The heterogeneous vehicle routing and truck scheduling problem in a multi-door cross-dock system

Cross-docking is a logistics technique applied by many industrial firms to get substantial savings in two warehousing costly functions like storage and order picking. Incoming shipments are unloaded from inbound trucks on a cross-dock terminal with minimal storage space and directly transferred to outbound vehicles that carry them to their destinations. The major decisions at the operational level are the vehicle routing and scheduling, the dock door assignment and the truck scheduling at the cross-dock. Because such decisions are interdependent, all of them are simultaneously considered in the so-called vehicle routing problem with cross-docking (VRPCD). Previous contributions on VRPCD assume that pickup and delivery tasks are accomplished by a homogeneous vehicle fleet, and they mostly ignore the internal transportation of goods through the cross-dock. This work introduces a new rigorous mixed-integer linear programming (MILP) formulation for the VRPCD problem to determine the routing and scheduling of a mixed vehicle fleet, the dock door assignment, the truck docking sequence and the travel time required to move the goods to the assigned stack door all at once. To improve the computational efficiency of the branch-and-cut search, an approximate sweep-based model is developed by also considering a set of constraints mimicking the sweep algorithm for allocating nodes to vehicles. Numerous heterogeneous VRPCD examples involving up to 50 transportation requests and a heterogeneous fleet of 10 vehicles with three different capacities were successfully solved using the proposed approaches in acceptable CPU times.     

A sweep-heuristic based formulation for the vehicle routing problem with cross-docking

Cross-docking is a warehousing strategy in logistics used by process industries making products with high proportions of distribution costs. It is described as the process of moving goods from suppliers to customers through a cross-dock terminal without a long-term storage in this facility. The vehicle routing problem with cross-docking (VRPCD) consists of fulfilling a set of transportation requests using a fleet of homogeneous vehicles to sequentially accomplish the pickup and delivery tasks. Between those operations, there is a consolidation process of incoming shipments at the cross-dock. This work introduces a monolithic formulation for the VRPCD that determines pickup/delivery routes and schedules simultaneously with the truck scheduling at the terminal. To derive a more efficient formulation, a constraint set mimicking the widely known sweep algorithm was incorporated into the rigorous model. The resulting model based on the sweep heuristic can find near-optimal solutions to large problems at very acceptable CPU times.     

Optimal management of logistic activities in multi-site environments

The new emerging area of Enterprise Wide Optimization (EWO) has focused the attention in effectively solving the combined production/distribution scheduling problem. The importance of logistic activities performed in multi-site environments comes from the relative magnitude of the associated transportation costs and the good chance of getting large savings on such expenses. This paper first develops an exact MILP mathematical formulation for the multiple vehicle time-window-constrained pickup and delivery (MVPDPTW) problem. The approach is able to account for many-to-many transportation requests, pure pickup and delivery tasks, heterogeneous vehicles and multiple depots. Optimal solutions for a variety of benchmark problems with cluster/random distributions of pickup and delivery locations and limited sizes in terms of customer requests and vehicles have been discovered. However, the computational cost exponentially grows with the number of requests. For large-scale m-PDPTW problems, a local search improvement algorithm steadily providing a better solution through two evolutionary steps is also presented. A neighborhood structure around the starting solution is generated by first allowing multiple request exchanges among nearby trips and then permitting the reordering of nodes on every individual route. If a better set of routes is found, both steps are repeated until no improved solution is discovered. Compact MILP mathematical formulations for both sub-problems have been developed and solved through an efficient branch-and-bound algorithm. A significant number of large-scale m-PDPTW benchmark problems, some of them including up to 100 transportation requests, were successfully solved in reasonable CPU times.     

The multi-echelon vehicle routing problem with cross docking in supply chain management

Multi-echelon distribution networks are quite common in supply chain and logistics. Deliveries of multiple items from factories to customers are managed by routing and consolidating shipments in warehouses carrying on long-term inventories. On the other hand, cross-docking is a logistics technique that differs from warehousing because products are no longer stored at intermediate depots. Instead, cross-dock facilities consolidate incoming shipments based on customer demands and immediately deliver them to their destinations. Hybrid strategies combining direct shipping, warehousing and cross-docking are usually applied in real-world distribution systems. This work deals with the operational management of hybrid multi-echelon multi-item distribution networks. The goal of the N-echelon vehicle routing problem with cross-docking in supply chain management (the VRPCD-SCM problem) consists of satisfying customer demands at minimum total transportation cost. A monolithic optimization framework for the VRPCD-SCM based on a mixed-integer linear mathematical formulation is presented. Computational results for several problem instances are reported.     

Rule-based scheduling of multi-stage multi-product batch plants with parallel units

A novel rule-based model for multi-stage multi-product scheduling problem (MMSP) in batch plants with parallel units is proposed. The scheduling problem is decomposed into two sub-problems of order assignment and order sequencing. Firstly, hierarchical scheduling strategy is presented for solving the former sub-problem, where the multi-stage multi-product batch process is divided into multiple sequentially connected single process stages, and then the production of orders are arranged in each single stage by using forward order assignment strategy and backward order assignment strategy respectively according to the feature of scheduling objective. Line-up competition algorithm (LCA) is presented to find out optimal order sequence and order assignment rule, which can minimize total flow time or maximize total weighted process time. Computational results show that the proposed approach can obtain better solutions than those of the literature for all scheduling problems with more than 10 orders. Moreover, with the problem size increasing, the solutions obtained by the proposed approach are improved remarkably. The proposed approach has the potential to solve large size MMSP.     

金融危机后的世界轮胎市场

一、全球轮胎市场发展趋势 对于轻型汽车（LV,包括轿车胎和轻型商用车胎）和中重型载重卡车（M／H CV）来说,国内生产总值（GDP）的增长是其需求增长的动力。金融危机和经济衰退使欧美等发达国家GDP减少．导致全球轮胎销售额的大幅下降,其中原配轮胎（OE）市场受到的影响更为严重。通常,中重型载重卡车轮胎中,原配胎销售额占其总销售额的20％;轻型汽车轮胎中,原配胎占25％。     

我国发展醇醚汽车清洁燃料的可行性和优越性

从保证我国能源安全和环境双重角度出发,认为由我国资源相对丰富的煤炭转化为汽车清洁代用燃料是符合我国国情的重要途径。介绍了由煤转化为汽车燃料技术上可行的主要路线煤的直接液化,煤的间接液化和煤基合成含氧醇、醚燃料等;对甲醇、二甲醚清洁燃料用于汽车进行了可行性和优越性分析;在我国醇醚汽车燃料发展已有的基础上,讨论了推广应用中注意的问题及解决途径。     

A branch-and-price approach to evaluate the role of cross-docking operations in consolidated supply chains

Supply-chain management and optimization aims at reducing costs and inventories. One way to increase the supply-chain efficiency is to use cross-docking for consolidating shipments from different suppliers. Cross-docking is a warehousing strategy used in logistics that consists on moving goods from suppliers to customers through a cross-dock facility. The employment of this strategy must be carefully evaluated because sometimes transportation requests can be better directly moved from source-sites to destination. A realistic problem studying the convenience of direct delivery, avoiding some cross-docking transfers, is here discussed. An efficient methodology for finding (near)optimal solutions is also described. The methodology is based on the use of column generation embedded into an incomplete branch-and-price tree. The approach provides (near)optimal solutions by solving the column generation sub-problems without necessarily considering all unexplored nodes in the search-tree. Finally, we show computational results on numerous test problems and on four configurations of the addressed case study.     

凝胶型连结料体系黏度模型的参数拟合

采用有机铝型螯合物凝胶剂、松香改性酚醛树脂、亚麻仁油和高沸点煤油制备胶印油墨用连结料,研究了亚麻仁油、高沸点煤油和凝胶剂的用量对连结料流变性能的影响。根据流变实验测定值,准确地拟合出该连结料体系的一系列黏度模型。根据该黏度模型,能够在改变原料配比、温度与剪切速率的条件下,相当精确地描述连结料体系的黏度。因此,若能利用汇编语言设计出连结料的配方计算软件,则可根据连结料体系中各因素变化来直接调节连结料的流变参数,达到快速调整连结料配方、缩短生产工艺时间、稳定连结料质量、适应高速印刷油墨要求的目的。     

Operational planning of forward and reverse logistic activities on multi-echelon supply-chain networks

Distribution activities arising from supply-chains of chemical and food industries involve the shipping of products directly and/or via distribution-centers. Also, due to growing ecology concerns, the recycling of recoverable-materials is becoming a common practice. In this paper, a distribution and recovering problem has been studied and modeled. The solution to the problem-model computes the forward and backward flows on a supply-chain network of a company that take into account ‘green logistics’ considerations. In this problem, vehicles departing from plants/distribution-centers perform delivery of products and pick-up of recyclables at the lowest network-level. At a higher level, larger vehicles re-supply distribution-centers with products and bring back to plants recyclable goods. The operation must coordinate the vehicles-tours to assure efficient forward and backwards flows. The paper presents a column-generation based decomposition-approach for finding near-optimal solutions to the problem. We also present computational results on test problems derived from a real case-study.     

Genetic algorithm for large-size multi-stage batch plant scheduling

This paper presents a heuristic approach based on genetic algorithm (GA) for solving large-size multi-stage multi-product scheduling problem (MMSP) in batch plant. The proposed approach is suitable for different scheduling objectives, such as total process time, total flow time, etc. In the algorithm, solutions to the problem are represented by chromosomes that will be evolved by GA. A chromosome consists of order sequences corresponding to the processing stages. These order sequences are then assigned to processing units according to assignment strategies such as forward or backward assignment, active scheduling technique or similar technique, and some heuristic rules. All these measures greatly reduce unnecessary search space and increase the search speed. In addition, a penalty method for handling the constraints in the problem, e.g., the forbidden changeovers, is adopted, which avoids the infeasibility during the GA search and further greatly increases the search speed.     

A tighter continuous time formulation for the cyclic scheduling of a mixed plant

In this paper, based on the cyclic scheduling formulation of Schilling and Pantelides [Schilling, G., & Pantelides, C. (1999). Optimal periodic scheduling of multipurpose plants. Computers& Chemical Engineering, 23, 635–655], we propose a continuous time mixed integer linear programming (MILP) formulation for the cyclic scheduling of a mixed plant, i.e. a plant composed of batch and continuous tasks. The cycle duration is a variable of the model and the objective is to maximize productivity. By using strengthening techniques and the analysis of small polytopes related to the problem formulation, we strengthen the initial formulation by tightening some initial constraints and by adding valid inequalities. We show that this strengthened formulation is able to solve moderate size problems quicker than the initial one. However, for real size cases, it remains difficult to obtain the optimal solution of the scheduling problem quickly. Therefore, we introduce MILP-based heuristic methods in order to solve these larger instances, and show that they can provide good feasible solutions quickly.     

An efficient MILP continuous-time formulation for short-term scheduling of multiproduct continuous facilities

This paper presents a new MILP mathematical formulation for the scheduling of resource-constrained multiproduct plants involving continuous processes. In such facilities, a sequence of continuous processing steps is usually carried out to produce a significant number of final products and required intermediates. In order to reduce equipment idle time due to unbalanced stage capacities, storage tanks are available for temporary inventory of intermediates. The problem goal is to maximize the plant economic output while satisfying specified minimum product requirements. The proposed approach relies on a continuous time domain representation that accounts for sequence-dependent changeover times and storage limitations without considering additional tasks. The MILP formulation was applied to a real-world manufacturing facility producing seven intermediates and fifteen final products. Compared with previous scheduling methodologies, the proposed approach yields a much simpler problem representation with a significant saving in 0–1 variables and sequencing constraints. Moreover, it provides a more realistic and profitable production schedule at lower computational cost.     

Proactive scheduling of batch processes by a combined robust optimization and multiparametric programming approach

We address short‐term batch process scheduling problems contaminated with uncertainty in the data. The mixed integer linear programming (MILP) scheduling model, based on the formulation of Ierapetritou and Floudas, Ind Eng Chem Res. 1998; 37(11):4341–4359, contains parameter dependencies at multiple locations, yielding a general multiparametric (mp) MILP problem. A proactive scheduling policy is obtained by solving the partially robust counterpart formulation. The counterpart model may remain a multiparametric problem, yet it is immunized against uncertainty in the entries of the constraint matrix and against all parameters whose values are not available at the time of decision making. We extend our previous work on the approximate solution of mp‐MILP problems by embedding different uncertainty sets (box, ellipsoidal and budget parameter regulated uncertainty), and by incorporating information about the availability of uncertain data in the construction of the partially robust scheduling model. For any parameter realization, the corresponding schedule is then obtained through function evaluation. © 2013 American Institute of Chemical Engineers AIChE J, 59: 4184–4211, 2013     

Scheduling of a mixed batch/continuous sugar milling plant using Petri nets

Scheduling of processes in mixed batch/continuous plants, due to their hybrid nature, can become very complex. This paper presents the Timed Hybrid Petri net (THPN) as a suitable tool for modelling and scheduling of hybrid systems. One of the major benefits over traditional methods is a significant reduction in complexity during problem formulation. A sugar milling plant containing both batch and continuous processing units is used to illustrate the application of the proposed scheduling algorithm.     

A new continuous-time formulation for scheduling crude oil operations

In today's competitive business climate characterized by uncertain oil markets, responding effectively and speedily to market forces, while maintaining reliable operations, is crucial to a refinery's bottom line. Optimal crude oil scheduling enables cost reduction by using cheaper crudes intelligently, minimizing crude changeovers, and avoiding ship demurrage. So far, only discrete-time formulations have stood up to the challenge of this important, nonlinear problem. A continuous-time formulation would portend numerous advantages, however, existing work in this area has just begun to scratch the surface. In this paper, we present the first complete continuous-time mixed integer linear programming (MILP) formulation for the short-term scheduling of operations in a refinery that receives crude from very large crude carriers via a high-volume single buoy mooring pipeline. This novel formulation accounts for real-world operational practices. We use an iterative algorithm to eliminate the crude composition discrepancy that has proven to be the Achilles heel for existing formulations. While it does not guarantee global optimality, the algorithm needs only MILP solutions and obtains excellent maximum-profit schedules for industrial problems with up to 7 days of scheduling horizon. We also report the first comparison of discrete- vs. continuous-time formulations for this complex problem.     

基于故障树方法的机动车燃油大气环境风险评价：以杭州市为例

由于城市化、工业化和机动车数量的快速增长,灰霾天气已成为中国许多大城市亟待解决的严重环境污染问题。大量石油燃料消耗产生的机动车尾气排放可能是引起城市灰霾污染的一个关键因素。以长江三角洲的代表性城市杭州市为具体案例,探索将安全工程领域的故障树方法应用在机动车燃油尾气排放大气环境风险评价和与灰霾天气的致因机理分析上。通过辨识导致城市机动车尾气过量排放的关键风险因子,构建了杭州市“灰霾天气–机动车尾气过量排放”的故障树。另外采用结构、概率以及临界重要度分析,对关键风险因子对顶上事件“灰霾天气–机动车尾气过量排放”的贡献和影响程度进行了定性和定量分析。分析结果表明,过量机动车使用,严重的交通堵塞、高污染机动车的不当使用以及监管不严是对杭州市机动车尾气过量排放影响较大的关键风险因子。可为城市机动车燃油环境风险因子评价以及管理提供一种简洁有效的方法和思路。     

Simultaneous mixed-integer dynamic scheduling of processes and their energy systems

Increasingly volatile electricity prices make simultaneous scheduling optimization desirable for production processes and their energy systems. Simultaneous scheduling needs to account for both process dynamics and binary on/off-decisions in the energy system leading to challenging mixed-integer dynamic optimization problems. We propose an efficient scheduling formulation consisting of three parts: a linear scale-bridging model for the closed-loop process output dynamics, a data-driven model for the process energy demand, and a mixed-integer linear model for the energy system. Process dynamics is discretized by collocation yielding a mixed-integer linear programming (MILP) formulation. We apply the scheduling method to three case studies: a multiproduct reactor, a single-product reactor, and a single-product distillation column, demonstrating the applicability to multiple input multiple output processes. For the first two case studies, we can compare our approach to nonlinear optimization and capture 82% and 95% of the improvement. The MILP formulation achieves optimization runtimes sufficiently fast for real-time scheduling.     

A Lagrangian relaxation approach for a multi-mode inventory routing problem with transshipment in crude oil transportation

An inventory routing problem in crude oil transportation is studied, in which crude oil is transported from a supply center to multiple customer harbors to satisfy their demands over multiple periods. In the problem, a heterogeneous fleet of tankers consisting of tankers owned by a distributor and tankers rented from a third party, a pipeline, and multiple types of routes are considered; both inventory level and shortage level at each customer harbor are limited. The objective is to determine for each period over a given time horizon the number of tankers of each type to be rented/returned at the supply center, the number of tankers of each type to be dispatched on each route, and the quantity of crude oil flowing through the pipeline that minimizes the total logistics cost.After formulating the problem as a mixed integer programming problem, a Lagrangian relaxation approach is developed for finding a near optimal solution of the problem. The approach is also applied to a variant of the problem in which both fully and partially loaded tankers are allowed in the transportation of crude oil. Numerical experiments show that this approach outperforms an existing meta-heuristic algorithm, especially for the instances of large sizes.