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In today's fast-paced world, delays or prolonged customer waiting times pose a threat to the firm's profitability. This study utilizes the mean-CVaR metric to incorporate the risk associated with prolonged customer waiting times into the optimal trade-off decisions. For this purpose, we consider a single inventory system that faces Poisson demand and utilizes a base-stock policy to replenish its inventory, which takes a fixed amount of time. The firm implements a preorder strategy, encouraging customers to place their orders a fixed amount of time in advance of their actual needs, a period referred to as the commitment lead time. The firm rewards customers with a bonus termed the commitment cost, which increases with the length of the commitment lead time. We aim to determine the optimal control policy, including the optimal base-stock level and optimal commitment lead time, that minimizes the long-run average cost. The cost includes inventory holding, commitment, and customer waiting costs, with the latter adjusted for the firm's degree of risk aversion. The optimal policy depends on the interdependence of the decisions, with the optimal commitment lead time following a "bang-bang" pattern, and the corresponding optimal base-stock level taking an "all-or-nothing" form. For linear commitment costs with a cost factor per time unit, we identify a threshold that increases with the firm's risk aversion degree. Firms with greater risk aversion typically favor the buy-to-order strategy, while those with lower risk aversion may opt for either buy-to-stock or buy-to-order depending on their assessment of waiting costs. [ABSTRACT FROM AUTHOR]

Pricing and inventory control in a competing environment, as separate entities, have attracted much attention from academics and practitioners. However, integrating these decisions in a competitive setting has not been significantly analyzed by academics, but is of great significance to practitioners. In this study, the joint decision on price and inventory control of a deterioration product is investigated in a duopoly setting. We consider two competing supply chains, each consisting of one manufacturer and one retailer. Each manufacturer, as the leader of their supply chain determines the wholesale price to maximize their profit, while the retailer as the follower should determine the retail price and inventory cycle to maximize his or her profit. Using a game theoretic approach, we formulate in-chain, and chain-to-chain competition as a bi-level programming problem, and analyze Stackelberg–Nash equilibrium of the problem. Furthermore, two versions of a nested algorithm are proposed to obtain the equilibrium. Both versions employ a modified threshold-accepting (TA) algorithm to solve the first level of the problem. However, while the first version utilizes the modified TA algorithm to deal with the second level of the problem, the second version applies a differential evolution (DE) approach. Eventually, a numerical study is carried out not only to compare two developed versions of the algorithm, but also to implement the sensitivity analysis of main parameters. Based on numerical experiments, although the accuracy of both versions of algorithm are alike, using TA is more computationally efficient than using DE. Furthermore, despite the permissibility of partial backlogging, it has never occurred in equilibrium points due to in-chain and chain-to-chain competition. • Joint pricing and inventory control for two competing supply chains is analyzed. • The problem with deterioration products is modeled as a bi-level optimization model. • A modified nested Threshold Accepting and a Differential Evolution algorithm are proposed. • Using TA is more computationally efficient than using DE. • Shortage has never occurred in equilibrium points due to competition. [ABSTRACT FROM AUTHOR]