Spatial-temporal hedging coordination in prefabricated housing production

Abstract This paper studies the spatial-temporal hedging coordination problem. In the prefabricated housing production, the heavy double handling cost is the major reason that causes profit loss of project manager. The spatial-temporal method is capable of mitigating this double handling cost by reducing assembly time uncertainty and on-site congestion probability substantially. However, adopting hedging methods adds extra cost and pressure to the adopters. To encourage the building contractor who makes the temporal hedging decision to choose a larger assembly time hedging (ATH) amount, a hedging effort-sharing (HES) mechanism is proposed. In this research, we built an analytical model to investigate how HES mechanism affects spatial-temporal hedging decisions, the performance of the building contractor, project manager as well as the whole supply chain. Three scenarios with fixed HES terms under decentralized setting are examined: (I) Nash model, (II) Building contractor led Stackelberg model, and (III) Project manager led Stackelberg model. Closed-form results regarding the optimal spatial-temporal hedging decisions are obtained. Besides, we derive the threshold of the HES terms, under which a win-win coordination can be achieved. Furthermore, an integrated setting with negotiable HES terms is explored. We derive the optimal values of HES terms, which incents the individual optimal spatial-temporal hedging decisions identical with that obtained in the centralized setting. By conducting numerical studies, it is found that a higher site congestion probability and a lower demand uncertainty encourage the project manager to share more ATH effort, while a lower unit ATH cost and higher site space hedging effort-sharing rate entice the building contractor to burden more ATH effort. Surprisingly, in the proposed HES mechanism, the building contractor's profit is increasing in site congestion probability and the project manager's profit increases in the demand uncertainty.