The freight allocation problem with all-units quantity-based discount: A heuristic algorithm

This paper studies a problem encountered by a buying office for one of the largest retail distributors in the world. An important task for the buying office is to plan the distribution of goods from Asia to various destinations across Europe. The goods are transported along shipping lanes by shipping companies, which offer different discount rates depending on the freight quantity. To increase the reliability of transportation, the shipper imposes a quantity limit on each shipping company on each shipping lane. To guarantee a minimum business volume, each shipping company requests a minimum total freight quantity over all lanes if it is contracted. The task involves allocating projected demand of each shipping lane to shipping companies subject to the above conditions such that the total cost is minimized.Existing work on this and related problems employs commercial linear programming software to solve their models. However, since the problem is NP−hard in the strong sense, it is unlikely to be solvable optimally in reasonable time for large cases. Hence, we propose the first heuristic-based algorithm for the problem, which combines a filter-and-fan search scheme with a tabu search mechanism. Experiments on randomly generated test instances show that as the size of the problem increases, our algorithm produces superior solutions in less time compared to a leading mixed-integer programming solver.     

A heuristic approach to long-haul freight transportation with multiple objective functions

This paper studies a long-haul freight transportation problem stimulated by a real-life application, whose underlying vehicle routing problem is a multi-objective one, where travel time and route cost are to be minimized together with the maximization of a transportation mean sharing index, related to the capability of the transportation system of generating economy scale solutions. In terms of constraints, besides vehicle capacity and time windows, transportation jobs have to obey additional constraints related to mandatory nodes (e.g., logistic platform nearest to the origin or the destination) and forbidden nodes (e.g., logistic platforms not compatible with the operations required). Based on the network definition, routes can be multimodal. To solve this problem, we propose a heuristic algorithm that can be applied in the tactical and the operational planning phase, and present the results of an extensive experimentation.     

Options Contracts with Overbooking in the Air Cargo Industry

In today's world economy, which is marked by intensified international trade, air cargo acts as a key facilitator. However, cargo airlines continue to struggle to be profitable because of very high asset costs and substantial demand uncertainty. To improve upon this situation, we propose an options contract. Our model captures the main features of cargo trade between an airline and a freight forwarder and allows us to derive an optimal reservation policy. We then go on to analyze the impact of overbooking on the profit of the cargo capacity provider. The model is subsequently applied to real‐life booking data provided by a major cargo carrier. This enables us to compare current contractual arrangements with the ones proven optimal in the model. A numerical study provides insights about the impact of overbooking on contract parameters and profitability. Managerial insights to be drawn conclude this study.     

An optimization model for line planning and timetabling in automated urban metro subway networks. A case study

In this paper we present a Mixed Integer Linear Programming model that we developed as part of a pilot study requested by the R&D company Metrolab® in order to design tools for finding solutions for line planning and timetable situations in automated urban metro subway networks. Our model incorporates important factors in public transportation systems from both, a cost-oriented and a passenger-oriented perspective, as time-dependent demands, interchange stations, short-turns and technical features of the trains in use. The incoming flows of passengers are modeled by means of piecewise linear demand functions which are parameterized in terms of arrival rates and bulk arrivals. Decisions about frequencies, train capacities, short-turning and timetables for a given planning horizon are jointly integrated to be optimized in our model. Finally, a novel matheuristic approach is proposed to solve the problem. The results of extensive computational experiments are reported to show its applicability and effectiveness to handle real-world subway networks.     

A case study of production expansion planning in a soft-drink manufacturing company

This paper discusses the problem of production expansion planning in a soft-drink manufacturing company. We first formulate the problem as a large-scale transportation problem by making some realistic simplifications. We then program the transportation algorithm on a spreadsheet software to help solve the problem on a microcomputer. Through this case study we demonstrate how some standard OR technique can be combined with low-cost microcomputers to become an effective management tool to handle complex management decision problems.     

Urban vans,e-commerce and road freight transport

Abstract

A city will typically depend on at least 150 supply chains and freight is a key part of them. This article aims to explore at the qualitative and qualitative level (1) how e-commerce drives both travel and urban freight transport and (2) to estimate how various manufacturing activities determine the stability of demand for freight transport. The article provides elasticity estimates on (1) the demand for road freight transport for five industry sectors using a time series framework; data on GDP (gross value added) and on fuel costs per t-km (tonne-km). A key finding is that e-commerce induces freight traffic, by vans, but the decline in freight intensity of the economy is only temporary since urban economic activity contributes to growth of freight. Our analysis of five manufacturing sectors confirms that cyclical sectors are more sensitive to energy price gyrations over time than non-cyclical sectors. Price elasticities are high but comparable to other studies. The income elasticity of freight transport is large for the five sectors, but the high heterogeneity of freight sectors means that predicting this activity is challenging. It is observed that truck freight intensity (km of trucks per GVA) declines but not enough to offset the rise in energy needed to fuel the entire freight transport sector; it is likely that the rise in van freight increases the demand for freight energy in urban regions.     

运输网络运量分配问题的模型及算法研究

针对我国在运量分配模型及算法方面研究比较薄弱的现状,本文对此问题进行了系统深人的研究,应用运筹学、计算机科学的新的理论和方法,建立了多目标运量分配优化模型,且在模型中,将一些重要特性考虑成运输流量的函数,从而可使分配结果更符合实际情况。同时为求解该模型,本文研究设计了鲁棒性强、高效、实用的自适应搜索算法。     

Multi-echelon supply chain network design in agile manufacturing

In this paper, we consider a supply chain network design problem in an agile manufacturing scenario with multiple echelons and multiple periods under a situation where multiple customers have heavy demands. Decisions in our supply chain design problem include selection of one or more companies in each echelon, production, inventory, and transportation. We formulate the problem integrating all decisions to minimize the total operational costs including fixed alliance costs between two companies, production, raw material holding, finished products holding, and transportation costs under production and transportation capacity limits. A Lagrangian heuristic is proposed in this paper. Optimizing a Lagrangian relaxation problem provides a lower bound, while a feasible solution is generated by adjustment techniques based on the solution of subproblems at each iteration. Computational results indicate the high quality solutions with less than 5% optimality gap are provided quickly by the approach in this paper. Further, compared to initiative managerial alternatives, an improvement of 15% to 25% is not unusual in certain cases for the proposed approach.     

A mathematical modeling approach to improving locomotive utilization at a freight railroad

Moving freight by rail remains one of the major transportation modes in today's business world. Although railcars compare unfavorably with trucks and airplanes with respect to mobility, flexibility, and speed, the shipping costs are lower and the energy-efficiency is higher. In order to become more competitive in the logistics industry, railroads have taken a number of new initiatives to improve their operations in recent years. One of such efforts made by Consolidated Rail Corporation (Conrail) is described in this paper. The main focus of the present study is on helping Conrail increase the utilization of its locomotive fleet by developing a mixed integer linear program (MILP) to determine the least-cost plan of allocating locomotives to yards and moving light engines between yards. The MILP is tested on a set of real data gathered at Conrail and it is proven to be superior to the existing method. A simple sensitivity analysis is also performed to gain insight into the trade-off between investment in additional locomotives and cost of light engine moves.     

Multi-echelon supply chain network design in agile manufacturing

In this paper, we consider a supply chain network design problem in an agile manufacturing scenario with multiple echelons and multiple periods under a situation where multiple customers have heavy demands. Decisions in our supply chain design problem include selection of one or more companies in each echelon, production, inventory, and transportation. We formulate the problem integrating all decisions to minimize the total operational costs including fixed alliance costs between two companies, production, raw material holding, finished products holding, and transportation costs under production and transportation capacity limits. A Lagrangian heuristic is proposed in this paper. Optimizing a Lagrangian relaxation problem provides a lower bound, while a feasible solution is generated by adjustment techniques based on the solution of subproblems at each iteration. Computational results indicate the high quality solutions with less than 5% optimality gap are provided quickly by the approach in this paper. Further, compared to initiative managerial alternatives, an improvement of 15% to 25% is not unusual in certain cases for the proposed approach.     

Cost analysis between options for the movement of freight

A mathematical model is developed for the comparison of cost by weight for the movement of freight between the Canadian Forces ‘Scheduled Road Freight Service’ and the commercial carriers. The study considers in detail the effect of inflation on the various factors which affect the cost that govern those economic consequences which differ between the two alternatives. An example is included to illustrate the methodology.     

An investigation into the vehicle routing problem with time windows and link capacity constraints

In this work, we investigate a new, yet practical, variant of the vehicle routing problem called the vehicle routing problem with time windows and link capacity constraints (VRPTWLC). The problem considers new constraints imposed on road links with regard to vehicle passing tonnage, which is motivated by a business project with a Hong Kong transportation company that transports hazardous materials (hazmats) across the city and between Hong Kong and mainland China. In order to solve this computationally challenging problem, we develop a tabu search heuristic with an adaptive penalty mechanism (TSAP) to help manage the company's vehicle fleet. A new data set and its generation scheme are also presented to help validate our solutions. Extensive computational experiments are conducted, showing the effectiveness of the proposed solution approach.     

碳交易政策下多式联运路径选择问题的鲁棒优化研究

针对碳交易政策下的多式联运路径选择问题,考虑运输时间和单位运费率不确定且其概率分布未知的情况,引入鲁棒优化建模方法对其进行研究。首先利用box不确定集合刻画分布未知的运输时间和运费率,然后在碳交易政策下确定模型的基础上,构建鲁棒性可调节的多式联运路径选择模型,并通过对偶转化得到相对易求解的鲁棒等价模型。实例分析表明,鲁棒模型能较好地处理参数概率分布未知的多式联运路径选择问题,方便决策者根据偏好调整不确定预算水平进行决策。运输时间和单位运费率的不确定性都会影响多式联运路径决策,但是作用机理有所不同。将上述碳交易政策下的模型拓展到其他低碳政策,结果表明多种低碳政策的组合能更好实现多式联运减排。     

基于合作性投资和价格策略的多式联运企业协作行为博弈分析

本文对联合运输中不同运输企业间的协作行为进行了研究。考虑了两家提供互补运输服务的寡头运输企业之间的合作和竞争的博弈决策问题,通过定义合作强度参数,并将其引入收益函数,构造了合作性投资和价格策略的两阶段动态博弈模型,讨论了该博弈子博弈完美Nash均衡解的存在条件,推出了一些重要的结论。研究发现:当双方投资效果系数组合在(0,1)区间时,随着市场潜量的增加,投资增长;随着价格弹性的增加,投资下降。而当双方投资效果系数组合在(1,2)区间时结论相反。最后通过算例和所设计的免疫遗传算法进行了不同参数环境下的数值模拟,验证了结论的正确性。     

An Integrated Approach for Modeling Sustainability Risks in Freight Transportation Systems

Integrating sustainability into freight transportation systems (FTSs) is a complex and challenging task due to the sheer number of inherent sustainability risks. Sustainability risks disrupt the economic, social and environmental objectives of freight operations and act as impediments in the development of sustainable freight transportation systems. The area of sustainability risk management is still unexplored and immature in the operational research domain. This study addresses these research gaps and contributes in a threefold manner. First, a total of 36 potential sustainability risks related to FTSs are identified and uniquely classified into seven categories using a rigourous approach. Second, the research proposes two prominent perspectives, namely, ontological and epistemological perspectives to understand risks and develops a novel framework for managing sustainability risks in FTSs. Third, a novel approach by integrating fuzzy evidential reasoning algorithm (FERA) with expected utility theory is developed to quantitatively model and profile sustainability risk for different risk preferences, namely, risk-averse, risk-neutral, and risk-taking scenarios. The proposed FERA is a flexible and robust approach, which transforms the experts’ inputs into belief structures and aggregates them using the evidence combination rule proposed in Dempster–Shafer theory to overcome the problem of imprecise results caused by average scoring in existing models. A sensitivity analysis is conducted to demonstrate the robustness of the proposed model. Unlike conventional perception, our study suggests that most of the high priority sustainability risks are behaviorally and socially induced rather than financially driven. The results provide significant managerial implications including a focus on skills development, and on social and behavioral dimensions while managing risks in FTSs.     

Sustainable supply chain network design: A case of the wine industry in Australia

This paper comes at a time when many companies and people are increasingly facing various global problems linked to sustainability challenges, and when the literature is still scarce on research incorporating all three dimensions of sustainability in supply chain analytical models. The paper conducts a case-based modeling study to address this gap in the area of supply chain network design. We investigate the wine supply chain and propose a generic model for sustainable wine supply chain network design encompassing economic, environmental and social objectives. The case company is a real large-sized wine company located in Australia, for which a customized model is provided. Both models are formulated as a multi-objective mixed-integer program and solved using the augmented ϵ-constraint method by CPLEX. Social implications of the feasible scenarios are examined through introducing social impact coefficients. Non-dominated solutions are obtained and some balanced scenarios are proposed. The results show trade-offs between the objectives, yet more interestingly demonstrate how large is the gap between the existing supply chain configuration and the proposed scenarios in terms of supply chain cost and emissions.     

航运供应链上的品牌价值、公平关切与契约协调

航运供应链上的船公司提高企业品牌价值需要付出大量的投资成本,因此将会更加关注供应链上利润分配的公平。本文以单个船公司和单个货代公司构成的两级航运供应链为研究对象,运用博弈论分析船公司的公平关切行为和品牌投入意愿对航运供应链上各方利润的影响,探讨决策者非理性条件下航运供应链的合作模式和协调机制。研究表明:1)船公司的公平关切提高了其在供应链上利润分配占比,但降低了整个航运供应链的利润和效率;2)随着品牌投入意愿的提高,船公司的公平关切对航运供应链上各方决策的负作用就越明显;3)"收益共享成本共担"式契约能够消除公平关切的负作用,达到航运供应链纵向联盟的最优收益和整体效率。     

城市土地利用规划的交通影响评价建模研究

本文提出了一种基于投入产出关系和连续逼近方法的城市土地利用的交通影响评价模型。作为城市土地可持续利用规划模型的一个子模型,本模型已在桂林市的总体规划中得以实施。在本文中,先简要介绍了城市土地可持续利用规模模型,接着基于投入产出关系估算了分行业的单位产值对应的货物运输量,然后以交通熵和交通周转量作为评价指标,基于投入产出关系、连续逼近方法和Huff商区模式分别建立了货物运输、居民工作出行和居民购物出行交通影响评价模型。     

Managing Demand Risk in Tactical Supply Chain Planning for a Global Consumer Electronics Company

We consider the problem of managing demand risk in tactical supply chain planning for a particular global consumer electronics company. The company follows a deterministic replenishment‐and‐planning process despite considerable demand uncertainty. As a possible way to formally address uncertainty, we provide two risk measures, “demand‐at‐risk” (DaR) and “inventory‐at‐risk” (IaR) and two linear programming models to help manage demand uncertainty. The first model is deterministic and can be used to allocate the replenishment schedule from the plants among the customers as per the existing process. The other model is stochastic and can be used to determine the “ideal” replenishment request from the plants under demand uncertainty. The gap between the output of the two models as regards requested replenishment and the values of the risk measures can be used by the company to reallocate capacity among different products and to thus manage demand/inventory risk.     

Bounding Optimal Expected Revenues for Assortment Optimization under Mixtures of Multinomial Logits

We consider assortment problems under a mixture of multinomial logit models. There is a fixed revenue associated with each product. There are multiple customer types. Customers of different types choose according to different multinomial logit models whose parameters depend on the type of the customer. The goal is to find a set of products to offer so as to maximize the expected revenue obtained over all customer types. This assortment problem under the multinomial logit model with multiple customer types is NP‐complete. Although there are heuristics to find good assortments, it is difficult to verify the optimality gap of the heuristics. In this study, motivated by the difficulty of finding optimal solutions and verifying the optimality gap of heuristics, we develop an approach to construct an upper bound on the optimal expected revenue. Our approach can quickly provide upper bounds and these upper bounds can be quite tight. In our computational experiments, over a large set of randomly generated problem instances, the upper bounds provided by our approach deviate from the optimal expected revenues by 0.15% on average and by less than one percent in the worst case. By using our upper bounds, we are able to verify the optimality gaps of a greedy heuristic accurately, even when optimal solutions are not available.