In this paper we model a consignment (CS) and vendor-managed inventory (VMI) policy for a single vendor and multiple buyers supply chain with known demand. We study three vendor–buyers partnerships: (i) the vendor and the buyers act independently, (ii) the vendor enters in a vendor-managed inventory consignment (VMI&CS) partnership with the buyers and (iii) the vendor and the buyer belong to a vertically integrated firm where a single decision maker decides about the ordering policies. We use relationships (i) and (ii) to study the benefits of the VMI&CS agreement. We provide analytical and numerical results. We find that such an agreement is more beneficial when the vendor has a flexible capacity. It is also more attractive to buyers when they have significant order costs and the vendor's setup cost is not large. Finally we find that under VMI&CS the vendor will tend to make more frequent shipments with smaller lots.

In this paper, the problem of determining the optimal maintenance and operation policies for a multi-state, multi-stage machine maintenance problem is considered. This problem has been formulated in the literature as a Partially Observed Markov Decision Process (POMDP). A new formulation that explicitly ties maintenance, operation, and quality within the POMDP framework is provided. The new formulation maximises Overall Systems Effectiveness for an n-state system with multiple speed and maintenance actions. The model provides, for each time epoch, a set of optimal maintenance and production-rate actions. The decision-maker (controller) can select the optimal policy depending on the system state occupancy vector (belief state). A realistic numerical model is presented to demonstrate the model utility.

Oil and gas supply chain (SC) plays key role in the global economy. Gas and oil separation plants (GOSP) belong to the upper stream of the oil and gas SC. In this article we study a GOSP that pumps oil to a stabilizing plant through a 600 KM pipeline. The purpose of this research is to develop an integrated mathematical model to support operational decision making regarding the optimal pumps scheduling and the dosage level of a drag reduction chemical, which is injected in the oil pipeline to stabilize the pressure and achieve higher oil flow rate. To the best of our knowledge, there is no MDP model in the literature that jointly considers pumps scheduling and oil flow control. A novel Markov decision process (MDP) model and two intuitive heuristic polices are proposed and simulated based on historical data. The heuristic policies are to operate the oil pumps in a cyclic weekly or biweekly patterns and to use a maximum likelihood rule to select the dosage level. Compared to the heuristic policies, our results demonstrated that MDP can lead to a substantial amount of savings in terms of the total system operating and maintenance costs. It also provides some practical insights on the interaction ways between the frequency of operating the pumps, the maintenance costs and the costs of a chemical that is used to control the pressure and flow rate of oil in a 600 KM oil pipeline.

To maximize profitability and meet customers’ expectations, assembly plants strive to enhance operations and minimize costs. Using discrete-event system simulation and optimization, this paper investigates different scenarios to enhance operations efficiency and presents a decision support tool to aid operational decision making in an air conditioner assembly plant. Specifically, simulation is used to evaluate the system-wide impact of an optimal sequencing rule at a bottleneck station; in addition, a multi-objective simulation optimization problem is solved to enable manufacturers to evaluate their time/cost tradeoffs. Using actual data from a previous month, the simulation study illustrates possible savings throughout the evaluated alternatives. To sustain the study outcomes, we combine mathematical programming and simulation into an integrated decision support tool. Overall, this study illustrates the benefits of solution approach to a real-life system.