Purchaser Problem Research Articles

Automatic Dress Size Detection for Online Shopping

This project addresses the challenge faced by online shoppers in accurately determining dress sizes, leading to dissatisfaction and high return rates. To mitigate this issue, the proposed system introduces an automated approach to dress size detection, integrating human detection and landmark extraction techniques using YOLOv7. The system aims to enhance the online shopping experience by providing shoppers with personalised dress size recommendations based on their body measurements. By leveraging the real-time object detection capabilities of YOLOv7, the system detects human bodies within input images, subsequently extracting key landmarks such as shoulder width, bust size, and waist circumference. These measurements are then utilised to estimate the most suitable dress size for the user, thus addressing the problem of ill-fitting purchases in online shopping. Evaluation of the system's performance involves accuracy metrics and usability testing to validate its effectiveness and reliability in automating dress size detection for online shoppers. Through this innovative solution, online retailers can significantly reduce return rates and enhance customer satisfaction, while shoppers can enjoy a more convenient and personalised shopping experience

The traveling purchaser problem for perishable foods

This paper addresses the well-known traveling purchaser problem (TPP) considering restrictions related to perishable food, which is called the traveling purchaser problem for perishable foods (TPP-PF). In addition to the main assumptions of the capacitated TPP, the TPP-PF takes into account the release times of perishable foods at markets. In this context, a product can only be purchased at a market after it becomes available for sale. The TPP-PF also considers that the quality of perishable products decreases due to waiting on the market shelves and during transportation, and it additionally takes the deterioration cost of the foods into account. The aim of the problem is to find the best procurement and route plan for the purchaser to minimize the total transportation cost of the temperature-controlled vehicle, the damage cost of perishable foods, and the purchasing cost. Since the deterioration cost of products based on the waiting time is determined through exponential functions, the problem is formulated as a mixed-integer non-linear programming model. To find efficient results for the problem, an adaptive large neighborhood search (ALNS) algorithm is introduced with new problem-specific destroy/repair operators. Furthermore, an additional mechanism is included in the ALNS to change the direction of the search when the algorithm cannot improve the best solution after a number of iterations. In order to analyze the performance of the proposed ALNS, a new benchmark problem set is generated using a well-known TPP benchmark set. The proposed ALNS is first performed for the TPP-PF instances and compared to GUROBI and a simulated annealing algorithm. Following the TPP-PF experiments, the ALNS is carried out for the TPP instances and compared to six state-of-the-art solution approaches. The computational results show that the proposed ALNS outperforms those approaches by finding better results for almost all instances.

Strategy design of fresh e-commerce pre-warehouse based on mass customization

Pre-warehouse is the last link in the delivery process of fresh e-commerce. The purchase link of pre-warehouse needs to balance two contradictory goals: meeting the diverse individual needs of customers and reducing the delivery cost through scale. The idea of mass customization is a trade-off between economy of scope and economy of scale. Based on this idea, this paper constructs a mass customization model, and formulates the purchase strategy for the pre-warehouse based on the prediction of customer demand, so as to maximize the long-term profit of enterprise. Through the simulation experiment, this paper compares the comprehensive performance of the mass customization model with five other contrastive strategy models: the highest satisfaction strategy, the demand priority strategy, the lowest purchasing price strategy, the highest selling price strategy and the highest gross margin strategy. Experimental results show that no matter how the parameters change, the mass customization model performs better than other models. What’s more, when the demand forecast is accurate, the substitutability between commodities is strong, and the delivery cost is high, the mass customization model has biggest advantage over other strategy models. The main innovation of this paper is introducing the idea of mass customization in pre-warehouse purchase problem, which not only expands the scope of application of mass customization, but also proposes a stably better and appliable pre-warehouse purchase strategy.

A Study of the Impact of Smart Contract Technology on the Understanding and Application of Contract Law from the Perspective of the Legal Profession

Abstract This paper constructs the logical framework of the smart contract model based on the sale and purchase contract transaction, integrating the legal framework and the smart contract. The smart contract model merges blockchain technology’s decentralized features with smart contracts to create a decentralized service mode for sale and purchase transactions. The classification of transaction attributes optimizes and adjusts the matching degree calculation method to meet the needs of both buyers and sellers. The matching degree determines the total satisfaction of both buyers and sellers. The simulation experiment model is designed to analyze the matching results, and the experimental results indicate that the overall utility value range is between 0.1 and 0.5, resulting in a higher level of satisfaction for both parties involved. Applying the model to actual purchase and sale transaction contracts, the expected profit of transaction users after using smart contracts is 26,300 yuan higher than that without using smart contracts. The accuracy and confidence scores of experienced users are 0,8886 and 4.0493, respectively, when evaluating their perceptions of the use of smart contracts. Smart contracts can help participants identify sales and purchase problems and provide innovative perspectives on the application of contract law.

A pattern recognition lexi-search approach to the variant traveling purchaser problem

A pattern recognition lexi-search approach to the variant traveling purchaser problem

Preprocessing Algorithms for the Traveling Purchaser Problem

Preprocessing Algorithms for the Traveling Purchaser Problem

The Traveling Purchaser Problem with Zone-Based Tariffs

The Traveling Purchaser Problem with Zone-Based Tariffs

Dual Hesitant q-Rung Orthopair Fuzzy Interaction Partitioned Bonferroni Mean Operators and Their Applications

The purpose of this paper is to introduce interaction partitioned Bonferroni mean operators under dual hesitant q-rung orthopair fuzzy environment. Motivated by the idea of q-rung orthopair fuzzy interaction operational laws, partitioned Bonferroni mean, and dual hesitant q-rung orthopair fuzzy sets, for dual hesitant q-rung orthopair fuzzy numbers, we present dual hesitant q-rung orthopair fuzzy interaction operational rules and propose several dual hesitant q-rung orthopair fuzzy interaction partitioned Bonferroni mean aggregation operators, including the interaction partitioned Bonferroni mean operator for dual hesitant q-rung orthopair fuzzy numbers, the weighted interaction partitioned Bonferroni mean operator for dual hesitant q-rung orthopair fuzzy numbers, the interaction partitioned geometric Bonferroni mean operator for dual hesitant q-rung orthopair fuzzy numbers, and the weighted interaction partitioned geometric Bonferroni mean operator for dual hesitant q-rung orthopair fuzzy numbers. Moreover, some properties and special cases associated with these proposed operators are also analyzed. For dual hesitant q-rung orthopair fuzzy numbers, based on the proposed operators, a multicriteria group decision-making method is proposed. Finally, an example for missile purchase problem is illustrated to demonstrate the superiority and feasibility by comparing with other existing multicriteria group decision-making methods.

Multipath traveling purchaser problem with time-dependent market structure using quantum-inspired variable length genetic algorithm

In developing countries, rural and semi-urban markets remain open for a particular period of time in a day, and items’ availabilities and prices vary within the opening hours of a market. Nowadays, the Internet of Things (IoT) and Type-2 fuzzy logic (T2FL) systems are used to make real-time decisions. With the logistical and infrastructural development around the world, there are several alternate routes for travel between different markets and cities. Taking the above realities into consideration, here, we develop and analyze IoT-enabled and T2FL system-based multipath traveling purchaser problems with time-dependent market structure (IoT-T2FL-MPTPPswTDMS). In this study, information regarding weather, road surface, and congestion concerning different paths obtained by the IoT are used as inputs in T2FL, and path-wise, the vehicle’s average velocity is obtained using the fuzzy rules. Item availability and price in a particular market change with time. The optimization problem is, for different journey starting times, to select the appropriate markets for the purchase and the corresponding optimum routing path for minimum cost (time) against time (cost) constraint. A multi parent, varied-offspring, variable length quantum-inspired genetic algorithm (MPVOVLQiGA) with quantum selection and mutation and multi parent, varied-offspring crossover is developed to solve the proposed problems. For children, mimicking the real-life parental system, In vitro fertilization (IVF) and adopted child (if IVF failed) are considered alongwith the usual comparison crossover. The algorithm’s superiority is established through a statistical test. A real-life model is numerically illustrated. The dependence on the minimum cost and time on the journey’s commencement time is demonstrated.

Optimal investment, consumption and life insurance purchase with learning about return predictability

This paper studies the optimal investment, consumption and life insurance purchase problem for a wage earner under the condition that the return on the risky asset is predictable. We assume that the market price of risk is an affine function consisting of an observable and an unobservable factor that follow the O-U processes, while the evolution of the interest rate is described by the Vasicek model. The optimal investment, consumption and life insurance strategies and the corresponding value function are derived by adopting the filtering technique and the dynamical programming principle approach. In addition, for comparative analysis, the suboptimal strategies and the utility losses are presented when the wage earner ignores learning or the randomness of the interest rate. Finally, some numerical examples are presented to illustrate the results.

On the influence of infrastructure availability on companies decisions toward modal shift and relocation of falicities

PurposeReduced availability of transport infrastructure affects highly dependent global supply chains (SCs), leading to modal shifts in the short term. Since relocation decisions of facilities may result in the long term, this paper evaluates companies’ business decisions in reaction to availability reductions of inland waterway transport (IWT). MethodologyA transport model evaluates the impact of reduced infrastructure availability through heuristic optimization based on the Traveling Purchaser Problem. The resulting increase in operational costs is used to assess the probability of relocating facilities based on a Facility Location Problem (FLP) which enables deriving the benefit from infrastructure conditions. FindingsThe study identifies critical thresholds for infrastructure availability that affect companies’ relocation decisions regarding the maintenance of public infrastructure. The case study exhibits actual critical infrastructure assets. Practical implicationsInsights into the decisive consequences of companies’ decisions are given, and awareness of the relevance of infrastructure investments on local areas’ attractiveness is raised. The results imply considering public infrastructure investments in maintenance for private business locations. OriginalityThe paper highlights a new way to sustain local industries and connects short-term agility and long-term resilience with companies’ decisions and the exogenous factor infrastructure availability. The applied use case focuses on the barely studied waterway infrastructure that gains importance in light of sustainability and climate change.

Multi-vehicle clustered traveling purchaser problem using a variable-length genetic algorithm

In this paper, we propose a multi-vehicle clustered traveling purchaser problem (MVCluTPP). Here, two types of procurement planning are proposed. In the first setup, the purchaser visits the markets clusterwise and, after satisfying the demand, returns to the depot with the purchased products, which are carried on the same path using a different vehicle. The other set up, in which products are purchased clusterwise and transported directly from the center of the cluster to the depot, but with the mandate that the purchaser visits the markets clusterwise. One of the multi-pronged aims of the model is to select the clusters and identify which markets to visit, determine the amount of procurement available in each market in a cluster, and develop an optimal routing plan in such a way that the overall system cost is minimized. The clusters are generated using k-means algorithm, and a variable-length chromosome genetic algorithm (VLC-GA) is proposed to optimize the cluster paths and to use a local heuristic to link the clusters to minimize the system cost. Furthermore, the superiority of the VLC-GA has been established through standard TPP and TSP instances, compared with exact methods, and some statistical tests are presented.

SOĞUK ZİNCİR LOJİSTİĞİNDE GEZGİN SATIN ALICI PROBLEMİ İÇİN İKİ AŞAMALI BİR ÇÖZÜM YÖNTEMİ

Bu çalışma, soğuk zincir lojistiğinde bozulabilir gıdaların satın alınması işlemini dikkate alarak literatürde iyi bilinen gezgin satın alıcı probleminin genişletilmiş bir halini sunmaktadır. Taşıma işlemlerinin bozulabilir ürün şartlarına dayalı gerçekleştirildiği bu problem, soğuk zincir lojistiğinde gezgin satın alıcı problemi (SZL-GSAP) olarak adlandırılmıştır. SZL-GSAP, sıcaklık kontrollü bir araç ile taşınması gereken çeşitli ürün taleplerinin belirli sayıda marketlerden satın alınması işlemini dikkate almaktadır. Problemin amacı, satın alıcı için sıcaklık kontrollü aracın enerji maliyetinin, ürün bozulma maliyetinin ve satın alma maliyetinin toplamını minimize edecek satın alma ve rota planını elde etmektir. Problem, doğrusal olmayan bir karma tamsayılı programlama modeli olarak formüle edilmiş ve iki aşamalı bir yöntem kullanılarak çözülmüştür. Çözüm yönteminin ilk aşamasında, bir çözüm kurucu yöntem kullanılarak problem için kaba bir sonuç elde edilmektedir. Daha sonra ilk aşamada elde edilen çözüm, GUROBI çözücüsü kullanılarak belirli bir zaman kısıtı içerisinde geliştirilmektedir. Sayısal uygulamalarda, önerilen çözüm yönteminin performansı, farklı büyüklükte örnekleri içeren bir problem seti üzerinde analiz edilmiştir.

The Multi-Depot Traveling Purchaser Problem with Shared Resources

Using shared resources has created better opportunities in the field of sustainable logistics and procurement. The Multi-Depot Traveling Purchaser Problem under Shared Resources (MDTPPSR) is a new variant of the Traveling Purchaser Problem (TPP) in sustainable inbound logistics. In this problem, each depot can purchase its products using the shared resources of other depots, and vehicles do not have to return to their starting depots. The routing of this problem is a Multi-Trip, Open Vehicle Routing Problem. A tailored integer programming model is formulated to minimize the total purchasers’ costs. Considering the complexity of the model, we have presented a decomposition-based algorithm that breaks down the problem into two phases. In the first phase, tactical decisions regarding supplier selection and the type of collaboration are made. In the second phase, the sequence of visiting is determined. To amend the decisions made in these phases, two heuristic algorithms based on the removing and insertion of operators are also proposed. The experimental results show that not only can purchasing under shared resources reduce the total cost by up to 29.11%, but it also decreases the number of dispatched vehicles in most instances.

Graph-Based Modeling for Optimal Strategy in Online Buying

Discount is a type of online purchase promotion that is presented based on the total value of consumer purchases. In this paper, an online purchase optimization problem will be studied, where a buyer is interested in buying several items (x≥2) by considering the total value discounts from different retailers, so that result in significant cost savings. The comparison shopping websites can be an alternative for consumers to find and compare information on items they want to buy from many online retailers. An integer programming formulation is proposed to obtain a near-optimal model of the online purchase problem. Then this formulation was developed into a graph-based modeling which was presented to build an optimization model (OptiGraph). The OptiGraph model obtained consists of the OptiNode set (subgraph) SG_a,SG_b,SG_c (retailer a, b, and c which contains nodes m_1 and m_2 in each subgraph representing the item to be purchased) and the OptiEdge set which describes the relationship between nodes in the subgraph. All nodes and edges contain the constraint function properties of the integer programming formulation of the online purchase problem with discount.

Regulating the rebound effect in the traveling purchaser problem

Despite engineers’ best intentions, technological innovations intended to reduce resource consumption are not assured to achieve their desired effects. As self-interested agents utilize the technological innovation, theretofore unprofitable activities may be rendered profitable, leading to a disparity between the expected reduction in resource consumption and what is actually achieved. This rebound effect is particularly salient given the current global climate crisis. In practice, many national governments are promoting fuel efficiency gains via new, cutting-edge technologies. However, to maximize the effectiveness of such technological innovations, additional regulation is likely required to avoid unfavorable rebound effects. To further study the dynamics of this setting, we set forth a logistics-based Stackelberg game underpinned by the Traveling Purchaser Problem. More specifically, we develop a bilevel programming formulation wherein the upper-level player is a regulator and the lower-level player is the purchaser. The regulator encounters a multi-objective problem and desires to reduce resource consumption while minimally disrupting commerce on the network. Mathematical conditions are derived and proved to determine when the null regulator action is optimal. These results are leveraged to develop a preprocessing algorithm and customized heuristic solution methodology. Extensive empirical testing is conducted on these methods and their results are analyzed using statistical techniques to quantitatively characterize their behavior. Analysis confirms the supposition that regulation and technological innovations are often required in tandem to achieve a regulator’s aims; however, it also reveals that, under select conditions, the regulator is better served by not intervening.

Decision-making under Risk: A Ship Sale and Purchase Problem by Utilizing Cumulative Prospect Theory

Decision-making under Risk: A Ship Sale and Purchase Problem by Utilizing Cumulative Prospect Theory

The traveling purchaser problem with fast service option

The traveling purchaser problem (TPP) is a generalization of the well-known traveling salesman problem, in which a list of products with different quantities has to be purchased from a subset of markets selling various products with different prices. The aim of the problem is to minimize total traveling and purchasing costs while satisfying the products demand in a unique tour. This study introduces a new variant of the TPP, in which the tour has to be completed within a duration time limit by taking into account the traveling and purchasing times of the purchaser. For the purchasing operations, two types of service options are allowed for the purchaser: standard and fast service. The fast service option of a market gives opportunities to the purchaser to complete the purchasing process in a shorter time with an additional cost. This problem is called the traveling purchaser problem with fast service option (TPP-FSO). In addition to presenting a new TPP variant to the literature, this paper proposes an adaptive large neighborhood search (ALNS) algorithm for the TPP-FSO. The proposed ALNS is enriched by a local search procedure, which consists of a set of route-change-based and procurement-change-based heuristics. To evaluate the performance of the ALNS on TPP-FSO, different-sized benchmark problems are generated by using a well-known TPP benchmark problem set. The results of the computations demonstrate the efficiency of the proposed algorithm by introducing better results in shorter computational times.

The rebound effect in transportation

Sometimes measurements taken to increase resource efficiency do not reach the estimated magnitude, as part of the reduction vanishes due to the reaction of entities to the changed environment. This so-called rebound effect is intensively discussed in the energy sector literature. Surprisingly, little is found for transportation, and the question arises whether rebound effects occur in logistics as well. In this paper, we fill this gap by examining a Traveling Purchaser Problem (TPP) transport network. The implementation, which is a generalization of the TSP, will serve as a decision space in which the procurement of various quantities of products inside a logistics network will be optimized in regard to total costs while the consumption of fuel is monitored. Different parameterizations of the environment will be analyzed and tested to determine factors for the occurrence and magnitude of rebound effects. We present an upper bound on the number of rebound effects and we prove that smaller steps in fuel efficiency increase let each rebound effect become a backfire.

Influence of divided attention on the attraction effect in multialternative choice

AbstractThe attraction effect in multialternative decision making reflects the context-dependent violation of rational choice axioms. This study examined the effect of concurrent divided attention in three-alternative visual choice tasks. The concurrent divided attention task is considered to consume the mental resources available for the choice task. There were three conditions: (a) the task-relevant condition, in which the auditory task should consume resources across multiple levels; (b) the task-irrelevant condition, in which the auditory stimuli should consume perceptual resources; (c) and the control condition, in which the resources should not be consumed by auditory stimuli. Thirty-three participants solved 24 hypothetical purchase problems with three alternatives that differed in terms of two attributes. The results indicated that the choice proportion of the target was significantly higher in the task-relevant condition than in the task-irrelevant and control conditions, thereby suggesting that a reduction in cognitive (and/or response) resources is critical for the attraction effect.