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This paper considers a fresh produce supply chain in which a retailer sources alternative produce from two competing suppliers. The suppliers make efforts to preserve product freshness, while the retailer, with demand forecast information, can share it with none, one or both of the suppliers strategically. By constructing a multistage game model, the optimal demand information sharing strategy in the supply chain is explored. The results show that the retailer has the willingness to disclose demand information only when the freshness elasticity is high. Significantly, with the improvement of the freshness elasticity, the retailer will transform from sharing information with both suppliers to one supplier. The suppliers are always willing to accept the information disclosed by the retailer. However, complete transparency of demand information may hurt the supply chain under certain conditions. To improve the supply chain performance, the retailer can charge both suppliers for information fees and adjust from sharing information with no suppliers to both suppliers with a relatively low freshness elasticity. Conversely, the retailer can change from sharing information with both suppliers to sharing information with one supplier through a charging contract with a relatively high freshness elasticity.

A product service supply chain (PSSC) supplies customers with product-service systems (PSS) consist of integrated products and services. The product manufacturing should match the service supply in the order delivery planning. For PSS orders are usually delivered under time window constraints, this paper is concerned with the integrated order acceptance and scheduling (OAS) decision of the PSSC. Defined the PSS orders by their revenues, product processing times, serving offering times and hard time window constraints, we formulate the OAS problem as a MILP model to optimize total revenue of PSSC and propose two effective value for big-M to solve the problem with small size optimally. The simulated annealing algorithm based on the priority rule of servable orders first (SOF-SA) and the dynamic acceptance and scheduling heuristic (DASH) algorithm are presented. The performance of the model and the two algorithms are proved through simulating instances with different order sizes. Computational tests show that the SOF-SA algorithm is more effective when used for small size problems while the DASH algorithm is more effective for problems with larger size; negotiating with customers to make reasonable delivery time windows should be beneficial to increasing total revenue and improving the decision efficiency.

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