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The purpose of this paper is to investigate the behavioral operations effect in production inventory decision of supply chain consisting of one manufacturer and one buyer, and analyze how the unfairness concerns impact the decision of production inventory in a supply chain system.

First, a model without the buyer’s unfairness concern is established; then, advantage unfairness concern and disadvantage unfairness concern behavior of buyer are taken into account in the production inventory system. The authors analyze how advantage unfairness concern and disadvantage unfairness concern impact the optimal decisions and channel coordination.

The result shows several important conclusions. First, the buyer’s optimal ordering quantity and expected utility show opposing trend when the buyer has advantage unfairness concern. Second, the stronger bargaining power of the manufacturer results in an increasing buyer’s optimal ordering quantity under the advantage unfairness concern case, but decreasing under the disadvantage unfairness concern case. Third, the supply chain production-inventory can be coordinated under advantage unfairness concern case, but cannot be coordinated under disadvantage unfairness concern.

The study can provide to practitioners with important implications that when the vendor or the buyer in supply chain wants to make the decision of inventory replenishment, taking unfairness concerns into account will lead to different results. Therefore, to effectively improve the operations performance of supply chain, partners of the supply chain should not only care about their own interest, but also need to consider the fairness concern of the other partner, reflecting the cooperation consciousness of supply chain management.

This paper contributes to the new field of creative management–behavioral operations, offering managerial implications for the decision and optimization of supply chain production-inventory problem.

Equipment reliability significantly impacts productivity, and in order to obtain high equipment reliability and productivity, maintenance and production decision should be made simultaneously to keep manufacturing system healthy. The purpose of this paper is to investigate the joint optimization of equipment maintenance and production decision for k-out-of-n system equipment with attenuation of product quality and to explore the impact of maintenance on the production and cost control for manufacturers.

A multi-period Markov chain model for k-out-of-n system equipment is set up based on the assumption that the deterioration of equipment is a pure birth process. Then, the maintenance cost, setup cost, inventory holding cost, shortage cost, production cost and the quality cost are analyzed with the uncertain demand and the attenuation of product quality stemmed from equipment deterioration. The total lowest cost per unit time and its specific calculation method are presented. Finally, the robustness and flexibility of the method are verified by a numerical example and the effects of equipment deterioration intensity and attenuation of product quality are analyzed.

The result shows that the joint decision model could not only satisfy the uncertain demand with low cost and strong robustness but also make the output products high quality level. In addition, the attenuation of product quality would influence the equipment maintenance and production decision and leads to the production waste and increases the operation cost greatly.

Implications derived from this study can help production maintenance managers and reliability engineers adequately select maintenance policy to improve the equipment efficiency and productivity with high quality level at a relatively low cost.

The purpose of this paper is to study a production lot sizing problem with consideration of imperfect manufacturing and emergency maintenance policy, providing managerial implication for practitioners.

In this study, the authors introduce two models, where in Model I, shortages are not allowed and repair times are negligible. In Model II, shortages are allowed and are partially backlogged, and repair times are assumed to be exponentially distributed, algorithm is developed to solve the models, numerical examples were demonstrated the applications.

Results show that in the Model I, demand rate is the most significant parameter affecting the average expected cost, whereas the time needed to breakdown after machine shift is the most significant factor affecting the production lot size. Therefore, reduction in the time needed to breakdown after machine shift would be helpful for determining an appropriate production lot size in Model I. In Model II, repair time parameter is the most significant factor affecting the average expected cost. Reducing the value of machine shift parameter would be helpful for determining an adequate production lot size and reducing decision risk.

This paper can provide important reference value for practitioners with managerial implication of how to effectively maintain equipment, i.e. how to make product lot size considering the influence of the maintenance policy.

From the aspect of academia, this paper provides a solution to the optimal production lot sizing decision for an imperfect manufacturing system with consideration of machine breakdown and emergency maintenance, which is a supplement to imperfect EMQ model.

Presents a mixed‐integer programme to simultaneously select part routeings and form machine cells in the presence of alternate process plans so that the total cost of operating and intercell material handling is minimized. Demand for parts, machine capacities, number of cells to be formed, and number of machines in a cell are included in the model. Includes an example to illustrate the solution technique of the problem of practical instance.

Service systems that require failure rate below a predetermined value usually need maintenance in order to operate at that level of reliability. One major issue in maintenance systems is the optimal maintenance schedule that incurs minimum total cost. Considering fixed inflation rate and failure rate variation after maintenance, an algorithm of balanced maintenance scheduling is developed. The algorithm provides an optimal number of replacement and preventive maintenance to minimize the total maintenance cost over a certain planning period. A numerical example is demonstrated and compared with the results reported by other researchers.

Automated guide vehicles (AGVs) are driverless vehicles that perform material handling operations in both flexible and conventional facilities. We provide here a review of recent work on the design of AGV guide paths and dispatching rules, including related issues such as idle vehicle location, and location of pickup and delivery stations. Different types of guide paths and related layouts, including optimal and heuristic approaches to the path design, are reviewed here. Dispatching rules and algorithms, including zone control, are also proposed and compared with commonly‐used rules.

The purpose of this paper is to study the impact of learning effect and demand uncertainty on aggregate production planning (APP), provide practitioners with some important managerial implications for improving production planning and productivity.

Motivated by the background of one labour-intensive manufacturing firm – a mosquito expellant factory – an APP model considering workforce learning effect and demand uncertainty is established. Numerical example and comparison with other two models without considering learning and uncertainty of demand are conducted.

The result shows that taking into account the uncertain demand and learning effect can reduce total production cost and increase flexibility of APP.

Managerial implications are provided for practitioners with four propositions on improving workforce learning effect, i.e. emphasizing employee training, combing individual and organizational learning and reduction of forgetting effect.

This paper has practice value in improving APP in labor-intensive manufacturing.