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Real time control and scheduling systems determine the vehicle routing plan based on the current status of the system. The status of a system can be represented by different attributes of demand such as location, quantity, and due date. The objective of this article is to propose a real time dynamic vehicle control and scheduling system for multi‐depot physical distribution. To perform the system objectives effectively, the proposed system includes five major modules. These are: global information collection system, depot controller, route planner, vehicle scheduler, vechicle route and time table feedback system. A simulation experiment is described at the end of the article to evaluate the performance of the proposed system. The results indicate that the proposed system is promising and can be implemented in practical operations.

The Home Depot case is a great story. It's about entrepreneurship, growth, CEO leadership, and the dramatic impact, good and bad, a CEO can have on a company's growth culture, strategy, and performance. Home Depot had faced market growth challenges for the last seven years as it tried in numerous ways to reignite its growth engine. The case explores the growth strategies of CEOs Bernie Marcus, Arthur Blank, and Blank's successor Bob Nardelli, a former GE executive. After examining Home Depot's growth history, the case challenges students to devise a growth strategy for the company under a new CEO.

This chapter proposes a hybrid heuristic method combining a clustering search (CS) metaheuristic with an exact algorithm to solve a two-stage capacitated facility location problem (TSCFLP). The TSCFLP consists of defining the optimal locations of plants and depots and the product flow from plants to depots (first stage) and from depots to customers (second stage). The problem deals commonly with cargo transportation in which products must be transported from a set of plants to meet customers’ demands passing out by intermediate depots. The main decisions to be made are related to define which plants and depots must be opened from a given set of potential locations, which customer to assign to each one of the opened depots, and the amount of product flow from the plants to the depots and from the depots to the customers. The objective is to minimize costs satisfying demand and capacity constraints. Computational results demonstrate that our method was able to find good solutions when comparing it directly with a commercial solver and a genetic algorithm (GA) reported in a recent chapter found in the literature, requiring less than 1.5% and 41% of the computational time performed by these methods, respectively. Thus, our hybrid method combining CS with an exact algorithm can be considered as a new matheuristic to solve the TSCFLP.

Supply chain systems play very significant roles to improve the quality of customer service and therefore, derive considerable attention from managers of enterprises in pursuit of competitive excellence. Different strategies are used to improve customer service and different measures are also used to quantify this. A detailed diagnostic study was conducted in a large Indian company dealing with fast moving consumer goods. In this study, a new measure called Depot Service India (DSI) was used to measure the quality of customer service and using which various statistical analyses were carried out to study how the DSI values vary over time and among various depots, how these values are auto‐correlated, what factors influence the variations in DSI values and how various decisions at strategic, tactical and operational levels affect the performance of the system. The details of how the results obtained were used to evolve remedial measures to improve the quality of customer service in the supply chain system of this company are also discussed in this paper.

The purpose of this paper is to figure out in which way a hinterland-based inland depot model can help a shipping company in solving the empty container problem at a regional level. The repositioning of empty containers is a very expensive operation that does not generate profits. Consequently, it is very important to provide an efficient empty container management.

In this paper, the empty container problem is discussed at a regional repositioning level. For solving this problem, a mixed-integer linear optimization model is developed and validated by using the German hinterland as a case.

The findings show that the hinterland-based solution is able to reduce the total system costs by 40 per cent. In addition, total of truck kilometres could be reduced by more than 30 per cent too.

This research is based on German data only.

This paper closes the gap in empty container repositioning research by looking at the hinterland dimension from a single shipping company point of view.

Preface The functions of business divide into several areas and the general focus of this book is on one of the most important although least understood of these—DISTRIBUTION. The particular focus is on reviewing current practice in distribution costing and on attempting to push the frontiers back a little by suggesting some new approaches to overcome previously defined shortcomings.

This paper proposed a bi-level mathematical model for location, routing and allocation of medical centers to distribution depots during the COVID-19 pandemic outbreak. The developed model has two players including interdictor (COVID-19) and fortifier (government). Accordingly, the aim of the first player (COVID-19) is to maximize system costs and causing further damage to the system. The goal of the second player (government) is to minimize the costs of location, routing and allocation due to budget limitations.

The approach of evolutionary games with environmental feedbacks was used to develop the proposed model. Moreover, the game continues until the desired demand is satisfied. The Lagrangian relaxation method was applied to solve the proposed model.

Empirical results illustrate that with increasing demand, the values of the objective functions of the interdictor and fortifier models have increased. Also, with the raising fixed cost of the established depot, the values of the objective functions of the interdictor and fortifier models have raised. In this regard, the number of established depots in the second scenario (COVID-19 wave) is more than the first scenario (normal COVID-19 conditions).

The results of the current research can be useful for hospitals, governments, Disaster Relief Organization, Red Crescent, the Ministry of Health, etc. One of the limitations of the research is the lack of access to accurate information about transportation costs. Moreover, in this study, only the information of drivers and experts about transportation costs has been considered. In order to implement the presented solution approach for the real case study, high RAM and CPU hardware facilities and software facilities are required, which are the limitations of the proposed paper.

The main contributions of the current research are considering evolutionary games with environmental feedbacks during the COVID-19 pandemic outbreak and location, routing and allocation of the medical centers to the distribution depots during the COVID-19 outbreak. A real case study is illustrated, where the Lagrangian relaxation method is employed to solve the problem.

A model for the location of depots is described; the model being a reconciliation between the ‘Feasible Set’ approach and the ‘Infinite Set’ approach, both of which have distinct advantages.

This monograph defines distributed intelligence and discusses the relationship of distributed intelligence to data base, justifications for using the technique, and the approach to successful implementation of the technique. The approach is then illustrated by reference to a case study of experience in Birds Eye Foods. The planning process by which computing strategy for the company was decided is described, and the planning conclusions reached to date are given. The current state of development in the company is outlined and the very real savings so far achieved are specified. Finally, the main conclusions of the monograph are brought together. In essence these conclusions are that major savings are achievable using distributed intelligence, and that the implementation of a company data processing plan can be made quicker and simpler by its use. However, careful central control must be maintained so as to avoid fragmentation of machine, language skills, and application taking place.