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Suggests that, in recent years, remarkable progress has been made in the development of the topological design of logistics networks, especially in the warehouse location problem. Extends the standard warehouse location problem to a generalization of multiproduct capacitated warehouse location problem, as opposed to differentiated variations of a single‐product warehouse location problem, where each warehouse has a given capacity for carrying each product. Presents an algorithm based on cross‐decomposition, to reduce the computational difficulty by incorporating Benders decomposition and Lagrangean relaxation. Computational results of this algorithm are encouraging.

The paper aims at the bi‐objective optimization of a two‐echelon distribution network model for facility location and capacity allocation where in a set of customer locations with demands and a set of candidate facility locations will be known in advance. The problem is to find the locations of the facilities and the shipment pattern between the facilities and the distribution centers (DCs) to minimize the combined facility location and shipment costs subject to a requirement that maximum customer demands be met.

To optimize the two objectives simultaneously, the location and distribution two‐echelon network model is mathematically represented in this paper considering the associated constraints, capacity, production and shipment costs and solved using hybrid multi‐objective particle swarm optimization (MOPSO) algorithm.

This paper shows that the heuristic based hybrid MOPSO algorithm can be used as an optimizer for characterizing the Pareto optimal front by computing well‐distributed non‐dominated solutions. These aolutions represent trade‐off solutions out of which an appropriate solution can be chosen according to industrial requirement.

Very few applications of hybrid MOPSO are mentioned in literature in the area of supply chain management. This paper addresses one of such applications.

This paper aims to deal with a real-life strategic conflict in joint operations (JOs) for facility location decision and planning in an oil and gas field that stretches over two countries and tries to develop a basis for mitigating such conflict.

This paper develops a novel approach using integer linear programming (ILP) to determine optimal facility location considering technical, economic and environmental factors. Strategic decision-making in JOs is also influenced by business priorities of individual partner, sociopolitical issues and other covert factors. The cost-related quantitative factors are normalized using inverse normalization function as these are to be minimized, and qualitative factors that are multi-decision-making criteria are maximized, thus transforming both qualitative and quantitative factors as a single objective of maximization in ILP model.

The model identifies the most suitable facility location based on a wide range of factors that would provide maximum benefit in the long term, which will help decision-makers and managers.

The model can be expanded incorporating other quantitative and qualitative factors such as tax incentives by the government, local bodies and government regulations.

The applicability of the model is not limited to JOs or oil/gas field, but is applicable to a wide range of sectors.

The model is transparent and based on rational and scientific basis, which would help in building consensus among the dissenting parties and aid in mitigating strategic conflict. Such type of model for mitigating strategic conflict has not been reported/used before.

The literature on the facilities location problem is quite extensive with a wide variety of solution methods for addressing these problems where the objective is cost minimization. Develops a branch and bound algorithm for solving the uncapacitated, multi‐period facility location problem where the objective is to maximize profits. The solution method uses a number of simplification and branching decision rules to solve the problem efficiently. Extensive computational results on the algorithm’s performance are provided. The results indicate that the algorithm provides optimal solutions in substantially less time than LINDO.

Effective inventory management is very critical to market success. The purpose of this paper is to formulate an integrated model for the location of warehouse, the allocation of retailers to the opened warehouses, and finding the perfect policy for inventory control to managing order quantity and safety stock level. The goal is to select the optimum numbers, locations, capacities of the opening warehouses and inventory policy so that all stochastic customer demands can be satisfied.

It is assumed that the location of plant has already been determined and the paper answers the following questions: what are the location decisions over the planning horizon? How retailers are allocated to the warehouses? What are the optimum capacities for the opened warehouses? What is the best inventory policy for this supply chain? What are the total minimum costs?

The model was developed as a non‐linear mixed integer programming and solved using Lagrange relaxation and sub‐gradient search for the location/allocation module and a procedure for the capacity planning module. The results for the randomly selected problems show that the average duality gap ranges are between 0.51 and 1.58 percent. Also, from the CPU time point of view, the performance of the proposed algorithm was very good.

The paper addresses an integrated location, allocation, and inventory decisions in the design of a supply chain distribution network. In addition sensitivity analyses are conducted to evaluate the effects of the multi‐capacity levels on some performance measures.

The purpose of this paper is to focus on the medium‐term planning problem in supply chain networks. Based on a literature review, a comprehensive analytical planning model for the three echelon tactical planning problem in supply chain networks is developed which is applicable in a hierarchical planning frame.

The approach used was mathematical mixed integer programming to model three echelon production‐distribution networks embedded in the supply chain planning matrix frame. Application of the model in a multi‐site planning process based on a case study from the industrial transformer supply chain was also undertaken.

Integrated multi‐period medium‐term planning with customer oriented single sourcing is an efficient method to implement mathematical optimal solutions in practice as it provides comprehensive tactical plans and network designs. These can be used for scenario analysis in a coordination process with independent supply chain partners.

The implementation of a mathematical optimal plan in a complex business network structure requires a big‐bucket model solution to grant the plan's stability via sufficient time buffers.

The paper displays development of a multi‐period three echelon tactical production‐distribution‐transportation model with different capacities, transportation modes, product types and single sourcing decisions.

Demand for goods/services has increased in Latin America due to urbanization, leading to a complex delivery system and increased logistical activities. In Quito, the Historic Center and La Mariscal are two zones that face logistical challenges. The objective of this chapter is to analyze the commercial logistic activities related to loading and unloading goods in these zones. To address this urban freight problem, this chapter proposes a solution through the calculation of the optimal number and location of loading and unloading bays in each zone based on actual commercial activity data. First, a delivery survey was completed in each zone regarding frequency and amount of deliveries. Then, based on the data obtained, an optimization model is proposed to determine the optimal number and location of loading and unloading bays. Finally, a simulation model of the delivery process is performed to readjust the bay’s optimal number. A total number of 75 and 98 bays were calculated to serve the total shopping district of a representative square kilometer (km²) of the Historic Center and La Mariscal. This solution aims to minimize the delivery time and the distance for deliveries, improve urban freight transportation, and reduce traffic. This study could be used as a baseline and guide for further research in urban logistics, especially in Latin America, where urban logistics is still under study. This chapter is part of a Research Project of Urban Logistics in Quito, led by Universidad San Francisco de Quito (USFQ), in association with the Megacity Logistics Lab of MIT.

With the increasing urbanization rates in emerging countries such as the ones in Latin America and the Caribbean, urban logistics solutions and initiatives are widely needed. Urban planners often consider only passenger transportation and leave freight transportation unattended, thus increasing externalities and degrading the transportation of goods. This chapter presents three urban logistics solutions, which intend to tackle problems related to urbanization and last mile delivery operations challenges by evaluating location models for loading and unloading bays, urban transfer centers location models, and freight trip generation models. The presented solutions were proposed by several researchers of the Institute of Innovation in Productivity and Logistics CATENA-USFQ over the last four years and remain theoretical at the moment. However, we present estimated results of potential implementations in three districts of Quito: Historic Center, Entertainment District, and Corporate District. This chapter not only presents the mentioned urban logistics solutions in Quito but also gives an overview of the followed methodology, which can be replicated in countries and cities of similar characteristics of the region.

Despite a growing body of research on the problem of increasing disaster preparedness by pre-positioning relief supplies at strategic locations, there is a lack of a benchmark set of problem instances that hinders thorough hypotheses testing, sensitivity analysis, model validation or solution procedure evaluation. The purpose of this paper is to address this issue by constructing a public library of diverse pre-positioning problem instances.

By carefully manipulating some of the instance parameters, the authors generated 30 case studies that were inspired by four instances collected from the literature that focus on disasters of different type and scale that occurred in different parts of the world. In addition, the authors developed a tool to algorithmically generate arbitrarily many diverse random instances of any size.

For many purposes, the problem library can eliminate or reduce the time-consuming process of data collection, conversion, digitization, calibration and validation, while simultaneously increasing the statistical significance of research results and allowing comparison with different works in the literature.

The case studies are inspired by only four disasters, and some of the instance parameters are defined in a reasonable, albeit arbitrary way. The instances are also limited by the underlying problem assumptions.

The instances provide a more comprehensive and balanced experimental setting (compared to a single case study) that can be used to study the pre-positioning and related problems, or derive managerial implications that can directly benefit the practitioners.

The instances can be used to derive practical guidelines that humanitarian workers can use on the ground to better plan their pre-positioning strategies and therefore minimize human suffering.

The case studies and the random instance generator are made publicly available to foster further research on the problem of pre-positioning relief supplies and humanitarian logistics in general.