In view of uncertainties caused by sudden accidents (SAs) and affecting retailers’ demand in many districts, it is difficult for suppliers to determine when and how many products to procure/produce. Considering a supply chain consisting of two types of competing suppliers and multi-retailer, this work studies the suppliers’ optimal emergency procurement/production decision (EPD) with information updating. Firstly, a probability evolution model with information updating to describe the probability of the retailers’ procurement behavior and the occurrence probability of supply disruption (SD) is inferred. Secondly, suppliers’ EPDs regarding retailers’ procurement behavior and occurrence probability of SD are discussed and a real-time updated emergency decision-making model (EDM) is proposed based on Stackelberg game and Bayesian inference. Thirdly, the value of information updating and the critical factors that affect the suppliers’ optimal EPD are quantitatively analysed. Numerical examples are finally provided to verify the EDM. Results indicate that information is the premise and foundation for the suppliers to deal with SA effectively; suppliers can easily determine when and how many products to procure/produce based on the proposed EDM; it is demonstrated that for any chosen supplier strategy, there exists a corresponding optimal procurement/production quantity for the suppliers that maximises the expected profits. Moreover, the suppliers’ EPD with information updating is affected by cost parameters, with the rank of information collection cost coefficient, unit procurement/production cost, unit sales price, unit holding cost and unit shortage cost, from apparently to slightly.

A quick and accurate identification of source information on sudden hazardous chemical leakage accident is crucial for early accident warning and emergency response. This study firstly regards source identification problem of sudden hazardous chemical leakage accidents as an inverse problem and presents a source identification model based on the Bayesian framework. Secondly, a new identification method is designed on the basis of particle swarm optimisation (PSO), differential evolution (DE) and the Metropolis–Hastings (M–H) sampling method. Lastly, the designed method, i.e. PSO-DE-MH, is verified by an outdoor experiment analyses in a section of the South–North Water Transfer Project. Results show that the number of iterations, the average absolute error, the average relative error and the average standard deviations of the identification results obtained by PSO-DE-MH are less than those of PSO-DE and DE-MH. Moreover, the relative error and the sampling relative error of the identification results under five different measurement errors (MEs) (σ = 0.01, 0.05, 0.1, 0.15, 0.2) are less than 9.5% and 0.2%, respectively. The designed method is effective even when the standard deviation of the ME increases to 0.2. Therefore, the designed method can effectively and accurately obtain the source information of sudden hazardous chemical leakage accidents. This study provides a new idea and method to solve the difficult problems of emergency management.

Lessees may abuse equipment during the lease period since lacking of ownership, thereby increasing lessors’ repair cost and lessees’ downtime losses. This study integrates lessees’ effort to protect leased equipment during the lease period with lessors’ preventive maintenance (PM) into maintenance service strategies. It is proved in a non-cooperative game, neither party achieves the cooperative game’s ideal revenue, but improvement in the lessee’s effort level and lessor’s PM degree can increase the other party’s revenue. A cost-sharing contract model is designed to achieve the maximum revenue as in a cooperative game and ensure Pareto improvement of the leasing parties. In the contract, the lessor grants the lessee a rental discount, and the lessor’s PM cost and lessee’s effort cost are shared with cost-sharing coefficients. Conditions under which the ideal revenue and Pareto improvement can be achieved are discussed. Numerical examples are provided to illustrate the effects of contract parameters, unit penalty on the effort level, and revenue. Managerial insights are finally proposed for leasing parties. The results show: the effect of the effort level and PM degree on equipment failures is marginally diminishing; proposed cost-sharing contract model can achieve the ideal revenue and Pareto improvement; the rental discount has a greater impact on the lessee, while the cost-sharing coefficients have a greater impact on the lessor; and increasing the unit penalty decreases (increases) the lessor’s (lessee’s) revenue but maintains the effort level at constant.

Constrained by production capacity and the pressure to reduce emissions, many original equipment manufacturers (OEMs) authorize third-party remanufacturers (TPRs) to remanufacture patented products. We investigate the operational decisions of OEMs and authorized TPRs under carbon cap-and-trade regulations in a two-echelon supply chain. We first formulate an operational decision model for OEMs before a TPR enters. Then, for the cases of centralized and decentralized decision making, we formulate an operational decision-making model for the TPR and, subsequently, establish one for the OEM after the TPR enters. We further analyze the effects of carbon emissions cap, trading price of carbon permits, yield rate, and consumer willingness to pay (WTP) on optimal decisions. Our results indicate: whether TPRs accept authorization remanufacturing depending on the ratio of carbon emissions cap to carbon emissions for producing per remanufactured product; royalty rate is negatively affected by trading price of carbon permits and per remanufactured product’ carbon emissions other than that for per new product, and can offset the threat caused by TPRs; the implementation of carbon cap-and-trade regulations causes OEMs to charge TPRs lower royalty rate; centralized decision making increases the total profit of the supply chain and delivers superior environmental benefits. As yield rate and WTP increase, the total profit increases, increasingly sensitive to WTP.

Policy-makers are developing regulation policies to drive down carbon emissions from industries. Independent remanufacturers (IRs), which remanufacture recycled products/components/parts, must manage and evaluate economic costs generated by the production under future carbon emission regulations. We present three optimisation models to determine the remanufacturing quantity that maximises the total profits under three common carbon emission regulation policies: (a) mandatory carbon emissions capacity, (b) carbon tax and (c) cap and trade. These models include sales revenue, remanufacturing cost, disposal cost, inventory holding cost, shortage cost and carbon emission cost. The max–min approach is used to solve the models, which assume limited information on demand distribution. We investigate how the three regulation policies affect remanufacturing decision-making for IRs and we also solve some numerical examples where we vary the magnitudes of incentives, penalties and stringency of constraints to provide implications to policy-makers. The results indicate that remanufacturers should aim to improve yield rate to maximise the profit irrespective of the implemented carbon emissions policy. Policy-makers should prefer the carbon tax policy, if any of the other two policies must be performed, a remanufacturing discount such as a higher carbon emission cap or lower penalty should be implemented to better promote the development of remanufacturers.

In order to solve the source identification problem of sudden water pollution accident accurately and quickly, a method based on the Differential Evolution and Markov Chain Monte Carlo (MCMC) is presented. First, the problem is considered as a Bayesian estimation problem, and the posterior probability distribution of the unknown parameters that include source's position, intensity and events' initial time are deduced with Bayesian inference. Second, these unknown parameters are estimated by sampling the posterior probability distribution using the Differential Evolution algorithm and Markov Chain Monte Carlo simulation, and the sources are further identified. To test the effectiveness and accuracy of the proposed method, numerical experiments are carried out, and the model result is compared to that of the Bayesian-MCMC method. The conclusions are as following: three fourth of the iterations can be reduced, the average relative error of the source's position, intensity and events initial time are reduced 1.23%, 2.23% and 4.15%, their mean errors are decreased 0.39%, 0.83% and 1.49% by using the proposed method. The latter is thus more stable and robust than the Bayesian-MCMC method, and is able to identify the sudden water pollution accidents' source effectively. Therefore, this study provides a new approach and method to solve the difficult traceability problem of sudden water pollution accidents.