Order picking (OP) is a critical yet time-consuming and labor-intensive warehouse operation within the supply chain. In picker-to-part systems with high demand, pickers are exposed to fatigue due to the excessive repetition of picking activities, which results in high human energy expenditure. The literature indicates that energy expenditure depends on the picking activity and the worker’s attributes, such as pickers’ weight, gender, and age. Studies have shown that as the weights of individuals increase, the energy consumed for the same task increases. This study proposes a two-stage stochastic programming model that minimizes assignment and overtime costs while avoiding excessive fatigue levels for pickers by incorporating rest allowance into the picking tour time. In the first stage, the number of pickers required is decided. In the second stage, orders are assigned to pickers considering uncertain energy expenditure. The two-stage stochastic programming model is solved by the sample average approximation algorithm. Results show that both OP cost and the number of pickers required to fulfill an order increase when the picker’s weight exceeds 80kg. In allocating orders, pickers weighing less than 80kg should be assigned to orders with more items, such as those containing 4- or 5-items. Conversely, pickers weighing more than 80kg should be assigned to orders with fewer items, like those containing 2- or 3-items, to avoid fatigue side effects.

The hub location problem (HLP) is one of the strategic planning problems encountered in different contexts such as supply chain management, passenger and cargo transportation industries, and telecommunications. In this paper, we consider a reliable uncapacitated multiple allocation hub location problem under hub disruptions. It is assumed that every open hub facility can fail during its use and in such a case, the customers originally assigned to that hub, are either reassigned to other operational hubs or they do not receive service in which case a penalty must be paid. The problem is modeled as two-stage stochastic program and a metaheuristic algorithm based on the adaptive large neighborhood search (ALNS) is proposed. Extensive computational experiments based on the CAB and TR data sets are conducted. Results show the high efficiency of the proposed solution method.

Coordination improves the profit of all the members in a supply chain. In this paper, a novel coordination mechanism is introduced in a retailer-manufacturer supply chain in which the retailer can adopt either an American option mechanism or a wholesale mechanism to satisfy the market demand throughout a multi-period horizon. The manufacturer follows a make-to-order production policy. This mechanism gives the retailer the right to select the more beneficial mechanism in each period. The retailer as the leader of the chain decides how many options should be purchased at the beginning of the horizon through a mixed-integer mathematical model. This model addresses the uncertainties in the market demand and the market price, simultaneously. A scenario planning approach is used to treat the random variables within the model. Also, an optimal scenario reduction model is adapted to reduce the computational complexity of the problem. Finally, a numerical experiment is designed to validate the performance of the model. The results demonstrate a remarkable improvement in the profit of both members. Moreover, a number of experiments are performed to show how the option price, the exercise price and the interest rate affect the performance of the contract.