Inventory Control with Multiple Setup Costs

Abstract

We consider an infinite-horizon, periodic-review, single-item production/inventory system with random demand and back-ordering, where multiple set-ups are allowed in any period, and a separate fixed cost is associated for each set-up. Contrary to majority of the literature on this topic, we do not restrict the order quantities to be integer multiples of the exogenously-given batch size and instead allow the possibility of partial batches, in which case the fixed cost for ordering the batch is still fully charged. We build a model that particularly takes the batch ordering cost structure into account. We introduce an alternative cost accounting scheme to analyze the problem, which we use for developing a computationally efficient optimal solution method and several properties of the optimal solution. In addition, we propose two heuristic policies, both of which perform extremely well computationally.

Optimal Ordering Policy for Inventory Systems with Quantity-Dependent Setup Costs

“…With the setup cost in (1.1), Iwaniec (1979) identified a set of conditions under which a full-batch-ordering policy is optimal. Alp et al (2014) allowed orders with partial batches in their policies and partially characterized the optimal ordering policy that minimizes the long-run average cost. Chao and Zipkin (2008) considered a simple quantity-dependent setup cost…”

Solving a Joint Pricing and Inventory Control Problem for Perishables via Deep Reinforcement Learning

“…After receiving the reward from the environment, the target Q-values are estimated by current rewards and discounted predicted Q-values from the next state, as shown in equation ( 9). e parameters of the network θ are updated by minimizing the difference between the predicted Q-values and the target Qvalues, as shown in equation (10). After a fixed number of steps, assign the value of parameter θ to θ. e details of the algorithm named perishables integrate age and quantity deep Q-network (PAQ-DQN) are shown in Algorithm 1:…”

“…ere is a considerable literature devoted to dynamic inventory control for nonperishable products; see, for example, Presman and Sethi [8], Caliskan-Demirag et al [9], Alp et al [10], Almaktoom et al [11], Azghandi et al [12], Li et al [13], and Gan et al [14]. e dynamic inventory control for perishable products has not been widely studied in the literature.…”

A hybrid solution approach for fuzzy multiobjective dual supplier and material selection problem of carton box production systems

“…The ideal supplier selection under uncertainty should consider all cost‐related parameters, capacity limitations, and information about the prices given by other suppliers, simultaneously. A nice selection of the supplier occurs if the regularity in delivery, high‐quality level of the products, and operative strategic cooperations are appropriately considered (Alp, Tim Huh, & Tan, ). Through a successful selection of the suppliers, the enterprises may stay competitive and able of satisfying the high requirements of the customers (see Ozgen & Gulsun, ).…”

“…In addition, Alp and Tan () have studied a multiperiod problem having two supply choices, with fixed cost of procurement. Alp et al () investigated another form for the problem with similar suppliers from an unlimited horizon with components with linear cost function. Neglecting the associated procurement fixed costs, Awasthi, Chauhan, Goyal, and Proth () deliberated a situation with multiple suppliers, lowest order amount, and/or highest capacity of supply.…”