A multi-agent framework for load consolidation in logistics

Logistics companies mainly provide land transportation services facing with difficulties in making effective operational decisions. This is especially the case of making load/capacity/route planning and load consolidation where customer orders are generally unpredictable and subject to sudden changes. Classical modelling and decision support systems are mostly insufficient for providing satisfactory solutions in a reasonable time solving such dynamic problems. Agent-based approaches, especially multi-agent paradigms that can be considered as relatively new members of system science and software engineering, are providing effective mechanisms for modelling dynamic systems generally operating under unpredictable environments and having a high degree of complex interactions. It seems that multi-agent paradigms have big potential for handling complex problems in land transportation logistics. Based on this motivation, the paper proposes a multi-agent based framework for load consolidation problems of third-party logistics companies

New lot sizing heuristics for demand and price uncertainties with service-level constraint

Simultaneous consideration of both demand and price uncertainties has not been studied extensively in the literature. This problem is mathematically intractable for cases where a complex problem structure exists. This paper addresses the multi-period single-item lot sizing problem with stochastic demand and price on a rolling horizon basis. Problem formulation permits lost sale and backordering. Unit holding cost depends on purchasing price. In this study, we propose three new lot sizing heuristics based on a rolling horizon for this problem. The first two heuristics are the modified versions of well-known silver-meal and least-unit cost heuristics. The last heuristic known as cost-benefit (CB) is based on a cost-benefit evaluation at decision points. An extensive simulation analysis is performed for different values of set-up cost, number of rolling horizon periods, coefficient of variation of demand and price, backorder ratio and service level factors. Simulation study also considers different demand and price scenarios. The proposed heuristics are compared with each other by considering different cost components and they are also compared with deviation from the Wagner-Whitin solution. Simulation experiments show that the proposed CB heuristic outperforms the other two heuristics in most of the scenarios.Simultaneous consideration of both demand and price uncertainties has not beenstudied extensively in the literature. This problem is mathematically intractablefor cases where a complex problem structure exists. This paper addresses themulti-period single-item lot sizing problem with stochastic demand and price ona rolling horizon basis. Problem formulation permits lost sale and backordering.Unit holding cost depends on purchasing price. In this study, we propose threenew lot sizing heuristics based on a rolling horizon for this problem. The first twoheuristics are the modified versions of well-known silver-meal and least-unit costheuristics. The last heuristic known as cost-benefit (CB) is based on a cost-benefitevaluation at decision points. An extensive simulation analysis is performed fordifferent values of set-up cost, number of rolling horizon periods, coefficient ofvariation of demand and price, backorder ratio and service level factors.Simulation study also considers different demand and price scenarios.The proposed heuristics are compared with each other by considering differentcost components and they are also compared with deviation from the Wagner-Whitin solution. Simulation experiments show that the proposed CB heuristicoutperforms the other two heuristics in most of the scenarios

A Comparative Study of Neural Networks and ANFIS for Forecasting Attendance Rate of Soccer Games

WOS: 000483103300004The main purpose of this study was to develop and apply a neural network (NN) approach and an adaptive neuro-fuzzy inference system (ANFIS) model for forecasting the attendance rates at soccer games. The models were designed based on the characteristics of the problem. Past real data was used. Training data was used for training the models, and the testing data was used for evaluating the performance of the forecasting models. The obtained forecasting results were compared to the actual data and to each other. To evaluate the performance of the models, two statistical indicators, Mean Absolute Deviation (MAD) and mean absolute percent error (MAPE), were used. Based on the results, the proposed neural network approach and the ANFIS model were shown to be effective in forecasting attendance at soccer games. The neural network approach performed better than the ANFIS model. The main contribution of this study is to introduce two effective techniques for estimating attendance at sports games. This is the first attempt to use an ANFIS model for that purpose

The Internal and External Customer Focused Process Improvement and the Performance Analysis Studies in Healthcare Systems

Purpose: The main contribution of this paper is to generate an optimum solution for capacity planning and appointment scheduling issues, which are frequently encountered in clinical flows with various route and treatment periods at dental hospitals. Design/methodology/approach: It is essential to define the system well in order to ensure that the working staff and patients use their time very efficiently and that the process flows continuously. By having examined a sample healthcare system through the help of a study addressed in such context, studies on process improvement in line with the dissatisfactions of the working staff and patients have been carried out. Within the scope of the study, the operation of 7 Departments in a dental hospital undergoing a treatment process have been reviewed and examined. The problems encountered as result of the observations made are discussed in detail, and formerly and recently designed system performance analyses are conducted by having performed the respective process improvement studies. The relevant samplings of this study are modeled via the Arena Simulation Program. The data of the previous four months is used in the parameters, which are used through the modellings. The system data are entered by taking into account seasonal characteristics of the data. Findings: The analyses are made as a consequence of such study that has been addressed, it is established that the efficiency of the internal customers of the hospital increases substantially, and that the waiting durations of the dental patients decrease and in turn, the external customer satisfaction increases drastically. Research limitations/implications: Under the scope of the present study, 7 different treatment processes are analysed in a dental hospital in Cukurova Region with a significant patient potential. The treatment clinics present in the hospital are radiology, periodontology, surgery, treatment, orthodontics and prosthesis. These clinics run their own appointment and treatment system independently. Thus, the study has limited with five departments among 7. Originality/value: With this study, given the flow of different existing treatment processes belonging to patients are optimized, and also the continuity of the system is ensured by minimizing the patient waiting times within the existing system

A multi-agent based approach to dynamic scheduling of machines and automated guided vehicles in manufacturing systems

In real manufacturing environments, the control of system elements such as automated guided vehicles has some difficulties when planning operations dynamically. Multi agent-based systems, a newly maturing area of distributed artificial intelligence, provide some effective mechanisms for the management of such dynamic operations in manufacturing environments. This paper proposes a multi-agent based scheduling approach for automated guided vehicles and machines within a manufacturing system. The proposed multi-agent based approach works under a real-time environment and generates feasible schedules using negotiation/bidding mechanisms between agents. This approach is tested on off-line scheduling problems from the literature. The results show that our approach is capable of generating good schedules in real time comparable with the optimization algorithms and the frequently used dispatching rules. (C) 2012 Elsevier B. V. All rights reserved

A multi-agent based approach to dynamic scheduling with flexible processing capabilities

A multi-agent based system is proposed to simultaneous scheduling of flexible machine groups and material handling system working under a manufacturing dynamic environment. The proposed model is designed by means of methodology and programmed in agent based systems development environment. Each agent in the model is autonomous and has an ability to cooperate and negotiate with the other agents in the system. Due to these abilities of agents, the structure of the system is more suitable to handle dynamic events. The proposed dynamic scheduling system is tested on several test problems the literature and the results are quite satisfactory because it generates effective schedules for both dynamic cases in the real time and static problem sets. Although the model is designed as an online method and has a dynamic structure, obtained schedule performance parameters are very close to those obtained from offline optimization based algorithms