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The paper aims to provide an efficient dispatching schedule for ready-mix concrete (RMC) trucks and create a balance between batch plants and construction sites.

The paper focused on developing a new metaheuristic swarm intelligence algorithm using Java code. The paper used statistical criterion: mean, standard deviation, running time to verify the effectiveness of the proposed optimization method and compared its derivatives with other algorithms, such as genetic algorithm (GA), Tabu search (TS), bee colony optimization (BCO), ant lion optimizer (ALO), grey wolf optimizer (GWO), dragonfly algorithm (DA) and particle swarm optimization (PSO).

The paper proved that integrating GWO and DA yields better results than independent algorithms and some selected algorithms in the literature. It also suggests that multi-independent batch plants could effectively cooperate in a system to deliver RMC to various construction sites.

The paper provides a compelling new hybrid swarm intelligence algorithm and a model allowing multi-independent batch plants to work in a system to deliver RMC. It fulfills an identified need to study how batch plant managers can expand their dispatching network, increase their competitiveness and improve their supply chain operations.

The dispatching of trucks in earthmoving and like operations is worthy of examination because of potential emission reductions and savings through the appropriate allocation of trucks to excavators and dump sites. The paper aims to discuss this issue.

Truck dispatching is performed through linear programming (LP) and the effect of truck allocation on unit emissions and unit costs established. Number of trucks, unit cost and unit emissions are all considered as objective functions. A cut and fill operation on a road project provides a numerical case study.

It is demonstrated analytically that the minimum unit emissions solution is the same as that for minimum unit cost. Numerical results from the case study, including sensitivity analyses on the underlying parameters, support this conclusion.

The LP dispatching solution, based on minimizing truck numbers and unit costs, accordingly impacts the environment the least in terms of emissions. The paper's results will be of interest to those designing and managing earthmoving and like operations for production, cost and emissions.

While LP has been used by others to examine optimum unit cost dispatching, this paper is original in examining the dispatching or truck allocation based on both unit cost and unit emissions, and showing the relationship between the optima for both.

For parts of the time on a typical construction site concrete pour, the site placing crew is idle waiting for the arrival of the next truckmixer delivery, whereas for other periods, truckmixers are idle on site waiting to be unloaded. Ideally, the work of the crew should be continuous, with successive truckmixers arriving on site just as the preceding truckmixer has been emptied, to provide perfect matching between site and concrete plant resources. However, in reality, sample benchmark data, representing 118 concrete pours of 69 m³ average volume, illustrate that significant wastage occurs of both crew and truckmixer time. The purpose of this paper is to present and explain the characteristics of the wastage pattern observed and provide further understanding of the effects of the factors affecting the productivity of this everyday routine site concreting system.

Analytical algebraic models have been developed applicable to both serial and circulating truckmixer dispatch policies. The models connect crew idle time, truckmixer waiting time, truckmixer round trip time, truckmixer unloading time and truckmixer numbers. The truckmixer dispatch interval is another parameter included in the serial dispatch model. The models illustrate that perfect resource matching cannot be expected in general, such is the sensitivity of the system to the values applying to those parameters. The models are directly derived from theoretical truckmixer and crew placing time-based flow charts, which graphically depict crew and truckmixer idle times as affected by truckmixer emptying times and other relevant parameters.

The models successfully represent the magnitudes of the resource wastage seen in real life but fail to mirror the wastage distribution of crew and truckmixer time for the 118 pour benchmark. When augmented to include the simulation of stochastic activity durations, however, the models produce pour combinations of crew and truckmixer wastage that do mirror those of the benchmark.

The basic contribution of the paper consists of the proposed analytical models themselves, and their augmented versions, which describe the site and truckmixer resource wastage characteristics actually observed in practice. A further contribution is the step this makes towards understanding why such an everyday construction process is so apparently wasteful of resources.

The purpose of this paper is to propose a model that determines the strategy of owning and renting trucks in combinations with internal truck scheduling and storage allocation problems in container terminals.

To deal with this complicated problem, a two-level heuristic approach is developed, in which the integration problem is decomposed into two levels. The first level determines the daily operations of the internal trucks, while the second level determines the truck employment strategy based on the calculation in the first level.

The results show that: even if the using cost of owned yard trucks is much lower than the cost of rented yard tucks, terminal companies should not purchase too many trucks when the purchasing price is high. In addition, the empirical truck employment strategies, which are purchasing all the trucks or renting all the trucks, are not cost-effective when compared with the proposed yard truck employment strategy.

The paper provides a novel insight for the internal truck employment strategy in container terminals which is the determination of the strategy of employing renting and outsourcing yard trucks to meet operational daily transportation requirements and minimize the long-term cost of employing yard trucks. A mathematical model is proposed to deal with the practical problem. Also, this study presents better solution than empirical method for employing different types of yard truck. Thus, in order to obtain more benefit, terminal companies should employ the proposed yard truck employment strategy.

Unprecedented endeavors have been made to take autonomous trucks to the open road. This study aims to provide relevant information on autonomous truck technology and to help logistics managers gain insight into assessing optimal shipment sizes for autonomous trucks.

Empirical data of estimated autonomous truck costs are collected to help revise classic, conceptual models of assessing optimal shipment sizes. Numerical experiments are conducted to illustrate the optimal shipment size when varying the autonomous truck technology cost and transportation lead time reduction.

Autonomous truck technology can cost as much as 70% of the price of a truck. Logistics managers using classic models that disregard the additional cost could underestimate the optimal shipment size for autonomous trucks. This study also predicts the possibility of inventory centralization in the supply chain network.

The findings are based on information collected from trade articles and academic journals in the domain of logistics management. Other technical or engineering discussions on autonomous trucks are not included in the literature review.

Logistics managers must consider the latest cost information when deciding on shipment sizes of road freight for autonomous trucks. When the economies of scale in autonomous technology prevail, the classic economic order quantity solution might again suffice as a good approximation for optimal shipment size.

This study shows that some models in the literature might no longer be applicable after the introduction of autonomous trucks. We also develop a new cost expression that is a function of the lead time reduction by adopting autonomous trucks.

Many bike rental organizations permit customers to pick-up bikes from one bike station and return them at a different one. However, this service may result in bike imbalance, as bikes may accumulate in stations with low demand. To overcome the imbalance problem, this paper aims to develop a decision model to minimize the total costs of unmet demand and empty bike transport by determining bike fleet size, deployments and the vehicle routing schedule for bike transports.

This paper developed a constrained mixed-integer programming model to deal with this bike imbalance problem. The proposed model belongs to the non-deterministic polynomial-time (NP)-hard problem. This paper developed a two-phase heuristic approach to solve the model. In Phase 1, the approach determines fleet size, deployment level and the number of satisfied demands. In Phase 2, the approach determines the routing schedule for bike transfers.

Computational results show the following results that the proposed approach performs better than General Algebraic Modeling System (GAMS) in terms of solution quality, regardless of problem size. The objective values and the fleet size of rental bikes allocated increase as the number of rental stations increases. The cost of transportation is not directly proportional to the number of bike stations.

The authors provide an integrated model to simultaneously deal with the problems of fleet sizing, empty-resource repositioning and vehicle routing for bike transfer in multiple-station systems, and they also present an algorithm that can be applied to large-scale problems which cannot be solved by the well-known commercial software, GAMS/CPLEX.

The role of business logistics for a water distribution company in Central Asia has become a major concern. As the marketing environment is getting more and more competitive, the company is forced to focus on the efficiency of its supply chain management operations both by improving customer service, increasing its profitability and productivity. The purpose of this paper is to report upon the designing of a responsive supply chain for water distribution in Central Asia.

A logistic plan to satisfy customer requirement for water distribution in a Central Asian city subject to satisfactory service levels both in the number of distribution centers (DCs) and truck delivery schedule is outlined in this paper. The logistics plan includes repositioning the DCs in relation to the customer location for efficient distribution. The problem is formulated as truck delivery schedule using a new algorithm where single distribution centre is converted into a multiple warehouse location problem. The problem is solved using WINQSB software. Further, the current DCs are appraised with the software and suggested possible new locations for convenience.

The application part of this case study consists of identifying water DCs in city limits. By developing improved distribution and logistics management, the study aims at economical operations, convenient zonal distributions, and responsive SCM characteristics. To this end, a spatial distribution plan and route sequencing solution is developed for water distribution.

The paper shows how to improve logistic network that results in cost savings, convenient zonal distributions, and responsive SCM operations. To this end, a spatial distribution plan and route sequencing is developed for water distribution.

The purpose of this article is to understand the role of individual and relational self‐concepts on various team processes and outcomes in a team context.

Participants (n=470) worked in dyads on a computer‐based truck dispatching task, deciding as a team which task activities to perform and in what order. The authors assessed differential relationships between individual and relational self‐concepts and various team processes (e.g. trust) and outcomes (satisfaction).

Subjective task complexity was influenced primarily by individual self‐concept, specifically their core self‐evaluations. Trust in others was influenced primarily by individuals' relational self‐concepts, specifically their teamwork predisposition. Intrinsic motivation and satisfaction were influenced by both individual and relational self‐concepts.

Future research should examine these effects in teams larger than dyads, with other types of tasks, over longer time periods, and with non‐college student samples.

Depending on the task type, a practitioner might cue different self‐concepts to increase individuals' focus on team performance, individual performance, or both. For example, if the team task is highly interdependent and reciprocal in nature, then the team can be trained together or provided information to cue relational self‐concept.

This paper focuses on the construct of individual and relational self‐concepts and their effects on individual functioning in a team context. The results support and extend prior research by demonstrating that outcomes in a team context can be identified and examined in relation to individual conceptions of the self, relational conceptions of the self, or by both.

Purpose – In this chapter, the potential of Milk Run logistics, a method for consolidating freight, is analysed. Milk Run logistics provides a host of possibilities for consolidating freight transport activities and thus using transport capacity efficiently. It utilizes one vehicle to conduct several pick-ups/deliveries in a round trip, which means that the pick-up/delivery points should be located in a limited area which can be covered in a one-day trip.

Findings – Milk Run logistics seems highly beneficial in congested urban environments in developed and developing countries although it may also work in other areas. Furthermore, it can be linked to long-distance logistics, by rail for example, in the national and world-wide network of large companies.

Application – Examples for three automotive companies are given: Toyota with its logistic concept for the Bangkok region, Webasto, a supplier of hardtops and other car parts, and Audi, a daughter company of Volkswagen. All of them have introduced green logistics concepts including Milk Runs, which help to reduce CO2, waste material and – last but not least – costs.

Implications – The chapter concludes with indicating the high potential of Milk Run logistics to China and its rapidly developing automotive industry.