Multi-item production routing problem with backordering: a MILP approach

In the scenario with many small base stations (SBSs) deployed, edge caching technology could bring contents closer to users by caching files at SBSs. Considering these SBSs with limited storage capacity, how to effectively cache files is a difficult and interesting problem. Many factors should be considered, such as the popularity of files, user download delay and average hit rate. In this paper, we investigate this problem and propose a minimizing user download delay caching (MUDDC) algorithm. It decides which files should be cached and where to cache them for reducing download delay. There is a conflict between hit rate and download delay with the limited SBS storage capacity. We target the average user download delay and model an optimization problem with the constraint of average hit rate and find the optimal solution. The simulation results show that the system performance is improved.

The main purpose of this paper wants to investigate the effect of trade credit policy with limited storage capacity within the economic order quantity (EOQ) framework. Goyal is frequently cited when the inventory systems under conditions of permissible delay in payments are discussed. But Goyal assumed that the retailer’s storage capacity is unlimited. This assumption is debatable. So, we want to extend Goyal’s model to the case that the retailer’s storage capacity is limited. Then, two theorems present solution procedures to locate the optimal cycle times of the annual total relevant cost function. In essence, the inventory model discussed in this paper generalizes that of Goyal. Finally, numerical examples are given to illustrate all theorems obtained in this paper.

The aim of the present work is to recognize printed and handwritten Telugu characters using artificial neural networks (ANNs). Earlier work on recognition of Telugu characters has been done using conventional pattern recognition techniques. We make an initial attempt here of using neural networks for recognition with the aim of improving upon earlier methods which do not perform effectively in the presence of noise and distortion in the characters. The Hopfield model of neural network working as an associative memory is chosen for recognition purposes initially. Due to limitation in the capacity of the Hopfield neural network, we propose a new scheme named here as the Multiple Neural Network Associative Memory (MNNAM). The limitation in storage capacity has been overcome by combining multiple neural networks which work in parallel. It is also demonstrated that the Hopfield network is suitable for recognizing noisy printed characters as well as handwritten characters written by different "hands" in a variety of styles. Detailed experiments have been carried out using several learning strategies and results are reported. It is shown here that satisfactory recognition is possible using the proposed strategy. A detailed preprocessing scheme of the Telugu characters from digitized documents is also described.

Optimal operation of water-energy microgrids; a mixed integer linear programming formulation

In this paper, we investigate downlink transmissions in a wireless communication system enabled by a swarm of unmanned aerial vehicles (UAVs) which are spatially dispatched to cooperatively deliver requested contents to ground users. First, we propose a communication scheme that exploits the flexible deployment of UAVs as well as their cooperative transmissions to improve in-network user admission. Unlike previous literature, a practical operational constraint of limited storage capacity for UAVs is considered. Then, from the knowledge that cooperation among UAVs depends on the availability of the contents in their limited storage space, we propose a novel joint optimization problem to determine the content placement, location planning, user admission decision and transmit beamforming to maximize the number of users experiencing a minimum required rate, so-called admitted users. Since the formulated problem is a mixed-integer non-linear program which is generally non-deterministic polynomial-time hard, we proposed a framework that is developed on the basis of difference-of-convex (DC) programming to transform the original problem into a series of approximate convex problems which can be iteratively solved until convergence. Our extensive simulation results reveal that the proposed scheme outperforms other schemes that have been introduced in previous work and reflect a notable trend that deploying more cooperative UAVs with fewer resources (power and storage capacity) is more efficient than deploying fewer UAVs with more resources. In particular, in one of our collected results, the total communication power can be reduced by roughly 40 dB when doubling the number of cooperative UAVs.

In the literature, e.g. [10], one can find the so-called basic UAV mission target assignment in which m UAVs each with a capacity limit q visit n targets in a cooperative manner (and return to their departure points) such that the cost incurred by each UAV’s travel is minimized. In [10], we proposed a mixed integer linear program (MILP) formulation which exactly solves the problem, as well as four alternative MILP formulations which are computationally less intensive (and therefore suited for real-time purposes) yet yield a theoretically guaranteed sub-optimal solution. In this chapter, we further consider timing constraints imposed on some p of the targets, so-called prime targets. This consideration is often required for scenarios in which prime targets must be visited in a pre-defined time interval, and mathematically results in the addition of several integer linear constraints to the previous MILP formulation making the problem computationally intractable. We propose a novel procedure of adding these cumbersome timing constraints to the previous MILP formulation, in order to avoid increasing too much computational cost under practically valid assumptions. We first show that the proposed procedure still guarantees the previously claimed theoretical solution quality associated with the basic mission. We then show through extensive numerical simulations that under certain conditions, our algorithms return solutions which are still computationally manageable.

A Stylized Model of the Oil Squeeze

One of the main obstacles in using mixed integer linear programming (MILP) formulations for large-scale open pit production scheduling is the size of the problem. The objective of this work is to develop, implement, and verify deterministic MILP formulations for long-term large-scale open pit production scheduling problems. The objective of the model is to maximize the net present value, while meeting grade blending, mining and processing capacities, and the precedence of block extraction constraints. We present four MILP formulations; the first two models are modifications of available models; we also propose, test and validate two new MILP formulations. To reduce the number of binary integer variables in the formulation, we aggregate blocks into larger units referred to as mining-cuts. We compare the performances of the proposed models based on net present value generated, practical mining production constraints, size of the mathematical programming formulations, the number of integer variables required in formulation, and the computational time required for convergence. An iron ore mine case study is represented to illustrate the practicality of the models as well.

With increasing demand on digital images, there is a need to compress the image to entertain the limited bandwidth and storage capacity. Recently, there is a growing interest among researchers focusing on compression of various types of images and data. Amongst various compression algorithms, transform-based compression is one of the promising algorithms. Despite the technological advances in transmission and storage, the demands placed on the bandwidth of communication and storage capacities by far outstrips its availability. This paper presents a review of image compression principle, compression techniques and various thresholding algorithms (pre-processing algorithms) and quantization algorithm (post-processing algorithms). This paper intends to give an overview to the relevant parties to choose the suitable image compression algorithms to suit with the need.

In order to meet the growing demands for multimedia service access and release the pressure of the core network, edge caching and device-to-device (D2D) communication have been regarded as two promising techniques in next generation mobile networks and beyond. However, most existing related studies lack consideration of effective cooperation and adaptability to the dynamic network environments. In this article, based on the flexible trilateral cooperation among user equipment, edge base stations and a cloud server, we propose a D2D-assisted heterogeneous collaborative edge caching framework by jointly optimizing the node selection and cache replacement in mobile networks. We formulate the joint optimization problem as a Markov decision process, and use a deep Q-learning network to solve the long-term mixed integer linear programming problem. We further design an attention-weighted federated deep reinforcement learning (AWFDRL) model that uses federated learning to improve the training efficiency of the Q-learning network by considering the limited computing and storage capacity, and incorporates an attention mechanism to optimize the aggregation weights to avoid the imbalance of local model quality. We prove the convergence of the corresponding algorithm, and present simulation results to show the effectiveness of the proposed AWFDRL framework in reducing average delay of content access, improving hit rate and offloading traffic.

A branch-price-and-cut algorithm is developed for a complex maritime inventory-routing problem with varying storage capacities and production/consumption rates at facilities. The resulting mixed-integer pricing problem is solved exactly and efficiently using a dynamic program that exploits certain “extremal” characteristics of the pricing problem. The formulation is tightened by using the problem's boundary conditions in preprocessing and to restrict the set of columns that are produced by the pricing problem. Branching schemes and cuts are introduced that can be implemented efficiently and that preserve the structure of the pricing problem. Some of the cuts are inspired by the capacity cuts known for the vehicle-routing problem, whereas others specifically target fractional solutions brought about by individual vessels “competing” for limited inventory at load ports and limited storage capacity at discharge ports. The branch-price-and-cut approach solves practically sized problems motivated by the operations of an oil company to optimality, and it provides reasonable bounds for larger instances.

Underground passageways for the transport of goods and people have fascinated mankind throughout the centuries. Recent initiatives in different parts of the world aim at rekindling the freight tunnel concept for urban logistics. According to this concept, freight is lifted into a tunnel, moved via electrically powered vehicles, rail cars, or maglev shuttles toward inner-city microhubs, and finally delivered with environmentally friendly vehicles to urban recipients. The main justification for this concept to outweigh the huge tunnel boring cost is a substantial reduction of road-based urban freight traffic. To warn about unfulfilled expectations, however, we show that the combination of time-critical goods, small inner-city hubs with restricted storage capacity, and the wrong booking procedure can hinder a loading of tunnel vehicles to capacity. To do so, we consider a polynomially solvable scheduling problem where due date-restricted shipments are to be assigned to tunnel vehicles and delivered to a given set of microhubs with limited storage capacity. Scarce hub capacities, tight due dates, and the wrong booking policy may not only lead to unused tunnel capacity but also to shipments that cannot be feasibly transported through the tunnel. If they are handed over to trucks as a fallback option, we show that a Braess-like paradox can occur: The time-critical, small-lot transports of the leftover shipments can produce even more surface traffic than trucking all shipments right from the start without the tunnel. Supplemental Material: The online appendices are available at https://doi.org/10.1287/serv.2023.0005 .

In the food industry products are usually characterized by their recipes, which are specified by various quality attributes. For end products, this is given by customer requirements, but for intermediate products, the recipes can be chosen in such a way that raw material procurement costs and processing costs are minimized. However, this product selection process is bound by production and storage capacity limitations, such as the number and size of storage tanks or silos. In this paper, we present a mathematical programming approach that combines decision making on product selection with production and inventory planning, thereby considering the production and storage capacity limitations. The resulting model can be used to solve an important practical problem typical for many food processing industries.

Currently, refined petroleum products are manufactured by six refineries in South Africa. The supply of refined petroleum products is supplemented by imports of refined petroleum products. These refined petroleum products are transported by pipeline, rail, road, sea and a combination thereof to depots across the country. These products are then distributed directly to commercial customers through points of resale in the form of retail service stations. The challenge is that the storage facilities and distribution networks in South Africa are currently utilised to its full capacity. There is a need to expand current storage infrastructure or to erect new storage facilities to facilitate imports. Durban Port that currently handles 70% of the country’s crude oil and 40% of refined products is also utilised to its full capacity. The investigated problem pertains to the limited storage and transport capacity available for the distribution of petroleum products in the country. The interview protocol, which was based on the literature reviewed, was used for compiling the primary data for the case study. Selected findings revealed that the respondents perceived that storage capacity is the most significant constraint militating against adequate supply by the supply chain. The findings also indicated that the respondents were of the opinion that transport rates and routes (cost of logistics) are the greatest contributor to the prices of products. As a result, there appears to be a major scope for improvement in terms of the usage of pipelines and rails for the movement of petroleum products.Keywords: Petroleum products, storage facilities, supply chain, transportation, South African energy sector.Disciplines: civil engineering, architecture, business studies, energy studies

Due to unreliable wireless media and lack of infrastructure in MANETs, providing certificate path is a big challenge and also faces a big security problem [1]. MANETs are dynamic Peer-to-Peer PKIs and certification path can be built in case any part of the infrastructure that is temporarily not reachable. Hence efficient path discovery is very important. Path discovery is difficult since trust relationship is bidirectional. Certification paths are easy to find in case of hierarchical PKI since there is only one path among two entities. To obtain resilience and efficient path discovery among Peer- to –Peer trusted PKI’ s for issuing entities we use a tool to address these problems by overlying a virtual hierarchical architecture. Since it is executed for short time in a dynamic atmosphere this is suitable in case of MANETs. Proposed technique to establish a virtual hierarchy in a Peerto- Peer PKI, based on the trustworthiness of the participating neighbors. The upward approach is used to build the hierarchical structure i.e. from the leaves to the root. The main importance is given for the multi-rooted approach in case of building a hierarchy, depending upon the possibility. In addition the protocol does not require to issue new certificates among PKI entities, facilitates the certification path discovery process and the maximum path length can be adapted to the characteristics of the users with limited processing and Storage capacity.