Dynamic scheduling of oil tankers with splitting of cargo at pickup and delivery locations: a Multi-objective Ant Colony-based approach

Maritime crude oil transportation problem have been drawing the attention of researchers for quite a long time. The cost incurred in the supply chain for the oil products is one of the biggest driving factors for these researchers. In the present paper, we have addressed the problem faced by the logistics section of the petroleum downstream industry. This industry mainly deals with the transportation of finished oil products like fuel oil, high speed diesel, etc. from refineries to the demand points. For this purpose, we have developed a mathematical model to represent the problem appropriately, aiming at total cost minimisation as well as service-level maximisation. The problem in hand is then tackled with a modified Multi-objective Ant Colony optimisation algorithm which besides considering more than one pheromone structure also involves non-dominated sorting of the results to give us the best-performing solution fronts. For the purpose of dealing with the uncertainties causing docking problems at a port, we have incorporated a second stage of route allocation for the vessels. Towards the end, we have carried out a sensitivity analysis for the parameters of the ant colony algorithm to get the combination of parameters for which this new type of algorithm performs best. The comparison of obtained results with one of the other contemporary algorithms also establishes the superiority of our heuristic.     

Particle swarm optimization for the multi-period cross-docking distribution problem with time windows

Cross-docking has emerged as a new technique in supply chain management to replace the warehouse concept in the retail industry. This paper proposes a multi-period cross-docking distribution problem that consists of manufacturers, cross-docks and customers. This model is formulated for cases that consider multiple products, consolidation of customer orders and time windows that are available in multiple periods. The objective function is to minimise the total cost, which includes transportation cost, inventory cost and penalty cost. The penalty cost arises when demand remains in each period that cannot be satisfied. To deal with the complexity of the problem, an algorithm is developed based on particle swarm optimisation (PSO) with multiple social learning terms, GLNPSO, with two solution representations. The solution representations are a one-period solution representation (OP-SR) and a multi-period solution representation (MP-SR). The GLNPSO-based algorithm performs well in solving this problem. Moreover, both representations are proven effective when comparing the solution quality and computational time with those results obtained from CPLEX. In terms of quality, the MP-SR solution is better than the OP-SR solution for both stable and fluctuating demand instances. However, MP-SR requires more computational effort than OP-SR.     

A genetic algorithm for dynamic inbound ordering and outbound dispatching problem with delivery time windows

This article considers an inbound ordering and outbound dispatching problem for a single product in a third-party warehouse, where the demands are dynamic over a discrete and finite time horizon, and moreover, each demand has a time window in which it must be satisfied. Replenishing orders are shipped in containers and the freight cost is proportional to the number of containers used. The problem is classified into two cases, i.e. non-split demand case and split demand case, and a mathematical model for each case is presented. An in-depth analysis of the models shows that they are very complicated and difficult to find optimal solutions as the problem size becomes large. Therefore, genetic algorithm (GA) based heuristic approaches are designed to solve the problems in a reasonable time. To validate and evaluate the algorithms, finally, some computational experiments are conducted.     

The two-echelon multi-products location-routing problem with pickup and delivery: formulation and heuristic approaches

The two-echelon location-routing problem (LRP-2E) considers the first-level routes that serve from one depot a set of processing centres, which must be located and the second-level routes that serve customers from the opened processing centres. In this paper, we consider an extension of the LRP-2E, where the second-level routes include three constraints that have not been considered simultaneously in the location routing literature, namely multi-product, pickup and delivery, and the use of the processing centre as intermediate facility in the second-level routes. This new variant is named two-Echelon Multi-products Location-Routing problem with Pickup and Delivery (LRP-MPPD-2E). The objective of LRP-MPPD-2E is to minimise both the location and the routing costs, considering the new constraints. The first echelon deals with the selection of processing centres from a set of potential sites simultaneously with the construction of the first-level routes, such that each route starts from the main depot, visits the selected processing centres and returns to the main depot. The second echelon aims at assigning customers to the selected processing centres and defining the second-level routes. Each second-level route, starts at a processing centre, visits a set of customers, through one or several processing centres, and then returns to the first processing centre. We present a mixed-integer linear model for the problem and use a Cplex solver to solve small-scale instances. Furthermore, we propose non-trivial extensions of the nearest neighbour and insertion approaches. We also develop clustering-based approaches that have not been extensively investigated with regards to location routing. Computational experiments are conducted to evaluate and to compare the performances of the proposed approaches. The results confirm the effectiveness of clustering approaches.     

Optimisation of cutting parameters using a multi-objective genetic algorithm

This paper presents a new optimisation technique based on genetic algorithms (GA) for determination of cutting parameters in machining operations. The cutting parameters considered in this study are cutting speed, feed rate and cutting depth. The effect of these parameters on production time, production cost and roughness is mathematically formulated. A genetic algorithm with multiple fitness functions is proposed to solve the formulated problem. The proposed algorithm finds multiple solutions along the Pareto optimal frontier. Experimental results show that the proposed algorithm is both effective and efficient, and can be integrated into an intelligent process planning system for solving complex machining optimisation problems.     

A traveling salesman problem with time windows for the last mile delivery in online shopping

With the fast growth of the parcel volume of online shopping, home delivery (delivering parcels to customers’ homes or workplaces) has accentuated the pressure on last mile delivery actors. Customer pickup, which allows customers to pick up their parcels from shared delivery facilities near them, has become widely popular. This study introduces a novel travelling salesman problem with time windows for the last mile delivery in online shopping. The purpose is to find a minimum cost tour over given customers and/or shared delivery facilities (SDFs) in which unvisited customers are assigned to the SDFs. A general variable neighbourhood search heuristic is developed to solve the problem. Computational results corroborate that the proposed heuristic is competitive relative to well-known algorithms.     

Integrated production scheduling and vehicle routing problem with job splitting and delivery time windows

In this paper, we study a production scheduling and vehicle routing problem with job splitting and delivery time windows in a company working in the metal packaging industry. In this problem, a set of jobs has to be processed on unrelated parallel machines with job splitting and sequence-dependent setup time (cost). Then the finished products are delivered in batches to several customers with heterogeneous vehicles, subject to delivery time windows. The objective of production is to minimize the total setup cost and the objective of distribution is to minimize the transportation cost. We propose mathematical models for decentralized scheduling problems, where a production schedule and a distribution plan are built consecutively. We develop a two-phase iterative heuristic to solve the integrated scheduling problem. We evaluate the benefits of coordination through numerical experiments.     

Solving the location-routing problem with simultaneous pickup and delivery by simulated annealing

The study concerns the location-routing problem with simultaneous pickup and delivery (LRPSPD) in which the pickup and delivery take place at the same time for each customer. The goal is to determine the facility locations and vehicle routes in order to minimise the total system cost as a sum of facility opening cost, vehicle fixed cost and vehicle travel cost. A simulated annealing (SA) heuristic is proposed for the problem and extensive computational experiments are conducted. The results show that the proposed SA effectively solves LRPSPD and outperforms existing exact approaches in terms of solution quality.     

Order picker routing with product returns and interaction delays

E-commerce companies often use manual order-picking systems in their warehouses since these systems can provide the required flexibility and scalability. Manual systems have been widely studied, but the operating policies may require significant changes for e-commerce settings. First, to maintain consumers’ loyalty, it is important to maintain delivery reliability even on the busiest days. When the number of order pickers in an area increases, however, more delays due to interactions may occur. For example, travel speed may need to be lowered when order pickers pass each other in narrow aisles. Second, many products sold through e-commerce are returned by consumers. Before these returned products can be sold again, they must be reintegrated in the stock. This paper presents hybrid genetic algorithms to determine routes for simultaneous pickup of products in response to consumers’ orders and delivery of returned products to storage locations. Furthermore, interactions between the order pickers are considered in the routing decisions. The developed algorithms use specific warehouse problem characteristics. We identify the mix of pickups and deliveries to realise the highest savings in practice. It is shown that order-picker interactions can be a significant cause for delay and should be accounted for in the routing.     

Integrated scheduling of production and delivery with time windows

This paper deals with an integrated scheduling problem in which orders have been processed by a distribution centre and then delivered to retailers within time windows. We propose a nonlinear mathematical model to minimise the time required to complete producing the product, delivering it to retailers and returning to the distribution centre. The optimal schedule and vehicle routes can be determined simultaneously in the model. In addition, two kinds of genetic-algorithm-based heuristics are designed to solve the large-scale problems. The conventional genetic algorithm provides the search with a high transition probability in the beginning of the search and with a low probability toward the end of the search. The adaptive genetic algorithm provides an adaptive operation rate control scheme that changes rate based on the fitness of the parents. The experimental results have shown that the solution quality of these two algorithms is not significant but that the adaptive genetic algorithm can save more time in finding the best parameter values of the genetic algorithm.     

带时间窗的同时取送货车辆路径问题求解算法

为了整合物流配送过程的退货与送货服务,依据实际情况建立带时间窗的同时取送货车辆路径规划模型,设计一种基于K-means聚类处理的Q-Leaning自启发式蚁群算法解决此类问题。根据配送服务的特性,在基本的K-means算法上作相应的改进,同时提高蚁群算法的局部搜索能力,完成两算法的合理衔接。选用相关文献数据和标准算例进行实验,验证所提算法具有较好的性能,可以解决所描述的此类问题。     

Optimisation of maintenance strategy for rural road network using genetic algorithm

The objective of this paper is to develop an optimised maintenance strategy for the rural road network of Kerala state. This is accomplished with the development of a bi-objective deterministic optimisation model which simultaneously satisfies the objectives of both minimisation of total maintenance cost and maximisation of performance of the road network. The model is capable of planning the maintenance activities over a multi-year planning period. The performance of the road network is accounted using the composite index, namely Pavement Condition Index. The constraint-based genetic algorithm was used as the optimisation tool since it very well takes care of the combinatorial nature of the network-level pavement maintenance programming. The applicability of the model is illustrated using a case study for the rural road network of Kerala state in India. The effort made through this research work to develop a suitable Pavement Maintenance and Management System for rural road network can lead to the proper maintenance and upkeep of the rural roads, thereby triggering a positive impact on the Indian economy.     

Optimisation of block erection scheduling based on a Petri net and discrete PSO

The shipyard block erection system (SBES) is a typical discrete-event dynamic system. To model multiprocessing paths and a concurrent assembly procedure, a timed Petri net (TPN) is proposed. The definition of a Petri net is extended to accord with the real-world SBES organisation. The basic TPN modules are presented to model the corresponding variable structures in the SBES, and then the scheduling model of the whole SBES is easily constructed. A modified discrete particle swarm optimisation (PSO) based on the reachability analysis of Petri nets is developed for scheduling of the SBES. In the proposed algorithm, particles are coded by welding transitions and selecting places of the TPN model, and then the collaboration and competition of particle individuals is simulated by crossover and mutation operators in a genetic algorithm. Numerical simulation suggests that the proposed TPN–PSO scheduler can provide an improvement over the conventional scheduling method. Finally, a case study of the optimisation of a back block erection process is provided to illustrate the effectiveness of the method.     

Perturbation analysis for continuous and discrete flow models: a study of the delivery time impact on the optimal buffer level

In this paper, a manufacturing system composed of a single-product machine, a buffer and a stochastic demand is considered. Two models are presented: continuous and discrete flow models including constant delivery times, machine failures and random demands. The objective is to determine the value of the optimal buffer level, for a hedging point policy which minimises the total average cost function. The cost function is the sum of inventory, transportation and lost sales costs. Infinitesimal perturbation analysis is used for optimisation of the failure-prone manufacturing system. The trajectories of buffer level are studied for the continuous and discrete cases and the infinitesimal perturbation analysis estimators are evaluated. These estimators are shown to be unbiased and then they are implemented in an optimisation algorithm which determines the optimal buffer level in the presence of constant delivery time. Numerical results are presented for continuous and discrete flow models and then compared in order to evaluate the application of the infinitesimal perturbation analysis on the discrete flow model.     

Field services design and management of natural gas distribution networks: a class of vehicle routing problem with time windows approach

Service operations management of metropolitan gas networks at operational level implies the optimisation of decisions related to logistic activities, taking into account multi-objectives and operational constraints. This paper proposes a metaheuristic approach for the operational planning of the daily logistic activities based on vehicle routing with time window model. Experimental results for a real planning case in a gas distribution network demonstrate the approach effectiveness.     

A calibration framework for the microparameters of the DEM model using the improved PSO algorithm

The discrete element method (DEM) is commonly used for simulating the mechanical characteristics of rock materials; however, constructing a DEM model requires the specification of a number of microparameters. In this paper, to obtain the microparameters of the DEM model, the improved particle swarm optimization (PSO) calibration method was presented. Based on numerical simulation examples, the new approach is considered valid for calibrating the microparameters of the DEM model. Moreover, it is concluded that different sets of microparameters can be determined when few macroparameters are used, which indicates that the empirical formula between microparameters and macroparameters is not reliable. From the analysis of the numerical simulation results, it is suggested that more macroparameters should be used to calibrate the microparameters of the DEM model, and the corresponding numerical simulation results could be more reliable; otherwise, the generated numerical model may not accurately simulate the mechanical characteristics of rock materials.     

Optimisation of one-stop delivery scheduling in online shopping based on the physical Internet

The circulation of online shopping commodities generally involves many entities, such as online manufacturers, wholesalers, retailers and customers. Goods from manufacturers to consumers undergo several exchange stages involving different economic entities. The logistics cost increases in certain supply chain nodes and the risk of backlog or running out of stock is high because of the different income and risk expectations of entities, as well as the varying degrees of information sharing between them. Clearly, a one-stop delivery mode crossing numerous transaction links in online shopping can effectively solve these problems and save social resources. However, realising this one-stop mode requires a series of specific premises, appropriate software and hardware technology support. This study introduces the Physical Internet into the one-stop delivery mode as an important logical and technical support, analyses the operating mechanism of the Physical Internet and discusses the operating conditions of one-stop delivery. It analyses the optimisation contents and objectives of one-stop delivery scheduling to construct a multi-objective optimisation mathematical model and propose a solving algorithm. Finally, this study uses an application case to verify the feasibility and effectiveness of the optimisation mechanism and algorithm.     

A cloud theory-based particle swarm optimization for multiple decision maker vehicle routing problems with fuzzy random time windows

This article puts forward a cloud theory-based particle swarm optimization (CTPSO) algorithm for solving a variant of the vehicle routing problem, namely a multiple decision maker vehicle routing problem with fuzzy random time windows (MDVRPFRTW). A new mathematical model is developed for the proposed problem in which fuzzy random theory is used to describe the time windows and bi-level programming is applied to describe the relationship between the multiple decision makers. To solve the problem, a cloud theory-based particle swarm optimization (CTPSO) is proposed. More specifically, this approach makes improvements in initialization, inertia weight and particle updates to overcome the shortcomings of the basic particle swarm optimization (PSO). Parameter tests and results analysis are presented to highlight the performance of the optimization method, and comparison of the algorithm with the basic PSO and the genetic algorithm demonstrates its efficiency.     

Analysis of closed loop supply chain using genetic algorithm and particle swarm optimisation

This paper describes a relational database system for semi-generative process planning for sheet metal parts that emulates expert system capabilities. The system integrates a feature-based relational database for the parts, a forward chaining rule-based strategy for machine selection, both global and feature-specific execution of the rules and a graph theoretic cost optimization model for optimal process plan selection. This system, which is currently being developed for a sheet metal fabrication company, suggests that, using the experience of shopfloor personnel, an efficient integration of feature-based process planning and expert system strategies can be accomplished.     

Optimisation of integrated process planning and scheduling using a particle swarm optimisation approach

Traditionally, process planning and scheduling are two independent essential functions in a job shop manufacturing environment. In this paper, a unified representation model for integrated process planning and scheduling (IPPS) has been developed. Based on this model, a modern evolutionary algorithm, i.e. the particle swarm optimisation (PSO) algorithm has been employed to optimise the IPPS problem. To explore the search space comprehensively, and to avoid being trapped into local optima, the PSO algorithm has been enhanced with new operators to improve its performance and different criteria, such as makespan, total job tardiness and balanced level of machine utilisation, have been used to evaluate the job performance. To improve the flexibility and agility, a re-planning method has been developed to address the conditions of machine breakdown and new order arrival. Case studies have been used to a verify the performance and efficiency of the modified PSO algorithm under different criteria. A comparison has been made between the result of the modified PSO algorithm and those of the genetic algorithm (GA) and the simulated annealing (SA) algorithm respectively, and different characteristics of the three algorithms are indicated. Case studies show that the developed PSO can generate satisfactory results in optimising the IPPS problem.