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Water delivery management in China’s irrigation districts has traditionally prioritized the main canal system, often overlooking the water-saving potential of the final canals and field irrigation, which offer substantial opportunities to enhance water use efficiency and conserve agricultural water resources. This study summarizes and defines the integrated water management of final canals and field irrigation as terminal water management. An optimization method was developed to improve terminal water management, which includes optimizing irrigation quotas based on water balance and scheduling final canal water delivery to minimize seepage losses. A genetic algorithm was employed to solve the problem. The method was applied to the Hongjin irrigation district in Jiangsu Province, China. In 2020, paddy water management was observed, revealing that the irrigation amount for organic and traditional rice was 1113 mm and 956 mm, respectively. Conventional irrigation and water delivery practices have led to extensive drainage, significant rainwater wastage, and inefficient water use. The optimized irrigation quotas for organic and traditional rice resulted in water savings of 302.5 mm and 325.9 mm, respectively, compared to the 2020 monitored data. An irrigation event in early August during a 75% hydrological frequency year was selected as an example. With conventional scheduling, optimized final canal water delivery scheduling reduced the seepage losses from 6.3% to 4.6%, shortened the irrigation time from 17 h to 14 h, and stabilized canal flow rates. The proposed optimization method is a valuable tool for enhancing terminal water management and supporting better irrigation decisions in irrigation districts.

As a newly proposed algorithm, fruit fly optimization algorithm (FOA) has been shown to have a strong capacity for solving numerical optimization problems. However, the basic FOA is faced with the challenges of poor diversity of the swarm and weak local search ability because of the improper osphresis operation and vision operation. To overcome these limitations synthetically, we propose an improved FOA based on hybrid location information exchange mechanism (HFOA) aiming at improving the swarm diversity in a more efficient way and well balance the global search and local search abilities. First, the proposed HFOA enables flies to communicate with each other and conduct local search in a swarm based approach. Second, osphresis operation is conducted in probability to balance the global search and local search processes. Finally, a mutation strategy called cataclysm policy is designed to help the flies jump out of the local extreme points. 18 complex continuous benchmark functions are used to test the performance of HFOA. Numerical experiments results indicate that HFOA outperforms main state-of-the-art algorithms. A classical non-deterministic polynomial hard problem—a widely-researched joint replenishment and delivery scheduling problem with resource restrictions is also used to further verify the ability of HFOA in solving practical operation management problems. Results show that HFOA can obtain lower operation cost than other widely used methods, demonstrating its ability to solve various complex optimization problems.

Today, there is a great need for greener urban freight transportations due to their ever-increasing environmental impact. The planet’s climate has been significantly affected as the temperature is constantly rising and extreme weather events are occurring more and more often. Aiming to reduce the environmental impact of freight transportation in urban areas, an advanced vehicle routing and scheduling system for improving urban freight transportations, has been developed. This paper presents the functionality of the advanced system, while also analyzing its subsystems and demonstrating its use in a case study. The system is provided as an integrated cloud-based software to support the needs of logistics companies, in order to efficiently schedule their deliveries and perform the routing of their vehicles. The utilized multi-objective algorithm produces solutions that minimize either the distribution cost or the environmental emissions or a combination of these parameters. An application of the system is performed for validation purposes, concerning the comparison of the system’s results with corresponding real-life data provided by a medium-sized logistics company. The results of the testing reveal its significant contribution to the reduction of the environmental impact of the company’s distribution services.

This paper addresses the economic lot and delivery scheduling problem (ELDSP) within three-echelon supply chains, focusing on the complexities of demand uncertainty, limited shelf-life of products, and sequence-dependency of setups. We develop a novel mixed-integer non-linear programming (MINLP) model for a supply chain comprising one supplier, multiple manufacturers with flexible flow shop (FFS) production systems, and multiple retailers, all operating over a finite planning horizon. The common cycle (CC) strategy is adopted as the synchronization policy. Our model employs fuzzy set theory, particularly the “ Me measure,” to effectively handle the retailers’ demand uncertainty. Our findings indicate that total supply chain costs escalate with an increase in demand, final components’ holding costs, and sequence-dependent setup costs, but decrease with increasing production rates. Furthermore, while total costs are significantly sensitive to changes in demand, they are relatively insensitive to fluctuations in sequence-dependent setup times. The models developed offer valuable managerial insights for optimizing costs in synchronized multi-stage supply chains, aiding managers in making informed decisions about production lot sizes and delivery schedules under both deterministic and fuzzy demand scenarios. Additionally, the proposed models bridge key research gaps and provide robust decision-making tools for cost optimization, enhancing supply chain synchronization in practical settings.

This paper presents a new mixed-integer nonlinear programming (MINLP) model for the economic lot and delivery scheduling problem in a flexible job shop with unrelated parallel machines on which the planning horizon length is finite and each product has a shelf life without any spoilage. This problem consists of lot sizing and sequencing in which a supplier produces multiple products in a flexible job shop and delivers components of different products to an assembly facility in batches. The presented MINLP model is based on the basic period strategy with shelf life consideration. It is so complex to optimally solve such a hard and large-sized problem in a reasonable time; thus, an efficient hybrid particle swarm optimization (PSO) is proposed. The computational results are compared with the optimal solutions and lower bounds for small- and large-sized problems, respectively. The results show that the performance of the proposed hybrid PSO is a very promising solution method for the given problem.

Water consumption is one of the most important concerns for a textile dyeing company in the era of global energy revolution. Different from the traditional production scheduling which focuses on the production efficiency improvement, the dyeing scheduling emphasizes the constraints of resource consumption. Besides, different from the traditional production scheduling where the task relevance is seldomly considered, the colour depth heavily impacts the water consumption and the dyeing scheduling results, which aggravates the uncertainty and complexity when modelling the dyeing scheduling optimization. Therefore, how to select proper dyeing orders among a large number of orders and schedule them for the purpose of generating more profit is challenging. To address the above issue, an optimization model with objective of maximizing profit and consideration of water consumption, delivery due time, and machine capacity, is built, and genetic algorithm is adopted to solve the problem. A numeric experiment is conducted to validate the effectiveness of the proposed method.

The integration of drones into the conventional truck delivery system has gained substantial attention in the business and academic communities. Most existing works restrict the launch and recovery of unmanned aerial vehicles (UAVs) to customer locations (or nodes) in the delivery network. Nevertheless, emerging technological advances can allow drones to autonomously launch/land from a moving vehicle. In addition, majority of the current literature assumes multiple UAVs to be deployed and/or recovered simultaneously, thereby ignoring the associated scheduling decisions, which are essential to ensure safe, collision-free operations. This research introduces the single truck multi-drone routing and scheduling problem with en route operations for last-mile parcel delivery. A mixed integer linear programming (MILP) model is developed to minimize the delivery completion time. In addition, a variant is introduced to minimize the total delivery cost. Since the problem under consideration is NP-hard, a relax-and-fix with re-couple-refine-and-optimize (RF-RRO) heuristic approach is proposed, where the associated decisions (truck routing and drone scheduling) are decomposed into two stages and solved sequentially. Besides, a deep learning-based clustering procedure is developed to establish an initial solution and accelerate the convergence speed of the RF-RRO heuristic. Notably, the proposed approach is extended to solve a multi-truck multi-drone variant using a deep learning-based cluster-first route-second heuristic. Our numerical results show that the proposed MILP model is able to solve problem instances with up to 20 customers optimally in a reasonable time. The proposed RF-RRO heuristic can achieve optimal (or near-optimal) solutions for small instances and is computationally efficient for large cases. Extensive experimental analysis shows 30% average savings in delivery completion time, and an average drone utilization of 62% if en route drone operations are considered. In addition, numerical results provide insights on the impact of heterogeneous drone fleet and customer density.

The Pickup and Delivery Problem with Time Windows and Scheduled Lines (PDPTW-SL) consists of routing and scheduling a set of vehicles, by integrating them with scheduled public transportation lines, to serve a set of freight requests within their time windows. This paper presents an exact solution approach based on a branch-and-price algorithm. A path-based set partitioning formulation is used as the master problem, and a variant of the elementary shortest path problem with resource constraints is solved as the pricing problem. In addition, the proposed algorithm can also be used to solve the PDPTW with transfers (PDPTW-T) as a special case. Results of extensive computational experiments confirm the efficiency of the algorithm: it is able to solve small- and medium-size instances to optimality within reasonable execution time. More specifically, our algorithm solves the PDPTW-SL with up to 50 requests and the PDPTW-T with up to 40 requests on the considered instances. The online appendix is available at https://doi.org/10.1287/trsc.2017.0798 .

This paper discusses the roles of communication and coordination (C&C) in the agile teams. C&C are important activities that a project manager has to deal with tactically during the development of software projects to avoid the consequences. Their importance further increases especially in case of distributed software development (DSD). C&C are considered as project drivers to accomplish a project successfully within budget and schedule. There are several issues associated to poor C&C those can lead to fail software projects such as budget deficit, delay in delivery, conflicts among team members, unclear goals of project and architectural, technical and integration dependencies. C&C issues are critical and vital for collocated teams but their presences in distributed teams are disastrous. Scrum is one of the most widely practiced agile models and it is gaining further popularity in the agile community. Therefore, a novel framework is proposed to address the issues that are associated to C&C using Scrum methodology. The proposed framework is validated through a questionnaire. The results are found supportive to reflect that it will help to resolve the C&C issues effectively and efficiently.

Every year natural and man-made disasters cause considerable human and economic losses. It is essential to prepare for different relief operations to prevent and reduce these losses. In this paper, we propose an integrated evacuation and distribution logistic system to obtain simultaneous routing and scheduling of vehicles to evacuate people from affected areas to shelters and provide them with necessary relief commodities. We assume that shelters and vehicles have limited capacity and the demand of each affected area and distribution center could be fulfilled by more than one vehicle (split delivery). The proposed problem is formulated as a Mixed-Integer Linear Programming model with the objective of minimization of the sum of arrival times of the vehicles at affected areas, shelters, and distribution centers. We also propose a Memetic Algorithm (MA) to solve this integrated model on large-scale problems efficiently after tuning the MA parameters using the Taguchi method. The proposed model and algorithm are used to solve a case study in Tehran, the capital of Iran. The evaluation of the results shows the effectiveness of the proposed disaster relief logistic system in minimizing the total waiting time of evacuees and delivery time of supplies. The results also show that the number of relief vehicles and capacity of shelters can considerably affect the total relief time in disaster relief operations.