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At container terminals, quay cranes, yard trucks, and yard cranes are mainly used to transfer containers. Driven by the demand for a green and low-carbon economy, an integrated scheduling problem considering three types of handling equipment of container handling systems is studied. As the task of transferring each container is completed by the three handling equipment sequentially, the optimal solution may not be found by only studying one type of equipment separately from a green operations perspective. The inter-dependency of different equipment should be considered to guarantee the overall performance of container handling systems with low-carbon operations so as to reduce energy consumption. In this paper, this integrated problem is formulated as a mixed integer linear programming (MILP). Since the MILP cannot be applied to solve large-sized practical problems, a genetic algorithm (GA) is developed. In the proposed GA, a three-dimension chromosome representation is proposed, which integrates the coordination of three handling equipment. A new mechanism including three pairs of crossover and mutation is used in parallel in GA with the aim of enhancing the efficiency of searching for good solutions. Each pair of crossover and mutation is specific to one dimension of a solution. Moreover, a novel heuristic mutation is developed to diversify solutions. The computational results indicate that the developed solution method for the integrated scheduling problem is promising and the heuristic mutation can highly improve the solution quality.

It has been a challenging task to manage perishable food supply chains because of the perishable product’s short lifetime, the possible spoilage of the product due to its deterioration nature, and the retail demand uncertainty. All of these factors can lead to a significant amount of shortage of food items and a substantial retail loss. The recent development of tracing and tracking technologies, which facilitate effective monitoring of the inventory level and product quality continuously, can greatly improve the performance of food supply chain and reduce spoilage waste. Motivated by this recent technological advancement, our research aims to investigate the joint decision of pricing strategy, shelf space allocation, and replenishment policy in a single-item food supply chain setting, where our goal is to maximize the retailer’s total expected profit subject to stochastic retail demand. We prove the existence of optimality for the design of the perishable food supply chain. We then extend the single-item supply chain problem to a multi-item setting and propose an easy-to-implement searching algorithm to produce the optimal allocation of shelf space among these items for practical implementation. Finally, we provide numerical examples to demonstrate the effectiveness of our solution.

Managing perishable food in a retail store is quite difficult because of the product’s short lifetime and deterioration. Many elements, such as price, shelf space allocation, and quality, which can affect the consumption rate, should be taken into account when the perishable food retail chain is designed. The modern tracking technologies provide good opportunities to improve the management of the perishable food retail chain. In this research, we develop a mathematical model for a single-item retail chain and determine the pricing strategy, shelf space allocation, and order quantity to maximize the retailer’s total profit with the application of tracking technologies. Then the single-item retail chain is extended into a multi-item one with a shelf space capacity and a simple algorithm is developed to find the optimal allocation of shelf space among these items. Finally, numerical experiments and real-life examples are conducted to illustrate the proposed models.

With the intensification of global warming and the levy of energy tax, more industries are paying attention to energy saving and reduction of carbon footprint. For the food industry, energy cost in the supply chain of perishable food is quite high because of cold-chain transport and storage. Therefore, the efficacies of cold chain management and inventory control are the key factors that increase the efficiency of food supply chain and make it more ecological. This research analyzes the degradation process of perishable food and determines the optimal temperature of the cold chain as well as the optimal price to maximize the channel profit. We prove that there is an optimal price with a certain temperature and develop an efficient search algorithm to find the optimal temperature. We also perform sensitivity analyses to test which parameters affect the channel profit significantly. Numerical experiments are conducted to illustrate the proposed models.