A maintenance driven scheduling cockpit for integrated production and maintenance operation schedule

The production and maintenance functions have objectives that are often in contrast and it is essential for management to ensure that their activities are carried out synergistically, to ensure the maximum efficiency of the production plant as well as the minimization of management costs. The current evolution of ICT technologies and maintenance strategies in the industrial field is making possible a greater integration between production and maintenance. This work addresses this challenge by combining the knowledge of the data collected from physical assets for predictive maintenance management with the possibility of dynamic simulate the future behaviour of the manufacturing system through a digital twin for optimal management of maintenance interventions. The paper, indeed, presents a supporting digital cockpit for production and maintenance integrated scheduling. The tool proposes an innovative approach to manage health data from machines being in any production system and provides support to compare the information about their remaining useful life (RUL) with the respective production schedule. The maintenance driven scheduling cockpit (MDSC) offers, indeed, a supporting decision tool for the maintenance strategy to be implemented that can help production and maintenance managers in the optimal scheduling of preventive maintenance interventions based on RUL estimation. The simulation is performed by varying the production schedule with the maintenance tasks involvement; opportune decisions are taken evaluating the total costs related to the simulated strategy and the impact on the production schedule.The full text can be downloaded at https://link.springer.com/article/10.1007/s40436-021-00380-z     

The entropic basis of collective behaviour

We identify a unique viewpoint on the collective behaviour of intelligent agents. We first develop a highly general abstract model for the possible future lives these agents may encounter as a result of their decisions. In the context of these possibilities, we show that the causal entropic principle, whereby agents follow behavioural rules that maximize their entropy over all paths through the future, predicts many of the observed features of social interactions among both human and animal groups. Our results indicate that agents are often able to maximize their future path entropy by remaining cohesive as a group and that this cohesion leads to collectively intelligent outcomes that depend strongly on the distribution of the number of possible future paths. We derive social interaction rules that are consistent with maximum entropy group behaviour for both discrete and continuous decision spaces. Our analysis further predicts that social interactions are likely to be fundamentally based on Weber''s law of response to proportional stimuli, supporting many studies that find a neurological basis for this stimulus–response mechanism and providing a novel basis for the common assumption of linearly additive ‘social forces’ in simulation studies of collective behaviour.     

A multi-agent model for the reasoning of uncertainty information in supply chains

In order to simulate the real world effectively, this paper proposes a multi-agent model that integrates a price negotiation support system based on the use of intelligent agents capable of processing information uncertainty. The certainty factor is integrated in expert systems to support the decision of agents. In the model, each agent is integrated with an expert system to deal with the uncertainty information. A real example of supply chains is chosen to show the validity of the proposed multi-agent model. Virtual companies can use the uncertainty information to support decisions. Multiple experiments are conducted to work on the coordination of the supply chain. The selling and purchasing prices in the supply chain are proposed in the experimental results. These prices are similar with the data in a real supply chain and are the optimal price strategy for the supply chain. The model was programmed using Jess and Swarm, which was run on Windows XP. The multi-agent model in the paper is beneficial to the reasoning of uncertainty information in multi-agent systems.     

Optimal procurement and sampling decisions under stochastic yield of returns in reverse supply chains

We study the problem of optimizing the sampling and procurement decisions in a remanufacturing system under stochastic yield of returns in a single-period setting. Returned products are characterized by uncertainty regarding their ability to be successfully remanufactured. This uncertainty is formulated as a variable probability of each returned unit in a batch to be remanufacturable (returns yield). We study the impact of returns yield on the optimal procurement policy and the benefits of sampling inspection of returns prior to the procurement decision. It is shown that sampling inspection improves the procurement decisions since it allows the Bayesian updating of the prior information regarding the returns yield. We derive analytical expressions for the determination of the economically optimal procurement quantity and structural properties that facilitate the optimization procedure and provide useful insights. The determination of the economically optimal sample size is also discussed and the benefits of sampling are illustrated through numerical examples.     

Analytical reasoning task reveals limits of social learning in networks

Social learning—by observing and copying others—is a highly successful cultural mechanism for adaptation, outperforming individual information acquisition and experience. Here, we investigate social learning in the context of the uniquely human capacity for reflective, analytical reasoning. A hallmark of the human mind is its ability to engage analytical reasoning, and suppress false associative intuitions. Through a set of laboratory-based network experiments, we find that social learning fails to propagate this cognitive strategy. When people make false intuitive conclusions and are exposed to the analytic output of their peers, they recognize and adopt this correct output. But they fail to engage analytical reasoning in similar subsequent tasks. Thus, humans exhibit an ‘unreflective copying bias’, which limits their social learning to the output, rather than the process, of their peers’ reasoning—even when doing so requires minimal effort and no technical skill. In contrast to much recent work on observation-based social learning, which emphasizes the propagation of successful behaviour through copying, our findings identify a limit on the power of social networks in situations that require analytical reasoning.     

Optimal decision making using cost accounting information

The primary objective of this paper was to compare the results of using four different cost accounting systems (traditional cost accounting, activity-based costing, direct costing, and throughput accounting) in a resource-constrained production environment in order to make two categories of decisions that managers frequently use cost accounting information to make. The research design includes a survey of manufacturing managers to determine what decisions cost accounting information is used to make, and a simulation model to determine the results of the decisions. In addition, the results of the four cost accounting models are compared with a linear programming solution. The study found that the through-put accounting model in all cases made the same decision as the linear programming model, but the other three cost accounting systems generally produced suboptimal results. Our conclusion is that for a cost accounting system to provide information for optimal decisions, it must (1) be aware of production constraints, and (2) not use allocated costs.     

The implication of capacity reservation contracts in assembly system with asymmetric demand information

This paper investigates an assembly system that consists of one assembler and two suppliers who produce complementary components. The assembler possesses some private demand information that is unobservable to the upstream suppliers, and the two suppliers have to choose their capacity reservations ahead of the selling season. To induce credible vertical information sharing, we design three capacity reservation contracts: the price-quantity contract, the price-only contract and the hybrid price-quantity and price-only contract, so as to identify how the firms’ equilibrium reservation strategies and pay-offs react under different contract schemes. The results show that the assembler obtains the highest pay-off under either the price-only contract or the hybrid contract, which is determined by whether the private demand information or the demand fluctuation is sufficiently high. The suppliers’ optimal contract types are further influenced by their decision sequence. In particular, the supplier who makes decision earlier prefers the hybrid contract, while the supplier who makes decision later prefers any of the three contracts. The supply chain generates the highest pay-off from either the price-quantity contract or the hybrid contract.     

Perfect Bayesian vs. Imperfect Bayesian in Small Decision Making Problems

This is an experimental economics research on human behaviour in “small decision making”. I set up ambiguity treatments in which there are two states of nature: a favourable state and an unfavourable state, but only one of them obtains on any given trial. The decision makers’ basic task is a binary choice between a risky option with higher expected value and a riskless option with lower expected value. The one-person game is iterated hundreds of times. Experimental results are reported with several findings, such as underweighting of rare events and deviations from expected value maximisation. Finally, I investigate the imperfect Bayesian decision makers observed in the experiments by exploring to what extent they can update prior probabilities and reflect it in making decisions.

     

Using game theory for analysing pricing models in closed-loop supply chain from short- and long-term perspectives

Closed-loop supply chain (CLSC) management is an environmental approach to supply chain management that aims to prevent hazardous material from entering the nature by means of creating a reverse flow. This paper studies the short- and long-term behaviour of agents in implementing the appropriate collecting strategy in a two-echelon CLSC. In short-term, based on the Stackelberg game, several novel pricing models for different collecting strategies are proposed and compared. Then, the optimal policies of the pricing decisions are determined for each model. The long-term behaviour of companies in implementing collecting process is examined by evolutionary game theory and the most stable strategy is selected. Furthermore, a numerical example is presented to compare the different collecting structures. Finally, a managerial insight is provided to indicate the effect of key parameters such as remanufacturing rate, marketing elasticity and government subsidies on selecting the appropriate strategy.     

The value of updated information on future price behaviour

This paper studies the value of information on future price behaviour. We consider a one-period inventory modelling framework with random period length and two order opportunities. The selling price is determined dynamically and the demand is price-sensitive. The second ordering-pricing decision reflects the updated information on future price behaviour on supply chain flexibility. We consider three models with different levels of flexibility: the static model, the quantity flexible model and the combined quantity and timing (fully) flexible model. We compare between the values of three different features in the supply chain: updated information on price behaviour, dynamic pricing and supply flexibility. And we demonstrate the effect of holding cost and demand uncertainty on these three values. We also consider a specific condition with fixed selling prices. We give explicit analysis on the optimal order decisions, and analytically show the impact of information and quantity flexibility on the optimal order decisions.     

An interval programming model for continuous improvement in micro-manufacturing

Continuous quality improvement in micro-manufacturing processes relies on optimization strategies that relate an output performance to a set of machining parameters. However, when determining the optimal machining parameters in a micro-manufacturing process, the economics of continuous quality improvement and decision makers’ preference information are typically neglected. This article proposes an economic continuous improvement strategy based on an interval programming model. The proposed strategy differs from previous studies in two ways. First, an interval programming model is proposed to measure the quality level, where decision makers’ preference information is considered in order to determine the weight of location and dispersion effects. Second, the proposed strategy is a more flexible approach since it considers the trade-off between the quality level and the associated costs, and leaves engineers a larger decision space through adjusting the quality level. The proposed strategy is compared with its conventional counterparts using an Nd:YLF laser beam micro-drilling process.     

A new game of information sharing and security investment between two allied firms

In consideration of business information sharing, this paper investigates a new game of information sharing and security investment between two allied firms. Firms’ strategies in three decision models (Nash Equilibrium decision, partially centralised decision and totally centralised decision) are analysed. We provide some quantitative analyses on how some parameters affect firms’ decisions in the three decision models. Our Nash Equilibrium analysis shows that when firms make decisions individually, they will share no information with each other. When information sharing is determined by a social planner, firms will share some information with each other, which increases the risk of information leakage. Thus, firms should increase their security investments to mitigate the higher information leakage risk. However, our analysis shows that instead of investing more in information security, firms will reduce their security investment, which will further aggravate the risk. Hence, a social planner is required to designate the security investments and information sharing levels for both firms. Our theoretical analysis shows that firms’ strategies can achieve global optimality in the totally centralised decision model. Furthermore, a numerical experiment is conducted and the result demonstrates that totally centralised decision model is more efficient than the other two decision models. At last, we propose two compensation mechanisms to help firms coordinate their strategies when making decisions individually.     

Modelling of coal trade process for the logistics enterprise and its optimisation with stochastic predictive control

In the paper, a typical coal trade process is described and modelled, where one logistics enterprise with blending equipments lies in the core and two types of common contracts are elucidated to define constraints. A mixed-integer model is built and featured by addressing contract violation, blending operation, real-time price information and arbitrarily distributed stochastic demands. To deal with the stochastic demands, probabilistic constraints are formed. Accordingly, stochastic model predictive control strategy with both receding horizon and decreasing horizon formulations is developed to handle the probabilistic constraints and exploit the value of newest price information. By solving a series of mixed-integer linear programmes, optimal coal trade decisions for the logistics enterprise can be obtained, including procurement decision, selling decision and operational decision of the blending equipments. Thorough simulation experiments are carried out and compared with three different strategies, which interpret the effectiveness of the proposed strategy.     

Effects of forecast errors on optimal utilisation in aggregate production planning with stochastic customer demand

The hierarchical structure of production planning has the advantage of assigning different decision variables to their respective time horizons and therefore ensures their manageability. However, the restrictive structure of this top-down approach implying that upper level decisions are the constraints for lower level decisions also has its shortcomings. One problem that occurs is that deterministic mixed integer decision problems are often used for long-term planning, but the real production system faces a set of stochastic influences. Therefore, a planned utilisation factor has to be included into this deterministic aggregate planning problem. In practice, this decision is often based on past data and not consciously taken. In this paper, the effect of long-term forecast error on the optimal planned utilisation factor is evaluated for a production system facing stochastic demand and the benefit of exploiting this decision’s potential is discussed. Overall costs including capacity, backorder and inventory costs, are determined with simulation for different multi-stage and multi-item production system structures. The results show that the planned utilisation factor used in the aggregate planning problem has a high influence on optimal costs. Additionally, the negative effect of forecast errors is evaluated and discussed in detail for different production system environments.     

Production control of hybrid manufacturing–remanufacturing systems under demand and return variations

Hybrid manufacturing systems that use both raw materials and returned products as a supply for their production process are considered. Specifically, the system under study contains two machines: one uses raw materials for manufacturing, while another utilises end-of-life products returned from the market for remanufacturing. Machines are failure-prone, demand and return rates fluctuate in time reflecting market behaviour due to economical, seasonal and environmental changes. The system performance is characterised by a long-term discounted cost that integrates several partial costs (those of manufacturing, remanufacturing, disposal, holding costs in serviceable and return inventories). Optimisation of the hybrid system behaviour requires to determine the combined manufacturing, remanufactruring and disposal policy, withstanding machine failures under dynamic market conditions. Optimality conditions in the form of Hamilton–Jacoby–Bellman equations are obtained and a novel numerical approach, based on the estimation of value function timederivative, is proposed in order to deal with demand and return variations. Extensive simulations are performed to address the numerous scenarios corresponding to evolving relationship between manufacturing capacities and varying demand and return levels. Simulation results show that the optimal policies have an important property of anticipating the future changes in the demand and return, and making the timely decisions relevant to these changes.     

Ants work harder during consensus decision-making in small groups

Individuals derive many benefits from being social, one of which is improved accuracy of decision-making, the so-called ‘wisdom of the crowds’ effect. This advantage arises because larger groups can pool information from more individuals. At present, limited empirical data indicate that larger groups outperform smaller ones during consensus decision-making in human and non-human animals. Inaccurate decisions can lead to significant costs, and we might therefore expect individuals in small groups to employ mechanisms to compensate for the lack of numbers. Small groups may be able to maintain decision accuracy if individuals are better informed than those in larger groups and/or by increasing the proportion of the group involved in collective decision-making relative to larger groups. In this study, we use interactive computer vision software to investigate individual contributions to consensus decision-making during house-hunting in different sized groups of the ant Myrmecina nipponica. We show that individuals in small colonies invest greater effort in the consensus decision process than those in large colonies and should be better informed as a result. This may act to ameliorate the limitations of group size, but could leave smaller groups more susceptible to additional stresses.     

Optimal lower bounds on the contribution margin in the case of stochastic order arrival

In customer order driven production, decisions on the acceptance of customer orders usually have to be based on variable costs and contribution margins (abbreviated CM), since in the short term only these quantities can be influenced. If we assume that customer orders arrive according to a stochastic process and that the decisions on order acceptance have to be made on each order separately, a customer order usually should be accepted only if its contribution margin exceeds a positive lower bound. This paper shows by means of a stochastic model that, under certain assumptions, this lower bound on the contribution margin can be determined using full costing, provided that the available capacity (constant over time) and the arrival process are balanced. This insight justifies, to a certain extent, the use of full costs to support decisions on the short-term production volume, which is a behaviour that can be observed in practice rather frequently. We also demonstrate the extension of the modelling approach to state-dependent lower bounds on the contribution margin.     

Procurement decisions and information sharing under multi-tier disruption risk in a supply chain

We consider a manufacturer's procurement decision in a three-tier supply chain (SC) under disruption risk. The manufacturer sources components from a single first-tier supplier (FT). The FT, in turn, sources raw materials from a single second-tier supplier (ST). Suppliers in both tiers are unreliable, i.e. prone to disruption risk. Increasing SC visibility through information sharing is a potential disruption management strategy for the manufacturer. While the manufacturer can obtain disruption risk information for the FT, disruption risk information for the ST is not easily accessible to the manufacturer except through the FT, who may not be willing to share ST information. We study different mechanisms under which the manufacturer can obtain ST information, and its impact on manufacturer's and FT's decisions and potential profits. We show that information sharing makes the manufacturer's procurement decisions more conservative, i.e. carrying more inventories, but the FT's procurement decision is contingent on the ST's reliability; more proactive (conservative) when ST is unreliable (reliable), i.e. carrying less (more) inventories. We demonstrate that there are two ways to induce the FT to share its information, and numerically show that their effectiveness is contingent on multiple factors, including FT and ST reliabilities and information sharing costs.     

Cournot game with incomplete information based on rank-dependent utility theory under a fuzzy environment

In most existing literatures on Cournot game, game behaviour between players is based on the hypothesis that people are complete rationality. However, players’ decisions are often affected by their behavioural characteristic and psychology preference. Traditional Cournot model also doesn’t deal with ambiguous information. Based on rank-dependent utility theory, this paper develops an incomplete information Cournot game in an ambiguous decision environment, where the form of ambiguity is described by a set of fuzzy parameters, and behavioural pattern is reflected by means of emotional function in rank-dependent utility. Further, we investigate the Nash equilibrium quantity of each manufacture in this kind of fuzzy Cournot game with incomplete information. Finally, the proposed Cournot model is applied to a case study, and dynamic variation and sensitivity analysis of optimal quantity with respect to decision-maker’s behaviour pattern are discussed in detailed, which illustrates that the proposed Cournot model is more reasonable than traditional Cournot model.     

A lateral transshipment model for perishable inventory management

Since inventory costs account for half of logistics costs, optimal inventory management to minimise total inventory costs remains a sustainable competitive advantage. Lateral transshipment (LT) is evidently a proven strategy to minimise total inventory costs. The additional LT costs are more than compensated by lowering the stock-out costs. Previous LT models have not been applied to perishable products. Our proposed LT model embodies spoilage costs in the total inventory costs function with the other cost components (purchase from a regular supplier, LT, backordering and holding), and optimises the trade-off among these five key cost components. Numerical examples from a supermarket chain case study demonstrate that, as compared against the no or lower spoilage costs scenarios, lower LT costs are required to trigger the decision point for implementing LT in the higher spoilage costs scenario. However, common to both the with and without spoilage costs scenarios, LT is still the preferred strategy to minimise total inventory costs, given the decision rules are satisfied.