOR]

Cross-docking is a logistics procedure implemented in a warehouse to achieve a competitive advantage by consolidating and transferring goods directly from an inbound supplier to an outbound customer on short notice and with no or limited storage. Today, one of the challenges related to cross-docking for both practitioners and researchers is handling the uncertainty. Robust cross-docking solutions bring a part of the answer to this challenge. This paper proposes an overview of robust and real-time models for cross-dock problems with a focus on scheduling problems, notably in the road-to-road cross-dock environment. To this end, the conducted systematic literature review addresses the collection, identification, screening, eligibility, and inclusion steps to extract the most relevant literature. The gaps in the literature are identified, and some perspectives to support future studies are proposed. [ABSTRACT FROM AUTHOR]

Blockchain technologies can support traceability, transparency and trust among participants. This has primarily been explored in established supply chains and not in the growing use of business networks or ecosystems, which is a notable limitation since supply chains typically are organised with a dominant actor that ensures common information systems and standards that negate blockchain benefits. Hence, this study explores the design of a blockchain-based collaborative framework for resource sharing using smart contracts. These are particularly well-suited for supporting operations in broader networks or ecosystems beyond supply chains with established collaborations and hierarchies. Based on a systematic literature review, a demonstrator framework was developed for stakeholder interactions through a procurement and distribution unit backed with blockchain technology. The framework consists of (a) network architecture to demonstrate partner interactions; (b) rules for network working principles based on supply collaboration requirements; (c) UML diagram to define smart contract interaction sequence; and (d) algorithm for smart contract network verification and validation. Applicability of these smart contracts was verified by deployment on an Ethereum blockchain. The demonstrator framework ensures quality and data authenticity in supply networks, so it is useful for effective resource utilisation in networks where outsourcing and production surpluses are major issues. [ABSTRACT FROM AUTHOR]

In this paper, a two-stage robust optimisation is presented for an uncapacitated hub location problem in which demand is uncertain and the level of conservatism is controlled by an uncertainty budget. In the first stage, locations for establishing hub facilities were determined, and allocation decisions were made in the second stage. An accelerated Benders decomposition algorithm was used to solve the problem. Computational experiments showed better results in terms of number of iterations and computation time for Benders decomposition with Pareto-optimal cuts in comparison with the classical Benders decomposition algorithm. According to numerical analysis, it was concluded that increasing the uncertainty budget also increased total costs for more established hubs. To determine the uncertainty budget in an appropriate manner, a new expected aggregate function was introduced. The numerical studies demonstrated the usefulness of the proposed method in defining the appropriate uncertainty budget in the presence of uncertainty. [ABSTRACT FROM AUTHOR]

This paper mainly studies a task allocation and scheduling problem in the multi-thread fulfilment process of electronic order, which seeks to minimise the makespan under thread constraints and order precedence constraints. The problem is formulated as a Mixed Integer Programming (MIP) model and a novel depth-first heuristic is proposed to solve it. The depth-first heuristic shows high effectiveness and efficiency, compared with the current policy and the genetic algorithm in both small/medium-scale and large-scale cases from the real transaction data. In addition, two extensions on precedence constraint reduction and resource allocation are discussed to further improve and manage the e-order fulfilment process. [ABSTRACT FROM AUTHOR]

Since the Third Industrial Revolution, technology and the global economy have developed rapidly. Driven by market demand and the development of science and technology, the organisational model of the production system has evolved, which has in turn caused changes in the methods of production scheduling. In the context of the newest industrial revolution (Industry 4.0), this review aims to examine the evolution of production scheduling in terms of economics and technology. First, literature on production scheduling is summarised and analysed from the perspectives of centralised/decentralised scheduling, distributed scheduling, and cloud manufacturing scheduling. Second, future challenges and trends in the development of production scheduling are discussed in view of the globalisation of manufacturing and changes in production modes enabled by new technologies. Finally, based on the findings of this review, we make a prediction for the future expansions of the customer-centric value chain as well as changes in product design and production methods brought by product personalisation. [ABSTRACT FROM AUTHOR]

In the last century, there was a general perception that scheduling theory was highly irrelevant to scheduling practice. Many recent studies, however, have suggested that the applicability of scheduling approaches is highly intertwined with the manufacturing environment in which the scheduling task is carried out. In this paper we used the constructs of Contingency Theory to suggest specific fits between scheduling approaches and manufacturing environments, after suggesting that the theory-practice gap in production scheduling research has been caused by three issues: (a) simplification of scheduling problems, (b) simplification of the practical scheduling task as a decision process, and (c) lack of relevance of the traditional scheduling approach to all manufacturing environments. Furthermore, we suggest that the dynamism of the state of the system and the complexity of the scheduling problem are the two constituting vectors that define the complexity of the scheduling task. We use both vectors to identify different types of manufacturing environments and propose specific fits with scheduling approaches. Finally, we hypothesize that the fit between scheduling approaches and manufacturing environments is only relevant in environments with high resource utilization where the scheduling task could have a bigger impact on a firm's performance, and present three case studies to better exemplify the relevance of the conceptual framework. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of scheduling independent jobs on identical parallel machines with a single server to minimise the makespan. We propose mixed integer programming (MIP) formulations to solve this problem. Each formulation reflects a specific concept on how the decision variables are defined. Moreover, we present inequalities that can be used to improve those formulations. A computational study is performed on benchmark instances from the literature to compare the proposed MIP formulations with other known formulations from the literature. It turns out that our proposed time-indexed variables formulation outperforms by far the other formulations. In addition, we propose a very efficient MIP formulation to solve a particular case of the problem with a regular job set. This formulation is able to solve all regular instances for the case of 500 jobs and 5 machines in less than 5.27 min, where all other formulations are not able to produce a feasible solution within 1 h. [ABSTRACT FROM AUTHOR]

The adoption of technologies by the operations and supply chain management (OSCM) field is leading to extraordinary disruptions. And with the rapid emergence of cutting-edge and more disruptive technologies, the OSCM is striving to take advantage of such innovations, but they are bringing in their wake a number of challenges. One of those disruptive technologies is blockchain, which is increasingly accepted in virtually all industries. This study aims to investigate the blockchain technology (BCT) adoption behaviour and possible barriers in the Brazilian OSCM context. We developed a model drawing on the unified theory of acceptance and use of technology (UTAUT) model, the supply chain literature, and the emerging literature on BCT. We empirically validated the proposed model with Brazilian operations and supply chain professionals by using the partial least squares structural equation modelling (PLS-SEM). Our findings revealed that facilitating conditions, trust, social influence, and effort expectancy are the most critical constructs that directly affect BCT adoption. Unexpectedly, performance expectancy appeared not decisive in terms of predicting BCT adoption. This study contributes to advancing and stimulating the theory about BCT adoption behaviour in supply chains, as well as important managerial implications, which may be more critical for emerging economies. [ABSTRACT FROM AUTHOR]

Traceability has become a critical element in supply chain management, particularly in safety-sensitive sectors like food, pharmaceuticals, etc. Upstream (manufacturers, producers, etc.) and downstream (distributors, wholesalers, etc.) supply chain members need to store and handle traceability-related information for providing proof of regulatory compliance to both state authorities and more demanding customers. Consumers also place high expectations on food supply chains (FSC) with specific emphasis on facets related to safety. However, the complexity of modern FSC networks and their fragmentation act as barriers for the development of sound traceability mechanisms. In this paper a distributed trustless and secure architecture for FSC traceability is developed and tested. For assessing the feasibility of the proposed approach, a food traceability case study from a dairy company is presented. The applicability of the model is further illustrated by the development of fully functional smart contracts and a local private blockchain. Moreover, the various links between the proposed blockchain-based model and its managerial implications are presented. The overall benefits of the proposed model are discussed along with fruitful areas for future research. The results are of significant value to both practitioners and researchers. [ABSTRACT FROM AUTHOR]

Blockchain is a disruptive technology that promises to embed visibility and trustworthiness in supply chains. This paper examines the perceived role of blockchain in improving SCM and profitability of organisations in the manufacturing industry. It establishes the blockchain benefits for the manufacturing industry using the process of scale development. The proposed hypotheses related to the indirect effects are based on the resource-based view of the firm. The conditional indirect effects for four organisational factors are tested. The research framework is operationalised based on data from 236 practitioners. The findings show that blockchain is perceived to drive improvement in six supply chain dimensions of the manufacturing industry. The breadth of organisation size and geographical dispersion moderate the mediation relationship between blockchain benefits and incremental profitability. Furthermore, the conditional indirect effects are found significant at mean and ±1σ values of integration intensity and IT integration. According to managers of manufacturing industry, blockchain can bring significant improvement in delivery reliability and mass customisation, which would result in increasing the profitability of the organisation. Organisations with low integration intensity, high IT integration and small size organisations are likely to be the early adopters of blockchain technology. [ABSTRACT FROM AUTHOR]

This article revisits the scheduling problem in a two-machine flow-shop system with the total late work criterion, which penalizes parts of jobs executed after their due dates. Firstly, it is shown that a lower bound presented previously in the literature, in the context of a branch-and-bound algorithm proposed for the same problem, is invalid. Then a novel proposal of the branch-and-bound method is given equipped with a new lower-bound technique, as well as an upper-bound and dominance rules. Numerical experiments show that the newly proposed lower-bound technique works well in cutting unpromising branches. [ABSTRACT FROM AUTHOR]

Multi-factory production planning and scheduling problems have been increasingly studied by scholars recently due to market uncertainty, technological trends like Industry 4.0 and increasing collaboration. Geographically dispersed factories may provide cost-saving potential and increase efficiency while also being subjected to varying capabilities and restrictions such as capacity constraints and labour costs. Traditional approaches in production planning and scheduling focus on the allocation of demand to a single factory and obtain sequences of operations on machines in this factory. In the multi-factory or distributed setting, an additional task includes assigning orders to potential factories beforehand. Starting with the first case studies in the late 1990s, research has increasingly been devoted to this research field and has considered numerous variations of the problem. We review 128 articles on multi-factory production planning and scheduling problems in this contribution and classify the literature according to shop configuration, network structure, objectives, and solution methods. Bibliometric analysis and network analysis are utilised to generate new findings. Research opportunities identified include integration with other planning stages, an investigation of key real-life objectives such as due date compliance and examining dynamic characteristics in the context of Industry 4.0. Besides, empirical studies are necessary to gain new practical insights. [ABSTRACT FROM AUTHOR]

An outbreak of deadly COVID-19 virus has not only taken the lives of people but also severely crippled the economy. Due to strict lockdown, the manufacturing and logistics activities have been suspended, and it has affected the demand and supply of various products as a result of restrictions imposed on shopkeepers and retailers. Impacts of COVID-19 are observed ubiquitously in every type of units from different sectors. In this study, a simulation model of the public distribution system (PDS) network is developed with three different scenarios to demonstrate disruptions in the food supply chain. Difficulties have been increased in matching supply and demand in a vast network of PDS because of changing scenarios with the growth of infected cases and recovery. This paper also highlights the importance of a resilient supply chain during a pandemic. Our proposed simulation model can help in developing a resilient and responsive food supply chain to match the varying demand, and then further assist in providing decision-making support for rerouting the vehicles as per travel restrictions in areas. Paper has been summarised with significant highlights and including future research scope for developing a more robust food supply chain network. [ABSTRACT FROM AUTHOR]

It is a widely accepted fact that almost every research or business revolves around Data. Data from various business sectors has been growing sharply and the management of this massive amount of data is the biggest professional crunch these days. The notion of Big Data Analytics (BDA) is a prominent facet that delivers the best possible solution to decision-makers for efficiently handling the problems related to huge data. The key role of BDA in the area of Supply Chain Management (SCM), Logistics Management (LM), and Inventory Management (IM) is of utmost significance as it optimises the business operations by analyzing customer behaviour. Motivated with the promising paybacks of the BDA, a recent review from the year 2015–2019 is presented in this paper. Further, the significance of BDA in SCM, LM, and IM has been highlighted by studying 58 papers, which have been sorted after a detailed study of 260 papers, collected through the Web of Science (WoS) database. The findings and observations give state-of-the-art insights to scientists and business professionals by presenting an exhaustive list of the progress made and challenges left untackled in the field of BDA in SCM, LM, and IM. [ABSTRACT FROM AUTHOR]

In this paper, production scheduling for rotomoulded plastics manufacturing in a multi-machine environment is considered. The objective is to minimise total tardiness. The problem has some commonality with hybrid flow shop scheduling with batching, where additional constraints are needed to control which machines may be used at each stage. The problem is shown to be NP-hard and is formulated as a mixed integer program. Given consequently large solve times to obtain optimal solutions, simulated annealing and tabu search algorithms were developed alongside a constructive heuristic to obtain near-optimal solutions within a practical time-frame. The solution algorithms were tuned and tested using randomly generated problem instances. The best results in terms of solution quality were generally obtained by simulated annealing. The problem instances were generated to be representative of a real production environment located in Queensland, Australia. [ABSTRACT FROM AUTHOR]

This paper describes the basis of a Decision Support System (DSS) designed to schedule fertiliser production orders to be delivered within time windows, in plants made up of multiple heterogeneous parallel processors (production lines), considering that fertiliser production rates and nomenclatures depend on lines, that setup times depend on sequence and lines, and taking into account downtime constraints (preventive maintenance ...). A mixed linear programming model is encapsulated in the DSS which considers the schedule's impacts, immediately upstream and downstream of plants in the supply chain. These side-effects may make the proposed solution unfeasible and the DSS helps redefining the problem to avoid them. [ABSTRACT FROM AUTHOR]

Efficient collaboration between various sub-processes of steel production is of considerable significance, which directly affects a product's production cycle and energy consumption. However, current collaborative optimisation models and methods in steel production are still limited: (1) Most of the current collaborative manufacturing problems in steel production focus on obtaining joint schedule between steel-making and continuous casting (SCC), and the works considering continuous casting and hot rolling (CCHR) are very few. (2) The processing time is assumed as a constant in most of the existing SCC scheduling models. However, the rolling time of a product in hot rolling operation is actually uncertain and deteriorating. (3) Exact algorithms cannot be applied to solve the complicated collaborative optimisation problems because of their high complexities. To address these problems, we propose an integrated CCHR and batch delivery scheduling model where interval rolling time and linear deterioration effect are considered. With the concept of min–max regret value, we formulate the collaborative optimisation problem as a robust optimisation problem. Instead of using the exact algorithm, we develop an Improved Variable Neighborhood Search (IVNS) algorithm incorporated a novel population update mechanism and neighbourhood structures to solve the robust optimisation problem. Moreover, we develop an exact algorithm that combines CPLEX solver and two dynamic programming algorithms to obtain the maximum regret value of a given rolling sequence. The results of computational experiments show the excellent performance of the proposed algorithms. Abbreviations: IVNS: improved variable neighbourhood search; TOPSIS: technique for order of preference by similarity to ideal solution; PUM-TOPSIS: population update mechanism based on TOPSIS; DP: dynamic programming; NSs-PUC: neighbourhood structures based on the parameterised uniform crossover; SNRT: shortest normal rolling time; SNRT-DP: DP algorithm based on SNRT rule; BRKGA: biased random-key genetic algorithm; SCC: steelmaking and continuous casting; MINP: mixed integer nonlinear programme; CCHR: continuous casting and hot rolling; PSO: particle swarm optimisation; GA: genetic algorithm; VNS-HS: variable neighbourhood search and harmony search; HPSO + GA: hybrid PSO and GA; SA: simulated annealing; B&B: branch-and-bound; TPSO: two-phase soft optimisation; TSAUN: tabued simulated annealing with united-scenario neighbourhood; VNS: variable neighbourhood search; ABC: artificial bee colony; PRVNS: population-based reduced variable neighbourhood search; NS1: neighbourhood structure 1; NS2: neighbourhood structure 2; DE: differential evolution; WSR: Wilcoxon signed-rank test; ENS: exchange neighbourhood structure; IVNS-ENS: IVNS with ENS; RPI: relative percentage increase; ARPI: average RPI; SD: standard deviation. [ABSTRACT FROM AUTHOR]

The growing importance of humanitarian operations has created an imperative to overcome the complications currently recorded in the field. Challenges such as delays, congestion, poor communication and lack of accountability may represent opportunities to test the reported advantages of emergent disruptive technologies. Meanwhile, the literature on humanitarian supply chains looks at isolated applications of technology and lacks a framework for understanding challenges and solutions, a gap that this article aims to fill. Using a case study based on the flood of Tabasco of 2007 in Mexico, this research identifies solutions based on the use of emergent disruptive technologies. Furthermore, this article argues that the integration of different technologies is essential to deliver real benefits to the humanitarian supply chain. As a result, it proposes a framework to improve the flow of information, products and financial resources in humanitarian supply chains integrating three emergent disruptive technologies; Artificial Intelligence, Blockchain and 3D Printing. The analysis presented shows the potential of the framework to reduce congestion in the supply chain, enhance simultaneous collaboration of different stakeholders, decrease lead times, increase transparency, traceability and accountability of material and financial resources, and allow victims to get involved in the fulfilment of their own needs. [ABSTRACT FROM AUTHOR]

The research and thinking pertaining to blockchain have thus far focused on cryptocurrency and Bitcoin. However, there is increased interest in using the technology to solve operational challenges in manufacturing and service supply chains. In this study, we introduce a new implication of using blockchain technology and propose two unique contributions. First, we introduce the notion of computational costs (measured in units of gas) as an essential mechanism for completing operational transactions in the blockchain environment. Second, we discuss the use of smart contracts and their influence on operational transactions. To investigate the link between blockchain transaction and computational costs, this study uses an experimental methodology. We develop and implement a fully functional virtual public blockchain to store, validate, and maintain transactions. The methodology provides a process to measure the computational costs, frequency, and intensity of transactions. This research contributes to conceptual research on the blockchain implementation paradigm. Its novelty stems from the identification of computational costs for operational transactions and use of an experimental methodology. This research provides managers an insight into the design of smart contract transactions in a supply chain from a cost perspective. [ABSTRACT FROM AUTHOR]

We propose a trade-off balancing (TOB) heuristic in a no-wait flow shop to minimise the weighted sum of maximum completion time ( C max ) and total completion time (TCT) based on machine idle times. We introduce a factorisation scheme to construct the initial sequence based on current and future idle times at the operational level. In addition, we propose a novel estimation method to establish the mathematical relationship between the objectives min( C max ) and min(TCT) at the production line level. To evaluate the performance of the TOB heuristic, computational experiments are conducted on the classic Taillard's benchmark and one-year historical data from University of Kentucky HealthCare (UKHC). The computational results show that minimisations of C max and TCT yield inconsistent scheduling sequences, and these two sequences are relatively uncorrelated. We also show that our TOB heuristic performs better than the best existing heuristics with the same computational complexity and generates stable performances in balancing trade-offs. [ABSTRACT FROM AUTHOR]

Recently, the applications of Blockchain technology have begun to revolutionise different aspects of supply chain (SC) management. Among others, Blockchain is a platform to execute the smart contracts in the SC as transactions. We develop and test a new model for smart contract design in the SC with multiple logistics service providers and show that this problem can be presented as a multi-processor flexible flow shop scheduling. A distinctive feature of our approach is that the execution of physical operations is modelled inside the start and completion of cyber information services. We name this modelling concept 'virtual operation'. The constructed model and the developed experimental environment constitute an event-driven dynamic approach to task and service composition when designing the smart contract. Our approach is also of value when considering the contract execution stage. The use of state control variables in our model allows for operations status updates in the Blockchain that in turn, feeds automated information feedbacks, disruption detection and control of contract execution. The latter launches the re-scheduling procedure, comprehensively combining planning and adaptation decisions within a unified methodological framework of dynamic control theory. The modelling complex developed can be used to design and control smart contracts in the SC. [ABSTRACT FROM AUTHOR]

Research in industry 4.0 is growing, driven by the innovations in production systems on a continuous basis. In this study, we identified the evolution of themes inherent in the industry 4.0 using a bibliometric software, namely SciMAT (Science Mapping Analysis Software Tool). The analyses included 1882 documents, 4231 keywords, and the relevant information was extracted based on frequency of co-occurrence of keywords. The clusters were plotted in two-dimensional strategic diagrams and analysed using the bibliometric indicators such as the number of publications, number of associated documents, and h-index. The results revealed that 2017 had the largest number of publications. Expert authors in the field and the periodicals that published the most were identified. The science mapping presented 31 clusters in which the most representative motor themes were CPS (Cyber-Physical System), IoT (Internet of Things), and Big Data. In addition, it was possible to identify fields with high investment of efforts by the scientific community such as the union between lean production and industry 4.0, production-centered CPS (CPPS), IoT (Industrial Internet of Things - IIoT), among others. The overlapping map showed an increase in the number of keywords from 338 to 1231 over the period of data. The map of scientific developments supported by an exhaustive research, it was possible to show the state of the art, the main challenges and perspectives for future research in the field of industry 4.0 such as Technology, Collaboration/Integration, Management and Implementation. [ABSTRACT FROM AUTHOR]

Information on the editors of this journal is presented, including Alexandre Dolgui, Frank Werner and J. A. Harding.

Extended producer responsibility (EPR) is a regulatory measure to enforce the life cycle management of electrical and electronic equipment, however, the implementation of EPR programmes is not as effective as expected. In the face of the fourth industrial revolution that commonly labelled as 'Industry 4.0,' this paper proposes an integrated architecture to achieve effective and efficient EPR from the manufacturer perspective, and attention is specifically paid on promoting information sharing. On the basis of the selected case study, a smart refrigerator plant of Haier, the architecture integrates information systems and facilitates life cycle management. Particularly, eco-design and end-of-life disposal, the two lasting problems in the current practises of implementing EPR, can be enforced based on product modularisation and high level of information availability that provided by the architecture. The outcomes of this study provide a valuable reference for other sectors that involve EPR or product life cycle management. [ABSTRACT FROM AUTHOR]

In this study, a coordinated production and transportation planning problem is addressed. A fleet of heterogeneous vehicles is considered. The transportation costs consist of two terms: a term represents the fixed cost for each vehicle used and another term for the variable cost which is the marginal transportation cost times the transportation quantity. The problem is formulated as a mixed-integer linear programming model (MILP) and a Non-Linear Programming model (NLP). The problem is unsolvable when the size increases beyond a certain magnitude. Therefore, two heuristics are developed. One of them is a decomposition-based heuristic (termed DBH). It combines solutions by decomposing the original problem into production and transportation subproblems and iteratively improves the combined solutions. The other heuristic is based on Lagrangian relaxation (called LRBH). The performance of these heuristics is evaluated by comparing their results with optimal solutions for small-sized instances and with Lagrangian-relaxation-based lower bounds for medium- or large-sized instances. The results indicate that although these heuristics use distinct mechanisms, they are both efficient and have comparable performances. The average cost gap for DBH as well as LRBH is around compared with optimal solutions. They are 4.43 and , respectively, when compared with Lagrangian-relaxation-based lower bounds. [ABSTRACT FROM AUTHOR]

In the vehicle routing problem with cross-docking (VRPCD), it is assumed that the selected suppliers and the quantity of the products purchased from each supplier are known. This paper presents an MILP model which incorporates supplier selection and order allocation into the VRPCD in a multi-cross-dock system minimising the total costs, including purchasing, transportation, cross-docking, inventory and early/tardy delivery penalty costs. The sensitivity of the model on the key parameters of the objective function is analysed and the supply decisions are evaluated when the coefficients of the distribution cost are changed. A two-stage solution algorithm (TSSA) is proposed and the results of the TSSA for small-sized instances are compared with the exact solutions. Finally, a large-sized real case of an urban freight transport is solved using the TSSA. [ABSTRACT FROM AUTHOR]

An uncertain single-machine scheduling problem is considered, where the processing time of a job can take any real value from a given segment. The criterion is to minimise the total weighted completion time of the n jobs, a weight being associated with each given job. We use the optimality box as a stability measure of the optimal schedule and derive an O(n)-algorithm for calculating the optimality box for a fixed permutation of the given jobs. We investigate properties of the optimality box using blocks of the jobs. If each job belongs to a single block, then the largest optimality box may be constructed in time. For the general case, we apply dynamic programming for constructing a job permutation with the largest optimality box. The computational results for finding a permutation with the largest optimality box show that such a permutation is close to an optimal one, which can be determined after completing the jobs when their processing times became known. [ABSTRACT FROM AUTHOR]

Models and optimisation approaches are developed for a flexible job shop scheduling problem with lot streaming and lot sizing of the variable sublots. A two-stage optimisation procedure is proposed. First, the makespan value is minimised with the smallest sublots defined for the problem instance. This makes it possible to shorten the makespan significantly, because each sublot is transferred separately to the next operation of a job. In the second stage, the sizes of the sublots are maximised without increasing the obtained makespan value. In this way, the quantity of sublots and transport activities is limited together with the related manufacturing cost. Two objectives are defined for the second stage. The first one is the maximisation of the sum of the sublot sizes of all operations, the second one is the maximisation of the number of the operations which do not need to be split at all. Mixed-integer linear programming, constraint programming and graph-based models are implemented for the problem. Two optimisation approaches are developed and compared in computational experiments for each stage and objective, one approach is based on a third-party solver, and the second one on an independent own implementation, namely a tabu search and a greedy constructive heuristic. [ABSTRACT FROM AUTHOR]

In the conditions of an increased worldwide competition, supply chains are struggling to respond to an increasingly volatile and complex environment. With technological advances, current practices to build efficient supply chains have changed. Companies are seeking to use internet in order to cope with the flexible and dynamic nature of logistics networks. The purpose of this article is to address the flexible dynamic e-procurement context under asynchronous and repetitive variations over time. The supply chain considered is composed of two levels (buyer-suppliers) operating in highly agile environment. The questions facing the buyer is how many units of product should be purchased and from which supplier in response to variation in term of price and capacity. Because of this highly changing environment characterised by frequent changes in a short time, most of the classical optimisation approaches seem inadequate to address these problems. Recently, dynamic optimisation has been proposed to deal with such problems. However, we have no knowledge of its application in a supply chain context. We suggest a dynamic genetic approach which is applied to an e-procurement context in aim to optimise the procurement process during time. [ABSTRACT FROM AUTHOR]

We study a single machine scheduling problem (SMSP) with uncertain job release times (JRTs) under the maximum waiting time (MWT) criterion. To deal with the uncertainty, a robust model is established to find an optimal schedule, which minimises the worst-case MWT (W-MWT) when JRTs vary over given time intervals. Although infinite possible scenarios for JRTs exist, we show that only n scenarios are needed for calculating the W-MWT, where n is the number of jobs. Based on this property, the robust (SMSP) with uncertain JRTs to minimise the W-MWT is formulated as a mixed integer linear programming problem. To solve large-size problem instances, an efficient two-stage heuristic (TSH) is proposed. In the first stage, n near-optimal schedules are obtained by solving n deterministic scenario-based SMSPs, and their W-MWTs are evaluated. To speed up the solution and evaluation process, a modified Gusfield’s heuristic is proposed by exploiting the inner connections of these SMSPs. To further improve the schedule obtained in the first stage, the second stage consists of a variable neighbourhood search method by combining both swap neighbourhood search and insert neighbourhood search. We also develop a method to calculate the lower bound of the proposed model so that we can evaluate the performance of the solutions given by the TSH. Experimental results confirm the robustness of schedules produced and advantages of the proposed TSH over other algorithms in terms of solution quality and run time. [ABSTRACT FROM AUTHOR]

One of the main challenges in the operation of robotic assembly lines is the occurrence of failures. Due to the connection of the stations via a material handling system, failures at one station often result in throughput losses. To some extent, these throughput losses can be reduced by installing buffers between the stations. However, the installation of buffers requires considerable investments and scarce factory space. Due to the advances of manufacturing technologies that form the foundation of ‘Industry 4.0’, new solutions to reduce failure-related throughput losses open up. One solution is a redundant configuration, in which downstream (backup) stations automatically take over the operations of failed stations during repair time. The throughput loss in these situations depends on the allocation of operations and the assignment of backup stations. Existing approaches in the literature that consider redundancies in the configuration of automated lines neglect the resulting production rate. Instead, the lines’ level of redundancy is used as a surrogate measure for optimisation. We present a genetic algorithm for the redundant configuration of robotic assembly lines with stochastic failures to maximise the production rate of the line. In a numerical analysis, it is demonstrated that this approach allows for productivity improvements. [ABSTRACT FROM AUTHOR]

This article considers the min–max version of a single-machine scheduling problem with generalized due dates under processing time uncertainty. The objective is to minimize the maximum number of tardy jobs over all scenarios. For a problem with a common due date, denoted d, it is shown that the case with a fixed number of scenarios is weakly NP-hard and has no fully polynomial time approximation scheme, although it has a polynomial time approximation scheme. Furthermore, it is shown that the case with an arbitrary number of scenarios has no α-approximation algorithm for any constant 1 < α < d. For a problem with identical due date intervals, it is shown that the case with two scenarios is strongly NP-hard and has no α-approximation algorithm for any constant α > 1. As a practical solution approach, a genetic algorithm is proposed and numerical experiments are conducted to verify its performance. [ABSTRACT FROM AUTHOR]

In light of the strange contradiction between the self-critical commemoration of the Holocaust on the one hand and patriotic history regarding colonialism on the other, this contribution maintains the need to transcend simplified notions of allegedly exemplary institutional I Vergangenheitsaufarbeitung i and far-right patriotic history. With regard to both strategies of making patriotic history, conservative and liberal politics of memory are often devious, indicating an intersection between patriotic history and underlying ideological concerns. In fact, in postcolonial and post-reunification Germany, I Aufarbeitung i and state-sponsored patriotic history co-exist in an uneasy tension: while fixated on the Holocaust, large segments of the German political and intellectual elites suffer from "colonial amnesia", as African and Afro-German activists as well as researchers of postcolonial and Black studies observe. Germany has long been championed for its exemplary I Vergangenheitsaufarbeitung i : its self-critical "working through the past."[1] Centred around the commemoration of the Holocaust as a "breach of civilization" ( I Zivilisationsbruch i ),[2] the emphatic rejection of antisemitism, and loyalty to the state of Israel, the country's official politics of memory and public culture of remembrance allegedly renounce nationalistic interpretations of the past. [Extracted from the article]

Industry 4.0 has fused digitalisation with traditional industrial processes bridging the physical and virtual worlds and opening unimagined possibilities for 21st century business growth. Research is still evolving towards the development of frameworks linking Industry's 4.0 enabling technologies to specific goals and to their impact on the manufacturers' businesses. A systematic review of all peer-reviewed managerial research is performed to extract Industry's 4.0 enabling technologies, barriers and goals. A framework linking technologies, barriers, and goals is then developed together with a web application based on its contents. The use of the framework is demonstrated through its application to two specific empirical case studies. The review shows that there are 9 classes of Industry's 4.0 enabling technologies, whose different arrangements lead to the achievement of up to 15 business goals that can be thwarted by 21 barriers. The pursuit of these goals leads to three different destinations, i.e. three diverse digital transformations of the manufacturing firms. This paper provides a holistic framework that analyses the relationships among Industry's 4.0 enabling technologies, barriers and goals. It appeals to managers who can use its contents along with the web application to derive recommendations and suggestions tailored to their Industry's 4.0 journeys. [ABSTRACT FROM AUTHOR]

Discusses how the industrialization of food production has led to numerous additives and chemical substances entering the food chain. How growth hormones and antibiotics are administered to animals, and pesticides and genetically-modified organisms (GMO); Concern among consumers.

The management of a military supply chain (SC) involves the integration of production, packaging, warehousing, repair, maintenance and transportation of army supplies. In this paper, we focus on the integrated demand-responsive scheduling of maintenance and transportation operations within the SC. We consider a modular representation of a SC, in which the maintenance and transportation operations constitute the corresponding modules. An efficient integration of these operations is carried out by an additional controlling (commanding) module. We describe and analyse the optimisation problems arising in each module. The main contribution of the paper is that the analysis and optimisation of the controlling module permit to enhance the performance of the entire SC. Computational experiments prove the validity and effectiveness of the suggested models. [ABSTRACT FROM PUBLISHER]

Continuous flow scheduling problems have their place in many industries such as gas, oil, chemicals, glass and fluids production as well as production of granular goods and steel details. The disruptions in processing capacities may result in schedule performance decrease. In this paper, we develop a new method for robustness analysis of those schedules that are formulated in continuous time in the state-space domain. The developed method is based on attainable sets (ASs) that allow computing a form to represent the states and performance of schedules in regard to different capacity degradation levels. Having such a form, it becomes possible to estimate the schedule robustness. The technical development and approximation of ASs are presented. A robustness index is developed on the basis of the minimax regret approach, and it can be used for decision-makers regarding the trade-off ‘performance vs. robustness’. As such, it becomes possible to compare maximal possible profits in situations without disruptions and realistic profits subject to some robustness investments and costs of protection against disruptions. With the presented results, it becomes possible to obtain ASs for interval data with no a priori information about perturbation impacts, i.e. for non-stationary perturbations. ASs permit to consider perturbations and schedule performances astime functions. Perturbation functions may be set up for different uncertainty scenarios, including interval perturbations. [ABSTRACT FROM AUTHOR]

Smart factories Industry 4.0 on the basis of collaborative cyber-physical systems represents a future form of industrial networks. Supply chains in such networks have dynamic structures which evolve over time. In these settings, short-term supply chain scheduling in smart factories Industry 4.0 is challenged by temporal machine structures, different processing speed at parallel machines and dynamic job arrivals. In this study, for the first time, a dynamic model and algorithm for short-term supply chain scheduling in smart factories Industry 4.0 is presented. The peculiarity of the considered problem is the simultaneous consideration of both machine structure selection and job assignments. The scheduling approach is based on a dynamic non-stationary interpretation of the execution of the jobs and a temporal decomposition of the scheduling problem. The algorithmic realisation is based on a modified form of the continuous maximum principle blended with mathematical optimisation. A detailed theoretical analysis of the temporal decomposition and computational complexity is performed. The optimality conditions as well as the structural properties of the model and the algorithm are investigated. Advantages and limitations of the proposed approach are discussed. [ABSTRACT FROM AUTHOR]