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This paper deals with coordinating delivery schedules and inventory replenishments in a supply chain operated under Vendor Managed Inventory (VMI) system. We propose a new model of Vendor Managed Inventory Routing (VMIR) based on the existing Period Traveling Salesman Problem (PTSP) model. The proposed model will be referred to as the Integrated Inventory and Period Traveling Salesman Problem (IPTSP). In the IPTSP, the delivery frequency is treated as a decision variable instead of a given parameter. We formulate the problem and develop a heuristic approach to solve it. We put emphasis on the procedure for seeking the best delivery frequency for retailers called "delivery consolidation". Assuming that each retailer is initially visited every day during the m-day period, the procedure tries to consolidate the current deliveries in order to make a trade-off between traveling costs and inventory holding costs in order to minimize system-wide costs. The numerical experiment results show that by treating the delivery frequency as a decision variable some benefits can be produced.

In this paper we formulate a bi-criteria search strategy of a heuristic learning algorithm for solving multiple resource-constrained project scheduling problems. The heuristic solves problems in two phases. In the pre-processing phase, the algorithm estimates distance between a state and the goal state and measures complexity of problem instances. In the search phase, the algorithm uses estimates of the pre-processing phase to further estimate distances to the goal state. The search continues in a stepwise generation of a series of intermediate states through search path evaluation process with backtracking. Developments of intermediate states are exclusively based on a bi-criteria new state selection technique where we consider resource utilization and duration estimate to the goal state. We also propose a variable weighting technique based on initial problem complexity measures. Introducing this technique allows the algorithm to efficiently solve complex project scheduling problems. A numerical example illustrates the algorithm and performance is evaluated by extensive experimentation with various problem parameters. Computational results indicate significance of the algorithm in terms of solution quality and computational performance.

Dealing with change is an essential capability for manufacturing systems. In this paper, we reviewed 30 different kinds of manufacturing systems and identified four common approaches in how they deal with change, i.e. modularity, autonomy, distribution, and integration. We conducted a survey of Chinese manufacturing firms and used regression analysis to find relationships between their competitive priorities and the four approaches. The results provide some useful explanations regarding how a firm's emphases on the approaches in its manufacturing systems are influenced by its competitive priorities. Utilizing linkages between the approaches and competitive priorities, we developed a framework for matching manufacturing systems with manufacturing strategy in an environment filled with change and uncertainty.