Column generation based heuristic for tactical planning in multi-period vehicle routing

The periodic vehicle routing problem (PVRP) consists in establishing a planning of visits to clients over a given time horizon so as to satisfy some service level while optimizing the routes used in each time period. The tactical planning model considered here restricts its attention to scheduling visits and assigning them to vehicles while leaving sequencing decisions for an underlying operational model. The objective is twofold: to optimize regional compactness of the routes in a desire to specialize routes to restricted geographical area and to balance the workload evenly between vehicles. Approximate solutions are constructed using a truncated column generation procedure followed by a rounding heuristic. This mathematical programming based procedure can deal with problems with 50–80 customers over five working days which is the range of size of most PVRP instances treated in the literature with meta-heuristics. The paper highlights the importance of alternative optimization criteria not accounted for in standard operational models and provides insights on the implementation of a column generation based rounding heuristic.     

A bi-objective hierarchical program scheduling problem and its solution based on NSGA-III

In enterprise project management systems, a program at the tactical level coordinates and manages multiple projects at the operational level. There are close relationships between multiple projects in a program, which are typically manifested as shared resources and precedence relationships. Most research efforts have concentrated on the resource sharing by projects, while the precedence relationships between projects have yet to be comprehensively investigated. In this paper, a bi-objective hierarchical resource-constrained program scheduling problem proposed, where both resource sharing and precedence relationships between projects are considered in a distributed environment. The problem contains two different sub-problems at the operational level and the tactical level, and they are modeled in the same way as two bi-objective multi-mode scheduling problems. Shared resources are allocated from the tactical level to the operational level, and once they are allocated to a project, they can only be re-allocated to other projects once the current project is finished. Subsequently, a two-phase algorithm based on NSGA-III is developed. The algorithm runs at the operational level and the tactical level in turn. According to the Pareto fronts of projects that are submitted from the operational level, the bi-objective program planning at the tactical level is conducted under the constraints of precedence relationships and shared resources. The results of computational simulations demonstrate the satisfactory performance of the improved algorithm. By coordinating the local optimization of projects and the global optimization of the program in a hierarchical framework, the method proposed in this paper provides an effective integrated scheduling method for decision-makers at various levels of a program.

     

Forest planning at the tactical level

Forest management and planning can involve large tracts of land involving numerous areal units. One approach to plan activities for a forested region over decades involves breaking down decision making into three components: strategic, tactical, and operational. Each level of the management hierarchy can involve the development and application of optimization models. These models typically aid in exploring management alternatives as well as multi-objective tradeoffs. Even though a strategic model can provide support for long term management at a broad scale, solutions may not be feasible at an operational level. Tactical level modeling helps to bridge solutions reached at a strategic level using operational planning information. This paper presents several tactical level planning models that have been developed as a part of a research effort supported by the US Forest Service. These models have been utilized in land use management and planning by the US Forest Service through a specially developed spatial decision support system.     

Spares provisioning under performance-based logistics contract: profit-centric approach

Performance-based logistics (PBL) is emerging as a preferred logistic support strategy within the public sector, especially the Department of Defence. Under a PBL strategy, the customer buys performance, such as operational availability, mission readiness and operational reliability, instead of contracting for a specified collection of resources defining the underlying support infrastructure. The literature on PBL is still in its infancy and additional research is required to optimize logistic resources such as spare parts, equipment, facilities, labour etc within a PBL context. In this paper, an optimization model is developed for spares provisioning under a multi-item, multi-echelon scenario. The objective of the optimization model is to maximize the profit to the supplier under a PBL contract.     

Internal transport in automated semiconductor manufacturing systems

This paper summarizes the main results presented by the author in his PhD thesis (Montoya-Torres 2005), supervised by Stéphane Dauzèere-Pérés, Jean-Pierre Campagne and Hélène Marian, and defended on 29 November 2005 at the école des Mines de Saint-étienne and Université Jean-Monnet. The thesis is written in French and is available upon request from the author. This work deals with a real-life transportation problem in the semiconductor industry. It proposes a new approach by integrating tactical and operational decisions for the control of the automated transport system. At the tactical level, the problem is modeled using integer linear programming models inspired from Location Theory. At the operational level, the solution obtained from the tactical optimization is coupled with a discrete-event computer simulation program and some policies for transportation operations are implemented and compared.     

An Overview of Strategy and Tactics in System Dynamics Optimization

A review of the methods and purposes of optimization in system dynamics models is undertaken. It is suggested that, given the typical client set involved, an optimization strategy embracing search-based techniques is preferable to one based on optimal control methods. When conducting an optimization, various tactical problems may need to be resolved and ways of doing this are described. The resolution of the problems is independent of the particular software tool being used for search-based optimization. A range of studies is discussed and these studies both calibrate and exemplify the adoption of a search routine to optimize a system dynamics model.     

基于时间的随机需求二级分销网络物流系统集成优化研究

分销网络设计包括设施选址、库存控制、运输等方面的设计与优化,但以往只是从战略层、战术层、运作层来分别进行各自的研究。实际上,这三个层次的决策要素之间存在着复杂的互动关系,并存在着广泛的效益悖反关系,这些在变化的环境下显得尤为突出。本文充分考虑时间因素的重要性,从物流系统的集成优化高度出发,研究建立需求随机的多分销中心多顾客的设施选址———运输路线安排———库存控制问题(ILRIP)的模型,对此设计了一个两层粒子群优化(PSO)算法,并给出了计算实例。研究结果有助于供应链分销网络的集成优化,缩短商品流转周期,提高顾客服务水平,提升竞争力。     

A theoretical analysis of the cross-nested logit model

The emergence of Intelligent Transportation Systems and the associated technologies has increased the need for complex models and algorithms. Namely, real-time information systems, directly influencing transportation demand, must be supported by detailed behavioral models capturing travel and driving decisions. Discrete choice models methodology provide an appropriate framework to capture such behavior. Recently, the Cross-Nested Logit (CNL) model has received quite a bit of attention in the literature to capture decisions such as mode choice, departure time choice and route choice. %The CNL model is an extension of the Nested Logit model, providing %more flexibility at the cost of some complexity in the model formulation. In this paper, we develop on the general formulation of the Cross Nested Logit model proposed by Ben-Akiva and Bierlaire (1999) and based on the Generalized Extreme Value (GEV) model. We show that it is equivalent to the formulations byby Papola (2004) and Wen and Koppelman (2001). We also show that the formulations by Small(1987) and Vovsha(1997) are special cases of this formulation. We formally prove that the Cross-Nested Logit model is indeed a member of the GEV models family. In doing so, we clearly distinguish between conditions that are necessary to prove consistency with the GEV theory, from normalization conditions. Finally, we propose to estimate the model with non-linear programming algorithms, instead of heuristics proposed in the literature. In order to make it operational, we provide the first derivatives of the log-likelihood function, which are necessary to such optimization procedures.     

An integrated optimization model for managing the global value chain of a chemical commodities manufacturer

The realization of supply chain management concepts goes along with the introduction of comprehensive software systems for supporting decisions at the strategic, tactical, and operational planning level. Moreover, in industry the focus has shifted from a pure logistics-oriented view towards the integration of pricing and revenue issues into cross-functional value chain planning models. This paper presents a practical decision support tool for global value chain planning in the production of chemical commodities. The proposed linear optimization model consists of various modules that reflect sales, distribution, production, and procurement activities within a company-internal value chain. The objective of the model is to maximize profit by coordinating all activities within the supply chain. The model formulation is related to a real industry case. It is shown how the model can be used to support decision making from sales to procurement by volume and value.     

An integrated simulation-optimization framework for the operational planning of seaport container terminals

In this article the operational planning of seaport container terminals is considered by defining a suitable integrated framework in which simulation and optimization interact. The proposed tool is a simulation environment (implemented by using the Arena software) representing the dynamics of a container terminal. When the system faces some particular conditions (critical events), an optimization procedure integrated in the simulation tool is called. This means that the simulation is paused, an optimization problem is solved and the relative solution is an input for the simulation environment where some system parameters are modified (generally, the handling rates of some resources are changed). For this reason, in the present article we consider two modelling and planning levels about container terminals. The simulation framework, based on an appropriate discrete-event model, represents the dynamic behaviour of the terminal, thus it needs to be quite detailed and it is used as an operational planning tool. On the other hand, the optimization approach is devised in order to define some system parameters such as the resource handling rates; in this sense, it can be considered as a tool for tactical planning. The optimization procedure is based on an aggregate representation of the terminal where the dynamics is modelled by means of discrete-time equations.     

Tactical Planning for a Cutting Stock System

Wood processing on a centralized conversion site can be considered as a cutting stock problem. The multiperiod optimization of the production system is a very hard problem to solve. A balance has to be made between value recovery optimization and service rate maximization. In this research a decision support system was designed and built to support production planning. Strategic, tactical and operational planning are covered, but here the focus is primarily on tactical planning.     

Optimizing revenue in CDMA networks under demand uncertainty

Mathematical programming models for telecommunications network design are prevalent in the literature, but little research has been reported on stochastic models for cellular networks. We present a stochastic revenue optimization model for CDMA networks inspired by bid pricing models from the airline industry. We describe the optimality conditions for the model and develop a supergradient algorithm to solve it. We provide computational results that show the effects of the distribution and variance of demand. Finally, we discuss areas of future research, including a method to optimize the locations of the towers.     

Multimodal freight transportation planning: A literature review

Multimodal transportation offers an advanced platform for more efficient, reliable, flexible, and sustainable freight transportation. Planning such a complicated system provides interesting areas in Operations Research. This paper presents a structured overview of the multimodal transportation literature from 2005 onward. We focus on the traditional strategic, tactical, and operational levels of planning, where we present the relevant models and their developed solution techniques. We conclude our review paper with an outlook to future research directions.     

Optimization model for a production,inventory, distribution and routing problem in small furniture companies

Production and distribution are two key decisions in supply chain planning. In order to achieve an effective operational performance, it is important for these two decisions to be integrated, especially in supply chains with low inventory levels. In this paper, we propose a mixed integer programming model to integrate production, inventory, distribution and routing decisions in a single framework. The model was inspired by small Brazilian furniture companies and focuses on production and distribution decisions at an operational level. In particular, we consider a scenario in which only one production line and one vehicle, which makes multiple trips over the planning horizon, are available to produce items and deliver final products, respectively. We also take into account some features rarely considered in the literature, but commonly found in real-world applications, such as producing and stocking multiple items, distribution routes extending over one or more periods, multiple time windows and customers’ due dates. Computational tests on a set of randomly generated instances were carried out using a well-known optimization software and six relax-and-fix heuristics, which explore different criteria for partitioning and fixing variables. We also implemented two hybrid heuristics in which an initial solution is first constructed and then fed into the optimization software to improve it. The results showed that one relax-and-fix and the two hybrid heuristics performed better than the solver on the largest instances.     

Cascading Knapsack Inequalities: reformulation of a crude oil distribution problem

In this paper we consider the problem in which crude oil is shipped from platforms to terminals using oil tankers at minimum transportation cost. This subproblem, which arises in petroleum supply chain models, can be surprisingly expensive to solve with a straightforward formulation involving inventory balances. We present a reformulation that has a special structure defined in this paper as Cascading Knapsack Inequalities. This is used as the basis for deriving tight reformulations for special cases with a limited number of classes of tankers. Numerical results are presented to demonstrate that significant computational savings can be accomplished with the proposed reformulations.     

Optimal Short Horizon Distribution Operations in Reusable Container Systems

Manufacturers in many countries are facing increasing market pressures to use returnable containers. Few studies address the day-to-day operational problems of controlling production and distribution that are inherent in closed systems that reuse containers. In this paper we present a formulation of an optimal configuration of this type of system. In particular, we model the reusable bottle production and distribution activities of a large soft drink manufacturer located in Mexico City, Mexico. Two types of operational research models are combined to form the overall optimization system. A pair of linear programs (one aggregated, the other disaggregated) determines a master plan that is subsequently fined-tuned on a shift-by-shift basis using a difference equation simulation model. The simulation model mimics the heuristic ‘rules of thumb’ used by production planners for product distribution and container reuse planning. The results of our study indicate that this formulation provides a timely response in the field to key operational problems addressed by no previous approach. Included are better organizational control (through providing one-week production and distribution plans), feedback allowing modification of heuristic rules previously used in controlling the distribution of product and container reuse, and improvement in inventory behaviour such as avoiding shortages. These improvements have resulted in considerable market share gains since the models were implemented.     

基于Logit离散选择模型的品类优化问题综述

品类优化问题(Assortment Optimization Problem)是收益管理的经典问题.它研究零售商在满足运营约束的前提下,应如何从给定产品集合中选择一个子集提供给消费者,以最大化预期收益.该问题的核心在于如何准确地刻画消费者在面对细分产品时的选择行为、建立相应的优化模型并设计高效率的求解算法.基于Logit离散选择模型的品类优化问题:首先,介绍了基于Multinomial Logit模型的品类优化问题.然后介绍了两个更复杂的变种:第一个是基于两层以及多层Nested Logit模型的品类优化问题,这类问题可合理刻画细分产品之间的"替代效应";第二个是基于Mixtures of Multinomial Logits模型的品类优化问题,这类问题可充分考虑消费者群体的异质性.随后,介绍了数据驱动的品类优化问题的相关进展.最后,指出该问题未来可能的若干研究方向.     

Combining discrete-event simulation and system dynamics in a healthcare setting: A composite model for Chlamydia infection

This paper presents a composite model in which two simulation approaches, discrete-event simulation (DES) and system dynamics (SD), are used together to address a major healthcare problem, the sexually transmitted infection Chlamydia. The paper continues an on-going discussion in the literature about the potential benefits of linking DES and SD. Previous researchers have argued that DES and SD are complementary approaches and many real-world problems would benefit from combining both methods. In this paper, a DES model of the hospital outpatient clinic which treats Chlamydia patients is combined with an SD model of the infection process in the community. These two models were developed in commercial software and linked in an automated fashion via an Excel interface. To our knowledge this is the first time such a composite model has been used in a healthcare setting. The model shows how the prevalence of Chlamydia at a community level affects (and is affected by) operational level decisions made in the hospital outpatient department. We discuss the additional benefits provided by the composite model over and above the benefits gained from the two individual models.     

Optimization of generalized desirability functions under model uncertainty

Desirability functions are increasingly used in multi-criteria decision-making which we support by modern optimization. It is necessary to formulate desirability functions to obtain a generalized version with a piecewise max type-structure for optimizing them in different areas of mathematics, operational research, management science and engineering by nonsmooth optimization approaches. This optimization problem needs to be robustified as regression models employed by the desirability functions are typically built under lack of knowledge about the underlying model. In this paper, we contribute to the theory of desirability functions by our robustification approach. We present how generalized semi-infinite programming and disjunctive optimization can be used for this purpose. We show our findings on a numerical example. The robustification of the optimization problem eventually aims at variance reduction in the optimal solutions.